Tabu-Search-Based Combinatorial Subset Selection Approach to Support Investigation of Built Environment and Traffic Safety Relationship

Date

2022-07-01

Author

Alisan, Onur
Tüydeş Yaman, Hediye
Ozguven, Eren Erman

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Traffic crashes are a leading cause of death globally, with an increasing rate in urban areas. Thus, this study focuses on the relationship between built environment (BE) and traffic safety (TS), by constructing a relationship model using BE variables. The aim of this paper is to determine the best subset of BE variables through a generalizable methodology. The BE is operationalized through the D-classification (e.g., density, diversity, and design), and various datasets are collected from different agencies. TS is operationalized through motor vehicle involved (MOT) and vulnerable road user (VRU)-involved crash frequencies at the zonal level. A preliminary GIS-based process is conducted to associate the crash data at the census block group (BG) level, followed by examining the BE-TS relationships through a series of negative binomial models optimized for subset selection. The model generation is performed automatically by an embedded Tabu Search procedure. Two case studies are presented: a single-county case (Leon County, Florida, U.S.) and a tri-county case (Miami-Dade, Broward, and Palm Beach Counties, Florida, U.S.). Results show that some BE variables such as total population, age of housing stock, number of bus stops, and traffic volume have consistently positive relationships with crash occurrences. In contrast, several factors show varying effects by crash type or location. For example, motorized mode percentage has a negative relation with crash occurrences in the single-county case whereas it is insignificant for the tri-county case where the non-motorized mode percentage has a positive effect on crash occurrences.

Subject Keywords

safety, crash analysis, GEOGRAPHICALLY WEIGHTED REGRESSION, PEDESTRIAN INJURY COLLISIONS, CRASH SPATIAL HETEROGENEITY, MULTIPLE LINEAR-REGRESSION, HOUSEHOLD CAR OWNERSHIP, VULNERABLE ROAD USERS, MOTOR-VEHICLE CRASHES, LAND-USE, MODELING APPROACH, PREDICTION MODELS

URI

https://hdl.handle.net/11511/100705

Journal

TRANSPORTATION RESEARCH RECORD

DOI

https://doi.org/10.1177/03611981221108161

Collections

Department of Civil Engineering, Article

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Citation Formats

O. Alisan, H. Tüydeş Yaman, and E. E. Ozguven, “Tabu-Search-Based Combinatorial Subset Selection Approach to Support Investigation of Built Environment and Traffic Safety Relationship,” TRANSPORTATION RESEARCH RECORD, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100705.