Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

Hindawi Mathematical Problems in Engineering Volume 2019, Article ID 5306247, 10 pages https://doi.org/10.1155/2019/5306247 Research Article Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach Shaohua Wang ,1,2 Yanyan Chen ,1 Jianling Huang,1,3 Ning Chen ,1 and Yao Lu1 1 Beijing University of Technology, Beijing Key Laboratory of Traffic Engineering, Beijing 100124, China Tianjin University of Technology and Education, Tianjin Collaborative Innovation Center of Traffic Safety and Control, Tianjin 300222, China 3 Beijing Transportation Information Center, Beijing 100161, China 2 Correspondence should be addressed to Yanyan Chen; Received 29 January 2019; Revised 27 March 2019; Accepted 21 April 2019; Published 6 May 2019 Academic Editor: Mahmoud Mesbah Copyright © 2019 Shaohua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security. 1. Introduction In 2013, 1.25 million people were killed by the road traffic crashes worldwide and more than 50 million were injured [1]. Moreover, casualty rates caused by traffic crashes were significantly higher in the low or middle-income countries than that in the high-income countries. Taking China as an example, traffic crashes caused 58,523 deaths and 211,882 injuries in 2014[2]. In the same period, the crashes caused 32,744 deaths and 2,338 thousand injuries in 2014 in the United States [3]. With the rapid growth of economic development and autoownership, traffic crashes have become a leading cause of mortality in many developing countries, which attracted increasing attention from both the government and the public. Thus, it has become increasingly necessary for all the countries in the world to put considerable efforts to enhance the road safety, particularly in the developing countries. The microlevel research focuses on the specific influencing factors of traffic crashes and casualties in the field of traffic safety research. The purpose of the microlevel research is to propose targeted measures to improve the vehicle, road, and environment. It is easy to understand the correlation between these direct contributing factors and traffic crashes. However, from another perspective, the macrolevel research focuses on the relationship between traffic crashes and society, economy, and environment. Compared with the microlevel safety research, the macrolevel safety analysis can identify safety problems more effectively in a larger area, which is more useful in helping establish a long-term planning policy to improve the traffic safety [4]. Though great progress had been made, the obtainment of data about traffic crashes and related influence factors is the main obstacle for crash analysis in under-developed countries [5]. In China, some scholars have used foreign 2 crashes data for analysis [6]. Other researchers focused on traffic violations such as drunk driving and speeding based on the traffic survey [7]. However, the situation is gradually changing. The road safety research platform (RSRP) was built to share traffic accident data by the Ministry of Public Security of People’s Republic of China at 2015. More importantly, with the continual development of data mining technology, open source data has raised more and more attention in recent years. The point-of-interest (POI) data are the more specific data of land use factors with exact information of location which are supposed to be highly related to the user characteristics and traffic crashes in both macro- and microaspects [5]. A POI database can be applied to describe the specific influence factors which are spatially correlated to the distribution of macrolevel traffic crashes. This study focuses on the spatial autocorrelation between the crashes and the impact of the different types of POI densities on the occurrences of crashes in the target units and adjacent units. The purpose of this paper is twofold: (a) to investigate the optimal spatial econometric model and (b) to evaluate the spatial direct effect and spillover effects of contributory factors that related to traffic crashes by using the POI dataset. The remainder of the paper is organized as follows. In Section 2, a literature review of previous researches on traffic crashes and corresponding measurement methods are presented. Section 3 describes the POI data and crash data(N=26,121) which are collected and processed within Traffic Analysis Zones (TAZs, N=2,114) in Tianjin municipality of China. In Section 4, the author focuses on the spatial econometric model used from the following 3 aspects: (1) using Moran’s I Index to check the spatial autocorrelation of the traffic crashes; (2) introducing the traditional Ordinary Least Square regression (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and testing the model by Lagrange Multiplier (LM); (3) furthermore, introducing the Spatial Durbin Model (SDM) to further estimate the spatial performance of the related factors. In Section 5, the empirical results are quantitative presented and analyzed. Section 6 provides the discussion, including policy implications and suggestions for further studies. Finally, the conclusions are presented and followed by references. 2. Literature Review 2.1. Safety Covariates. A wide variety (...truncated)