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.
|Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models. Transportation Research Part C.. 2021.||
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (...
|Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks. Transportation Research Part B.. 2021.||
Researchers often treat data-driven and theory-driven models as two disparate or even conflicting methods in travel behavior analysis. However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive. Using their complementary nature, this study designs a theory-based residual neural network (TB-ResNet) framework, which synergizes discrete...
|Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Transportation Research Part B.. 2021.||
Although researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain obstacles in theory and practice. This study presents a statistical learning theoretical framework to examine the tradeoff between estimation and approximation errors, and between the quality of prediction and of interpretation. It provides an upper bound on the estimation error of the prediction quality in DNN, measured by zero-one and log losses,...
|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. Journal of Choice Modelling.. 2020.||
It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We first demonstrate that the MTLDNNs are theoretically more general than the NL models because...
|Predicting Travel Mode Choice with 86 Machine Learning Classifiers: An Empirical Benchmark Study. Transportation Research Board 99th Annual Meeting.. 2020.||
Researchers are applying a large number of machine learning (ML) classifiers to predict travel behavior, but the results are data-specific and the selection of ML classifiers is author-specific. To obtain generalizable results, this paper provides an empirical benchmark by using 86 classifiers from 14 model families to predict the travel mode choice based on the National Household Travel Survey (NHTS) 2017 dataset. The 86 ML classifiers from 14 model families incorporate all the important ML...
|Machine-learning-augmented analysis of textual data: application in transit disruption management. IEEE Open Journal of Intelligent Transportation Systems.. 2020.||
Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this paper, we develop a...
|Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-resolution Spatio-Temporal Deep Learning Approach. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS.. 2020.||
Short-term demand predictions, typically defined as less than an hour into the future, are essential for implementing dynamic control strategies and providing useful customer infor- mation in transit applications. Knowing the expected demand enables transit operators to deploy real-time control strategies in advance of the demand surge, and minimize the impact of abnormalities on the service quality and passenger experience. One of the most useful applications of demand prediction models 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...
|Inferring patterns in the multi-week activity sequences of public transport users. Transportation Research Part C: Emerging Technologies. 64:1-16.. 2016.||
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,...