Collaborators and Affiliations - University of Washington



New Research Project ProposalEnhancing Safe Traffic Operations Using Connected Vehicles Data and Technologies{06/16/2015 – 06/15/2016} Collaborators and AffiliationsPI: Ali Hajbabaie, Assistant Professor, Department of Civil and Environmental Engineering, Washington State UniversityCo-PI: Yinhai Wang, Professor, Department of Civil and Environmental Engineering, University of WashingtonCo-PI: Leila Hajibabai, Clinical Assistant Professor, Department of Civil and Environmental Engineering, Washington State UniversityProject GoalThe ultimate goal of the proposed research is to use connected vehicles (CVs) data and technologies to improve traffic safety on mixed-use roadway networks (e.g., freeways and intersections). This goal is relevant to two of the three themes of PacTrans, namely Technological Impacts on Safety and safe travel on mixed-use roads. To achieve this goal, the research team has identified four objectives: a) Develop a cost-effective communication note (CN) device that is capable of communicating with connected vehicles via Dedicated Short Range Communications (DSRC) and with pedestrians, bicyclists, and unconnected vehicle through cell phones and other mobile devices via Bluetooth, WiFi, or other suitable communication protocols. Such CN devices can serve both as notes of the CV system and as data access and dissemination points for certified mobile devices, including cell phones, tablet personal computers, and laptop computers. These CN devices can set up ad hoc networks that extends the detection to desired locations as illustrated by Figure 1. By placing and properly using such CN devices in the collision-prone locations, traffic safety for all kinds of road users can be significantly improved. b) Develop a mobile application (app) that allows pedestrians, bicyclists, and drivers of unconnected vehicles to communicate with the CN device and vice versa. Considering the popularity of Android phones and other mobile devices, the mobile app to be developed in this study will be based on the Android system. As a result, the app will allow collecting data on systems users’ location, speed, etc. and sending them appropriate warning messages in response to a particular unsafe scenario. c) Develop an algorithm to identify unsafe conditions and determine appropriate CV based safety countermeasures (a.k.a. CV safety application) to be presented to system users. In other words, the team will determine what kind of message to be shown to which system users under a specific hazardous scenario. d) Develop a connected vehicle simulation test-bed to evaluate the safety benefits of the proposed methodology under various traffic and landscape conditions. The CN device system will be implemented in the CV test corridors for the Washington State Department of Transportation (WSDOT). Field observations offers the data needed to calibrate the simulation test-bed. Figure 1: Illustration of the CN Device Application in a Vision Restricted Street ScenarioThe expected outcomes of the proposed research include an innovative technology to combine personal mobile devices to the CV system and a new mechanism with its mobile implementation to identify and inform different road users about unsafe conditions on a roadway network. Both are important in enhancing safe traffic operations and travels on mixed-use roadways. Relevancy of Institutional PartnershipsWashington State University (WSU) is joined by the University of Washington (UW) Smart Transportation Applications and Research Laboratory (STAR Lab) to form a unique team with expertise in the area of traffic safety, connected vehicles, traffic operations, information technologies, logistics systems, and optimization to conduct the proposed study using both simulation and field data. Dr. Ali Hajbabaie brings his expertise in the areas of traffic safety, traffic operations, and simulation analysis to the team. He is expected to lead the team’s efforts on developing the CV simulation test bed for this project and contribute to developing CV safety applications. Dr. Yinhai Wang has both transportation and computer science backgrounds and is an expert in the areas of traffic operations and safety, traffic simulation, and information technology. He is the principal investigator for developing two CV test beds for WSDOT in Seattle, WA. He is expected to lead the team’s efforts on developing the CN device and the mobile app. Furthermore, he will lead the team’s efforts on implementing the proposed CV safety applications in the real-world CV test beds. Dr. Leila Hajibabai is an expert in transportation and logistics systems, asset management, and modeling and optimization. She is expected to lead the team’s efforts in developing CV safety applications and to contribute to analyzing the simulation and real-world results. Research Background and Problem StatementIn 2012, there were more than 5.6 million crashes, including almost 31,000 fatalities, and more than 1.6 million injuries on U.S. roads as the result of vehicle crashes. These figures call for developing and implementing effective methods to reduce the number and severity of crashes on US roadway system to move towards zero fatalities in the future. Connected Vehicle technology presents great potential to increase the safety of our roadway system by increasing driver situational awareness and reducing or eliminating crashes through vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-device systems (V2X) communications. Connected Vehicle technology employs dedicated short range wireless communication systems (or cellular systems) to share basic safety messages (e.g. vehicle location, speed, acceleration rate, etc.) between other equipped vehicles, road users, and infrastructure ten times a second. According to a U.S. Department of Transportation (DOT) report, combined V2V and V2I systems potentially address about 81 percent of all-vehicle target crashes; 83 percent of all light-vehicle target crashes; and 72 percent of all heavy-truck target crashes annually. Connected vehicles will also help roadway users (e.g. vehicle drivers, pedestrians, and bicyclists) to either avoid crashes or reduce their severity. In a connected environment, road users (drivers and pedestrians/bicyclists) will receive warnings that inform them of potential hazards through visual displays, audible warning messages, etc. These warnings will increase roadway users’ awareness of hazards and other dangerous situations they may not otherwise be aware of. As a result, drivers for example, can be alerted to imminent crash situations such as head-on collisions or rear-end crashes. Furthermore, when going through an intersection, roadway users can be alerted about another system user who has failed to observe the right of way so that these roadway users can take necessary actions to avoid traffic crashes. There have been several studies focusing on safety and operational benefits of V2V and V2I communications (as will be presented below); however, the safety benefits of V2X communications were not found in published materials so far. Using CV data and technology to improve traffic operations has been the topic of many studies over the past five years. Some examples include Lee and Park (2012), He et al. (2012), He et al. (2014), Christofa et al. (2013), Hu et al. (2015), Feng et al. (2015), Guler et al. (2014), and Lee et al. (2013). There are also a number of research projects that aim at evaluating the effectiveness of a number of CV safety applications. For instance, the effectiveness of six V2V safety applications are currently being tested in Ann Arbor, MI. Those applications are forward collision warning, emergency electronic brake light, blind spot/lane change warning, intersection movement assist, and left turn assist. The University of Michigan is performing this study and has a fleet of 3000 connected vehicles. In addition, the USDOT has sponsored and performed research in the area of V2V communications for safety. The research focus has been on finding out if vehicle based safety application using V2V communications are effective and if they have the perceived benefits. A report published in 2013 (Najm et al.) described pre-crash scenarios that might be addressed by V2V communications. The research focused on crashes involving at least one light vehicle with a gross vehicle weight rating of 10,000 pounds or less. The research showed that most crashes occurred on straight roads, dry surfaces, in clear weather conditions, and during daylight hours. About 56 percent of drivers were male and 60 percent were of middle age. About 27 percent of all drivers were inattentive, 4 percent were under the influence of alcohol and/or drugs, and 10 percent were fatigued. Speeding was a factor in 13 percent of all crashes. The average effective deceleration level was over 0.6g in the ‘lead vehicle moving’ and ‘lead vehicle decelerating’ pre-crash scenarios, when braking was initiated 2 to 3 seconds before the crash. In a follow up research, Najm (et al.) presented a template of pre-crash scenarios to depict national crash statistics and kinematic information on time-to-collision. These information were used to design appropriate crash countermeasures based on V2V communications. The study suggested a set of ten pre-crash scenarios that account for about 87% of the comprehensive economic cost and functional years lost of all V2V pre-crash scenarios, as follows:Junction crossing: Straight crossing paths (SCP) at non-signalized junctions Left turn across path/opposite directions (LTAP/OD) at controlled and non-controlled junctions Rear-end: Lead vehicle stopped Rear-end: Lead vehicle decelerating Rear-end: Lead vehicle moving at constant speed Opposite direction: One vehicle attempting a maneuver such as passing Opposite direction: No maneuvering involved such as drifting Lane change/same direction: Changing lanes Lane change/same direction: Turning at a junction Lane change/same direction: Drifting The report presented time-to-collision equations and the crash statistics for each of these priority pre-crash scenarios. Furthermore, each of the 10 pre-crash scenarios was mapped to a corresponding V2V safety application. In a third report, Toma et al. (2013) discussed light-vehicle crash countermeasure profiles to the 10 pre-crash scenarios they identified based on V2V communications. They determined time-to-collision and avoidance maneuvers for each pre-crash scenarios to identify information needed for the crash countermeasures. The report also identified two target pre-crash scenarios that would require new safety applications not developed in prior projects, including the LTAP/OD and “opposite direction/no vehicle maneuver” pre-crash scenarios. While there are several ongoing research projects on safety benefits of CV, there are few published journal articles in this area. As mentioned before, most of the ongoing research activities focus on how V2V and V2I communications may increase safety for drivers. V2X impacts on road user safety are typically left out by these existing studies while they can offer a great potential for further improvement in the safety of mixed-use roadways. To address the gaps, the proposed research project will focus on developing a CN device that facilitates communications between connected vehicles, pedestrians, bicyclists, and un-connected vehicles (as long as they have a smart phone and the mobile app). The devices can be placed on spots or links that are known to have safety problems. The design of the CN devices is a challenging task and needs to meet time-to-collision constraints. Such devices will allow communications between CVs and road users without CV devices to connect via regular mobile electronic devices. A mobile app that enables communication with the CV system will be developed to facilitate communications and displays of safety messages. An algorithm will also be built in the app to evaluate safety condition and identify response messages to communicate. Clearly, this research will focus on developing CV safety applications (a.k.a. CV based safety countermeasures) to improve the safety of collision-prone locations and will evaluate the performance of the proposed CV safety applications using a CV simulation test bed and real-world data. This activity is expected to augment USDOT studies that have mainly focused on identifying pre-crash scenarios for light vehicles. ApproachThis project will focus on using CV information to identify locations prone to conflicts between motorized and non-motorized users alike, especially on mixed-use roadways. With this information in hand, transportation system users can be alerted of potential conflicts they may be involved in, prior to their occurrence, allowing these users to take preventative actions, perhaps by making evasive maneuvers. This activity is expected to augment the USDOT studies that have mainly focused on light vehicles by providing a wealth of new information on the actions of non-motorized users in a connected vehicle environment. Specifically, successful implementation of a V2X communication network to work alongside existing V2V and V2I communications will be a key goal of this study. It is expected that implementation of V2I technologies will be able to extend the benefits of a connected vehicle environment (primarily safety-related) to travelers who would not typically be connected (e.g., pedestrians and bicyclists) due to lack of a communication device able to communicate with connected motor-vehicles and infrastructure.In addition to gathering data via simulation procedures, the research team will collect field data from two CVs test corridors in Seattle. These two test corridors, I-5 NB from Boeing field to the I-90 interchange and Mountlake Blvd, are instrumented by Battelle and the UW as part of their efforts for Federal Highway Administration (FHWA) and Washington State Department of Transportation (WSDOT) funded research projects. Additionally, the team will develop a CV simulation test-bed to allow the evaluation of the safety benefits of the proposed methodology under various traffic and landscape conditions. Furthermore, the CV simulation test-bed developed by the STAR Lab for the City of Bellevue can be improved and used as the basis for developing the simulation test-bed.The team has identified the following seven tasks to meet the objective of this research: Task 1: Review of literatureThe team will begin with a comprehensive review of the literature with several objectives. The first objective will involve investigation of existing vehicle-to-X (V2X, where X is any non-motorized vehicle or non-infrastructure user/source) projects. Special attention will be given to articles addressing V2X safety applications, particularly those that focus on distributing safety information to pedestrians and bicyclists as existing literature on such a topic seems relatively sparse. Another portion of the literature review will focus on determining ideal locations for implementing V2X systems in terms of their safety characteristics. More specifically, the team will focus on identifying what types of crashes or events leading to a crash could be addressed and mitigated by V2X communications in conjunction with what facility types are prone to these types of crashes. Examples include intersections with objects obstructing the visibility of both motorists and non-motorized road-users as well as intersections with speeding issues. Yet, another objective of the literature will be to investigate methods to allow a sensor to communicate via both dedicated short range communications (DSRC, the existing connected vehicle standard for V2V and V2I communications) and via Bluetooth, WiFi or another communication protocol commonly used by mobile devices. Of the utmost importance for this objective will be determining the best method to communicate with non-motorized users’ cellular devices (i.e., via Bluetooth, WiFi etc.). Another objective of the literature review will be to investigate existing surrogate safety measures (e.g., time to collision) and their use in conflict- and safety-prediction algorithms. The final portion of the literature review will involve investigation of techniques and methods used to simulate connected vehicle environments, especially V2V, V2I, and V2X communications. Based on the literature review a preliminary system architecture will be developed.Task 2: Develop the CN device capable of communicating between CV network and certified mobile devicesAn initial literature search has shown that researchers have investigated V2X communications and developed devices and methodologies to communicate information on possible safety issues with pedestrians to drivers of motor vehicles (Sugimoto et al., 2008; Hisaka and Kamijo, 2011). Although these papers have made initial strides in the V2X arena, they focus on the safety issue from one perspective only, namely, that of the motorist. To date, little work has been done to address safety issues, particularly conflicts between motorized and non-motorized users, in a connected vehicle environment from the perspective of the non-motorized user. We feel providing safety information to the non-motorized users is just as important as providing similar information to drivers as both parties may ultimately be able to take preventative actions to avoid conflicts. In this step, the goal is to develop a solar-energy driven, small, and lightweight communication note device (similar to the solar-energy sensor shown in Figure 2 developed by the STAR Lab) that could be installed along the roadside. This sensor would receive messages from connected vehicles and infrastructure (such as signal controller cabinets) and transmit pertinent safety information/warning messages to users’ cellular devices via Bluetooth or another suitable cellular communication protocol. This would allow for system users, especially non-motorized users who do not have access to DSRC devices to send/receive critical safety information and greatly increase the potential number of “connected” users across all modes on a given facility. Besides allowing for communication between vehicles, infrastructure, and non-motorized users, the sensors would be able to transmit information from one sensor to another. By developing a small, and relatively inexpensive sensor, multiple sensors could be easily installed in critical areas to overcome issues of transmission range and allow users (regardless of mode) to be informed of safety issues/potential conflicts well in advance of their arrival at the site of the potential conflict. Figure 2: STAR Lab’s Solar Energy Traffic Sensor (Left) and Testing Screen (Right)Task 3: Develop a mobile app for communication of safety information45091354381500Figure 3: STAR Lab’s Mobile Sensing Phone App Developed on the Android SystemFigure 3: STAR Lab’s Mobile Sensing Phone App Developed on the Android System4509410232553100Once the sensor designed to receive information from connected vehicles and infrastructure has been designed, the next step will be to develop a mobile device application for use of receiving information from the sensors and presenting pertinent safety information to users who have the application installed on their phones or other mobile devices. Such information will include warnings about potential conflicts that may allow users to take corrective or evasive actions to prevent the conflicts from ever happening. Again, the goal of developing the mobile app and the CN device is to allow for transmission of safety information to a larger percentage of the traveling population, regardless of their mode, as at least in the present time, non-motorized users do not have DSRC-capable devices. Existing research, such as Roodell (2009), has addressed communication of safety information in a connected vehicle environment between DSRC and cellular phones, but primarily from the perspective of the driver and not in terms of presenting warnings/information to pedestrians and bicyclists. Cyber security and privacy are important issues to address in this research. The research team will applied the best available technology to ensure cyber security and protect user privacy through the design of the mobile app.Task 4: Develop safety prediction algorithmsAs noted in Task 3, a key goal of the mobile app will be to provide warning messages to users on potential conflicts with other travelers. The UW STAR Lab has several experiences in developing Android phone apps (see Figure 3 as an example) and these previous experience definitely help the mobile app development work in this research. Situations in which the warnings will be provided will be as determined from Task 1. These warnings will be based upon safety prediction algorithms (to be developed under this Task) that attempt to model when a conflict may occur, and perhaps how severe said conflict may be, based upon information (e.g., position, speed, acceleration etc.) collected from vehicles and non-motorized users. Thus, by determining when a conflict could possibly occur, all parties that may potentially be involved in the conflict (assuming they have the application installed and running on their phones) could be alerted of the conflict.Task 5: Test algorithms in real-world test-bed sitesOnce the algorithms have been developed and validated, the next step will be to implement a real-world field test under which the CN devices of Task 2 will be installed at the chosen test sites and the mobile app from Task 3 will be provided to users for download. An ideal test site would be the Montlake Boulevard corridor that will be the subject of another connected vehicle project being conducted by WSDOT and UW. Through having access to a field test site, actual data on effects of penetration rate, algorithm failure (i.e., false negatives), sensor effectiveness, and ideal sensor placement configuration can be thoroughly tested.Task 6: Develop simulation test-bed and test the proposed algorithmsTo further test the safety prediction algorithm in various traffic and environmental scenarios beyond those observable at the test-beds, a simulation test-bed needs to be developed. This will be based on the CV test-bed previously developed by the STAR Lab for the City of Bellevue, WA. The simulation model will be developed using VISSIM and its CAR2X interfaces. A key component of the simulation testing will be to investigate the effectiveness under varying penetration rates of both connected vehicles and application usage. Key metrics used to determine algorithms’ effectiveness, presumably based upon correct prediction of conflicts under surrogate safety threshold values and lack of incorrect predictions (i.e., false negatives), will be decided upon following, and based upon, the literature review. Based upon the results of the simulation, the algorithms will be modified to account for any issues that may have been overlooked in their initial design. Task 7: Analyze results and draft final reportThe project team will document the project with a final report. The report will be a concise accounting of the methods and results, with appendices that contain all data compiled and analyzed, along with any outputs from the models developed in this project. The project will start on June 16, 2015 and be completed by June 15, 2016. A proposed sequence of Tasks 1 through 7 is included in Table 1. Table 1: Project Schedule?MonthTask 123456789101112Task 1XXTask 2XXXXXTask 3XXXXTask 4XXXXXTask 5XXXXTask 6XXXXTask 7XXXResearch Outcomes and Technology Transfer PlanThe proposed research presents the following significant outcomes: CN Device: the CN device will facilitate communications between DSRC systems and cell phones thus, allowing connected vehicles to share information with pedestrians, bicyclists, and unconnected vehicles, and vice versa. This is an important product of this research that allows further research on V2V, V2I, and more importantly V2X, and X2I. The CN device will allow developing CV safety applications for mixed-use roadways. Mobile App: the Android mobile app allows the exchange of data between the CN devices and unconnected systems users (i.e., users that are equipped with smartphones). The app will collect data on user locations, speeds, etc. and will share those with the CN devices to allow sending proper warning messages to different system users. CV Safety Applications: the team will develop a number of CV data and technology based safety countermeasures to improve the safety of mixed-use roadways. This outcome will include identifying potential dangerous situations and determining what kind of message to be share with which users to improve safety.CV Simulation test-bed: the CV simulation test bed will allow evaluation of different CV safety apps in a simulated environment without all risks and costs that are associated with real-worlds testing. This would be an important product of this research that will allow state DOTs and other transportation agencies to evaluate different CV safety countermeasure before real-world implementations. The outcome of this project will be disseminated to different audiences through making presentations at different conference, organizing workshops at State DOTs involved in the PacTrans consortium, publishing papers in reputable transportation safety journals, and developing a final report. The team will create a video as a technology transfer product to highlight the finding of this research and to train roadway users how to best benefit the information CV technology provides them with. Furthermore, Washington State University and the STAR Lab at University of Washington will hold webinars on the outcome of this project. Finally, the outcome of this research will be showcased in the ACS Lab and STAR Lab Websites. Amount of UTC Funds RequestedThe total requested UTC funds for this project is $180K. Washington State University will receive $100K and University of Washington will receive $80k. Non-federal MatchLetters for match are enclosed. The Department of Civil and Environmental Engineering at Washington State University and the WSDOT/UW have committed to provide a one to one match of a total of $180K. ReferencesJ. Lee and B. Park, Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment. IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No. 1, 2012, pp 81–90.Q. He, Head K. L., and Ding J., PAMSCOD: Platoon-based arterial multi-modal signal control with online data. Transportation Research Part C, 20, 2012, pp 164–184.Q. He, Head K. L., and Ding J., Multi-modal traffic signal control with priority, signal actuation and coordination. Transportation Research Part C, 46, 2014, pp 65–82.E. Christofa, Argote J., and Skabardonis A., Arterial Queue Spillback Detection and Signal Control Based on Connected Vehicle Technology. Transportation Research Record: Journal of the Transportation Research Board, No. 2356, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 61–70. J. Hu, Park B. B., and Lee Y., Coordinated transit signal priority supporting transit progression under Connected Vehicle Technology. Transportation Research Part C. In Press. Y. Feng, Head K. L., Khoshmagham S., Zamanipour M., A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C, In Press. S. I. Guler, Menendez M., Meier L., Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C, 46, 2014, pp 121–131. J. Lee, Park B. B., Malakorn K., and So J., Sustainability assessments of cooperative vehicle intersection control at an urban corridor. Transportation Research Part C, 32, 2013, pp 193–206.W. G. Najm, Ranganathan R., Srinivasan G., Toma J. S., Swanson E., and Burgett A. Description of Light-Vehicle Pre-Crash Scenarios for Safety Applications Based On Vehicle-to-Vehicle Communications. US Department of Transportation. DOT HS 811 731, May 2013, Washington DC. W. G. Najm, Toma J. S., and Burgett A. Depiction of Priority Light-Vehicle Pre-Crash Scenarios for Safety Applications Based on Vehicle-to-Vehicle Communications. US Department of Transportation. DOT HS 811 732, April 2013, Washington DC. J. S. Toma, Swanson E., and W. G. Najm. Light Vehicle Crash Avoidance Needs and Countermeasure Profiles for Safety Applications Based on Vehicle-to-Vehicle Communications. US Department of Transportation. DOT HS 811 733, April 2013, Washington DC. S. Hisaka and S. Kamijo, On-board wireless sensor for collision avoidance: Vehicle and pedestrian detection at intersection.?Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, pp.198,205, 5-7 Oct. 2011.C. Sugimoto, Nakamura, Y., and Hashimoto, T. Prototype of pedestrian-to-vehicle communication system for the prevention of pedestrian accidents using both 3G wireless and WLAN communication.?Wireless Pervasive Computing, 2008. ISWPC 2008. 3rd International Symposium on, pp.764,767, 7-9 May 2008.B. D. Roodell, Vehicle Driver Message Alert System Using DSRC/WAVE and Bluetooth. Masters’ Thesis. University of Minnesota, 2009.ALI HAJBABAIE, Ph.D.Assistant Professor, Department of Civil and Environmental EngineeringWashington State University (WSU)Professional PreparationUniversity of Illinois at Urbana-ChampaignIL, USACivil Engineering Ph.D., 2012University of Illinois at Urbana-ChampaignIL, USAIndustrial Engineering M.Sc., 2011Sharif University of TechnologyTehran, IranCivil Engineering M.Sc., 2006Sharif University of TechnologyTehran, IranCivil Engineering B.Sc., 2003University AppointmentsAssistant Professor, Department of Civil and Environmental Engineering, WSU October 2014 – PresentPostdoctoral Research Scholar, North Carolina State University, NCSU January 2012 – September 2014Research Assistant, University of Illinois at Urbana – Champaign, UIUC August 2006 – December 2011Selected ProjectsPI, Regional Map Based Analytical Platform for State-Wide Highway Safety Performance Assessment, Pacific Northwest Transportation Consortium (PacTrans), University Transportation Center, $180,000, 01/2015 – Present. Co-PI, Safety Data Management and Analysis: Addressing the Continuing Education Needs for the Pacific Northwest, PacTrans, $175,000, 01/2015 – Present.WSU PI, Production of a Major Update to the 2010 HCM, NCHRP 03-115, $12,000,10/2014 – 01/2015.Project Manager, Work Zone Monitoring and Assessment, NCDOT, 08/2013 – 09/2014.ITRE Project Manager, Work Zone Capacity Methods for the HCM, (NCHRP 03-107), 08/2012 – 09/2014.Selected Peer-Reviewed Journal ArticlesHajbabaie A. and R. F. Benekohal. A Program for Simultaneous Network Signal Timing Optimization and Traffic Assignment. IEEE Transactions on Intelligent Transportation Systems, 2015, In Press.Hajbabaie A., N. M. Rouphail, B. J. Schroeder, and R Dowling. A Planning-Level Methodology for Freeway Facilities. Transportation Research Record: Journal of the Transportation Research Board, 2015, In Press. Aghdashi S., A. Hajbabaie, N. M. Rouphail, and B. J. Schroeder. Freeway Reliability Scenario Generation: A Hybrid Approach. Transportation Research Record: Journal of the Transportation Research Board, 2015, In Press.Yoem. C., A. Hajbabaie, B. J. Schroeder, Vaughan C., X. Xuan, and N. M. Rouphail. Innovative Work Zone Capacity Models from Nationwide Field and Archival Sources. Transportation Research Record: journal of the Transportation Research Board. 2015, In Press.Aghdashi S., N. M. Rouphail, A. Hajbabaie, and B. J. Schroeder. Generic Speed Flow Models for Basic Freeway Segments on General Purpose and Managed Lanes. Transportation Research Record: Journal of the Transportation Research Board, 2015, In Press.Hajbabaie A. and R. F. Benekohal. Traffic Signal Timing Optimization: Selecting the Objective Function. Transportation Research Record: Journal of the Transportation Research Board, No. 2355, 2013, pp 10-19. Medina J. C., A. Hajbabaie, an R. F. Benekohal. Effects of Metered Entry Volume on an Oversaturated network with Dynamic Signal Timing. Transportation Research Record: Journal of the Transportation Research Board, No. 2356, 2013, pp 53-60.Aghdashi S., N. M. Rouphail, and A. Hajbabaie. Estimating Incident Propensity Analysis in the HCM. Transportation Research Record: Journal of the Transportation Research Board, No. 2395, 2013, pp 123-131.Hajbabaie A., J. C. Medina, M. Wang, M. V. Chitturi, and R. F. Benekohal. Sustained and Halo Effects of Various Speed Reduction Treatments in Highway Work Zones. Transportation Research Record: Journal of the Transportation Research Board, No. 2265, 2011, pp 118-128.Medina J. C., A. Hajbabaie, and R. F. Benekohal. Detection Performance of Wireless Magnetometers at Signalized Intersection and Railroad Grade Crossing under Different Weather Conditions. Transportation Research Record: Journal of the Transportation Research Board, No. 2259, 2011, pp 233-241. Medina J. C., R. F. Benekohal, A. Hajbabaie, M. Wang, and M. V. Chitturi. Downstream Effects of Speed Photo–Radar Enforcement and Other Speed Reduction Treatments on Work Zones. Transportation Research Record: Journal of the Transportation Research Board, No. 2107, 2009, pp 24-33.Benekohal R. F., M. V. Chitturi, A. Hajbabaie, M. Wang, and J.C. Medina. Automated Speed Photo Enforcement Effects on Speeds in Work Zones. Transportation Research Record: Journal of Transportation Research Board, No. 2055, 2008, pp11-20.YINHAI WANG, Ph.D.Professor and DirectorPacific Northwest Transportation Consortium (PacTrans)USDOT University Transportation Center for Federal Region 10University of Washington, Seattle, WA 98195-2700Tel: (206) 616-2696 Fax: (206) 543-5965Email: yinhai@uw.eduEducationPh.D., Transportation Engineering, University of Tokyo, Tokyo, Japan, Sept. 1998Master of Computer Science and Engineering, Univ. of Washington, Seattle, Dec. 2002Master of Construction Management, Tsinghua University, Beijing, China, June 1991Bachelor of Civil Engineering, Tsinghua University, Beijing, China, July 1989University ExperienceDirector, PacTrans, Univ. of Washington, Jan. 2012 – present. Professor, Dept. of Civil and Env. Eng., Univ. of Washington, Sept. 2011 – present.Associate Professor, Dept. of Civil and Env. Eng., Univ. of Washington, Sept. 2007 – Sept. 2011.Assistant Professor, Dept. of Civil and Env. Eng., Univ. of Washington, Sept. 2003 – Aug. 2007.Selected AwardsThomas and Marilyn Nielson Endowed Professor, Univ. of Washington, Nov. 2006.ASCE Journal of Transportation Engineering Best Paper Award for 2003.Excellent Presentation Award, 51st Annual Conference of Japan Society of Civil Engineers,1996.Selected Journal PublicationsMa, Xiaolei, Yao-Jan Wu, Yinhai Wang, Feng Chen, and Jianfeng Liu. “Mining Smart Card Data for Transit Riders’ Travel Patterns.” Transportation Research Par C. Vol. 36, 1-12. 2013.Zhang, Guohui, Xiaolei Ma, and Yinhai Wang. “Self-Adaptive Dynamic Tolling Strategy for Optimal Operations on High Occupancy Toll Lanes.” IEEE Transactions on Intelligent Transportation Systems. Volume 15, Issue 1, 306-317. 2013.Lao, Yunteng, Guohui Zhang, Yinhai Wang, and John Milton. “Generalized Nonlinear Models for Rear-End Crash Risk Analysis.” Accident Analysis and Prevention. Vol. 62, 9-16, 2014.Malinovskiy, Yegor, Yao-Jan Wu, and Yinhai Wang. “Video-Based Vehicle Detection and Tracking Using Spatiotemporal Maps.” Transportation Research Record, No. 2121, 81-89, 2009.Zhang, Guohui, Shuming Yan, and Yinhai Wang. “Simulation-based Investigation on High Occupancy Toll (HOT) Lane Operations for Washington State Route 167.” ASCE Journal of Transportation Engineering. Vol. 35, No. 10, 677-686, 2009.Selected Research ProjectsPI. Quantifying Incident Induced Delay Using Field Observations, Washington State Department of Transportation (WSDOT), $176,053, July 1, 2009 – June 30, 2011.PI with Nagui Rouphail (co-PI). Analysis of Managed Lanes on Freeway Facilities, Cooperative Highway Research Program (NCHRP) 03-96, $350,000, Apr. 1, 2009 – March 31, 2011.PI. Arterial Performance Measurements, TransNow, $123,208, July 2008 – June 2010.PI with Mark Hallenbeck (co-PI). Development of a Statewide Traffic Data System, TransNow and WSDOT, $320,000, July 2007 – June 2009.Co-PI with Sverre Vedal (PI). Integrated Epidemiologic and Toxicologic Cardiovascular Studies to Identify Toxic Components and Sources of Fine Particulate Matter, Health Effects Institute (HEI), $3,851,000, Sept. 2006 – Aug. 2010.Leila Hajibabai, Ph.D.Clinical Assistant Professor, Department of Civil and Environmental EngineeringWashington State University (WSU)Professional PreparationUniversity of Illinois at Urbana-ChampaignIL, USACivil Engineering Ph.D., 2014University of Illinois at Urbana-ChampaignIL, USAIndustrial Engineering M.Sc., 2013University of TehranTehran, IranCivil/Surveying Engineering M.Sc., 2006K.N.Toosi University of TechnologyTehran, IranCivil/Surveying Engineering B.Sc., 2004AppointmentsClinical Assistant Professor, Department of Civil and Environmental Engineering, WSU 01/2015 – Present Adjunct Assistant Professor, Department of Civil and Environmental Engineering, WSU 10/2014 – 12/2014Research Assistant, University of Illinois at Urbana – Champaign, UIUC 08/2008 – 09/2014Senior Research Associate, National Cartographic Center, Tehran, Iran 08/2006 – 07/2008Research Associate, K.N.Toosi University of Technology, Tehran, Iran 05/2002 – 08/2006Selected ProjectsCo-PI, Mitigation of lane departure crashes in the Pacific Northwest through coordinated outreach, Pacific Northwest Transportation Consortium (PacTrans), University Transportation Center, $175,000, 01/2015 – Present.ProductsSelected Peer-Reviewed Journal ArticlesHajibabai, L., Bai, Y., and Ouyang, Y. (2014). Joint Optimization of Supply Chain Network Design and Highway Pavement Rehabilitation Plan under Traffic Equilibrium. Transportation Research Part B: Methodological, 63(1) 38-52.Hajibabai, L., Nourbakhsh, S.M., Ouyang, Y., and Peng, F. (2014). Network Routing of Snow Plows with Resource Replenishment and Plowing Priorities: Formulation, Algorithm, and Application, Transportation Research Record: Journal of the Transportation Research Board, No. 2440, pp. 16–25.Hajibabai, L. and Ouyang, Y. (2013). Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to Biofuel Industry. Computer-Aided Civil and Infrastructure Engineering, 28(4): 247-259.Hajibabai, L., Aziz, Z., and Pe?a-Mora, F. (2011). Visualizing Green House Gas Emissions from Construction Activities. Journal of Construction Innovation: Information, Process, Management, 11(3): 356-370.Hajibabai, L. and Ouyang, Y. (2014). Dynamic Snow Plow Fleet Management under Uncertain Demand and Service Disruption. Submitted to IEEE Transactions on Intelligent Transportation Systems.Selected Peer-Reviewed Conference ProceedingsHajibabai, L. and Ouyang, Y. Dynamic Snow Plow Fleet Management under Uncertain Demand and Service Disruption, In: Proceedings of the 94th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C., January 2015.Hajibabai, L., Nourbakhsh, S.M., Ouyang, Y., and Peng, F. Snow Plow Routing Optimization under Resource Constraints: Formulation, Algorithm, and Decision-Support System, In: Proceedings of the 92nd Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C., January 2013.Hajibabai, L., Bai, Y., and Ouyang, Y. Joint Optimization of Supply Chain Network Design and Highway Pavement Rehabilitation Plan, In: Proceedings of the 92nd Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C., January 2013.Hajibabai, L., Saat, M.R., Ouyang, Y., Barkan, C.P.L., Yang, Z., Bowling, K., Somani, K., Lauro, D., and Li, X. Wayside Defect Detector Data Mining to Predict Potential WILD Train Stops. Annual Conference and Exposition of the American Railway Engineering and Maintenance-of-Way Association (AREMA), Chicago, IL, September 2012.Hajibabai, L. and Ouyang, Y. Integrated Planning of Biofuel Supply Chain Networks and Multimodal Infrastructure Expansion. In: Proceedings of the 91st Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C., January 2012.Hajibabai, L., and Pe?a-Mora, F. Visualizing Green House Gas Emissions during the Construction Phase of Infrastructure Projects Using Geo-Spatial Information Systems, 2009 ASCE International Workshop on Computing in Civil Engineering, ASCE, Austin, TX, 2009. ................
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