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Chi Xie

Tongji University, Transportation and Logistics, Professor

Hainan University, Economics, Guest Professor

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Dr. Chi Xie obtained his Ph.D. in Systems Engineering from Cornell in 2008. He has since been teaching at UT Austin, SJTU and Tongji and is currently a Professor at Tongji. His research interest is focused on transportation and logistics systems analysis, especially urban transportation networks, electrified and shared transportation systems, and freight distribution and transportation systems. Over past years, his research activities have been sponsored by the U.S. National Science Foundation, National Natural Science Foundation of China, National Key Research and Development Program of China, Doctoral Research Fund of China, China Recruitment Program of Global Experts, and other national and international funding sources; his research results have appeared in more than 150 peer-reviewed journal and conference papers, book chapters, and technical reports and led to 5 Best Paper/Research Awards at international or domestic conferences.
Dr. Xie is a 2005 champion of the Richard E. Rosenthal Competition sponsored by the U.S. Military Operations Research Society, a 2013 recipient of the Young Thousand-Talent Award from the China Recruitment Program of Global Experts, a 2017 Outstanding Young Scholar of Frontiers of Engineering elected by the Chinese Academy of Engineering, and a 2022 recipient of the International Road Transportation Science and Technology Leadership Award from the China Highway and Transportation Society.
Supervisors: Travis Waller, Mark Turnquist, Paul Shuldiner, and Kelvin Cheu
Phone: +86 (21) 3420-8385; +86 (21) 5959-0130
Address: A405 Tongda Bldg., 4800 Cao'an Hwy., Shanghai 201804, China

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Book Chapters by Chi Xie

A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

Hybrid Algorithms for Service, Computing and Manufacturing Systems, 2011

Hybridization offers a promising approach in designing and developing improved metaheuristic meth... more Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

Journal Papers by Chi Xie

Disrupted Transportation Networks under Different Information Availability and Stochasticity Situations

Transportation Research Part C, 2023

Disruptive events may result in severe performance degradations in transportation networks. Prope... more Disruptive events may result in severe performance degradations in transportation networks. Properly evaluating the performance deterioration of a disrupted network with taking individual’s behavioral response into account is a critical step within many operational and planning decisions, such as emergency rescue, traffic management, evacuation planning, infrastructure reinforcement, and so on. The focus of this paper is on the development and analysis of a set of disrupted network evaluation methods, considering different disruption information availability conditions and further an adaptation of these methods into a stochastic traffic environment and stochastic disruption context. Specifically, we define two typical information availability conditions: 1) information globally available (IGA); 2) information locally available (ILA), in terms of different data collection and transmission capabilities and traveler response behaviors. The IGA condition presumes that travelers know the complete set of disruption information before they make trips, while the ILA condition, however, represents another extreme information attainment condition, implying that travelers can observe or notice the occurrence of a disruption only when they reach in person the proximity of the disruption site. A new dynamic adaptive routing process is constructed to model individual routing behaviors for the ILA condition. The above modeling framework implies that, the total travel cost in the IGA condition is always better than the ILA situation in the deterministic traffic environment, and this conclusion is also applicable to the total traveler surplus in the IGA and ILA conditions in the stochastic traffic environment. On the other hand, the resulting ILA network flow pattern in the stochastic traffic environment can be fully accommodated by a Markovian traffic assignment process. When disruptions probabilistically occur, the proposed network evaluation method resorts to a combinatorial number of network evaluations and poses a very time-consuming process. For tackling this computational complexity, we suggested a Monte Carlo simulation-based evaluation framework and found that it can very precisely conduct network evaluations in a much more efficient manner, especially when the number of independently occurring disruptions is large.

An Empirical Analysis of Electric Vehicles’ Charging Patterns

Transportation Research Part D, 2023

Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the i... more Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the influence of shrinking public incentives for purchasing electric vehicles (EVs), it is crucial to deepen the understanding of the existing users’ use cases and clear the barriers for potential EV owners in daily usage. To this end, our study analyzes an 11-month usage data from a large sample of EVs, including 3,777 battery EVs and 5,973 plug-in hybrid EVs. Based on their daily travel intensity, the EVs are clustered into two groups used primarily for private and commercial purposes. We leverage the data to uncover how, when, and where users in each group charge their EVs; investigate EVs’ energy consumption efficiency and its relationship to charging; and discuss EV-related policy implications. The outcome of this study may aid in the development of more targeted EV promotion policies and thus contributes to the carbon neutrality.

Emerging Technologies and Policies for Carbon-Neutral Transportation

Transportation Science and Technology, 2023

Many countries and regions across the world have been actively implementing their commitments to ... more Many countries and regions across the world have been actively implementing their commitments to achieve carbon neutrality in response to the 2030 Agenda for Sustainable Development and the Paris Agreement on Climate Change (United Nations, 2021). This paper highlights some evolutions and revolutions currently experienced by the transportation industry in different contexts with respect to some common carbon neutrality and environmental sustainability goals. The evolutionary and revolutionary changes are powered by the adoption and implementation of emerging technologies and policies on three aspects: Low-carbon transportation infrastructure, transportation electrification and decarbonization, and intelligent transportation systems management. It is hoped that the experience and lessens we learned from these accumulative and adaptive practices can help us more precisely select and adjust the technological and policy pathways for achieving carbon-neutral transportation.

Impacts of Photovoltaic and Energy Storage System Adoption on Public Transport: A Simulation-Based Optimization Approach

Renewable and Sustainable Energy Reviews, 2023

Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promi... more Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative toward reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study aims to fill this research gap by presenting an impact analysis framework incorporating multi-source datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces annual charging costs and carbon emissions of an example bus route by 17.6% and 8.8% on average, respectively. Results suggest that recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also considerably impact the scheduling pattern of PESS. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the total budget is limited.

An Optimal Charging Scheduling Model and Algorithm for Electric Buses

Applied Energy, 2023

Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving... more Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints.
This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver.

Temporal Equilibrium for Electrified Ride-Sourcing Markets Considering Charging Capacity and Driving Fatigue

Transportation Research Part C, 2023

Electric vehicles (EVs) are now being introduced to the ride-sourcing market to catalyze the real... more Electric vehicles (EVs) are now being introduced to the ride-sourcing market to catalyze the realization of sustainable transportation. Given their demand for charging, ride-sourcing EV drivers may have distinct work schedules from gasoline vehicle (GV) drivers, yielding significant impacts on the market supply when their penetration becomes high. Driving fatigue is another factor affecting ride-sourcing drivers’ working hours and work schedules, but has been seldom deliberated. This paper proposes an equilibrium modeling framework based on a time-expanded network to describe the work schedules of EV and GV drivers with the consideration of their driving fatigue and EVs’ charging opportunities subject to the limited charging infrastructure. Specifically, a novel cost formulation is proposed to capture the impact of driving fatigue on drivers’ choices of work schedules. To solve the equilibrium model, we develop a gap functionbased method coupled with the column generation scheme in which a non-additive shortest path (NSP) problem appears as a subroutine. A customized multi-objective label correcting algorithm enhanced with several problem-tailored speed-up techniques is then designed to solve the NSP problem efficiently. Experimental results suggest that the speed-up techniques contribute significantly to the reduction of computational time. Numerical examples reveal that the temporal equilibrium of the electrified ride-sourcing market is moderated by the charging capacity, EV penetration as well as the competition among drivers; and driving fatigue may yield considerable impacts on drivers’ work schedules and the market performance.

Local Container Drayage Problem with Improved Truck Platooning Operations

Transportation Research Part E, 2023

Truck platooning has been identified as a promising tool to significantly save labor force for th... more Truck platooning has been identified as a promising tool to significantly save labor force for the local container drayage problem (LCDP). However, such a favorable platooning system actually suffers from truck under-utilization due to the existing inefficient platooning operation mode. To overcome this issue, this paper investigated the LCDP under an improved platooning operation mode, in which the drivers are not necessarily attached to their respective leading trucks but can move by alternative transport modes to undertake subsequent tasks, leading to a more flexible drayage system. The problem is to minimize the total operational costs including the driver employment cost, truck deployment cost, fuel consumption cost of trucks, and travel cost of the alternative transport mode while satisfying all the customer requests. A novel mixed-integer linear programming (MILP) model is developed to capture the features pertaining to the new platooning mode. A heuristic construction method incorporating the simulated annealing scheme is proposed to address the problem. Numerical experiments are conducted to validate the proposed model and solution method and quantify the advantages of the improved platooning mode. Sensitivity analysis is also conducted to examine the impacts of some major influential factors, e.g., labor cost and maximum platoon length, on the system performance and derive managerial insights for further system management.

Optimal En-Route Charging Station Locations for Electric Vehicles: A New Modeling Perspective and a Comparative Comparison of Network-Based and Metanetwork-Based Approaches

Transportation Research Part C, 2022

In many regions across the world, the distribution density of electricity-charging stations is ye... more In many regions across the world, the distribution density of electricity-charging stations is yet sufficient for taming the range anxiety concern of electric vehicle drivers. In such driving circumstances, making detours to find alternative charging opportunities is often an operational remedy to accomplish long-distance trips. To reduce the frequency and extra mileage of detouring, public charging infrastructures should be coordinately sited and constructed in a regional network. With the goal of minimizing the possible detour mileage, this paper describes an optimal charging station location problem for intercity highway networks and develops two approaches for modeling and solving it. Specifically, two mixed linear integer programming models are constructed in different network modeling paradigms: The first one is formed and solved on the basis of the original node-link network topology, with newly introduced integer variables such as path and subpath activation indicators to quantify the utilization status of paths and subpaths, respectively. The classic branch-and-bound algorithm encapsulating a bi-criteria label-correcting algorithm is designed for solving this network-based model. The other one is formulated and solved relying on the station-subpath metanetwork topology, as the result of a two-phase process that decomposes an individual routing and charging decision into two parts. The two phases are conducted on the node-link network and station-subpath metanetwork levels, respectively, where the first phase is done by solving a network-based, distance-constrained minimum-cost path problem, while the second phase collapses to a metanetwork-based, simple minimum-cost path problem. The findings we obtained through conducting this research are twofold: First, analytical and computational studies clearly identify the effectiveness of the two modeling and solution approaches, while the metanetwork-based approach exhibits its appealing computing advantage for solving problems of large size; second, the application of the solution approaches for a real-world network instance reveals how budget limit and distance limit impact the charging station locations, individual routing and charging decisions, and network cost and accessibility levels.

A Multimodal Multicommodity Network Equilibrium Model with Service Capacity and Bottleneck Congestion for China-Europe Containerized Freight Flows

Transportation Research Part E, 2022

As a traditional way for transporting containerized cargoes, international liner shipping has lon... more As a traditional way for transporting containerized cargoes, international liner shipping has long played a dominant role in the China-Europe freight transportation market. In recent years, China Railway Express has offered a new and competitive freight transportation option, contributing to a significant increase in the trade volume by rail between China and Europe. With the goal of providing a holistic computational tool that can predict and analyze the spatial distribution of containerized freight flows and suggest strategic transportation infrastructure and service expansion options, this paper develops and implements a multimodal multicommodity freight transportation network equilibrium model, based on a newly constructed supernetwork integrating international liner shipping lines, international freight rail services, and international and domestic highway networks in relevant countries. The combined transportation mode-route choice behavior of individual shippers is characterized by the widely used multinomial logit model, where the values of time of different commodity categories are included in its utility functions. The congestion effects arising from bottleneck facilities along liner shipping and freight rail lines (i.e., seaports, water channels, and break-of-gauge stations) are first captured by queueing models and then approximated by polynomial delay functions, while the accommodation capacity of individual service lines is explicitly embedded into the model as hard constraints. This supernetwork equilibrium model is tackled by a subgradient algorithm in the Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm. Computational experiments are conducted with a multi-layer, hetero-structure set of supply and demand data collected from multiple data repositories, government agencies, freight carriers, and research papers and reports. The preliminary results exhibit the computational performance of the solution procedure and reveal the variation of congestion levels of bottleneck facilities, capacity usage profiles of service lines, and competitiveness conditions between liner shipping and freight rail services under different monthly demand patterns.

Joint Train Line Planning and Timetabling of Intercity High-Speed Rail with Time-Dependent Demand

Transportation Science and Technology, 2023

Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan... more Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan areas and has become an essential part of sustainable transportation systems in some countries. One of the most challenging problems is how to collectively optimize the train line plan and timetable with a simultaneous consideration of efficiency, cost, and level of service. In this paper, we focus on jointly optimizing the train line plan and timetable of intercity HSR using real-world time-dependent demand while considering the practical operation constraints including overtaking. First, we formulate the optimization problem as a mixed integer nonlinear programming (MINLP). Its objective is to minimize the total cost comprised of the trains’ fixed and variable costs, passengers’ travel time cost, penalties for passengers’ departure time deviation and passengers’ failing to board a train. We then design a double-layer simulated annealing (SA) algorithm, in which the inner layer algorithm optimizes the stop plan, the departure and arrival times at each station, the outer layer algorithm optimizes the number of trains, their origin-destination stations, and their departure times at origin stations. Next a case study is conducted based on real-world data to demonstrate the effectiveness of the proposed model and solution algorithm. The numerical results show that the proposed model can help significantly reduce the number of trains used by increasing the average seat occupancy with little impact on the average passenger travel time and departure time. The study provides valuable guidance for optimizing HSR train line plan and timetable based on real-world time-dependent demand.

Resilient Route Design for Collection of Material from Suppliers with Split Deliveries and Stochastic Demands

Computers and Operations Research, 2022

Efficient design of routes and schedules for moving materials into manufacturing or assembly plan... more Efficient design of routes and schedules for moving materials into manufacturing or assembly plants is a central element of freight logistics systems. However, such route design faces many challenges from practice, including uncertain supplies and operational effectiveness. The work in this paper addresses both the uncertainty in supplier quantities and the need for operational constraints on supplier splitting in the route design process. The model developed here accomplishes this resilient design of routes using a stochastic version of a capacitated clustering formulation. The model is constructed as a two-stage stochastic mixed integer program and an effective solution method is developed using progressive hedging. An application to logistics operations in the automotive industry is used to validate the model, and the efficiency and stability of the proposed algorithm has been verified by computational experiments.

Location and Capacity Planning for Preventive Healthcare Facilities with Congestion Effects

Journal of Industry and Management Optimization, 2023

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost im... more A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing.

On the Primal and Dual Formulations of Traffic Assignment Problems with Perception Stochasticity and Demand Elasticity

Transportation Letters, 2022

In a traffic assignment problem with perception stochasticity and demand elasticity, the equilibr... more In a traffic assignment problem with perception stochasticity and demand elasticity, the equilibrium conditions are achieved on two levels, namely, the stochastic user equilibrium on the path level and the supply-demand equilibrium on the origin-destination level. By recognizing that both equilibrium levels imply random individual perceptions and decision makings, this paper reinvestigates the mathematical formulations of this kind of problems in both the system optimum and user equilibrium principles. Unlike previous research devoted to the development of solution methods for some specific versions of this kind of traffic assignment problems, our focus is given to a pair of new general formulations that pose a duality relationship to each other. The primal formulation has a convex programming form with nonlinear constraints, while the dual one poses a concave programming problem. In this primal-dual modeling framework, we found that the equilibrium or optimality conditions of a traffic assignment problem with perception stochasticity and demand elasticity can be redefined as a combination of three sets of equations and an arbitrary feasible solution of either the primal or dual formulation satisfies only two of them. We further rigorously proved the solution equivalency and uniqueness of both the primal and dual formulations, by using derivative-based techniques. We also found that when a problem of this type collapses to a logit-based case, both formulations can be conveniently written into a tractable (i.e., analytically evaluable) path flow-based form and the primal formulation becomes a convex programming problem with linear constraints. While the two formulations pose their respective modeling advantages and drawbacks, our preliminary algorithmic analysis and numerical test results indicate that the dual formulation-based algorithm, i.e., the Cauchy algorithm, can be more readily implemented for large-scale problems and converge evidently faster than the primal formulation-based one, i.e., the Frank-Wolfe algorithm, at least in the logit-based case.

Modeling and Evaluating the Impact of Electricity Price on Commute Network Flows of Electric Vehicles

Transportation Letters, 2022

In many urban areas and urban-rural regions, the price of electricity is not only subject to time... more In many urban areas and urban-rural regions, the price of electricity is not only subject to time-of-use tariffs, but also dependent on the its usage purpose or service sector, significantly varying across different geographic locations or land uses. Such a spatial price difference greatly affects the charging behavior of electric vehicle drivers. This paper models and analyzes the possible impact of electricity-charging price on the routing choice and flow pattern of electric vehicles in a home-workplace commute traffic network. The particular circumstance in such a network accommodating electric vehicles is that commuters driving battery electric vehicles have opportunities to charge their vehicles at both home and workplace and take into account different electricity-charging prices in their travel choices. For the illustration and evaluation purposes, we presented a small synthetic example and proposed a convex programming model for characterizing the commute network flow pattern of electric vehicles that may be charged at home and/or workplace, encapsulating a piecewise electricity-charging cost function that implies commuters’ combined home-based and workplace-based charging behaviors. Given different electricity-charging prices at home and workplace, solving this problem requires tracking all individual paths in the network. As such, a path-based solution algorithm sketched in the disaggregated simplicial decomposition framework, with some modifications in the path generation phase and an added k-shortest path search procedure, is designed for problem solutions. We implemented the modeling and solution methods for deriving the network flow pattern of electric vehicles and comparing it with that of gasoline vehicles, as well as evaluating the network performance change caused by varying electricity-charging prices with multiple network-level and link-level evaluation matrices.

Robust Liner Ship Routing and Scheduling under Uncertain Weather and Ocean Conditions

Transportation Research Part C, 2022

Since sailing along the Northern Sea Route (NSR) through the Arctic Ocean comes into reality, com... more Since sailing along the Northern Sea Route (NSR) through the Arctic Ocean comes into reality, commercial shipping developments including opening new liner services along this route have been put on the agenda. However, uncertainties within weather and ocean conditions in this region, especially during temperature-varying seasons, may cause failure to on-time arrivals, resulting unignorable or even unaffordable monetary losses. As a result, these uncertainties will inevitably affect cargo shippers’ willingness of choosing this being-explored route, even if on average its shipping time is much shorter than that of the traditional Asia-Europe shipping lines. In this context, the present paper describes a liner ship routing and scheduling problem considering schedule-sensitive demand and late-arrival penalty for operating incoming NSR shipping lines in the future. By using two types of uncertainty sets, bounded and budget-bounded uncertainty sets, we construct and solve two robust counterparts of this problem. The deterministic model and bounded robust model can be seen as special cases of the budget-bounded robust model when the uncertainty budget is set to null and full, respectively. A multifaceted case study of planning liner shipping routes and schedules along the NSR is conducted to validate the efficacy and efficiency of the proposed models and identify the effects of uncertainty bounds and budgets on the solution performance. The following facts and conclusions are revealed from the optimization results of the case study: The budget-bounded robust model functions the best in the worst condition; low revenue rate and high demand sensitivity are the major factors for excluding a candidate port from the route; the order of a visited port in the route decides the weight of its uncertainty budget on the entire shipping line; setting the uncertainty budget in a decelerative way will enhance the scheduling robustness of solutions.

Allocating Exclusive and Intermittent Transit Lanes in Dynamic Traffic Networks with Connected and Automated Vehicles

Transportation Science and Technology, 2021

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. ... more Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.

Optimal Station Locations for En-Route Charging of Electric Vehicles in Congested Intercity Networks: A New Problem Formulation and Exact and Approximate Partitioning Algorithms

Transportation Research Part C, 2021

This paper addresses a new electricity-charging station location problem for congested intercity ... more This paper addresses a new electricity-charging station location problem for congested intercity or regional networks, in which electric vehicles with a limited driving range require recharges for their long-distance trips. When the distribution of charging stations does not sufficiently cover all used routes, some drivers may take a detour to find charging opportunities (or switch to another transportation mode). The goal of this station location problem is to find, under a limited construction budget, an optimal set of charging station locations such that all vehicles finish their trips by choosing a self-optimal route with necessary charging opportunities while the overall network congestion caused by possible detours is minimized. The problem is written as a bi-level mixed nonlinear integer programming model, where the upper level of the model is set to regulate the selection of station locations subject to the construction budget while the lower level is used to characterize the equilibrium flow pattern of electric vehicles with the charging requirement. Selected exact and approximate algorithms, namely, the branch-and-bound algorithm and the nested partitions algorithm are adopted to solve this station location problem. While both algorithms imply a divide-and-conquer strategy, the branch-and-bound algorithm poses a deterministic, exact procedure that utilizes the bounding mechanism to prune impossible solution subspaces, whereas the nested partitions algorithm performs a stochastic search and selection process in terms of the optimality probability for near-optimal solutions. To get numerical insights on the algorithmic performance and solution behavior, we test these algorithms through a couple of benchmark network instances. A performance comparison of the algorithms indicates that, the branch-and-bound algorithm can quickly obtain the global optimum when the driving range is relatively low, while the nested partitions algorithm can find optimal solutions or extremely near-optimal solutions in all cases and typically spend on average only one fourth of the computing time of the branch-and-bound algorithm. The network flow solutions clearly show, compared to gasoline vehicle drivers, how an insufficient driving range or number of charging stations may significantly reduce the number of feasible paths and force electric vehicle drivers to choose more costly paths, which thus reshapes flow patterns and possibly increases congestion levels.

Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions

Transportation Research Record, 2021

It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to ser... more It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time, however, is heavily subject to the variation of sea ice conditions along this route. Any participating shipping company must consider how to mitigate the ill effects on itinerary planning caused by sailing time and cost uncertainty. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions, and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profit over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables, and Benders decomposition is applied to solve the linearized problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.

Mitigating the Spread of Epidemics by Managing and Controlling Urban Transportation Systems and Travel Activities

Journal of Transportation Engineering and Information, 2021

This research proposes in an integrated framework a set of network-wide management and control st... more This research proposes in an integrated framework a set of network-wide management and control strategies on transportation systems and travel activities for the epidemic intervention and control and discusses the implementation issues and methods of this strategies. The strategies could be categorized into four groups: 1) travel demand management (including compulsory and advisory demand management strategies); 2) transportation system control (limiting or adjusting the operations of certain transportation modes, facilities and areas in space and time); 3) travel population screening (quickly identifying those virus infectors from the travel population and tracing their activity trajectories in an urban transportation system or network); 4) quarantine facility planning (for isolating, accommodating, monitoring, inspecting and curing suspected or infected cases). The focus of this paper is on a discussion of the rationale, purpose and efficacy of these strategies, along which the resulting technical problems, data requirements and solution methods for implementing these strategies are briefly suggested as well.

A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

Hybrid Algorithms for Service, Computing and Manufacturing Systems, 2011

Hybridization offers a promising approach in designing and developing improved metaheuristic meth... more Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

Disrupted Transportation Networks under Different Information Availability and Stochasticity Situations

Transportation Research Part C, 2023

Disruptive events may result in severe performance degradations in transportation networks. Prope... more Disruptive events may result in severe performance degradations in transportation networks. Properly evaluating the performance deterioration of a disrupted network with taking individual’s behavioral response into account is a critical step within many operational and planning decisions, such as emergency rescue, traffic management, evacuation planning, infrastructure reinforcement, and so on. The focus of this paper is on the development and analysis of a set of disrupted network evaluation methods, considering different disruption information availability conditions and further an adaptation of these methods into a stochastic traffic environment and stochastic disruption context. Specifically, we define two typical information availability conditions: 1) information globally available (IGA); 2) information locally available (ILA), in terms of different data collection and transmission capabilities and traveler response behaviors. The IGA condition presumes that travelers know the complete set of disruption information before they make trips, while the ILA condition, however, represents another extreme information attainment condition, implying that travelers can observe or notice the occurrence of a disruption only when they reach in person the proximity of the disruption site. A new dynamic adaptive routing process is constructed to model individual routing behaviors for the ILA condition. The above modeling framework implies that, the total travel cost in the IGA condition is always better than the ILA situation in the deterministic traffic environment, and this conclusion is also applicable to the total traveler surplus in the IGA and ILA conditions in the stochastic traffic environment. On the other hand, the resulting ILA network flow pattern in the stochastic traffic environment can be fully accommodated by a Markovian traffic assignment process. When disruptions probabilistically occur, the proposed network evaluation method resorts to a combinatorial number of network evaluations and poses a very time-consuming process. For tackling this computational complexity, we suggested a Monte Carlo simulation-based evaluation framework and found that it can very precisely conduct network evaluations in a much more efficient manner, especially when the number of independently occurring disruptions is large.

An Empirical Analysis of Electric Vehicles’ Charging Patterns

Transportation Research Part D, 2023

Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the i... more Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the influence of shrinking public incentives for purchasing electric vehicles (EVs), it is crucial to deepen the understanding of the existing users’ use cases and clear the barriers for potential EV owners in daily usage. To this end, our study analyzes an 11-month usage data from a large sample of EVs, including 3,777 battery EVs and 5,973 plug-in hybrid EVs. Based on their daily travel intensity, the EVs are clustered into two groups used primarily for private and commercial purposes. We leverage the data to uncover how, when, and where users in each group charge their EVs; investigate EVs’ energy consumption efficiency and its relationship to charging; and discuss EV-related policy implications. The outcome of this study may aid in the development of more targeted EV promotion policies and thus contributes to the carbon neutrality.

Emerging Technologies and Policies for Carbon-Neutral Transportation

Transportation Science and Technology, 2023

Many countries and regions across the world have been actively implementing their commitments to ... more Many countries and regions across the world have been actively implementing their commitments to achieve carbon neutrality in response to the 2030 Agenda for Sustainable Development and the Paris Agreement on Climate Change (United Nations, 2021). This paper highlights some evolutions and revolutions currently experienced by the transportation industry in different contexts with respect to some common carbon neutrality and environmental sustainability goals. The evolutionary and revolutionary changes are powered by the adoption and implementation of emerging technologies and policies on three aspects: Low-carbon transportation infrastructure, transportation electrification and decarbonization, and intelligent transportation systems management. It is hoped that the experience and lessens we learned from these accumulative and adaptive practices can help us more precisely select and adjust the technological and policy pathways for achieving carbon-neutral transportation.

Impacts of Photovoltaic and Energy Storage System Adoption on Public Transport: A Simulation-Based Optimization Approach

Renewable and Sustainable Energy Reviews, 2023

Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promi... more Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative toward reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study aims to fill this research gap by presenting an impact analysis framework incorporating multi-source datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces annual charging costs and carbon emissions of an example bus route by 17.6% and 8.8% on average, respectively. Results suggest that recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also considerably impact the scheduling pattern of PESS. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the total budget is limited.

An Optimal Charging Scheduling Model and Algorithm for Electric Buses

Applied Energy, 2023

Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving... more Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints.
This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver.

Temporal Equilibrium for Electrified Ride-Sourcing Markets Considering Charging Capacity and Driving Fatigue

Transportation Research Part C, 2023

Electric vehicles (EVs) are now being introduced to the ride-sourcing market to catalyze the real... more Electric vehicles (EVs) are now being introduced to the ride-sourcing market to catalyze the realization of sustainable transportation. Given their demand for charging, ride-sourcing EV drivers may have distinct work schedules from gasoline vehicle (GV) drivers, yielding significant impacts on the market supply when their penetration becomes high. Driving fatigue is another factor affecting ride-sourcing drivers’ working hours and work schedules, but has been seldom deliberated. This paper proposes an equilibrium modeling framework based on a time-expanded network to describe the work schedules of EV and GV drivers with the consideration of their driving fatigue and EVs’ charging opportunities subject to the limited charging infrastructure. Specifically, a novel cost formulation is proposed to capture the impact of driving fatigue on drivers’ choices of work schedules. To solve the equilibrium model, we develop a gap functionbased method coupled with the column generation scheme in which a non-additive shortest path (NSP) problem appears as a subroutine. A customized multi-objective label correcting algorithm enhanced with several problem-tailored speed-up techniques is then designed to solve the NSP problem efficiently. Experimental results suggest that the speed-up techniques contribute significantly to the reduction of computational time. Numerical examples reveal that the temporal equilibrium of the electrified ride-sourcing market is moderated by the charging capacity, EV penetration as well as the competition among drivers; and driving fatigue may yield considerable impacts on drivers’ work schedules and the market performance.

Local Container Drayage Problem with Improved Truck Platooning Operations

Transportation Research Part E, 2023

Truck platooning has been identified as a promising tool to significantly save labor force for th... more Truck platooning has been identified as a promising tool to significantly save labor force for the local container drayage problem (LCDP). However, such a favorable platooning system actually suffers from truck under-utilization due to the existing inefficient platooning operation mode. To overcome this issue, this paper investigated the LCDP under an improved platooning operation mode, in which the drivers are not necessarily attached to their respective leading trucks but can move by alternative transport modes to undertake subsequent tasks, leading to a more flexible drayage system. The problem is to minimize the total operational costs including the driver employment cost, truck deployment cost, fuel consumption cost of trucks, and travel cost of the alternative transport mode while satisfying all the customer requests. A novel mixed-integer linear programming (MILP) model is developed to capture the features pertaining to the new platooning mode. A heuristic construction method incorporating the simulated annealing scheme is proposed to address the problem. Numerical experiments are conducted to validate the proposed model and solution method and quantify the advantages of the improved platooning mode. Sensitivity analysis is also conducted to examine the impacts of some major influential factors, e.g., labor cost and maximum platoon length, on the system performance and derive managerial insights for further system management.

Optimal En-Route Charging Station Locations for Electric Vehicles: A New Modeling Perspective and a Comparative Comparison of Network-Based and Metanetwork-Based Approaches

Transportation Research Part C, 2022

In many regions across the world, the distribution density of electricity-charging stations is ye... more In many regions across the world, the distribution density of electricity-charging stations is yet sufficient for taming the range anxiety concern of electric vehicle drivers. In such driving circumstances, making detours to find alternative charging opportunities is often an operational remedy to accomplish long-distance trips. To reduce the frequency and extra mileage of detouring, public charging infrastructures should be coordinately sited and constructed in a regional network. With the goal of minimizing the possible detour mileage, this paper describes an optimal charging station location problem for intercity highway networks and develops two approaches for modeling and solving it. Specifically, two mixed linear integer programming models are constructed in different network modeling paradigms: The first one is formed and solved on the basis of the original node-link network topology, with newly introduced integer variables such as path and subpath activation indicators to quantify the utilization status of paths and subpaths, respectively. The classic branch-and-bound algorithm encapsulating a bi-criteria label-correcting algorithm is designed for solving this network-based model. The other one is formulated and solved relying on the station-subpath metanetwork topology, as the result of a two-phase process that decomposes an individual routing and charging decision into two parts. The two phases are conducted on the node-link network and station-subpath metanetwork levels, respectively, where the first phase is done by solving a network-based, distance-constrained minimum-cost path problem, while the second phase collapses to a metanetwork-based, simple minimum-cost path problem. The findings we obtained through conducting this research are twofold: First, analytical and computational studies clearly identify the effectiveness of the two modeling and solution approaches, while the metanetwork-based approach exhibits its appealing computing advantage for solving problems of large size; second, the application of the solution approaches for a real-world network instance reveals how budget limit and distance limit impact the charging station locations, individual routing and charging decisions, and network cost and accessibility levels.

A Multimodal Multicommodity Network Equilibrium Model with Service Capacity and Bottleneck Congestion for China-Europe Containerized Freight Flows

Transportation Research Part E, 2022

As a traditional way for transporting containerized cargoes, international liner shipping has lon... more As a traditional way for transporting containerized cargoes, international liner shipping has long played a dominant role in the China-Europe freight transportation market. In recent years, China Railway Express has offered a new and competitive freight transportation option, contributing to a significant increase in the trade volume by rail between China and Europe. With the goal of providing a holistic computational tool that can predict and analyze the spatial distribution of containerized freight flows and suggest strategic transportation infrastructure and service expansion options, this paper develops and implements a multimodal multicommodity freight transportation network equilibrium model, based on a newly constructed supernetwork integrating international liner shipping lines, international freight rail services, and international and domestic highway networks in relevant countries. The combined transportation mode-route choice behavior of individual shippers is characterized by the widely used multinomial logit model, where the values of time of different commodity categories are included in its utility functions. The congestion effects arising from bottleneck facilities along liner shipping and freight rail lines (i.e., seaports, water channels, and break-of-gauge stations) are first captured by queueing models and then approximated by polynomial delay functions, while the accommodation capacity of individual service lines is explicitly embedded into the model as hard constraints. This supernetwork equilibrium model is tackled by a subgradient algorithm in the Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm. Computational experiments are conducted with a multi-layer, hetero-structure set of supply and demand data collected from multiple data repositories, government agencies, freight carriers, and research papers and reports. The preliminary results exhibit the computational performance of the solution procedure and reveal the variation of congestion levels of bottleneck facilities, capacity usage profiles of service lines, and competitiveness conditions between liner shipping and freight rail services under different monthly demand patterns.

Joint Train Line Planning and Timetabling of Intercity High-Speed Rail with Time-Dependent Demand

Transportation Science and Technology, 2023

Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan... more Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan areas and has become an essential part of sustainable transportation systems in some countries. One of the most challenging problems is how to collectively optimize the train line plan and timetable with a simultaneous consideration of efficiency, cost, and level of service. In this paper, we focus on jointly optimizing the train line plan and timetable of intercity HSR using real-world time-dependent demand while considering the practical operation constraints including overtaking. First, we formulate the optimization problem as a mixed integer nonlinear programming (MINLP). Its objective is to minimize the total cost comprised of the trains’ fixed and variable costs, passengers’ travel time cost, penalties for passengers’ departure time deviation and passengers’ failing to board a train. We then design a double-layer simulated annealing (SA) algorithm, in which the inner layer algorithm optimizes the stop plan, the departure and arrival times at each station, the outer layer algorithm optimizes the number of trains, their origin-destination stations, and their departure times at origin stations. Next a case study is conducted based on real-world data to demonstrate the effectiveness of the proposed model and solution algorithm. The numerical results show that the proposed model can help significantly reduce the number of trains used by increasing the average seat occupancy with little impact on the average passenger travel time and departure time. The study provides valuable guidance for optimizing HSR train line plan and timetable based on real-world time-dependent demand.

Resilient Route Design for Collection of Material from Suppliers with Split Deliveries and Stochastic Demands

Computers and Operations Research, 2022

Efficient design of routes and schedules for moving materials into manufacturing or assembly plan... more Efficient design of routes and schedules for moving materials into manufacturing or assembly plants is a central element of freight logistics systems. However, such route design faces many challenges from practice, including uncertain supplies and operational effectiveness. The work in this paper addresses both the uncertainty in supplier quantities and the need for operational constraints on supplier splitting in the route design process. The model developed here accomplishes this resilient design of routes using a stochastic version of a capacitated clustering formulation. The model is constructed as a two-stage stochastic mixed integer program and an effective solution method is developed using progressive hedging. An application to logistics operations in the automotive industry is used to validate the model, and the efficiency and stability of the proposed algorithm has been verified by computational experiments.

Location and Capacity Planning for Preventive Healthcare Facilities with Congestion Effects

Journal of Industry and Management Optimization, 2023

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost im... more A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing.

On the Primal and Dual Formulations of Traffic Assignment Problems with Perception Stochasticity and Demand Elasticity

Transportation Letters, 2022

In a traffic assignment problem with perception stochasticity and demand elasticity, the equilibr... more In a traffic assignment problem with perception stochasticity and demand elasticity, the equilibrium conditions are achieved on two levels, namely, the stochastic user equilibrium on the path level and the supply-demand equilibrium on the origin-destination level. By recognizing that both equilibrium levels imply random individual perceptions and decision makings, this paper reinvestigates the mathematical formulations of this kind of problems in both the system optimum and user equilibrium principles. Unlike previous research devoted to the development of solution methods for some specific versions of this kind of traffic assignment problems, our focus is given to a pair of new general formulations that pose a duality relationship to each other. The primal formulation has a convex programming form with nonlinear constraints, while the dual one poses a concave programming problem. In this primal-dual modeling framework, we found that the equilibrium or optimality conditions of a traffic assignment problem with perception stochasticity and demand elasticity can be redefined as a combination of three sets of equations and an arbitrary feasible solution of either the primal or dual formulation satisfies only two of them. We further rigorously proved the solution equivalency and uniqueness of both the primal and dual formulations, by using derivative-based techniques. We also found that when a problem of this type collapses to a logit-based case, both formulations can be conveniently written into a tractable (i.e., analytically evaluable) path flow-based form and the primal formulation becomes a convex programming problem with linear constraints. While the two formulations pose their respective modeling advantages and drawbacks, our preliminary algorithmic analysis and numerical test results indicate that the dual formulation-based algorithm, i.e., the Cauchy algorithm, can be more readily implemented for large-scale problems and converge evidently faster than the primal formulation-based one, i.e., the Frank-Wolfe algorithm, at least in the logit-based case.

Modeling and Evaluating the Impact of Electricity Price on Commute Network Flows of Electric Vehicles

Transportation Letters, 2022

In many urban areas and urban-rural regions, the price of electricity is not only subject to time... more In many urban areas and urban-rural regions, the price of electricity is not only subject to time-of-use tariffs, but also dependent on the its usage purpose or service sector, significantly varying across different geographic locations or land uses. Such a spatial price difference greatly affects the charging behavior of electric vehicle drivers. This paper models and analyzes the possible impact of electricity-charging price on the routing choice and flow pattern of electric vehicles in a home-workplace commute traffic network. The particular circumstance in such a network accommodating electric vehicles is that commuters driving battery electric vehicles have opportunities to charge their vehicles at both home and workplace and take into account different electricity-charging prices in their travel choices. For the illustration and evaluation purposes, we presented a small synthetic example and proposed a convex programming model for characterizing the commute network flow pattern of electric vehicles that may be charged at home and/or workplace, encapsulating a piecewise electricity-charging cost function that implies commuters’ combined home-based and workplace-based charging behaviors. Given different electricity-charging prices at home and workplace, solving this problem requires tracking all individual paths in the network. As such, a path-based solution algorithm sketched in the disaggregated simplicial decomposition framework, with some modifications in the path generation phase and an added k-shortest path search procedure, is designed for problem solutions. We implemented the modeling and solution methods for deriving the network flow pattern of electric vehicles and comparing it with that of gasoline vehicles, as well as evaluating the network performance change caused by varying electricity-charging prices with multiple network-level and link-level evaluation matrices.

Robust Liner Ship Routing and Scheduling under Uncertain Weather and Ocean Conditions

Transportation Research Part C, 2022

Since sailing along the Northern Sea Route (NSR) through the Arctic Ocean comes into reality, com... more Since sailing along the Northern Sea Route (NSR) through the Arctic Ocean comes into reality, commercial shipping developments including opening new liner services along this route have been put on the agenda. However, uncertainties within weather and ocean conditions in this region, especially during temperature-varying seasons, may cause failure to on-time arrivals, resulting unignorable or even unaffordable monetary losses. As a result, these uncertainties will inevitably affect cargo shippers’ willingness of choosing this being-explored route, even if on average its shipping time is much shorter than that of the traditional Asia-Europe shipping lines. In this context, the present paper describes a liner ship routing and scheduling problem considering schedule-sensitive demand and late-arrival penalty for operating incoming NSR shipping lines in the future. By using two types of uncertainty sets, bounded and budget-bounded uncertainty sets, we construct and solve two robust counterparts of this problem. The deterministic model and bounded robust model can be seen as special cases of the budget-bounded robust model when the uncertainty budget is set to null and full, respectively. A multifaceted case study of planning liner shipping routes and schedules along the NSR is conducted to validate the efficacy and efficiency of the proposed models and identify the effects of uncertainty bounds and budgets on the solution performance. The following facts and conclusions are revealed from the optimization results of the case study: The budget-bounded robust model functions the best in the worst condition; low revenue rate and high demand sensitivity are the major factors for excluding a candidate port from the route; the order of a visited port in the route decides the weight of its uncertainty budget on the entire shipping line; setting the uncertainty budget in a decelerative way will enhance the scheduling robustness of solutions.

Allocating Exclusive and Intermittent Transit Lanes in Dynamic Traffic Networks with Connected and Automated Vehicles

Transportation Science and Technology, 2021

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. ... more Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.

Optimal Station Locations for En-Route Charging of Electric Vehicles in Congested Intercity Networks: A New Problem Formulation and Exact and Approximate Partitioning Algorithms

Transportation Research Part C, 2021

This paper addresses a new electricity-charging station location problem for congested intercity ... more This paper addresses a new electricity-charging station location problem for congested intercity or regional networks, in which electric vehicles with a limited driving range require recharges for their long-distance trips. When the distribution of charging stations does not sufficiently cover all used routes, some drivers may take a detour to find charging opportunities (or switch to another transportation mode). The goal of this station location problem is to find, under a limited construction budget, an optimal set of charging station locations such that all vehicles finish their trips by choosing a self-optimal route with necessary charging opportunities while the overall network congestion caused by possible detours is minimized. The problem is written as a bi-level mixed nonlinear integer programming model, where the upper level of the model is set to regulate the selection of station locations subject to the construction budget while the lower level is used to characterize the equilibrium flow pattern of electric vehicles with the charging requirement. Selected exact and approximate algorithms, namely, the branch-and-bound algorithm and the nested partitions algorithm are adopted to solve this station location problem. While both algorithms imply a divide-and-conquer strategy, the branch-and-bound algorithm poses a deterministic, exact procedure that utilizes the bounding mechanism to prune impossible solution subspaces, whereas the nested partitions algorithm performs a stochastic search and selection process in terms of the optimality probability for near-optimal solutions. To get numerical insights on the algorithmic performance and solution behavior, we test these algorithms through a couple of benchmark network instances. A performance comparison of the algorithms indicates that, the branch-and-bound algorithm can quickly obtain the global optimum when the driving range is relatively low, while the nested partitions algorithm can find optimal solutions or extremely near-optimal solutions in all cases and typically spend on average only one fourth of the computing time of the branch-and-bound algorithm. The network flow solutions clearly show, compared to gasoline vehicle drivers, how an insufficient driving range or number of charging stations may significantly reduce the number of feasible paths and force electric vehicle drivers to choose more costly paths, which thus reshapes flow patterns and possibly increases congestion levels.

Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions

Transportation Research Record, 2021

It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to ser... more It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time, however, is heavily subject to the variation of sea ice conditions along this route. Any participating shipping company must consider how to mitigate the ill effects on itinerary planning caused by sailing time and cost uncertainty. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions, and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profit over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables, and Benders decomposition is applied to solve the linearized problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.

Mitigating the Spread of Epidemics by Managing and Controlling Urban Transportation Systems and Travel Activities

Journal of Transportation Engineering and Information, 2021

This research proposes in an integrated framework a set of network-wide management and control st... more This research proposes in an integrated framework a set of network-wide management and control strategies on transportation systems and travel activities for the epidemic intervention and control and discusses the implementation issues and methods of this strategies. The strategies could be categorized into four groups: 1) travel demand management (including compulsory and advisory demand management strategies); 2) transportation system control (limiting or adjusting the operations of certain transportation modes, facilities and areas in space and time); 3) travel population screening (quickly identifying those virus infectors from the travel population and tracing their activity trajectories in an urban transportation system or network); 4) quarantine facility planning (for isolating, accommodating, monitoring, inspecting and curing suspected or infected cases). The focus of this paper is on a discussion of the rationale, purpose and efficacy of these strategies, along which the resulting technical problems, data requirements and solution methods for implementing these strategies are briefly suggested as well.

Demand Information Sharing in Port Concession Arrangements

Transportation Research Part B, 2020

This paper investigates the effects of demand information sharing on concession arrangements and ... more This paper investigates the effects of demand information sharing on concession arrangements and market equilibria, when two ports, each managed by a welfare-maximizing port authority and a profit-maximizing port operator, compete for demands. The problem is formulated and analyzed as a multi-stage game, in which the authority and the operator at each port first decide whether to share demand information and make concession arrangements; then, the port operators compete à la Cournot. Alternative scenarios are compared to identify the effects of information sharing and market structure. Our analytical results identify the conditions under which demand information sharing is beneficial in port concession arrangements and highlight the importance of the underlying market structure and congestion levels in achieving these benefits. Specifically, we show that information sharing is a source of welfare improvement, and the effects are more significant when the positive externality of information sharing on welfare is large, inter-port competition is strong, and port congestion is costly. However, with no compensation, the port operators have no incentive to share their private information because otherwise, this is likely to increase concession unit-fees, limit their ability to compete effectively with each other, and ultimately reduce their expected profits. Therefore, transfer payments are necessary to encourage information sharing. With this arrangement and the assumed symmetric cost and service structure, we show that a port operator prefers sharing information if the externality of it on welfare exceeds a threshold. Furthermore, when this externality is sufficiently large, the operators at both ports benefit from sharing information. Finally, when the two ports compete in price, we show that a port operator’s single-side information sharing may not always benefit its port authority.

Transportation Network Analysis

Tongji University, 2018

This course introduces the basic and advanced models and algorithms for analyzing, managing and d... more This course introduces the basic and advanced models and algorithms for analyzing, managing and designing urban and regional transportation networks. Main topics covered by this course include network routing problems, network flow problems, and network equilibrium problems. Other topics of interest might be introduced if time allows. The focus of this course is on the mathematical modeling and solution methods. To make the course self-contained, relevant mathematical programming and optimization techniques (e.g., linear programming, nonlinear programming, and network programming) will be covered, and elaborated if necessary.

The instructional language of this course is English, which means that teaching, quizzes, exams, and all course-related paperwork will be delivered in English.

Intelligent Transportation Systems

Shanghai Jiaotong University, 2014

This course introduces the basic elements and functionalities of intelligent transportation syste... more This course introduces the basic elements and functionalities of intelligent transportation systems (ITS) and how they are designed, deployed and operated for various traffic and transportation management purposes. Main topics covered by this course include transportation systems management and analysis, historical development, functional areas and institutional issues of ITS, advanced traffic management systems, advanced traveler information systems, advanced vehicle control systems, advanced public transportation systems, commercial vehicle operations, and role of ITS in travel demand management. Other topics of interest might be introduced and guest lectures be presented if time allows. The focus of this course is on the technological, systems and institutional aspects of ITS and its subsystems. To make the course self-contained, relevant transportation systems analysis methods will be covered, and elaborated if necessary.

The instructional language of this course is English, which means that teaching, quizzes, exams, and all course-related paperwork will be delivered in English.

Logistics Theory and Methods

Shanghai Jiaotong University, 2013

This course introduces the basic theory and methods used for analyzing, managing and designing mo... more This course introduces the basic theory and methods used for analyzing, managing and designing modern logistics systems. Main topics covered by this course include routing problems, network flow problems, facility location problems, and network design problems. Other topics of interest might be introduced if time allows. The focus of this course is on the mathematical modeling and solution methods. To make the course self-contained, relevant mathematical programming and optimization techniques (e.g., linear programming, integer programming, network programming, heuristic and metaheuristic methods, etc.) will be covered, and elaborated if necessary.

The instructional language of this course is English, which means that teaching, quizzes, exams, and all course-related paperwork will be delivered in English.

Evacuation Network Optimization: Models, Solution Methods and Applications

Cornell University, 2008

On finding the most effective ways to minimize the traffic congestion and disaster threat over an... more On finding the most effective ways to minimize the traffic congestion and disaster threat over an urban or regional evacuation network, the focus of this study is to develop a set of analytical tools and computational methods for seeking optimal allocation of existing network capacity and connectivity. The core problem posed in this text is a network optimization problem with regard to two lane-based planning strategies: lane reversal on roadway sections and crossing elimination at intersections. These strategies supplement one another by increasing capacity in specific traffic directions and creating an interruption-free traffic environment throughout the network.

The joint consideration of these strategies greatly increases the problem complexity and combinatorial effect. A Lagrangian-relaxed, tabu-based solution method has been developed to solve this otherwise intractable problem, which takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction and whose algorithmic design is based on the principles of tabu search metaheuristic.

The requirement of emergency vehicle assignment is also incorporated into the above modeling and solution framework, which creates a bi-objective evacuation network optimization problem. A lexicographic optimization approach is developed to identify the Pareto-optimal set of routing and network solutions for scenario analysis and decision making.

The set of evacuation planning models and solution methods have been tested and evaluated with both numerical examples and an evacuation case study in Monticello, Minnesota with varying network settings and conditions. The evaluation results prove the applicability, reliability and robustness of the developed methodology in both theoretical and practical network circumstances and provide useful insights and directions for further research.

Arterial Route Travel Time Estimation and Prediction Integrating Section- and Point-Based Traffic Data

University of Massachusetts, 2005

A regional advanced traveler information system (ATIS)—the Regional Traveler Information Center (... more A regional advanced traveler information system (ATIS)—the Regional Traveler Information Center (RTIC)—is being developed and implemented in western Massachusetts to provide real-time pre-trip and en-route travel time information to the public. The main functions of the RTIC include traffic data collection, processing, and information dissemination. The core of the data processing component is an arterial route travel time estimation and prediction model. This model is being developed and tested based on the combination of traffic data from two types of traffic sensors—license plate readers (LPR) and microwave-ultrasonic-infrared (MUI) sensors—in the RTIC testbed, a 3.7-mile critical arterial corridor on State Route 9. Individual route travel times captured by the LPR system and standard point traffic measures collected by the MUI sensors are integrated in order to overcome the inherent disadvantages of previous travel time estimation techniques based on either section-based or point-based data sources. The model functions on two levels: (1) to provide current travel time information for on-trip route choice; and (2) to predict short-term travel time variations from the current travel times for pre-trip route planning. Several separate functional modules are integrated into this model: (1) a heuristic LPR data filtering algorithm with underlying statistical components for validation of individual LPR travel time measurements; (2) a dynamic LPR data examination and aggregation module for providing aggregate LPR route travel time estimates; and (3) state space models built based on conditional independences between traffic variables, which are capable of correcting lagging LPR travel time estimates through monitoring the variation of MUI traffic flow data, and of deriving predictive route travel times in near-future time horizons in terms of the recent evolution of MUI traffic flow data.

An Arterial Speed Estimation Model Using Mobile and Stationary Sensors

National University of Singapore, 2001

This research contributes to the development of a link average speed estimation system through th... more This research contributes to the development of a link average speed estimation system through the data fusion combining probe vehicle and loop detector data as well as signal control parameters in a typical arterial street network. Such a road network, the Clementi New Town area in Singapore, is chosen as the simulation testbed for this study. Real-time data used for speed estimation are derived from two distinct sources: (1) mobile probe vehicles equipped with global positioning system (GPS) receivers, and (2) stationary inductive loop detectors (ILDs) embedded into pavements and signal control systems installed at the intersections, as all simulated and collected in an integrated microscopic simulation environment. Two independent estimation models, as subsystems in the whole architecture, are developed respectively for the two types of data sources based on the analysis of the sample size requirement of probe vehicles and the principle of travel time variation and composition over arterial links. Moreover, data fusion algorithms combining the above two estimates are also developed by two approaches of (1) multiple linear regression (MLR) and (2) multi-layer feedforward neural network (MLF). The results prove that data fusion has the capability of making more reliable and accurate arterial link speed estimation than either of the single source-based models operating alone, and the MLF model shows a better performance than the MLR model for this real-time data fusion so that it is recommended for the implementation in the data fusion architecture.

Allocating exclusive and intermittent transit lanes in dynamic traffic networks with connected and automated vehicles

International Journal of Transportation Science and Technology

Securing Cooperative Adaptive Cruise Control in Vehicular Platoons via Cooperative Message Authentication

IEICE Transactions on Information and Systems, 2020

Travel Behavior and Transportation Systems Analysis of Electric Vehicles

Journal of Advanced Transportation, 2018

Service network design in inland waterway liner transportation with empty container repositioning

European Transport Research Review, 2015

Modeling collusion-proof port emission regulation of cargo-handling activities under incomplete information

Transportation Research Part B: Methodological, 2017

Integrated Evacuation Network Optimization and Emergency Vehicle Assignment

Transportation Research Record: Journal of the Transportation Research Board, 2009

Three treatments are combined in the design of evacuation network operating plans: reversing lane... more Three treatments are combined in the design of evacuation network operating plans: reversing lanes, eliminating intersection crossings, and reserving lanes for use by emergency vehicles. An optimization approach is taken and casts the problem as a form of discrete network design, with constraints imposed by emergency vehicle lane assignment. What have previously been separate strands of work examining ways of configuring road networks for effective evacuation performance are integrated. A case study for a network around a nuclear power plant illustrates the usefulness of this integrated approach.

Equilibrium Analysis of Low-Conflict Network Designs

Transportation Research Record: Journal of the Transportation Research Board, 2014

A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows

EURO Journal on Transportation and Logistics, 2013

Path-Constrained Traffic Assignment

Transportation Research Record: Journal of the Transportation Research Board, 2012

This paper presents a mathematical programming model and solution method for the path-constrained... more This paper presents a mathematical programming model and solution method for the path-constrained traffic assignment problem, in which route choices simultaneously follow the Wardropian equilibrium principle and yield the distance constraint imposed on the path. This problem is motivated by the need for modeling distance-restrained electric vehicles in congested networks, but the resulting model and solution method can be applied to various conditions with similar path-based constraints. The equilibrium conditions of the problem reveal that any path cost in the network is the sum of corresponding link costs and a path-specific out-of-range penalty term. The suggested method, based on the classic Frank–Wolfe algorithm, incorporates an efficient constrained shortest-path algorithm as its subroutine. This algorithm fully exploits the underlying network structure of the problem and is relatively easy to implement. Numerical results from the examples of problems provided show how the equ...

A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

Routing and Scheduling Solutions

Hybridization offers a promising approach in designing and developing improved metaheuristic meth... more Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

Maximum Entropy Method for Subnetwork Origin–Destination Trip Matrix Estimation

Transportation Research Record: Journal of the Transportation Research Board, 2010

In the context of sketch planning, a simplified network (i.e., an abstract network or subnetwork)... more In the context of sketch planning, a simplified network (i.e., an abstract network or subnetwork) model is expected to accurately approximate travel demand patterns and level-of-service attributes obtained from its full-network counterpart. A data prerequisite in this approximation process is the trip matrix of the simplified network. This paper discusses a maximum entropy method for the subnetwork trip matrix estimation problem, relying only on link flow rates estimated with the use of full-network traffic assignment or as observed link-level vehicle counts. A linearization algorithm of the Frank–Wolfe type is devised for problem solutions in which a column–generation approach is used iteratively to solve the linearized subproblem without path enumeration. Encouraging results from sample applications of different size and topology suggest that this method holds much promise for generating trip matrices that can be used to evaluate traffic flow patterns under various network changes.

Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks

Transportation Research Record: Journal of the Transportation Research Board, 2003

Among discrete choice problems, travel mode choice modeling has received the most attention in th... more Among discrete choice problems, travel mode choice modeling has received the most attention in the travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives or classes. The capability and performance of two emerging pattern recognition data mining methods, decision trees (DT) and neural networks (NN), for work travel mode choice modeling were investigated. Models based on these two techniques are specified, estimated, and comparatively evaluated with a traditional multinomial logit (MNL) model. For comparison, a unique three-layer formulation of the MNL model is presented. The similarities and differences of the models' mechanisms and structures are identified, and the mechanisms ...

Two-Stage Equilibrium Travel Demand Model for Sketch Planning

Transportation Research Record: Journal of the Transportation Research Board, 2012

This paper describes a two-stage equilibrium travel demand model. The unique feature of this mode... more This paper describes a two-stage equilibrium travel demand model. The unique feature of this model is that it takes time-of-day traffic counts instead of land use and demographic data as inputs to derive spatial and temporal travel demand patterns. The first stage of the model is a trip matrix estimator based on traffic count; the second stage is an elastic-demand network flow estimator, which recognizes latent demand shifts while performing mode split, time-of-day split, and traffic assignment in a multilevel equilibration. The main purpose of this paper is to describe model development, algorithm design, and software implementation experiences. An example application illustrates how the model is used to evaluate multiclass, multimode, and multiperiod network flow patterns for sketch planning purposes.

Intersection Origin-Destination Flow Optimization Problem for Evacuation Network Design

Transportation Research Record: Journal of the Transportation Research Board, 2011

Origin-Based Algorithms for Traffic Assignment

Transportation Research Record: Journal of the Transportation Research Board, 2015

This paper presents an extensive analytical and numerical investigation of a class of origin-base... more This paper presents an extensive analytical and numerical investigation of a class of origin-based algorithms for the user equilibrium–based traffic assignment problem. Nine known algorithms in this class are first clustered into four algorithmic structures on the basis of their structural differences and similarities in algorithm design. A complexity analysis of these algorithmic structures is conducted by calculating the frequency of executing node and link operations; this approach provides a simple analytical way to estimate their per iteration computation costs. To deliver a comprehensive and fair comparison of their convergence performance, all nine algorithms are implemented on the same programming platform and run to solve a few representative large-scale traffic networks by sizes and congestion levels. A close look at the convergence performance statistics further justifies the consistency of the complexity analysis and numerical evaluation results of the computational effi...

A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation

Transportation Research Part B: Methodological, 2011