Updating of Travel Behavior Model Parameters and Estimation of Vehicle Trip Chain Based on Plate Scanning

This paper proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The paper adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is individual profile of daily performing activities. To obtain the reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the Expectation-Maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux-Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases.