Meenakshy Vasudevan

Connected vehicles have the potential to transform travel as we know it by combining leading edge technologies— advanced wireless communications, on-board computer processing, advanced vehicle-sensors, Global Positioning System (GPS) navigation, smart infrastructure, and others—to address safety, mobility, and environmental challenges. Over the last five years, application prototyping and assessment has been a focus of federal connected vehicle research and development activity, resulting in more than three dozen connected vehicle application concepts. This effort also included assessments to measure safety, mobility and environmental impacts from four U.S. Department of Transportation (USDOT) connected vehicle Vehicle-to-Infrastructure (V2I) research programs (V2I Safety, Dynamic Mobility Applications (DMA), Applications for the Environment: Real-Time Information Synthesis (AERIS), and Road-Weather Management). Considering results to date from these assessment activities, there is ...

This Analysis Modeling Simulation (AMS) Analysis Plan for the I-15 Corridor outlines the various tasks associated with the application of the Integrated Corridor Management (ICM) AMS tools and strategies to this corridor in order to support benefit-cost assessment for the successful implementation of ICM. The report provides a brief description of the I-15 Corridor in San Diego, California, and the methodology used for the AMS. It lays out ICM strategies that will be tested and provides a list of the AMS scenarios. This report then defines performance measures that will be utilized in the analysis of the ICM strategies on the Pioneer Corridor and sets out the simulation model calibration requirements and the data needs for this calibration. Finally, the last two sections present an overview of the Pioneer Corridor AMS document that will be developed to summarize the results of the AMS effort and provides a schedule and a resource guide for the AMS tasks. Appendixes are included that...

2013PDFTech ReportFHWA-JPO-13-097DTFH61-11-D-00018SimulationComputer modelsTest bedsIntelligent transportation systemsWireless communication systemsReal time informationMobilityRisk assessmentUnited StatesUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeVasudevan, MeenakshyWunderlich, KarlNoblis, Inc.United States. Federal Highway AdministrationUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeIntelligent Transportation Systems Joint Program Office;Federal Highway AdministrationAnalysis Modeling and Simulation (AMS)Testbeds can make significant contributions in identifying the benefits of more effective, more active systems management, resulting from integrating transformative applications enabled by new data from wirelessly connected vehicles, travelers, and infrastructure. To this end, the Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM)Programs have jointly sponsored the planning of multiple AMS Testbeds to support the two programs in evaluating and demonstrating the system-wide impacts of deploying application bundles and strategies in an AMS environment.The purpose of this report is to document a preliminary plan for evaluating impacts of individual DMA applications, individual DMA bundles, and logical combinations of bundles and applications, and identifying conflicts and synergies for maximum benefit. Elements that are covered include: Key research questions and hypotheses that should be tested in the AMS Testbed; Performance measures that underpin the hypotheses; Description of analysis scenarios; Key technology and market penetration assumptions; Sensitivity analyses; and, Results reporting.A companion document provides a preliminary plan for ATDM strategies. These plans are intended to assist AMS Testbed developers in preparing an overarching evaluation methodology as well as detailed analytical plans tailored to specific testbed locations and analytical approaches.93

The United States Department of Transportation (USDOT), the Road Bureau of Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan, and the European Union’s European Commission Directorate General for Communications Networks, Content & Technology (DG CONNECT) have a long history of sharing information on ITS (Intelligent Transportation Systems) activities. A US-Japan-Europe ITS Task Force was established specifically to facilitate the exchange of information and identify areas for collaborative research for the development and deployment of ITS in the three nations. The Task Force identified the following four high-priority areas for conducting collaborative research: 1. International Standards; 2. Evaluation Tools and Methods; 3. Probe Data; and 4. Automation in Road Transport. To continue the initial joint US-Japan work documented in the US-Japan Collaborative Research on Probe Data: Assessment Report, published in November 2013, this report documents the work con...

16326352016PDFTech ReportFHWA-JPO-16-349DTFH61-11-D-00018Vehicle to roadside communicationsVehicle to vehicle communicationsMobile communication systemsIntelligent vehiclesConnected VehiclesOutreachStakeholder EngagementUnited StatesUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeWang, PeiweiDeurbrouck, TaylorNoblis, Inc.United States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeFederal Highway AdministrationThis document provides guidance material in regards to the outreach activities in the CV Pilot Deployment ConceptDevelopment Phase and deployment outreach plan in the Deployment Phases. This guidance provides keyrequirements and references in developing the Deployment Outreach Plan in Task 11, lists relevant deliverables andthe relevant elements in each deliverables, identifies key challenges the site deployers may encounter with respect toTask 11, and provides a summary of relevant technical support events.This document does not replace or alter the work statement defined in the Broad Agency Announcement (BAA); rather it provides technical guidance to the pilot deployers in completing the tasks and deliverables described in the statement of work

Analysis Modeling and Simulation (AMS) Testbeds can make significant contributions in identifying the benefits of more effective, more active systems management, resulting from integrating transformative applications enabled by new data from wirelessly connected vehicles, travelers, and infrastructure. To this end, the Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs have jointly sponsored the planning of multiple AMS Testbeds to support the two programs in evaluating and demonstrating the system-wide impacts of deploying application bundles and strategies in an AMS environment. The purpose of this report is to document an initial screening of AMS Testbed locations that was conducted to identify seven to ten potential candidate locations that may be further down-selected in a future effort for developing AMS Testbeds to conduct an evaluation of DMA and ATDM applications and strategies. The criteria used for screening the testbed loc...

Previous research using a simulated yoked trial evaluation methodology showed that commuter benefits from a notification-based pre-trip traveler information service are highly concentrated for congested PM peak trips. One implication of this finding is that traveler information benefits increase with increasing congestion. This paper explores how much benefit pre-trip traveler information provides on some of the worst commuting days in Washington, DC. The worst commuting days were determined as those having the highest system-wide aggregate travel times. The impacts on the worst commuting days were significant for a simulated commuter who did not rely on traveler information; typically, the number of late trips doubled and travel disutility cost jumped by 30%. The benefits of pre-trip traveler information were high on the worst commuting days; lateness risk was cut by more than half, and travel disutility cost was reduced by more than 20%. The ten worst days garnered 9% of the total...

2016PDFTech ReportFHWA-JPO-16-356DTFH61-11-D-00018CooperationIntelligent transportation systemsIntelligent transportation systems programsInternational transportationInternational relationsProbe dataprobe systemscooperative systemsconnected vehicleITS SpotETC2.0BSMmobility environmentalweatherUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeMcGurrin, MichaelVasudevan, MeenakshyWang, PeiweiMcHale, GeneThompson, DaleSakai, KoichiWatanabe, RyoichiTanaka, YoshihiroKanoshima, HideyukiMawatari, ShingoTsukiji, TakahiroFlament, MaximeBarnard, YvonneDreher, StephaneSalanova, Josep MariaNoblis, Inc.National Institute for Land and Infrastructure Management (Japan)European UnionUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeNTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMSFederal Highway AdministrationThe United States Department of Transportation (USDOT), the Road Bureau of Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan, and the European Union\u2019s European Commission Directorate General for Communications Networks, Content & Technology (DG CONNECT) have a long history of sharing information on ITS (Intelligent Transportation Systems) activities. A US-Japan-Europe ITS Task Force was established specifically to facilitate the exchange of information and identify areas for collaborative research for the development and deployment of ITS in the three nations. The Task Force identified the following four high-priority areas for conducting collaborative research:1. International Standards2. Evaluation Tools and Methods3. Probe Data4. Automation in Road TransportTo continue the initial joint US-Japan work documented in the US-Japan Collaborative Research on Probe Data: Assessment Report [1], published in November 2013, this report documents the work conducted from the fall of 2013 through the fall of 2015 to:1. Add probe system and probe data inputs from the European team;2. Significantly expand on the initial assessment, providing an extensive expansion on assessing the three applications;3. Discuss cross-cutting issues that affect all three applications; and4. Introduce the next steps for future collaboratio

This document presents a combined set of requirements for an Analysis, Modeling, and Simulation (AMS) Testbed based on the foundational research already conducted under the Active Transportation and Demand Management (ATDM) Program, and the analytical needs of the Dynamic Mobility Applications (DMA) bundles. A total of 103 requirements are identified to represent the following: (1) System Entities (“nodes”) (System Users, Connected Vehicles/Devices, Communications Systems, Operational Data Environments, and System Managers); (2) Data and Information Flows (“arcs”) connecting system entities, including attributes of latency, aggregation, message content, range, reliability, and accuracy (error); (3) ATDM Strategies and DMA Applications enhancing current decision-making by System User or System Managers or enabling new forms of decision-making by Users and Managers; (4) Operational Conditions (ambient demand, incident, and weather conditions); (5) System Performance Measurement (mobil...

The Integrated Corridor Management (ICM) initiative requires the calibration and validation of simulation models used in the Analysis, Modeling, and Simulation of Pioneer Site proposed integrated corridors. This report summarizes the results and process for the calibration of microsimulation network for the I-15 Corridor in San Diego, California. As one of the Pioneer Sites for the ICM project, the I-15 Corridor was simulated in TransModeler microsimulation. Various versions of TransModeler were utilized as additional capabilities became available. The work was conducted by Cambridge Systematics, Inc. (CS), in partnership with San Diego Association of Governments (SANDAG). The eventual model was satisfactorily calibrated according to the guidelines set forth in the Experimental Plan. The report is organized into two major parts. The first part provides the final calibration results, and provides a quick summary of the model calibration criteria and comparison against real data. The ...

This paper studies the advantage of integrating bus-preemption with adaptive signal control. Two integrated models for adaptive bus- preemption control are used in the absence and presence of Automatic Vehicle Location systems. Instead of using prespecified strategies the proposed models make a preemption decision based on a performance index. The study also contains an extensive simulation evaluation with respect to the integration of adaptive control with bus-preemption.

2016PDFTech ReportFHWA-JPO-16-326DTFH61-11-D-00018CooperationIntelligent transportation systemsIntelligent transportation systems programsInternational transportationInternational relationsCooperative systemsconnected vehiclesprobe systemevaluationcost-benefitperformance measuresindicatorsJapan;United StatesUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeVasudevan, MeenakshyThompson, KathyJacobi, AmyMercer, MichaelBrooks, MichaelLawrence, StephanieVickery, DanielSakai, KoichiWatanabe, RyoichiKanoshima, HideyukiMawatari, ShingoTsukiji, TakahiroMachek, EliThompson, DaleNoblis, Inc.United States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeNational Institute for Land and Infrastructure Management (Japan)NTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMSFederal Highway AdministrationThe United States (U.S.) and Japan have similar transportation challenges, and share a common belief that cooperative systems can deliver significant societal benefits for road users, especially in terms of safer, more energy-efficient, and environmentally friendly surface transportation. The two regions recognize that coordinated research can reduce costs and accelerate the development, deployment, and adoption of cooperative systems. The report is an outcome of the U.S.-Japan bi-lateral collaborative research on evaluation tools and methods. The report includes:\u2022 Case studies of cost-benefit evaluations, including performance indicators, and measurement methods, of Intelligent Transportation Systems (ITS) and cooperative systems in the U.S. and Japan\u2022 Comparison and assessment of existing evaluation methods used for evaluating ITS and cooperative systems in the U.S. and Japan\u2022 Consistent glossary of terms for evaluations for use in the U.S. and Japan\u2022 Consistent categorization and organization of performance indicators and measurement methodsThe following are the key lessons learned from the assessment:\u2022 Evaluations should be performed by an independent party who has no vested interest or stake in the project itself to eliminate potential bias.\u2022 More rigorous experimental design is needed to better isolate benefits of cooperative systems or ITS implementations\u2022 Consistent dollar values should be applied when monetizing benefits.\u2022 Acceptance of cooperative systems based on short-term exposures can be misleading.\u2022 Longer-term impact of cooperative systems should be examined prior to large scale deployment.The following are some opportunities for future collaboration between the U.S. and Japan, including:\u2022 Development of consistent methodology for evaluations\u2022 Application of the consistent methodology to evaluate a cooperative system deployment, either in the U.S. or in Japan (or one each in both nations)

Identifying applications that can reduce fuel consumption and emissions from surface transportation is an important strategy in solving transportation’s environmental dilemma. The U.S. Department of Transportation (U.S. DOT) has set out to investigate the problem through several initiatives, one of which is connected vehicle research. The purpose of this report is to document the state of the practice for applications that have demonstrated environmental benefits through intelligent transportation system (ITS) technologies, or have the potential to do so, and identify opportunities to leverage existing research. The findings outlined in the report will assist the U.S. DOT in planning and implementing the Applications for the Environment: Real-Time Information Synthesis (AERIS) Program. The findings will also provide the basis for identifying opportunities to further examine and research applications for improving environmental decisions by public agencies and consumers and for impro...

In a connected-vehicle environment, wireless subsecond data exchange connects vehicles, the infrastructure, and travelers’ mobile devices. These data have the promise to transform the geographic scope, precision, and latency of transportation system control; fulfillment of that promise could result in significant safety, mobility, and environmental benefits. However, the new data influx also has the potential to overburden legacy computational and communication systems. Although connected-vehicle technology can facilitate ubiquitous system coverage, the existing prediction methods, computational platforms, and data management methods are insufficient to process the data within a reasonable time frame for real-time predictions. An investigation of the ways in which advanced (big-data) analytics might be applied to realize the full potential of connected-vehicle technology is particularly relevant now as this technology evolves from research to deployment. This paper presents an appro...

This white paper examines the available quantitative information on the potential mobility benefits of the connected vehicle Dynamic Mobility Applications (DMA). This work will be refined as more and better estimates of benefits from mobility applications emerge. The set of mobility applications provide more than just mobility benefits. They also have the potential to generate energy benefits (e.g., decrease in fuel consumption), environmental benefits (e.g., decrease in greenhouse gases (GHGs), and air pollutant emissions) and safety benefits (e.g., decrease in number and severity of primary and secondary crashes, reduced incident response time). This white paper, however, focuses on mobility benefits. The results of the analysis show that the high priority Dynamic Mobility Applications will reduce congestion while simultaneously increasing safety and benefiting the environment. Based on the limited data currently available from modeling and field trials of similar applications, fu...

This work presents a method for constructing an archive of broadcast radio traffic report content from Web advisories using neural networks. Broadcast traffic reports are free and widely used as a source of traveler information. However, there has been no study done to establish what impacts, if any, these traffic reports have in terms of improving listener travel reliability. We developed an analytical technique to quantify travel reliability impacts and conducted a preliminary case study for the Washington, DC, metropolitan area, using radio traffic reports recorded from a local radio station and manually coded for 37 weekdays. However, as coding of radio traffic reports is highly labor-intensive, we used neural networks to construct a database of radio traffic advisories from an existing archive of Web traffic advisories. This paper presents the model developed using feed-forward neural network with back propagation of error that can, given a list of Web advisories, predict roadway segments that would also have an advisory mentioned on the radio. The overall accuracy during the morning peak period was 72%, implying that a commuter listening to constructed advisories would have a 72% chance of listening to an actual advisory mentioned on the radio. During the afternoon peak period, the accuracy was 78%. The missed prediction rates in the morning and afternoon peak periods were 28% and 23%, respectively. Given that we can construct a full year of radio traffic advisories we are able to conduct a more representative study for a longer period of time since traffic conditions on 37 weekdays cannot be used to generalize typical trip experiences of a commuter. Thus, neural networks proved to be a viable low-cost approach to solve the problem of lack of data.

The mobility and disutility-reduction benefits to users of the 511 advisory service currently deployed in Salt Lake City, Utah, were evaluated through a modeling study conducted using the HOWLATE (Heuristic On-Line Web-Linked Arrival Time Estimation) methodology. The benefits of the existing 511 advisory service to seasoned users of the service and the potential additional benefit to providing travel-time estimates via the 511 service were evaluated. Data were provided by the Utah Department of Transportation. Four types of commuters were modeled: (a) a nonuser, who ignored traveler information; (b) a 511 advisory user, who made use of the 511 advisory service currently available to commuters in Salt Lake City; (c) a 511 travel-time user, who made use of a hypothetical 511 service that provided travel-time estimates; and (d) a dynamic message sign (DMS) user, who made use of DMS deployed along 20 mi of I-15. Analysis showed that the existing 511 advisory service resulted in mobility...

An approach is presented for quantifying commute disutility measures. The approach was demonstrated through a case study conducted for the Washington, D.C., metropolitan area by using an analytical technique called the heuristic on-line web-linked arrival time estimation. “Commute disutility” was defined as having pretrip, en route, and posttrip components on the assumption that there is disutility associated with a commuter's expectation of the trip before trip start, the en route trip experience, and the actual outcome of the trip. Three types of regular commuters were modeled: the nonuser who does not use any traveler information and two kinds of traveler information user–the radio listener who listens to commercial broadcast traffic advisories and the advanced traveler information service user who uses a notification-based service that provides route-specific travel time estimates. Analysis showed that compared to nonusers, traveler information users had lower commute disutility. They had fewer late arrivals at their destinations, and in fewer instances their trip expectation before trip start did not match their actual trip experience. They had fewer instances of feeling at intermediate waypoints along a trip that they were running behind schedule. They modified their trip start times or took alternate routes on more than 65% of the trips. This may have resulted in some disutility because of changes to the regular commute behavior, but they are more informed and therefore more confident of the potential trip outcome than is a nonuser.

To explore the advantages of integrating bus preemption and adaptive signal control, an integrated model for adaptive bus-preemption control in the absence of automated vehicle location systems was developed. In the proposed system, unconditional priority is not given to buses over passenger cars. Instead of using pre-specified strategies such as phase extension, phase early start, or special bus phase, preemption decision is based on a performance index, which includes vehicle delay, bus schedule delay, and passenger delay. An extensive simulation evaluation with respect to the integration of adaptive control with preemption is also presented. The developed model displays promising results.

16440912016PDFTech ReportFHWA-JPO-17-498DTFH61-11-D-00018Traffic simulationTraffic modelsIntelligent vehiclesTraffic flowTraffic forecastingMachine learningpredictiontraffic regimescongestionbig dataconnected vehiclesSparkUnited StatesUnited States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeVasudevan, MeenakshyCurtis, ChrisLowman, AlexaO'Hara, JamesNoblis, Inc.United States. Department of Transportation. Intelligent Transportation Systems Joint Program OfficeFederal Highway AdministrationThe key objectives of this study were to:1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine accuracy for various levels of market penetration2. Examine the tradeoff between information insight and cost of data processing and managementData from a virtual (simulated) testbed for the I-405 corridor in Seattle was used to conduct the study. The field data and VISSIM simulation model were obtained from WSDOT. The simulation model went through rigorous calibration and validation process as part of a separate study conducted by Noblis for FHWA Traffic Analysis Tools Program. The Trajectory Conversion Algorithm (TCA V2.3), an open source tool developed by Noblis for the USDOT, was used to emulate SAE J2735 Basic Safety Messages (BSM).Traffic flow regimes (free flow, speed at capacity, and congested) were predicted for 100\u2019 x 100\u2019 boxes overlaid on the I-405 traffic network, every 5 minutes an hour ahead of time using the simulated BSMs. The study made use of Apache Spark\u2019s machine learning libraries for Logistic Regression, Decision Tree and Random Forest to develop models to predict the traffic flow regimes. The computational resources and analytic environment used for this work were provisioned via the Microsoft Azure cloud environment. The computing cluster used for the analysis consisted of four nodes in total: 2 head nodes for job submission and management and 2 worker nodes for computation. Prediction accuracy was tested for two types of communication technologies (Cellular, Dedicated Short Range Communications (DSRC)), two market penetrations (20%, 75%), and six traffic operational conditions. The three algorithms were tested for 6, 8, and 11 principal components. In addition, the Decision Trees and Random Forest algorithms were tested using two node impurity metrics (entropy, Gini), and Random Forest was tested for multiple ensembles of trees (10, 250, 1000). The model that used the Random Forest algorithm with 11 principal components, 250-tree ensemble, and the Gini node impurity metric, had the best results with an average F1 score of 0.83 over all scenarios. The F1 scores were 0.87 for free flow, 0.67 for at capacity and 0.95 for congested traffic regimes. The model was able to fully process an hour\u2019s worth of BSMs into the 100\u2019 x 100\u2019 grid boxes, and make a prediction for the following hour, at 5-minute intervals for each of the 100\u2019 x 100\u2019 boxes in 6 to 16 minutes

Because of high data collection costs, analysts are commonly faced with the problem of limited data in the evaluation of intelligent transportation systems. How reliable are conclusions based on small samples? If limited data are available, how does one maximize their value? These questions were addressed to evaluate the potential benefits of prospective notification-based traveler information services used to deliver pre-trip travel time information to simulated drivers in a Cincinnati, Ohio, case study. In Cincinnati, travel time data were initially available for only 30 weekdays. An analysis that used this small data set indicated that an advanced traveler information system (ATIS) user would reduce disutility by 32% versus a comparable nonuser. However, since trip experiences on 30 weekdays may not characterize the typical experience of a commuter, conclusions drawn from the small sample may not accurately represent a more generalized assessment of the benefits of ATIS. Hence, a...