Yuebing Liang

• Doctor of Philosophy
• Visiting PhD student at Massachusetts Institute of Technology

Due to its various social and environmental benefits, bike sharing has been gaining popularity worldwide and, in response, many cities have gradually expanded their bike sharing systems (BSSs). For a growing station-based BSS, it is essential to plan new stations based on existing ones, which requires predicting not only the overall trip intensity...

As in a typical two-sided market, the competition between transportation network companies (TNCs) can lead to market fragmentation and loss of matching efficiency between passengers and drivers, whereas a monopoly market may result in the dominant TNC abusing its market power. Therefore, whether to encourage or discourage competition between TNCs i...

For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transport...

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires...

Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. While several recent studies have started to explore the applicability of deep learning for route choice modeli...

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires...

For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transport...

Vacant cruising is an inevitable part of taxi services caused by spontaneous demand, and the efficiency of cruising strategies has purported impact on the profit of individual drivers. Extensive studies have been conducted to analyze taxi cruising patterns and propose effective cruising strategies. However, existing studies mainly focused on the co...

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Recent research has employed graph neural networks (GNNs) for spatiotemporal data imputation and achieved promising performance. However, there still exist two limitations to be addressed: first, existing approaches are generally...

Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand prediction, ignoring the fact that the demands for different transportation modes can be correlated with each other. Despite some recent efforts, existing approaches to multimod...

Route choice modeling, i.e., the process of estimating the likely path that individuals follow during their journeys, is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. While several r...

Alleviating crime and improving urban safety is important for the sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI da...

Bike sharing is an increasingly popular part of urban transportation systems. Accurate demand prediction is the key to support timely re-balancing and ensure service efficiency. Most existing models of bike-sharing demand prediction are solely based on its own historical demand variation, essentially regarding bike sharing as a closed system and ne...

For city regulators, whether to encourage or discourage competition between transportation network companies (TNCs) is a debatable question. As a typical two-sided market, the competition between TNCs can lead to market fragmentation and loss of matching efficiency between passengers and drivers, whereas a monopoly market may result in the dominant...

Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand prediction, ignoring the fact that the demands for different transportation modes can be correlated with each other. Despite some recent efforts, existing approaches to multimod...

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the...

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation, there still exist two limitations to be addressed: first, existing approaches fail to capture the complex spatiotemporal dependencies in traffic data, especially the dy...

Trajectory prediction of vehicles at the city scale is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based...

Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand prediction, ignoring the fact that the demands for different transportation modes can be correlated with each other. Despite some recent efforts, existing approaches to multimod...

Coworking space is a recent manifestation of the emerging sharing economy. This is largely due to two core driving forces: a new working style in the creative and knowledge economies, and the sharing economy, which promotes resource usage efficiency. This paper develops an analytical framework for the spatial perspectives on coworking spaces accord...