Macro and micro models for zonal crash prediction with application in hot zones identification

Huang, Helai; Song, Bo; Xu, Pengpeng; Zeng, Qiang; Lee, Jae Young; Abdel-Aty, Mohamed

doi:10.1016/j.jtrangeo.2016.06.012

Cited by 104 publications

(50 citation statements)

References 45 publications

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“…These findings were in line with other previous studies, as reported by Huang et al (2016), Pirdavani et al (2012) and Lee et al (2015). Table 2.1 details the explanatory variables and techniques, which were investigated in the above mentioned previous studies.…”

Section: Xu Huang Dong and Wongsupporting

confidence: 90%

“…microscopic, for a specific road segment or intersection, or at macroscopic-level for a larger area, such as a municipality (Pirdavani, Daniels, van Vlierden, Brijs, & Kochan, 2017). We suggest Huang et al (2016) for more detailed information concerning both aggregation levels. Specifically for macro-level analysis, efforts have been made to associate crashes and predictive variables that have macro-level characteristics, such as socioeconomic, exposure and network variables (e.g.…”

Section: Examples Of Traffic Crash Risk Exposure Measures Are Aadt Ormentioning

confidence: 99%

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Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

Gomes¹

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Section: Xu Huang Dong and Wongsupporting

confidence: 90%

Section: Examples Of Traffic Crash Risk Exposure Measures Are Aadt Ormentioning

confidence: 99%

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

Gomes¹

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“…The model developed in this study included company-level factors and regional-level factors, both of which are macro-level factors. Many previous studies have also developed models that include macro-level factors [34][35][36][37][38]. The factors in the previous studies included demographic, socioeconomic, land use, road network, and transit characteristics, and they were aggregated using various geographical units, e.g., traffic analysis zones, block groups, and census tracts [37].…”

Section: Macro-level Factors In Traffic Safetymentioning

confidence: 99%

“…These factors provide useful insights in terms of long-term improvements in traffic safety. Thus, by analyzing these factors, researchers can suggest implications for improving traffic safety in both traffic engineering and non-traffic engineering, which can lead to many aspects of policies and decisions related to traffic safety investments [37,38].…”

Section: Macro-level Factors In Traffic Safetymentioning

confidence: 99%

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

et al. 2019

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Bus-pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus-pedestrian crashes. However, bus-pedestrian crashes that are affected by a company's and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus-pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus-pedestrian crashes, and a safety policy is suggested to enhance bus-pedestrian safety.

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“…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 Mathematical Problems in Engineering crashes data for analysis [6]. Other researchers focused on traffic violations such as drunk driving and speeding based on the traffic survey [7].…”

Section: Introductionmentioning

confidence: 99%

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

Wang

Chen

Huang

et al. 2019

Mathematical Problems in Engineering

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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.

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Macro and micro models for zonal crash prediction with application in hot zones identification

Cited by 104 publications

References 45 publications

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

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