Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
Ridesourcing systems: A framework and review
With the rapid development and popularization of mobile and wireless communication
technologies, ridesourcing companies have been able to leverage internet-based platforms …
technologies, ridesourcing companies have been able to leverage internet-based platforms …
Deep learning for intelligent transportation systems: A survey of emerging trends
M Veres, M Moussa - IEEE Transactions on Intelligent …, 2019 - ieeexplore.ieee.org
Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
The vehicle dispatching system is one of the most critical problems in online ride-hailing
platforms, which requires adapting the operation and management strategy to the dynamics …
platforms, which requires adapting the operation and management strategy to the dynamics …
Temporal multi-graph convolutional network for traffic flow prediction
M Lv, Z Hong, L Chen, T Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …
[HTML][HTML] DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …
management in an urban road network. How to build an effective large-scale traffic state …
Mobility-aware charging scheduling for shared on-demand electric vehicle fleet using deep reinforcement learning
With the emerging concept of sharing-economy, shared electric vehicles (EVs) are playing a
more and more important role in future mobility-on-demand traffic system. This article …
more and more important role in future mobility-on-demand traffic system. This article …
Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network
Region-level passenger demand prediction plays an important role in the coordination of
travel demand and supply in the urban public transportation system. The complex urban …
travel demand and supply in the urban public transportation system. The complex urban …
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
With the rapid development of mobile-internet technologies, on-demand ride-sourcing
services have become increasingly popular and largely reshaped the way people travel …
services have become increasingly popular and largely reshaped the way people travel …