Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.
Using Deep Learning to Understand Travel Demands in Different Urban Districts
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2675 , 7 ; 117-128
2021-03-03
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Seemingly unrelated static and dynamic urban travel demands
Elsevier | 1978
|Defining Sampling Districts for Household Travel Surveys
Transportation Research Record | 2007
|Pedestrian travel rates in central business districts
Engineering Index Backfile | 1968
|Analyses of Canadian Air Travel Demands.
Online Contents | 1993
|Analyses of Canadian Air Travel Demands
British Library Online Contents | 1993
|