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

Abstract

This article 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 article 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 an individual profile of daily performed activities. To obtain 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.

Keywords:

• EM Algorithm
• Maximum-Likelihood Estimation
• Plate Scanning
• Trip Chaining

Notes

A traffic zone is a special area designed by the state and usually consists of one or more census blocks.

The activity chain is the combined decision of travelers on an activity pattern and trip chain.

Additional information

Funding

The authors are indebted to the General Research Fund of the Hong Kong Research Council (RGC) (Project: PolyU 5290/09E) for support of this research.