Estimation of Queue Lengths, Probe Vehicle Penetration Rates, and Traffic Volumes at Signalized Intersections using Probe Vehicle Trajectories

Abstract

With the development of connected vehicle technologies and the emergence of e-hailing services, a vast amount of vehicle trajectory data is being collected every day. This massive amount of trajectory data could provide a new perspective for sensing, diagnosing, and optimizing transportation networks. There has been some literature estimating traffic volumes and queue lengths at intersections using the data collected from these probe vehicles. Nevertheless, some of the existing methods only work when the penetration rate of the probe vehicles is high enough. Some other methods require two critical inputs, the distribution of the queue lengths and the penetration rate of the probe vehicles. However, these two inputs might vary a lot both spatially and temporally and are not usually known in the real world. To fill the gap, this paper proposes a novel method for the estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections. The key step is to estimate the penetration rate of the probe vehicles from the distribution of their stopping positions at the intersections. Then, scaling up the number of probe vehicles in the queues and in the traffic according to the estimated penetration rate will give an estimate of the total queue length and the total traffic volume, respectively. The proposed method has been validated by both simulation data and real-field data. The testing results have shown that the method is ready for large-scale real-field applications.

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

“…Given all the observed partial queues, as derived in Zhao et al (2019), the conditional expectation of the total length of the observable queues can be expressed as…”

“…Therefore,Ĉ l , the difference betweenc l , the count of stopping probe vehicles at position l + 1, andc l+1 , the count of stopping probe vehicles at position l, can be used to approximate pE(C l ). When the difference is negative, a least-squares method can be applied to ensure the nonnegativity ofĈ l (Zhao et al, 2019).…”

“…Although the method proposed by Zhan et al (2017) can be applied in large scale, it requires the ground-truth traffic volume data on some road segments to build a connection between their high-level features and the actual volume categories, which implies that the method depends on not only the trajectory data but also other sources of data. Zhao et al (2019) proposed a simplified method of finding the penetration rate of the probe vehicles based on Bayes' theorem. Extending the method in Zhao et al (2019), this paper aims to propose a general framework and a series of methods that can estimate queue length and traffic volume both accurately and efficiently.…”

“…Zhao et al (2019) proposed a simplified method of finding the penetration rate of the probe vehicles based on Bayes' theorem. Extending the method in Zhao et al (2019), this paper aims to propose a general framework and a series of methods that can estimate queue length and traffic volume both accurately and efficiently.…”

Replicating Advanced Detection using Low Ping Frequency Probe Vehicle Trajectory Data to Optimize Signal Progression

“…As individual vehicle trajectories can be traced through a road network, this data has been used to estimate traffic states, traffic volumes, queue lengths, and travel times ( 10 – 12 ). Previous studies also demonstrated that trajectory data can be used to optimize traffic signal timings; however, they concluded that this required penetration rates between 30% and 100%, which is much higher than what is currently commercially available in the United States ( 13 – 16 ).…”