A traffic prediction enabled double rewarded value iteration network for route planning
J Li, D Fu, Q Yuan, H Zhang, K Chen… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Effective route planning is the key to improving transportation efficiency. By leveraging the in-
depth knowledge of road topology and traffic trends, experienced drivers (eg, taxi drivers) …
depth knowledge of road topology and traffic trends, experienced drivers (eg, taxi drivers) …
Urban multiple route planning model using dynamic programming in reinforcement learning
With the development of the economy and the acceleration of urbanization, traffic congestion
has become a worldwide problem. Advances in mobile Internet and sensor technologies …
has become a worldwide problem. Advances in mobile Internet and sensor technologies …
Multi-task travel route planning with a flexible deep learning framework
Travel route planning aims to map out a feasible sightseeing itinerary for a traveler covering
famous attractions and meeting the tourist's desire. It is very useful for tourists to plan their …
famous attractions and meeting the tourist's desire. It is very useful for tourists to plan their …
AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning
Taxi cruising route planning has attracted considerable attention, and relevant studies can
be broadly categorized into three main streams: recommending one or multiple areas …
be broadly categorized into three main streams: recommending one or multiple areas …
NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms
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 …
location-based applications such as vehicle navigation, traffic management, and location …
Route optimization via environment-aware deep network and reinforcement learning
Vehicle mobility optimization in urban areas is a long-standing problem in smart city and
spatial data analysis. Given the complex urban scenario and unpredictable social events …
spatial data analysis. Given the complex urban scenario and unpredictable social events …
Modeling heterogeneous routing decisions in trajectories for driving experience learning
J Zheng, LM Ni - Proceedings of the 2014 ACM International Joint …, 2014 - dl.acm.org
Road latent cost, which quantifies how desirable each road is for traveling, is important
information to enable many smartcity applications such as route recommendation. Arguably …
information to enable many smartcity applications such as route recommendation. Arguably …
AlphaRoute: large-scale coordinated route planning via Monte Carlo tree search
This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-
scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree …
scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree …
Online vehicle routing with neural combinatorial optimization and deep reinforcement learning
Online vehicle routing is an important task of the modern transportation service provider.
Contributed by the ever-increasing real-time demand on the transportation system …
Contributed by the ever-increasing real-time demand on the transportation system …
Multi-scale and multi-scope convolutional neural networks for destination prediction of trajectories
Precise destination prediction from partial trajectories have a huge potential impact on
intelligent location-based approaches. Traditional prediction approaches, which treat …
intelligent location-based approaches. Traditional prediction approaches, which treat …