Route Optimization via Environment-Aware Deep Network and Reinforcement Learning
@article{Guo2021RouteOV, title={Route Optimization via Environment-Aware Deep Network and Reinforcement Learning}, author={Pengzhan Guo and Keli Xiao and Zeyang Ye and Wei Zhu}, journal={ACM Trans. Intell. Syst. Technol.}, year={2021}, volume={12}, pages={74:1-74:21} }
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, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to…
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References
SHOWING 1-10 OF 42 REFERENCES
Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning
- Computer ScienceKnowl. Based Syst.
- 2020
Optimize taxi driving strategies based on reinforcement learning
- Computer ScienceInt. J. Geogr. Inf. Sci.
- 2018
The results show that the novel method based on reinforcement learning to optimize taxi driving strategies for global profit maximization improves profits and efficiency for cabdrivers and increases the opportunities for passengers to find taxis as well.
Augmenting Decisions of Taxi Drivers through Reinforcement Learning for Improving Revenues
- Computer ScienceICAPS
- 2017
A Reinforcement Learning (RL) based system to learn from real trajectory logs of drivers to advise them on the right locations to find customers which maximize their revenue and it is demonstrated that an RL based system can provide significant benefits to the drivers.
Optimizing Taxi Driver Profit Efficiency: A Spatial Network-Based Markov Decision Process Approach
- EconomicsIEEE Transactions on Big Data
- 2020
A novel Spatial Network-based Markov Decision Process (SN-MDP) with a rolling horizon configuration to recommend better driving directions given a set of historical taxi records and the current status of a vacant taxi to maximize the profit in the near future is proposed.
HUNTS: A Trajectory Recommendation System for Effective and Efficient Hunting of Taxi Passengers
- Computer Science2013 IEEE 14th International Conference on Mobile Data Management
- 2013
This paper proposes a dynamic scoring system to evaluate each road segment in different time periods by considering both picking-up rate and profit factors, and introduces a novel method, called trajectory sewing, based on a heuristic method and the Skyline technique, to produce an approximate optimal trajectory in real-time.
TaxiRec: Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines
- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2018
TaxiRec is proposed, a framework for evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers and can use with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable.
T-drive: driving directions based on taxi trajectories
- Computer ScienceGIS '10
- 2010
This paper mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provides a user with the practically fastest route to a given destination at a given departure time.
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning
- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2022
An enhanced version of the weighted aggregating stochastic gradient descent scheme, WASGD+, is shown to be a significant improvement over its prototype and firmly validated the superiority of the WASGD scheme in accelerating the training of deep architecture.
Multi-User Mobile Sequential Recommendation for Route Optimization
- Computer ScienceACM Trans. Knowl. Discov. Data
- 2020
This work enhances the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective, and develops a parallel framework based on the simulated annealing to numerically solve the MMSR problem series.
Anomalous Taxi Route Detection System Based on Cloud Services
- Computer Science
- 2019
This paper proposes a solution which is a cloud-based system and applies machine learning algorithms to detect anomaly taxi trajectory for the passenger and demonstrates the system architecture design in detail.