On-line Estimation of Departure Time-Based Link Travel Times from Spatial Detection System

Abstract

The link travel time (LTT) estimation logic, using the spatial detection system, can be classified into two classes based on the aggregation point of view: departure time-based (DTB) and arrival time-based (ATB) aggregations. The former is conceptually advantageous over the latter because it provides the LTTs, which are experienced by vehicles departing at the same time period. However, the existing studies on link travel time (LTT) estimation have focused on ATB- LTT estimation, rather than on DTB-LTT estimation. Although some research in this area has developed DTB-LTT estimation algorithms, they are not applicable in a real-time mode. The objective of this study is to develop a DTB-LTT estimation algorithm, which is applicable in a real-time mode. This study compares the mean DTB- and ATB-LTT estimates from the two estimation perspectives: on-line and off-line. Then the tradeoff between the accuracy and timeliness of the on-line DTB-LTT estimates and their implications on the LTT estimation procedure are discussed. Lastly; this study develops an on-line DTB-LTT estimation algorithm which utilizes the Bayesian inference logic and demonstrates it using spatial travel time data from the Toll Collection System (TCS) of the Korea Highway Corporation. It was found that the proposed approach could estimate DTB-LTTs in a real-time context with an acceptable level of accuracy and timeliness.