T3e Webinar Overview
Data and Connected Vehicle Support of Active Traffic Management Strategies
Date: Monday, October 31, 2016
Time: 2:00 PM – 3:00 PM ET
Cost: All T3 webinars are free of charge
PDH: 1.0 View PDH Policy
T3 and T3e Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation's (U.S. DOT) ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the U.S. DOT.
This webinar will feature four student presentations from Florida International University, hosted by Dr. Mohammed Hadi. Xuanwu Chen, Homa Fartash, Samaneh Khazraeian, and Md Shahadat Iqbal are PhD candidates in the Department of Civil and Environmental Engineering and will be presenting on their current research in Intelligent Transportation Systems (ITS). Topics include signal timing operations based on automatic vehicle matching technologies, warrants for ramp metering operations, connected vehicle (CV) data usage for back-of-queue estimation, and CV market penetration in light of socioeconomic characteristics, all under a unifying theme of data and CV support of active traffic management strategies.
The target audience includes transportation agencies and researchers.
- Recognize the potential of using automatic vehicle matching data to support signal timing optimization.
- Learn the importance of introducing system-based warrants in offline planning and real-time operations.
- Introduction to connected vehicle data usage for back-of-queue (BOQ) estimation, comparing the performance of connected vehicle BOQ estimation versus point detector based BOQ estimation, and evaluating the safety benefits of the implemented queue warning system (QWS) (effect of the QWS on rear-end collision reduction).
- Provide a method that assesses the variation of connected vehicle market penetration due to the variability in socioeconomic characteristics.
Dr. Mohammed Hadi, P.E., Associate Professor and Graduate Program Director, Civil and Environmental Engineering, Florida International University
Dr. Hadi is a professor at Florida International University. He has 30 years experience (20 years in academic and research institutions and 10 years in the private sector) in a wide variety of transportation engineering areas with an emphasis on ITS, connected vehicles, data analytic and decision support, simulation and dynamic assignment modeling, performance measurements, traffic operations, and planning for operations. He produced several decision support tools and methods that have been used by transportation operation and planning agencies in Florida.
Mr. Xuanwu Chen, Graduate Research Assistant and PhD Candidate, Lehman Center for Transportation Research, Florida International University
Xuanwu Chen earned his Bachelor’s degree in Transportation Engineering at Southeast University, Nanjin, China. Xuanwu worked as an assistant engineer in a consultant company for two years and gained his Master’s degree in the same area at the University of Alaska, Fairbanks (UAF). He is currently a PhD candidate in Civil Engineering (Transportation) under Dr. Mohammed Hadi’s supervision and focuses on ITS. He served as president of FIU ITE Student Chapter and as vice president of FIU WTS Student Chapter. Xuanwu received the Bill McGrath Transportation Studies Scholarship and the Henry P. Boggs Student Paper Award. His works were awarded in multiple research poster competitions.
A Framework for Recommending Signal Timing Operations Based on Automatic Vehicle Matching Technologies
Continuously monitoring and automatically identifying existing problems in traffic signal operation is a challenging and time-consuming task. The current practices of retiming signals are still periodic and based on few days’ aggregated turning movement counts. This research develops a framework of automatic signal operation diagnosis with the aim to support decision-making processes by assessing the signal control and identifying the signal retiming needs. The developed system uses a combination of relatively low-cost data from Wi-Fi sensors and historical signal timing records from existing signal controllers. The development involves applying multiple data matching and filtering algorithms to allow the estimation of travel times of vehicular traversals. The Travel Time Index (TTI) is then used as a measure to assess the traffic conditions of various movements. Historical signal timing records are also analyzed and an additional signal-timing measure, referred to as the max out ratio (MR), is proposed to evaluate the frequency in which the green time demand of a phase exceeded its preset value.
Ms. Homa Fartash, PhD Candidate, Transportation Engineering, Florida International University
Homa Fartash, a PhD candidate at FIU, is in her third year of a PhD program in Transportation Engineering. She received her Bachelor’s and Master’s degrees with the honor of Best Student from Iran University of Science and Technology. ITS is her area of interest. Currently, she is working on a funded research project titled “Guidelines for the Evaluation of Ramp Signaling Deployments in a Real-Time Operations Environment” under the supervision of Dr. Mohammed Hadi. Homa currently serves as president of the FIU ITE Student Chapter, and is a member of WTS International (Women’s Transportation Seminar), and FIU Society of Women Engineers. She was recognized as the recipient of the Anne Brewer Scholarship (ITS Florida), Sanchez Grand Prix Scholarship, and was runner up in the Florida Section ITE Poster Competition.
Development of Warrants for Installation and Activation of Ramp Metering
Warrants for ramp metering installation have been developed by a number of states around the nation. These warrants are generally simple and are based on the traffic, geometry, and safety conditions in the immediate vicinity of each ramp (local conditions). However, advanced applications of ramp metering utilize system-based metering algorithms that involve metering a number of on-ramps to address system bottleneck locations. These algorithms have been proven to perform better compared with local ramp metering algorithms. This has created a disconnect between existing agency metering warrants to install the meters and the subsequent management and operations of the ramp meters. Therefore, there is a need for developing warrants in addition to local warrants for ramp metering installation in the planning stage and activation in real-time operations under recurrent and nonrecurrent conditions. Such warrants will need to be developed to prevent breakdowns at bottleneck locations and to improve the performance measures of freeway segments beyond the local ramp locations.
Ms. Samaneh Khazraeian, PhD Candidate, Transportation Engineering, Florida International University
Samaneh Khazraeian is pursuing her PhD in Transportation Engineering with a focus on ITS. She completed her Master’s degree in Transportation Engineering with honors at Iran University of Science and Technology in the fall of 2010. She has served as president of FIU WTS (Women’s Transportation Seminar) Student Chapter. She was the recipient of the 2016 Transportation Research Board (TRB) Best Student Paper award, the 2016 WTS International Southwest Award, and the 2015 Helene M. Overly Memorial Scholarship Award. She is working on a research project on “Utilization of Connected Vehicle Data to Support Traffic Management Decisions.” So far, she has presented four research papers at TRB annual meetings (2015 and 2016), and three papers have been accepted for presentation in the upcoming TRB 2017.
Estimating Back of Queue Location Based on Connected Vehicle Technologies
Queue warning systems (QWS) have been implemented to increase traffic safety by informing drivers about queued traffic ahead, so that they can react in a timely manner to the presence of the queue. Existing QWS rely on fixed traffic sensors to estimate the back of queue. If the transmitted messages from the connected vehicles are utilized for this purpose, it is expected that the estimation can be faster and more accurate. In addition, with connected vehicles the messages can be delivered using on-board units instead of dynamic message signs (DMS), providing more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS based on connected vehicle data. The study evaluates the safety benefits of the QWS under different market penetrations of CV in future years based on safety surrogate measures estimated using simulation modeling combined with the Surrogate Safety Assessment Model (SSAM) tool.
Mr. Md. Shahadat Iqbal, PhD Candidate, Transportation Engineering, Florida International University
Md. Shahadat Iqbal is a doctoral candidate in Civil Engineering at FIU. He earned his Bachelor’s and Master’s degrees from Bangladesh University of Engineering and Technology (BUET). He also worked as a research engineer at the same university for two years before joining FIU in fall 2013. Now he is working as a graduate research assistant at the Lehman Center for Transportation Research (LCTR) at FIU with Dr. Mohammed Hadi’s research team. His focus research areas are intelligent transportation systems, connected vehicle, traffic simulation, and big data analysis. He has won several awards, including the ITS Florida Anne S. Brewer Scholarship.
Estimating Connected Vehicle Market Penetration with the Consideration of Socioeconomic Characteristics of a Region
Estimation of the market penetration of connected vehicles (CV) and automated vehicles is important in identifying the impacts of these technologies. Past efforts have assumed the growth in CV market penetration without considering the variations in the socioeconomical characteristics between regions and zones within a region. This study proposes a methodology to determine the variation of CV market penetration between regions, zones within a certain region, links within the region, and time of day. The methodology can be implemented with various CV implementation scenario assumptions and considers the variations in the socioeconomic characteristics of travelers of a region.
Applying the methodology of this paper to a case study indicates that the distribution of link-specific CV market penetration follows a lognormal distribution. The percentage variation in the market penetration is shown to be the highest in the first year of CV implementation and decreases exponentially with the number of years passing since the implementation. The market penetration variations between links are the highest on collectors, followed by arterials, followed by freeways. The study also shows that the average percentage increase in CV market penetration grows in the first several years and then remains almost constant before dropping sharply.