Estimation Method of Intersection Signal Cycle Based on Empirical Data

The signal cycle plays an important role in the design of timing optimization and evaluation for a signal intersection. However, in some cities, there is a lack of unified management of intelligent transportation system, and even the intersection signal cycle has not been archived online and updated in real time, resulting in a signal cycle that is not convenient and reliable. Therefore, it is necessary to use a simple and effective method to obtain the real-time signal cycle in a large regional area. This paper proposes an innovative traffic grid model, which matches the massive floating car data with the intersections across the entire urban road network, to estimate the signal cycle length of intersections accurately. This estimation method is composed of four major parts: (1) a grid model is built to transform intersections into discrete cells, and the floating car data are mapped to the grids through a simple assignment process; (2) based on the grid model, a set of key traffic parameters (e.g., the time stamp of vehicle stops or starts and the position of stop) is derived; (3) the augmented Dickey-Fuller (ADF) test and Pettitt test are used to identify the timing scheme constant fixed signal cycle in 1 day (CFSC) or variable fixed signal cycle in various periods of 1 day (VFSC); and (4) the k-means clustering algorithm with the trajectory similarity measurement is used to accurately estimate the cycle of each period. Taking the intersections of Beijing as an example, the effectiveness and feasibility of this method were demonstrated. The proposed cycle estimation method can provide valuable insights for the study of traffic signal control, and management and can be extended to other cities.

Automated summary

This paper proposes an innovative traffic grid model that matches floating car data with intersections across an entire urban road network to accurately estimate signal cycle lengths. The method involves building a grid model, identifying timing schemes, and applying clustering algorithms to obtain real-time signal cycles, providing valuable insights for traffic signal control and management.