Machine Learning for Transportation

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,...

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...