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- Transportation Engineering, Transportation Studies, Intelligent Transportation Systems, Traffic Simulation, Traffic Operations, Machine Learning, and 8 moreSimulation Based Optimization, Traffic Flow, Bus Rapid Transit, Dynamic Traffic Assignment, Congestion Pricing, Transportation Corridors, Traffic Management, and Stochastic Fundamental Diagramedit
- Dr. Xiqun (Michael) Chen is working in the College of Civil Engineering and Architecture, Zhejiang University, Hangzh... moreDr. Xiqun (Michael) Chen is working in the College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, as a "Hundred Talents Program" Professor, and Principal Investigator. Dr. Chen received his B.E. degree and Ph.D. degree in Civil Engineering from Tsinghua University, China, in 2008 and 2013, respectively. In 2011, he visited the California PATH Program, University of California at Berkeley as a joint training Ph.D. student. His Ph.D. thesis entitled "Stochastic Evolutions of Dynamic Traffic Flow: Modeling and Application" received the 2013 IEEE Intelligent Transportation Systems Society (ITSS) Best Ph.D. Dissertation Award. He worked in the Department of Civil and Environmental Engineering at the University of Maryland, USA, as a Faculty Research Assistant (Jan 2013 to Jun 2014), a Research Associate (Jul 2014 to Mar 2015). His past work forcused on the simulation based optimization, traffic flow theory, and intelligent transportation systems. From Oct 2013 to Mar 2015, Dr. Chen served as the Research Director of National Center for Strategic Transportation Policies, Investments and Decisions. He is a member of IEEE, Chinese Overseas Transportation Association (COTA), and an Associate Member of American Society of Civil Engineers (ASCE). He had published more than 20 peer-reviewed journal papers and over 30 conference papers.edit
Road traffic flow is intrinsic with stochastic and dynamic characteristics so that traditional deterministic theory no longer satisfies the requirements of evolution analysis. Stochastic traffic flow modelling aims to study the... more
Road traffic flow is intrinsic with stochastic and dynamic characteristics so that traditional deterministic theory no longer satisfies the requirements of evolution analysis. Stochastic traffic flow modelling aims to study the relationships of transportation components. The kernel is an investigation of both stochastic characteristics and traffic congestion evolution mechanism by using headway, spacing and velocity distributions. The primary contents include empirical observations, connections with microscopic and macroscopic traffic flow models, and traffic breakdown analysis of highway bottlenecks.
The thesis first analyzes the characteristics of empirical traffic flow measurements to reveal the underlying mechanism of complexity and stochastic evolutions. By using Eulerian measurements (inductive loop data) and Lagrangian measurements (vehicular trajectory data), we study headway/spacing/velocity distributions quantitatively and qualitatively.
Meanwhile, disturbances of congested platoons (jam queues) and time-frequency properties of oscillations, which establish the empirical foundation for stochastic traffic flow modelling. Then we establish the Markov car-following model by incorporating the connection between headway/spacing/velocity distributions and microscopic car-following models. Using the transition probability matrix to describe random choices of headways/spacings by drivers. Results show that the stochastic model more veritably reflects the dynamic evolution characteristics of traffic flow. As discussions of the connection between headway/spacing/velocity
distributions and the macroscopic fundamental diagram model, we analyze the probability densities and probabilistic boundaries of congested flow in flow-density plot by proposing a stochastic extension of Newell’s model to study wide scattering features of flow-density points.
For applications to highway on-ramp bottlenecks, we propose a traffic flow breakdown probability model based on headway/spacing distributions. We reveal the mechanism of transitions from disturbances to traffic congestion, and the phase diagram analysis based on a spatial-temporal queueing model that is beneficial to obtain optimal control strategies to improve the reliability of road traffic flow.""
The thesis first analyzes the characteristics of empirical traffic flow measurements to reveal the underlying mechanism of complexity and stochastic evolutions. By using Eulerian measurements (inductive loop data) and Lagrangian measurements (vehicular trajectory data), we study headway/spacing/velocity distributions quantitatively and qualitatively.
Meanwhile, disturbances of congested platoons (jam queues) and time-frequency properties of oscillations, which establish the empirical foundation for stochastic traffic flow modelling. Then we establish the Markov car-following model by incorporating the connection between headway/spacing/velocity distributions and microscopic car-following models. Using the transition probability matrix to describe random choices of headways/spacings by drivers. Results show that the stochastic model more veritably reflects the dynamic evolution characteristics of traffic flow. As discussions of the connection between headway/spacing/velocity
distributions and the macroscopic fundamental diagram model, we analyze the probability densities and probabilistic boundaries of congested flow in flow-density plot by proposing a stochastic extension of Newell’s model to study wide scattering features of flow-density points.
For applications to highway on-ramp bottlenecks, we propose a traffic flow breakdown probability model based on headway/spacing distributions. We reveal the mechanism of transitions from disturbances to traffic congestion, and the phase diagram analysis based on a spatial-temporal queueing model that is beneficial to obtain optimal control strategies to improve the reliability of road traffic flow.""
Research Interests:
Freeway traffic state estimation is a vital component of traffic management and information systems. Macroscopic model based traffic state estimation methods are widely used in this field and have gained significant achievements. However,... more
Freeway traffic state estimation is a vital component of traffic management and information systems. Macroscopic model based traffic state estimation methods are widely used in this field and have gained significant achievements. However, tests show that the inherent randomness of traffic flow and uncertainties in the initial conditions of models, model parameters as well as model structures all influence traffic state estimations. To improve the estimation accuracy, this paper presents an ensemble learning framework to appropriately combine estimation results from multiple macroscopic traffic flow models. This framework first assumes that any models existing are imperfect and have their own strengths/weaknesses. It then estimates the online traffic states in a rolling horizon scheme. This framework automatically ensembles the information from each individual estimation models, based on their performance during the selected regression horizon. In particular, we discuss three weighting algorithms: least square regression, ridge regression and lasso, which represent different presumptions of model capabilities. A field test based on real freeway measurements indicates that lasso ensemble best handles various uncertainties and improves estimation accuracy significantly. It should also be pointed out that the proposed framework is a flexible tool to assemble non-model based traffic estimation algorithms. This framework can also be extended for many other applications, including traffic flow prediction and travel time prediction.
Keywords: Traffic state estimation, multi-model ensemble, uncertainty, ridge regression, lasso
Keywords: Traffic state estimation, multi-model ensemble, uncertainty, ridge regression, lasso
Research Interests:
Bus rapid transit (BRT) is a popular strategy to increase transit attraction because of its high-capacity, comfortable service, and fast travel speed with the exclusive right-of-way. Various engineering designs of right-of-way and the... more
Bus rapid transit (BRT) is a popular strategy to increase transit attraction because of its high-capacity, comfortable service, and fast travel speed with the exclusive right-of-way. Various engineering designs of right-of-way and the violation enforcement influence interactions between BRT and general traffic flows. An empirical assessment framework is proposed to investigate traffic congestion and lane-changing patterns at one typical bottleneck along a BRT corridor. The BRT bottleneck consists of bus lane, BRT station, video enforcement zone, and transit signal priority intersection. We analyze oblique cumulative vehicle counts and oblique cumulative lane-changing maneuvers extracted from videos. The cumulative vehicle counts method widely applied in revealing queueing dynamics at freeway bottlenecks is extended to an urban BRT corridor. In the study site, we assume four lane-changing patterns, three of which are verified by the empirical measurements. Investigations of interactions between buses and general traffic show that abnormal behaviors (such as lane violations and slow moving of the general traffic) induce 16% reduction in the saturation rate of general traffic and 17% increase in bus travel time. Further observations show that the BRT station and its induced increasing lane-changing maneuvers increase the downstream queue discharge flows of general traffic. The empirical results also contribute to more efficient strategies of BRT planning and operations, such as alternative enforcement methods, various lane separation types, and optimized traffic operations. Copyright © 2014 John Wiley & Sons, Ltd.
Research Interests:
Research Interests:
Research Interests:
Travel time serves as a fundamental measurement for transportation systems and becomes increasingly important to both drivers and traffic operators. Existing speed interpolation algorithms use the average speed time series collected from... more
Travel time serves as a fundamental measurement for transportation systems and becomes increasingly important to both drivers and traffic operators. Existing speed interpolation algorithms use the average speed time series collected from upstream and downstream detectors to estimate the travel time of a road link. Such approaches often result in inaccurate estimations or even systematic bias, particularly when the real travel times quickly vary. To get rid of this problem, Coifman proposed a creative interpolation algorithm based on kinetic-wave models. This algorithm reconstructs vehicle trajectories according to the velocities and the headways of vehicles. However, it sometimes gives significant biased estimation, particularly when jams emerge from somewhere between the upstream and downstream detectors. To make an amendment, we design a new algorithm based on the temporal-spatial queueing model to describe the fast travel-time variations using only the speed and headway time series that is measured at upstream and downstream detectors. Numerical studies show that this new interpolation algorithm could better utilize the dynamic traffic flow information that is embedded in the speed/headway time series in some special cases.
Research Interests:
The scattering features of points in flow–density plot remain as an attractive topic in the last several decades. Some previous studies either assumed that the points of congested traffic flows were completely random or that the implicit... more
The scattering features of points in flow–density plot remain as an attractive topic in the last several decades. Some previous studies either assumed that the points of congested traffic flows were completely random or that the implicit rules of hidden distribution were difficult to describe. Although the scattering features are influenced by various factors (e.g. lane-changing manoeuvers, merging behaviours and driver heterogeneity), we believe that they are mainly dominated by the microscopic headway/spacing distributions. In this paper, we relax the assumption of deterministic headway/spacing in Newell's simplified car-following model and allow random headways/spacings in a homogeneous platoon (vehicles run closely at the same velocity). Further extending the conventional deterministic reciprocal relationship between flow rate and headway, we find that the reciprocal of average headway of a homogeneous platoon and the corresponding flow rate should follow the same distribution. Based on these two extensions, we can link the conditional distributions of average headway in a homogeneous platoon and the conditional distributions of flow rate, all with respect to velocity. When the aggregation time interval is small enough (e.g. 30 s), tests on Performance Measurement System (PeMS) data reveal that the seemingly disorderly scattering points in the macroscopic flow–density plot follow the estimated flow rate distributions from Next Generation Simulation vehicular trajectories. While if the aggregation time interval increases (e.g. to 5 min), the measured vehicles probably pass the loop detectors at different velocities and form heterogeneous platoons. It becomes difficult to find a definite distribution model that can fit average headway/spacing for heterogeneous platoons. However, most points in flow–velocity plot still locate within a certain 2D region, whose boundaries can be obtained from the homogeneous platoon model. Finally, tests on PeMS data verify the estimated boundaries.
Research Interests:
In this paper, we propose a simple temporal-spatial queueing model to quantitatively address some typical congestion patterns that were observed around on/off-ramps. In particular, we examine three prime factors that play important roles... more
In this paper, we propose a simple temporal-spatial queueing model to quantitatively address some typical congestion patterns that were observed around on/off-ramps. In particular, we examine three prime factors that play important roles in ramping traffic scenarios: the time τin for a vehicle to join a jam queue, the time τout for this vehicle to depart from this jam queue, and the time interval T for the ramping vehicle to merge into the mainline. Based on Newell's simplified car-following model, we show how τin changes with the main road flow rate qmain. Meanwhile, T is the reciprocal of the ramping road flow rate qramp. Thus, we analytically derive the macroscopic phase diagram plotted on the qmain-versus- qramp plane and τin-versus-T plane based on the proposed model. Further study shows that the new queueing model not only reserves the merits of Newell's model on the microscopic level but helps quantify the contributions of these parameters in characterizing macroscopic congestion patterns as well. Previous approaches distinguished phases merely through simulations, but our model could derive analytical boundaries for the phases. The phase transition conditions obtained by this model agree well with simulations and empirical observations. These findings help reveal the origins of some well-known phenomena during traffic congestion.
In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified... more
In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.
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ABSTRACT In this paper, we study the equilibriums of the general Cell transmission models (CTM) and the general Lagged Cell transmission models (LCTM). Theoretical analyses show that different CTMs and LCTMs converge to a two-segment... more
ABSTRACT In this paper, we study the equilibriums of the general Cell transmission models (CTM) and the general Lagged Cell transmission models (LCTM). Theoretical analyses show that different CTMs and LCTMs converge to a two-segment almost piecewise-linear equilibrium state with similar shapes, although the variety of the traffic flow-density relationships (fundamental diagram, FD) and the existence of the lagged effect lead to different patterns of convergence trajectories and speeds. These results indicate the usefulness of the CTMs and LTCMs in practice, especially when we are interested on the formations and dissipations of traffic congestions.
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This paper extends the intelligent driver model (IDM) with a multi-anticipative behavior and a reaction delay to describe the motion of the dynamical traffic flow. In the approach, the acceleration and deceleration manipulations of... more
This paper extends the intelligent driver model (IDM) with a multi-anticipative behavior and a reaction delay to describe the motion of the dynamical traffic flow. In the approach, the acceleration and deceleration manipulations of drivers depend on the velocity of the subject vehicle n, the netto gaps and velocity differences between vehicle n and a range of the preceding vehicles
Research Interests:
Location routing problem (LRP) is a significant subject in logistics systems, and genetic algorithm can obtain the near optimum solutions of large scale nonlinear mixed integer programming models which are NP-hard in nature. In the... more
Location routing problem (LRP) is a significant subject in logistics systems, and genetic algorithm can obtain the near optimum solutions of large scale nonlinear mixed integer programming models which are NP-hard in nature. In the process of algorithm design, two-dimensional chromosomes that satisfy constraints automatically and their corresponding genetic arithmetic operators are designed, such as selection, crossover, mutation of seeds, re-insert and so on. Three-layer process of genetic evolution is conducted and examined by a group of random experiments. This paper compares the precision, effectiveness and applicable scope between the proposed algorithm and current optimizing software. With increase number of variables and constrains, computing time and iterations of genetic algorithm increase almost linearly, while that of the current software LINGO present NP-hard. Results show the proposed genetic algorithm is effective and efficient in solving the LRP problem especially of large scale.
