Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning

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

Estimation of activity-based model parameters and trip chain demand from vehicle identification data

Department of Civil and Environmental EngineeringRefereed conference pape

Statistical approach for activity-based model calibration based on plate scanning and traffic counts data

2015-2016 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptRGCPublishe

Freight traffic analytics from national truck GPS data in Thailand

International Symposium of Transport Simulation (ISTS'18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS'18) - Emerging Transport Technologies for Next Generation Mobility, Aug 06-08, 2018 Ehime University, Matsuyama, Japan201909 bcrcVersion of RecordPublishe