Next Article in Journal
Violence Recognition Based on Auditory-Visual Fusion of Autoencoder Mapping
Next Article in Special Issue
Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow
Previous Article in Journal
Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison
Previous Article in Special Issue
The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network
Article

Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit

Department of Computer Engineering, Hongik University, Seoul 04066, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Ning Wang
Electronics 2021, 10(21), 2653; https://doi.org/10.3390/electronics10212653
Received: 2 October 2021 / Revised: 27 October 2021 / Accepted: 28 October 2021 / Published: 29 October 2021
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
This paper addresses the taxi fleet dispatch problem, which is critical for many transport service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue and profit a taxi platform can generate through the dispatch approaches designed with various criteria. We consider determining the proportion of taxi fleets to different destination zones given the expected rewards from the future states following the distribution decisions learned through reinforcement learning (RL) algorithms. We also take into account more straightforward greedy algorithms that look ahead fewer decision time steps in the future. Our dispatch decision algorithms commonly leverage contextual information and heuristics using a data structure called Contextual Matching Matrix (CMM). The key contribution of our paper is the insight into the trade-off between different design criteria. Primarily, through the evaluation with actual taxi operation data offered by Seoul Metropolitan Government, we challenge the natural expectation that the RL-based approaches yield the best result by showing that a lightweight greedy algorithm can have a competitive advantage. Moreover, we break the norm of dissecting the service area into sub-zones and show that matching passengers beyond arbitrary boundaries generates significantly higher operating income and profit. View Full-Text
Keywords: taxi dispatching; greedy algorithm; reinforcement learning; contextual matching taxi dispatching; greedy algorithm; reinforcement learning; contextual matching
Show Figures

Figure 1

MDPI and ACS Style

Kim, Y.; Yoon, Y. Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit. Electronics 2021, 10, 2653. https://doi.org/10.3390/electronics10212653

AMA Style

Kim Y, Yoon Y. Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit. Electronics. 2021; 10(21):2653. https://doi.org/10.3390/electronics10212653

Chicago/Turabian Style

Kim, Youngrae, and Young Yoon. 2021. "Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit" Electronics 10, no. 21: 2653. https://doi.org/10.3390/electronics10212653

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop