50 citations · 106 across the 14 of their papers we have counts for
15 papers
Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability
Joseph Rodriguez, Haris N. Koutsopoulos, Jinhua Zhao
Service reliability is critical to transit service delivery. This paper describes headway control pilots conducted in two high-ridership Chicago bus routes between 2022 and 2023. A…
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…
Reconstructing Transit Vehicle Trajectory Using High-Resolution GPS Data
Yuzhu Huang, Awad Abdelhalim, Anson Stewart +2
High-resolution location ("heartbeat") data of transit fleet vehicles is a relatively new data source for many transit agencies. On its surface, the heartbeat data can provide a we…
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Zepu Wang, Dingyi Zhuang, Yankai Li +4
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from…
Fairness-enhancing deep learning for ride-hailing demand prediction
Yunhan Zheng, Qingyi Wang, Dingyi Zhuang +2
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecastin…
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
Qingyi Wang, Shenhao Wang, Yunhan Zheng +4
Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. Ho…
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
Qingyi Wang, Shenhao Wang, Dingyi Zhuang +2
Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably…
Passenger Path Choice Estimation Using Smart Card Data: A Latent Class Approach with Panel Effects Across Days
Baichuan Mo, ZhenLiang Ma, Haris N. Koutsopoulos +1
Understanding passengers' path choice behavior in urban rail systems is a prerequisite for effective operations and planning. This paper attempts bridging the gap by proposing a pr…
Modeling Virus Transmission Risks in Commuting with Emerging Mobility Services: A Case Study of COVID-19
Baichuan Mo, Peyman Noursalehi, Haris N. Koutsopoulos +1
Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuti…
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…
Bulk Service Queueing for Transit Resilience under Short Random Service Suspensions
Baichuan Mo, Li Jin, Zhengzhong Ricky You +3
Short service suspensions are common in public transit systems, but their operational impacts remain difficult to quantify. We develop an analytical framework for measuring the res…
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…
Computer Vision for Transit Travel Time Prediction: An End-to-End Framework Using Roadside Urban Imagery
Awad Abdelhalim, Jinhua Zhao
Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This paper is the first to utilize roadside u…
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Dingyi Zhuang, Shenhao Wang, Haris N. Koutsopoulos +1
Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential…
Transit Frequency Setting Problem with Demand Uncertainty
Xiaotong Guo, Baichuan Mo, Haris N. Koutsopoulos +2
Public transit systems are the backbone of urban mobility systems in the era of urbanization. The design of transit schedules is important for the efficient and sustainable operati…