5 papers
Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach
Xiaocai Zhang, Neema Nassir, Lok Sang Chan +1
Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle wit…
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
Xiaocai Zhang, Neema Nassir, Milad Haghani
Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusin…
Analytical modeling of a stop-less modular bus line: Optimization, feasibility, and economies of scale
Haoran Zhao, Neema Nassir, Andres Fielbaum
Conventional bus services often struggle with inefficiencies including prolonged dwell times at heavily used stops, especially for through passengers. A stop-less autonomous modula…
A sparse identification approach for automating choice models' specification
Amir Ghorbani, Neema Nassir, Patricia Sauri Lavieri +1
The methodology discussed in this paper aims to enhance choice models' comprehensiveness and explanatory power for forecasting choice outcomes. To achieve these, we have developed…
A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction
Jianzhong Qi, Zhuowei Zhao, Egemen Tanin +3
Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose…