New York City in Winter
New York City in Winter



  • The BUILT Lab, led by Professor Joseph Chow, had 9 papers accepted for presentation at the upcoming 99th Transportation Research Board Annual Meeting in Washington, DC. The research topics revolve around MaaS, optimal learning for fleet management, and emerging technologies like e-scooters, modular autonomous vehicles, and air taxis. The papers include:
    • Flexible Bus Dispatching System with Modular and Fully Automated Bus Units (Igor Dakic, Kaidi Yang, Monica Menendez, Joseph Chow)
    • Forecasting e-Scooter Competition with Direct and Access Trips by Mode and Distance in New York City (Mina Lee, Joseph Chow, Gyugeun Yoon, Yueshuai He)
    • Air Taxi Skyport Location Problem for Airport Access (Srushti Rath, Joseph Chow)
    • Reinforcement Learning-based Sequential Transit Route Design under Demand Uncertainty (Gyugeun Yoon, Joseph Chow)
    • Day-to-Day Market Evaluation of Last-Mile Transit Operations using Modular Autonomous Vehicles with En-Route Transfers (Nicholas Caros, Joseph Chow)
    • A Many-to-Many Assignment Game Method to Evaluate Cost Allocations of Link Operators in a Mobility-as-a-Service Market without Route Enumeration (Saeid Rasulkhani, Theodoros Pantelidis, Joseph Chow)
    • Online Route Choice Modeling for Mobility-as-a-Service Networks with Non-Separable, Congestible Link Capacity Effects (Jia Xu, Joseph Chow, Song Gao)
    • A Path-Based Many-to-Many Assignment Game to Model Mobility-as-a-Service Networks (Theodoros Pantelidis, Saeid Rasulkhani, Joseph Chow)
    • Empirical Validation of Network Learning with Taxi GPS Data from Wuhan, China (Jia Xu, Qian Xie, Joseph Chow, Xintao Liu)


  • Kun Qin, Yuanquan Xu, Chaogui Kang, Stanislav Sobolevsky, Mei-Po Kwan. Modeling Spatio-Temporal Evolution of Urban Crowd Flows. ISPRS International Journal of Geo-Information.
    • Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestions, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, and yet very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix computation based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps as (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification and correlation into a unified and efficient analytical framework. We validate the proposed methodology under both synthetic and real world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity and spatial constraint are also discussed.

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