research-article

Exploring the Risky Travel Area and Behavior of Car-hailing Service

Authors:

Bo LangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 1

Article No.: 9, Pages 1 - 22

https://doi.org/10.1145/3465059

Published: 23 December 2021 Publication History

Abstract

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

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Cited By

Niu HSun YZhu HGeng CYang JXiong HLang B(2022)Exploring the tidal effect of urban business district with large-scale human mobility dataFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-1623-617:3Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/s11704-022-1623-6

Index Terms

Exploring the Risky Travel Area and Behavior of Car-hailing Service
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 1

February 2022

349 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3502429

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 23 December 2021

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 January 2021

Published in TIST Volume 13, Issue 1

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Cited By

Niu HSun YZhu HGeng CYang JXiong HLang B(2022)Exploring the tidal effect of urban business district with large-scale human mobility dataFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-1623-617:3Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/s11704-022-1623-6

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