Lane-Based Saturation Degree Estimation for Signalized Intersections Using Travel Time Data

Saturation degree estimation is a vital problem of signal timing optimization. However, classic loop-detector-based algorithms are not capable to capture the severity of oversaturation, since detectors are located in front of stop lines, and also cannot distinguish the saturated degree in different lane groups if detectors are located at an upstream position. In this paper, we present a new method to estimate the lane-based saturation degree using travel times. The method is simple and mainly depends on the parameters of signal cycles and the corresponding virtual cycles. The virtual cycle parameters are extracted by analyzing the data on travel times using the K-mean cluster analysis. Then, two models for the traffic demand saturated degree (TDSD) and the effectively used green time saturation degree (EUGTSD) are presented based on the traffic flow conservation during one signal cycle and the corresponding virtual cycle. The new method can overcome the defects of loop-detector-based algorithms, and it can be used to optimize the TDSD and the EUGTSD simultaneously. Finally, the precision of the two types of models is evaluated using field survey data. The results show that the new method has a higher precision for the TDSD and the same accuracy level for the EUGTSD compared to the existing methods. The findings of this paper have potential applicability to signal control systems.