Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and ...Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.展开更多
Due to the increasing trend of population growth and urbanization, pedestrians form one of the largest single road user groups. However, they are the most neglected group among all road users. Pedestrian safety is now...Due to the increasing trend of population growth and urbanization, pedestrians form one of the largest single road user groups. However, they are the most neglected group among all road users. Pedestrian safety is now a growing concern in the USA. Identifying the factors associated with fatal pedestrian crashes plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This study addresses safety issues by identifying contributory factors associated with fatal pedestrian crashes in Kansas and the USA. For Kansas, the study uses KARS (Kansas Accident Reporting System) database while for the USA FARS (Fatality Analysis Reporting System) database has been used. Different variables considered in this study are human variables (age, and gender), environmental variables (atmospheric condition and light condition), time (time of day, day of week, and crash month), location (intersection vs. mid-block), and roadway variables (speed limit). Different factors that are found to have an association with fatal pedestrian crashes are male pedestrians, older pedestrians, weekend, off peak hours, winter months, dark hours, non-intersection, clear atmospheric conditions, higher speed limit. The findings from Kansas have been compared with that from the USA. This study helps to implement potential countermeasures by identifying the factors that have an association with fatal pedestrian crashes.展开更多
The quantity and severity of traffic accidents have increased with the development of machinery life and traffic growth in cities and roads in the past 50 years. Among the road users, pedestrians are the most vulnerab...The quantity and severity of traffic accidents have increased with the development of machinery life and traffic growth in cities and roads in the past 50 years. Among the road users, pedestrians are the most vulnerable groups to be exposed to high risks. Vehicle crashes with pedestrian are almost inevitable and cause injury or death to pedestrian. Crash investigation and statistical studies indicate that percentage of pedestrian deaths caused by vehicle accidents are much more than all deaths. A considerable amount of accidents occur at signalized and urban intersections which are the intensive crash places. Therefore in this paper appropriate models that could specify safety indicators have been indicated with existing information by characterized parametric and nonparametric variables for twenty signalized intersections. Categories and correlations of variables also have been investigated. Three models including Regression, Poisson, and Negative binomial with defined variables have been determined. T and chi square tests, calibration and comparison of variables have been done by curve fitting. The role of each parameter was specified in pedestrian crashes. Validating models had the following outcomes: Pedestrian crash prediction models were based on none linear relations at intersections. Predictable variables, developing extended linear models and also pedestrian crash prediction are on the basis of Negative binomial distribution which is used due to more data dispersion. As observed, the Negative binomial regression because of its more R2 correlation factor has more validity among other regression models such as linear regression and Poisson. Calibrated models are put into sensitivity analysis to study the effect of each previously mentioned parameter in overall performance. Hence much better perception of future transportation plans can be achieved by development of safety models at planning levels.展开更多
Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted ...Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.展开更多
Crash occurrence is a complex phenomenon,and crashes associated with pedestrians and bicyclists are even more complex.Furthermore,pedestrian-and bicyclist-involved crashes are typically not reported in detail in state...Crash occurrence is a complex phenomenon,and crashes associated with pedestrians and bicyclists are even more complex.Furthermore,pedestrian-and bicyclist-involved crashes are typically not reported in detail in state or national crash databases.To address this issue,developers created the Pedestrian and Bicycle Crash Analysis Tool(PBCAT).However,it is labour-intensive to manually identify the types of pedestrian and bicycle crash from crash-narrative reports and to classify different crash attributes from the textual content of police reports.Therefore,there is a need for a supporting tool that can assist practitioners in using PBCAT more efficiently and accurately.The objective of this study is to develop a framework for applying machine-learning models to classify crash types from unstructured textual content.In this study,the research team collected pedestrian crash-typing data from two locations in Texas.The XGBoost model was found to be the best classifier.The high prediction power of the XGBoost classifiers indicates that this machine-learning technique was able to classify pedestrian crash types with the highest accuracy rate(up to 77%for training data and 72%for test data).The findings demonstrate that advanced machine-learning models can extract underlying patterns and trends of crash mechanisms.This provides the basis for applying machine-learning techniques in addressing the crash typing issues associated with non-motorist crashes.展开更多
文摘Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.
文摘Due to the increasing trend of population growth and urbanization, pedestrians form one of the largest single road user groups. However, they are the most neglected group among all road users. Pedestrian safety is now a growing concern in the USA. Identifying the factors associated with fatal pedestrian crashes plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This study addresses safety issues by identifying contributory factors associated with fatal pedestrian crashes in Kansas and the USA. For Kansas, the study uses KARS (Kansas Accident Reporting System) database while for the USA FARS (Fatality Analysis Reporting System) database has been used. Different variables considered in this study are human variables (age, and gender), environmental variables (atmospheric condition and light condition), time (time of day, day of week, and crash month), location (intersection vs. mid-block), and roadway variables (speed limit). Different factors that are found to have an association with fatal pedestrian crashes are male pedestrians, older pedestrians, weekend, off peak hours, winter months, dark hours, non-intersection, clear atmospheric conditions, higher speed limit. The findings from Kansas have been compared with that from the USA. This study helps to implement potential countermeasures by identifying the factors that have an association with fatal pedestrian crashes.
文摘The quantity and severity of traffic accidents have increased with the development of machinery life and traffic growth in cities and roads in the past 50 years. Among the road users, pedestrians are the most vulnerable groups to be exposed to high risks. Vehicle crashes with pedestrian are almost inevitable and cause injury or death to pedestrian. Crash investigation and statistical studies indicate that percentage of pedestrian deaths caused by vehicle accidents are much more than all deaths. A considerable amount of accidents occur at signalized and urban intersections which are the intensive crash places. Therefore in this paper appropriate models that could specify safety indicators have been indicated with existing information by characterized parametric and nonparametric variables for twenty signalized intersections. Categories and correlations of variables also have been investigated. Three models including Regression, Poisson, and Negative binomial with defined variables have been determined. T and chi square tests, calibration and comparison of variables have been done by curve fitting. The role of each parameter was specified in pedestrian crashes. Validating models had the following outcomes: Pedestrian crash prediction models were based on none linear relations at intersections. Predictable variables, developing extended linear models and also pedestrian crash prediction are on the basis of Negative binomial distribution which is used due to more data dispersion. As observed, the Negative binomial regression because of its more R2 correlation factor has more validity among other regression models such as linear regression and Poisson. Calibrated models are put into sensitivity analysis to study the effect of each previously mentioned parameter in overall performance. Hence much better perception of future transportation plans can be achieved by development of safety models at planning levels.
文摘Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.
文摘Crash occurrence is a complex phenomenon,and crashes associated with pedestrians and bicyclists are even more complex.Furthermore,pedestrian-and bicyclist-involved crashes are typically not reported in detail in state or national crash databases.To address this issue,developers created the Pedestrian and Bicycle Crash Analysis Tool(PBCAT).However,it is labour-intensive to manually identify the types of pedestrian and bicycle crash from crash-narrative reports and to classify different crash attributes from the textual content of police reports.Therefore,there is a need for a supporting tool that can assist practitioners in using PBCAT more efficiently and accurately.The objective of this study is to develop a framework for applying machine-learning models to classify crash types from unstructured textual content.In this study,the research team collected pedestrian crash-typing data from two locations in Texas.The XGBoost model was found to be the best classifier.The high prediction power of the XGBoost classifiers indicates that this machine-learning technique was able to classify pedestrian crash types with the highest accuracy rate(up to 77%for training data and 72%for test data).The findings demonstrate that advanced machine-learning models can extract underlying patterns and trends of crash mechanisms.This provides the basis for applying machine-learning techniques in addressing the crash typing issues associated with non-motorist crashes.