Macro-level safety analysis of pedestrian crashes in Shanghai, China

Wang, Xuesong; Yang, Junguang; Lee, Chris; Ji, Zhuoran; You, Shikai

doi:10.1016/j.aap.2016.07.028

Cited by 110 publications

(61 citation statements)

References 29 publications

(90 reference statements)

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“…This hypothesis may be inappropriate, particularly in cases where 25 the unobserved factors are correlated over space (Xu and Huang, 2015). To capture this 26 spatially structured variability in the effects of contributory factors, Xu and Huang (2015) 27 advocated the development of a model based on the principle that the estimated parameters 28 on a geographical surface are related to each other with closer values more similar than distant 29 ones. 30 To address this potential spatial correlation in varying coefficients, two competing 31 approaches are promising, i.e., the geographically weighted Poisson regression (GWPR; 32 Fotheringham et al, 2002;Nakaya et al, 2005) and the Bayesian spatially varying coefficients 33 (BSVC) models (Congdon, 1997;Assuncao et al, 2002;Congdon, 2003;Gelfand et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Huang

et al. 2017

Accident Analysis & Prevention

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This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.

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Section: Introductionmentioning

confidence: 99%

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Huang

et al. 2017

Accident Analysis & Prevention

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“…The BHM model handles data with Poisson or negative binominal distribution and has been widely used to analyze traffic crashes [43,44], crimes [45,46], and disease cases [47,48].…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Chen

Liu

Zhou

et al. 2017

IJGI

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Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

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“…A wide variety of exposure variables were described in the traffic crash models in the previous studies. All the factors can be divided into five parts: (1) human related factors, including age, fatigue driving, and drunk driving [8,9]; (2) vehicle factors, especially different types of vehicle and nonvehicle [10,11]; (3) road factors, including geometric design features [12], number of lanes at road, number of intersections, and road density; (4) environment factors, including traffic characteristics, land use type [4], and weather condition. Meanwhile, socioeconomic variables, such as population, employment, and household income, were reported to be connected with the frequency of traffic crashes.…”

Section: Safety Covariatesmentioning

confidence: 99%

“…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].…”

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-level safety analysis of pedestrian crashes in Shanghai, China

Cited by 110 publications

References 29 publications

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

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