Traffic State Estimation Using Probe Vehicle Data

Traffic problems are becoming a burden on cities across the world. To prevent traffic accidents, mitigate congestion, and reduce fuel consumption, a critical step is to have a good understanding of traffic. Traditionally, traffic conditions are monitored primarily by fixed-location sensors. However, fixed-location sensors only provide information about specific locations, and the installation and maintenance cost is very high. The advances in gls{gps}-based technologies, such as connected vehicles and ride-hailing services, provide us an alternative approach to traffic monitoring. While these types of gls{gps}-equipped probe vehicles travel on the road, a vast amount of trajectory data are being collected. As probe vehicle data contain rich information about traffic conditions, they have drawn much attention from both researchers and practitioners in the field of traffic management and control. Extensive literature has studied the estimation of traffic speeds and travel times using probe vehicle data. However, as for queue lengths and traffic volumes, which are critical for traffic signal control and performance measures, most of the existing estimation methods based on probe vehicles can hardly be implemented in practice. The main obstacle is the low market penetration of probe vehicles. Therefore, in this dissertation, we aim to develop probe vehicle based traffic state estimation methods that are suitable for the low penetration rate environment and can potentially be implemented in the real world. First, we treat the traffic state in each location and each time point independently. We focus on estimating the queues forming at isolated intersections under light or moderate traffic. The existing methods often require prior knowledge of the queue length distribution or the probe vehicle penetration rate. However, these parameters are not available beforehand in real life. Therefore, we propose a series of methods to estimate these parameters from historical probe vehicle data. Some of the methods have been validated using real-world probe vehicle data. Second, we study traffic state estimation considering temporal correlations. The correlation of queue lengths in different traffic signal cycles is often ignored by the existing studies, although the phenomenon is commonly-observed in real life, such as the overflow queues induced by oversaturated traffic. To fill the gap, we model such queueing processes and observation processes using a hidden Markov model (gls{hmm}). Based on the gls{hmm}, we develop two cycle-by-cycle queue length estimation methods and an algorithm that can estimate the parameters of the gls{hmm} from historical probe vehicle data. Lastly, we consider the spatiotemporal correlations of traffic states, with a focus on the estimation of traffic volumes. With limited probe vehicle data, it is difficult to estimate traffic volumes accurately if we treat each location and each time slot independently. Noticing that traffic volumes in different locations and different time slots are correlated, we propose to find the low-rank representation of traffic volumes and then reconstruct the unknown values by fusing probe vehicle data and fixed-location sensor data. Test results show that the proposed methods can reconstruct the traffic volumes accurately, and they have great potential for real-world applications. In summary, this thesis systematically studies traffic state estimation based on probe vehicle data. Some of the proposed methods have been implemented in real life. We expect the methods to be implemented on an even larger scale and help transportation agencies solve more real-world traffic problems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155289/1/zhaoyann_1.pd