Next Article in Journal
Computer Vision in Analyzing the Propagation of a Gas–Gunpowder Jet
Previous Article in Journal
Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches
Previous Article in Special Issue
A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment
Article

A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates

Smart Transportation Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco J. Martinez
Sensors 2022, 22(1), 5; https://doi.org/10.3390/s22010005
Received: 19 November 2021 / Revised: 16 December 2021 / Accepted: 16 December 2021 / Published: 21 December 2021
(This article belongs to the Special Issue Sensory Data Supported Traffic Safety Analysis for Smart City Era)
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety. View Full-Text
Keywords: crash rate modelling; spatial autoregressive; quantile regression; quantile effects; spatial autocorrelation crash rate modelling; spatial autoregressive; quantile regression; quantile effects; spatial autocorrelation
Show Figures

Figure 1

MDPI and ACS Style

Yu, T.; Gao, F.; Liu, X.; Tang, J. A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates. Sensors 2022, 22, 5. https://doi.org/10.3390/s22010005

AMA Style

Yu T, Gao F, Liu X, Tang J. A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates. Sensors. 2022; 22(1):5. https://doi.org/10.3390/s22010005

Chicago/Turabian Style

Yu, Tianjian, Fan Gao, Xinyuan Liu, and Jinjun Tang. 2022. "A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates" Sensors 22, no. 1: 5. https://doi.org/10.3390/s22010005

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