Delay Estimation Model Based on Low-Sampling-Rate Floating Car Data

Abstract

In the light that floating car data (FCD) in real operations are of low sampling rate, a delay estimation method at intersections based on low-sampling-rate floating car data is proposed. This study analyzes running state differences of vehicles on different road units, delimits the affected area of an intersection and expedites travel speed of a vehicle on a specific road, using two nearest GPS points outside the affected area to calculate time stamps of a vehicle entering and leaving the affected area. Taking Guangzhou as a case study, a typical intersection is chosen to evaluate the performance of the proposed method. Extensive experimental results have demonstrated the better potential of low-sampling-rate FCD in delay estimation, with 85% of calculating results within 10s in terms of absolute error. INTRODUCTIONThe intersection is one of the factors that has a significant effect on urban traffic. Previous studies have proposed several indexes to evaluate the operating state of intersections, such as queue length, stop rate, etc. Delay, as one of the most commonly used indexes, can reflect the operating state of intersections in an effective way.In terms of calculation of delay, the most commonly used methods are experiment and delay estimation model (Yu et al. 2009;Sun et al. 2010). An experiment obtains an intersection delay by conducting a survey, such as the license plate method (Li et al. 2011). Although an experiment can get accurate results, it tends to be labor intensive; another method is to construct a delay estimation model based on transportation theory. The parameters of these models are signal time, effective green time and so on. The most well known models are the Webster delay model (Webster 1958) and the HCM model (Fambro 1996). Robertson (Fambro, 1996) revised the Webster delay model to avoid a delay smaller than zero under oversaturation; Qiao (2002) proposed a model based on fuzzy logic; this model considers not only the technical indexes, such as traffic volume, but also the nontechnical indexes, such as weather. However these models require too many parameters and collecting these parameters costs too

Vehicle Trajectory Based Control Delay Estimation at Intersections Using Low-Frequency Floating Car Sampling Data

“…To accurately obtain control delay from low-frequency floating car data, the main challenge is how to detect where and when a vehicle starts to decelerate and stop accelerating. He and Ye (2014) proposed a method, which delimits the affected area of intersection on the basis of queue length and calculated the times when a vehicle enters and leaves the affected area from low-frequency sample points. Although this method is simple and has a high computational efficiency, the affected area of the intersection in this article is assumed to be stable.…”