SAMPLING APPROACH TO SPATIAL VARIATION FOR OBTAINING A SET OF TRAVEL DEMANDS

This study focuses on origin-destination (OD) demand estimation with limited datasets, using sampling to obtain a distribution of the OD demand pattern that includes spatial variation. Traditional methods, which obtain one most likely OD demand pattern, cannot include variations. Our proposed algorithm extracts generated trips and calculates destination choice probability and link travel time. A discrete destination choice model utilizes a stochastic utility from unobserved spatial correlations as a means of obtaining a distribution of OD demand patterns. The algorithm generates a set of sampled OD demand pattern using a Monte Carlo approach; this spatial correlation produces a variety of OD demand patterns, which cannot be obtained by a traditional entropy model. The algorithm's performance is verified by numerical examples in the Philadelphia network. The results show that our proposed algorithm is highly reproducible and computationally efficient.

Junji Urata is an assistant professor of Kobe University, Japan. He received Dr. Eng. (city planning) from the University of Tokyo in 2015. He moved to Kobe University as an assistant professor in 2016. He worked for a Post-K computer project “Priority Issue 3: Development of Integrated Simulation Systems for Hazard and Disaster Induced by Earthquake and Tsunami”. His research topics include behavioral modeling, travel behavior analysis under emergency situation and implementation of a traffic simulator with high-performance computing.