Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2018

Abstract

In most cities, taxis play an important role in providing point-to-point transportation service. If the taxi service is reliable, responsive, and cost-effective, past studies show that taxi-like services can be a viable choice in replacing a significant amount of private cars. However, making taxi services efficient is extremely challenging, mainly due to the fact that taxi drivers are self-interested and they operate with only local information. Although past research has demonstrated how recommendation systems could potentially help taxi drivers in improving their performance, most of these efforts are not feasible in practice. This is mostly due to the lack of both the comprehensive data coverage and an efficient recommendation engine that can scale to tens of thousands of drivers. In this paper, we propose a comprehensive working platform called the Driver Guidance System (DGS). With real-time citywide taxi data provided by our collaborator in Singapore, we demonstrate how we can combine real-time data analytics and large-scale optimization to create a guidance system that can potentially benefit tens of thousands of taxi drivers. Via a realistic agent-based simulation, we demonstrate that drivers following DGS can significantly improve their performance over ordinary drivers, regardless of the adoption ratios. We have concluded our system designing and building and have recently entered the field trial phase.

Keywords

Agent based simulation, Cost effective, Guidance system, Large-scale optimization, Local information, Real-time data, System designing, Transportation services

Discipline

Databases and Information Systems | Technology and Innovation | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the Thirtieth AAAl Conference on Innovative Applications of Artificial Intelligence, New Orleans, Louisiana, 2018, February 2-7

First Page

7779

Last Page

7785

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Publisher

Additional URL

https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17215/16386

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