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Title:

Characterizing the Importance of Criminal Factors Affecting Bus Ridership using Random Forest Ensemble Algorithm

Accession Number:

01700485

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

Public transit systems provide mass movement with substantial traffic operational and environmental benefits. Despite these benefits, they still represent a small market share in the United States. A comprehensive understanding of the determinants of transit ridership is essential for investment allocation to improve safety, mobility, and air quality in an urban area. Except socio-economic factors, crime has been identified as a determinant for the ridership. Most studies found that ridership and crime are linearly correlated to each other, whereas other studies believed the level of crime can result in a nonlinear effect on the ridership. The relationship between ridership and crime remains inconclusive. Besides, the simultaneous relationship between ridership and crime is scarcely addressed and most ridership studies only include one or a few external factors that affect crime opportunity. This paper proposes a random-forest-based feature selection method to characterize the importance of multiple variables, to bus ridership and total crime, respectively, at different levels. A case study in Houston, Texas, USA, for the year 2017 is provided to illustrate the feature selection and modeling process. A total of 110,885 crimes, ridership on 9004 bus stops, and related socio-economic information were collected. Results indicated that a medium or lower level of ridership is positively correlated to crime; the linear relationship can be broken down at a high level; and reducing the total crime per capita can promote bus ridership. Random-forest-based models were developed with the selected determinants, performing with high accuracy in ridership per capita estimates.

Report/Paper Numbers:

19-02932

Language:

English

Authors:

Li, Qing
Qiao, Fengxiang
Mao, Andrew
McCreight, Catherine

Pagination:

pp 864-876

Publication Date:

2019-4

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2673
Issue Number: 4
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Digital/other

Features:

Figures (9) ; Maps; References (40) ; Tables (1)

Uncontrolled Terms:

Random forest algorithm

Geographic Terms:

Houston (Texas)

Subject Areas:

Planning and Forecasting; Public Transportation; Society

Files:

TRIS, TRB, ATRI

Created Date:

Feb 27 2019 10:09AM