Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Liang, Yuebing"'
Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling a plethora of intelligent transportation applications such as dynamic traffic control or personalized route recommendation. Despite r
Externí odkaz:
http://arxiv.org/abs/2310.03617
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 st
Externí odkaz:
http://arxiv.org/abs/2303.11977
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, es
Externí odkaz:
http://arxiv.org/abs/2211.08903
Autor:
Zhao, Zhan, Liang, Yuebing
Publikováno v:
Transportation Research Part C: Emerging Technologies, 149, 104079 (2023)
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 sever
Externí odkaz:
http://arxiv.org/abs/2206.10598
Publikováno v:
In Land Use Policy December 2024 147
Publikováno v:
In Sustainable Cities and Society 1 November 2024 114
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
Externí odkaz:
http://arxiv.org/abs/2203.10961
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 m
Externí odkaz:
http://arxiv.org/abs/2112.08078
Publikováno v:
In Computers, Environment and Urban Systems September 2024 112
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 a
Externí odkaz:
http://arxiv.org/abs/2109.08357