University of California, Riverside

Department of Electrical and Computer Engineering

Final Defense , Qichi Yang -Arterial Traffic Activity Estimation

Final Defense , Qichi Yang -Arterial Traffic Activity Estimation

Final Defense , Qichi Yang -Arterial Traffic Activity Estimation

December 6, 2013 - 9:00 am
Winston Chung Hall, 205/206

With advances in sensing technologies along with innovative modeling and estimation methods, a variety of Intelligent Transportation System (ITS) technology and applications have been developed to mitigate traffic congestion and associated environment pollution problems. In the last decade, several advanced sensing technologies have been developed for dedicated traffic measurements. In this dissertation, we focus on traffic activity estimation techniques and algorithms for three types of advanced traffic sensing systems: 1) sparse mobile sensors for arterial roadway travel time estimation; 2) wireless magnetic sensors for arterial roadway energy/emission estimation; and 3) 3-D LiDAR for lane-level vehicle trajectory estimation.

Due to the interrupted traffic flow caused by traffic control devices, it is very challenging to estimate average travel time of traffic flow along a signalized arterial corridor using conventional inductive loop detectors (ILD). Vehicle position samples from rapidly-growing smart phones and commercial navigator technologies turn out to be another promising data source for this task. However, one of the major obstacles of using these technologies is the randomness of sampling location, which results in significant variation in measured distance between two consecutive samples, compared to the stationary infrastructure technology. In Chapter 3, we describe a novel probabilistic travel time model that has been developed to deal with this issue by decomposing the arterial travel time into two components: free-flow travel time and delayed time. Validated by field operational tests, the proposed model has exhibited a good fit on the travel time distribution under different congestion levels and has resulted in more reliable and robust vehicle’s activity classification to differentiate between stopped and free-flow maneuvers by each individual vehicle. With this benefit, we developed an unique arterial roadway energy/emission estimation approach that is described in Chapter 4, using wireless magnetic sensors which measure travel time directly for each re-identified vehicle. An approximated speed trajectory is then reconstructed for stopped vehicles and fed into a microscopic energy/emissions model to achieve more accurate energy/emissions estimation compared to today’s commonly used techniques.

Lane-level second-by-second vehicle trajectories are another important data source, which is particularly useful for traffic simulation models in order to calibrate their internal vehicle activity parameters. In Chapter 5, we present a novel mobile sensor platform consisting of a centimeter-level accurate positioning system and a 3-D LiDAR for detecting and extracting surrounding vehicle trajectories. A robust detection/tracking algorithm has been developed to extract a large number of trajectories from vehicles surrounding the sensor-equipped probe vehicle. Results from both freeway and arterial roadway types have shown great potential of such innovative sensing systems in building high quality trajectory repositories for future research.



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