7 papers
TorchDCM: A Unified PyTorch-Native Package for Discrete Choice Modeling
Baichuan Mo, Zhengzhong Ricky You, Xiqun Michael Chen +1
Estimating large and simulation-intensive discrete choice models (DCMs) requires repeated evaluation of utilities, probabilities, derivatives, and simulated likelihoods over many o…
Large Language Models for Travel Behavior Prediction
Baichuan Mo, Hanyong Xu, Ruoyun Ma +4
Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data. With recent adva…
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
Baichuan Mo, Qing Yi Wang, Xiaotong Guo +2
In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and…
Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty
Baichuan Mo, Haris N. Koutsopoulos, Zuo-Jun Max Shen +1
Public transit passengers need guidance during service disruptions. This study proposes an individual-based path (IPR) recommendation model. The model decides which paths to recomm…
Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Baichuan Mo, Yunhan Zheng, Xiaotong Guo +2
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, t…
Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty
Baichuan Mo, Haris N. Koutsopoulos, Max Zuo-Jun Shen +1
When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation mode…