Machine Learning for Transportation

Shenhao Wang, Jinhua Zhao.  Submitted.  Using Deep Neural Network to Analyze Travel Mode Choice with Interpretable Economic Information: An Empirical Example. Transportation Research Part C.

Recently deep neural network (DNN) has been increasingly applied to microscopic demand analysis. While DNN often performs with higher predictive accuracy than traditional multinomial logit (MNL) model, it is unclear whether we can obtain interpretable economic information from DNN-based choice model beyond prediction accuracy. This paper seeks to provide an empirical method of numerically extracting valuable economic information such as choice probability, probability derivatives (or...

Shenhao Wang, Jinhua Zhao.  Submitted.  Multitask Learning Deep Neural Network to Combine Revealed and Stated Preference Data. Transportation Research Part B.

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a general multitask learning deep neural network (MTLDNN) to combine RP and SP data and incorporate the traditional nest logit approach as a special case. Based on a combined RP and SP survey in Singapore to examine the demand for autonomous vehicles (AV), we designed, estimated and compared one hundred MTLDNN architectures with three major...

Nassir N, Jinhua Zhao, John Attanucci, Frederick P. Salvucci, Nigel Wilson.  Submitted.  Bayesian Inference of Passenger Boarding Strategies at Express Stops with Real-time Bus Arrival Information. Transportation Research Part C.

Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs. In this paper, a Bayesian inference method is proposed to analyze the AFC...

Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao.  2018.  Individual mobility prediction using transit smart card data. Transportation Research Part C. 89:19-34.

For intelligent urban transportation systems, the ability to predict individual mobility is crucial for personalized traveler information, targeted demand management, and dynamic system operations. Whereas existing methods focus on predicting the next location of users, little is known regarding the prediction of the next trip. The paper develops a methodology for predicting daily individual mobility represented as a chain of trips (including the null set, no travel), each defined as a...

Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao.  2018.  Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach. Transportation Research Part B.

Although stable in the short term, individual travel patterns are subject to changes in the long term. The ability to detect such changes is critical for developing behavior models that are adaptive over time. We define travel pattern change as "abrupt, substantial, and persistent changes in the underlying pattern of travel behavior" and develop a methodology to detect such changes in individual travel patterns. We specify one distribution for each of the three dimensions of travel behavior...

Peyman Noursalehi, Haris N. Koutsopoulos, Jinhua Zhao.  2018.  Real time transit demand prediction capturing station interactions and impact of special events. Transportation Research Part C.

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A...

Gabriel Goulet Langlois.  2016.  Inferring patterns in the multi-week activity sequences of public transport users. Transportation Research Part C: Emerging Technologies. 64:1-16.

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study,...

Shenhao Wang, Jinhua Zhao.  Submitted.  Framing Discrete Choice Model as Deep Neural Network with Utility Interpretation. Working paper.

Deep neural network (DNN) has been increasingly applied to travel demand prediction. However, no study has examined how DNN relates to utility-based discrete choice models (DCM) beyond simple comparison of prediction accuracy. To fill this gap, this paper investigates the relationship between DNN and DCM from a theoretical perspective with three major findings. First, we introduce the utility interpretation to the DNN models and demonstrate that DCM is one special case of DNN with shallow...

Jian Wen, Jinhua Zhao, Patrick Jaillet.  2018.  Rebalancing Shared Mobility-on-Demand Systems: A Reinforcement Learning Approach. Transportation Research Board 97th Annual Meeting.

Shared mobility-on-demand systems have very promising prospects in making urban transportation efficient and affordable. However, due to operational challenges among others, many mobility applications still remain niche products. This paper addresses rebalancing needs that are critical for effective fleet management in order to offset the inevitable imbalance of vehicle supply and travel demand. Specifically, the authors propose a reinforcement learning approach which adopts a deep Q network...

Han Qiu, Ruimin Li, Jinhua Zhao.  2018.  Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts. Transportation Research Board 97th Annual Meeting.

The authors consider a daily-level profit maximization of a shared mobility on-demand (MoD) service with request-level control. The authors use discrete choice models to describe traveler behavior, apply the assortment and price optimization framework to model the request-level dynamics, and leverage insights from dynamic programming to develop daily-level optimization problem. The authors solve this problem by designing parametric rollout policy and utilizing Covariance Matrix Adaptation...