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

Big data and understanding change in the context of planning transport systems

This paper considers the implications of so-called ‘big data’ for the analysis, modelling and planning of transport systems. The primary conceptual focus is on the needs of the practical context of medium-term planning and decision-making, from which perspective the paper seeks to achieve three goals: (i) to try to identify what is truly ‘special’ about big data; (ii) to provoke debate on the future relationship between transport planning and big data; and (iii) to try to identify promising themes for research and application. Differences in the information that can be derived from the data compared to more traditional surveys are discussed, and the respects in which they may impact on the role of models in supporting transport planning and decision-making are identified. It is argued that, over time, changes to the nature of data may lead to significant differences in both modelling approaches and in the expectations placed upon them. Furthermore, it is suggested that the potential widespread availability of data to commercial actors and travellers will affect the performance of the transport systems themselves, which might be expected to have knock-on effects for planning functions. We conclude by proposing a series of research challenges that we believe need to be addressed and warn against adaptations based on minimising change from the status quo

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