A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
Route choice modeling is a fundamental task in transportation planning and demand
forecasting. Classical methods generally adopt the discrete choice model (DCM) framework …
forecasting. Classical methods generally adopt the discrete choice model (DCM) framework …
SoVEC: Social vehicular edge computing-based optimum route selection
This paper proposes a new architecture Social Vehicular Edge Computing (SoVEC) by
integrating three domains: social network, vehicular ad-hoc network, and mobile edge …
integrating three domains: social network, vehicular ad-hoc network, and mobile edge …
RoutesFormer: A sequence-based route choice Transformer for efficient path inference from sparse trajectories
Sensor and machine learning technologies have improved the perception of traffic systems
by providing detailed data about individual vehicle trajectories. Combining data from …
by providing detailed data about individual vehicle trajectories. Combining data from …
Enhancing choice-set generation and route choice modeling with data-and knowledge-driven approach
Two central and interconnected problems arise in the specification of a ''complete''path-
based route choice model: choice-set generation and choice from a choice set. Choice-set …
based route choice model: choice-set generation and choice from a choice set. Choice-set …
Route recommendation method for frequent passengers in subway based on passenger preference ranking
To provide personalized and precise guidance services to subway passengers, a route
recommendation method for frequent subway riders based on passenger preference …
recommendation method for frequent subway riders based on passenger preference …