JTL’s machine learning cluster focuses on using novel machine-learning perspectives to understand travel behavior and solve transportation challenges. Moving beyond the traditional approach of using discrete choice models (DCM), we use deep neural network (DNN) to predict individual trip-making decisions and to detect changes in travel patterns.
Our studies harness insights from DCM to enrich DNN models to achieve both high predictability and interpretability. Since travel behavior is often uncertain, we model them through the synthesis of prospect theory and DNN. To examine sequential decision making under uncertainty, we apply dynamic programming and reinforcement learning algorithms. For example, we use these approaches to develop methods to rebalance fleets and develop optimal dynamic pricing for shared ride-hailing services.
Moreover, as activity patterns are important underlying factors for travel behavior, but only latently revealed in travel data, in several studies, we use graphical models and unsupervised learning methods to detect changes in activity patterns, with the goal of understanding the impacts of transit fare changes on rider groups.
|Discovering Latent Activity Patterns from Transit Smart Card Data: A Spatiotemporal Topic Model. Transportation Research Part C.. 2020.||
Although automatically collected human travel records can accurately capture the time and location of human
|Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation. Transportation Research Part C: Emerging Technologies.. 2020.||
While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives,...
|Deep Neural Networks for Choice Analysis: Architecture Design with Alternative-Specific Utility Functions. Transportation Research Part C.. 2020.||
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN’s interpretability and predictive power, and to identify effective regularization methods for specific tasks. To address these challenges, this study demonstrates the use of behavioral knowledge for designing a particular DNN architecture with alternative-specific utility functions (ASU-DNN) and thereby...
|Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data. Working Paper.. 2019.||
It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in...
|Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Working Paper.. 2019.||
While researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain as obstacles in theory and practice. By using statistical learning theory, this study presents a framework to examine the tradeoff between estimation and approximation errors, and between prediction and interpretation losses. It operationalizes the DNN interpretability in the choice analysis by formulating the metrics of interpretation loss as the...
|Individual mobility prediction using transit smart card data. Transportation Research Part C. 89:19-34.. 2018.||
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...
|Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach. Transportation Research Part B.. 2018.||
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...
|Real time transit demand prediction capturing station interactions and impact of special events. Transportation Research Part C.. 2018.||
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...
|Rebalancing Shared Mobility-on-Demand Systems: A Reinforcement Learning Approach. Transportation Research Board 97th Annual Meeting.. 2018.||
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...
|Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts. Transportation Research Board 97th Annual Meeting.. 2018.||
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...