Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

A matched case-control approach to identify the risk factors of fatal pedestrian crashes on a six-lane rural highway in India

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case-control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model

Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section.Parameters includingpedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roadsare significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety.

Factors explaining pedestrian-involved fatality crashes on National Highways in India

Pedestrians continue to face high risk of getting involved in fatal and serious injury crashes all over the world. In many high-income countries, pedestrian involvement in fatal crashes occur mostly in urban areas. However, in many low- and middle-income countries in Asia and Africa, pedestrian involvement in fatal crashes occur on intercity highways too. This research analyses fatal pedestrian crash characteristics, and identifies probable contributory factors to pedestrian involvement in fatal crashes using logistic regression for two-, four-, and six-lane National Highways. The fatal pedestrian crash density is found to be the highest at 1.37 crashes/km/year on six-lane divided NH-1. The binary logistic regression estimation results for pedestrian involvement in the fatal crash model revealed that the predictors: "number of lanes" and "time of crash" are found to be significant at 95% level. The model results for the variable "number of lanes" highlights the need to study pedestrian crossing behaviour on highways in detail. The design standards for pedestrian crossing facilities in urban areas may not be suitable for National Highways in particular multi-lane highways. In-depth research is required to understand the suitability of various traffic calming measures and other possible interventions which can ensure pedestrian safety on highways.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

INTRODUCTION

The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria.

METHODS

This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose.

RESULTS

The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar.

PRACTICAL APPLICATION

To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

Financial risk of road traffic trauma care in public and private hospitals in Addis Ababa, Ethiopia: A cross-sectional observational study

BACKGROUND

Road traffic injuries are among the most important causes of morbidity and mortality and cause substantial economic loss to households in Ethiopia. This study estimates the financial risks of seeking trauma care due to road traffic injuries in Addis Ababa, Ethiopia.

METHODS

This is a cross-sectional survey on out-of-pocket (OOP) expenditures related to trauma care in three public and one private hospital in Addis Ababa from December 2018 to February 2019. Direct medical and non-medical costs (2018 USD) were collected from 452 trauma cases. Catastrophic health expenditures were defined as OOP health expenditures of 10% or more of total household expenditures. Additionally, we investigated the impoverishment effect of OOP expenditures using the international poverty line of $1.90 per day per person (adjusted for purchasing power parity).

RESULTS

Trauma care seeking after road traffic injuries generate catastrophic health expenditures for 67% of households and push 24% of households below the international poverty line. On average, the medical OOP expenditures per patient seeking care were $256 for outpatient visits and $690 for inpatient visits per road traffic injury. Patients paid more for trauma care in private hospitals, and OOP expenditures were six times higher in private than in public hospitals. Transport to facilities and caregiver costs were the two major cost drivers, amounting to $96 and $68 per patient, respectively.

CONCLUSION

Seeking trauma care after a road traffic injury poses a substantial financial threat to Ethiopian households due to lack of strong financial risk protection mechanisms. Ethiopia's government should enact multisectoral interventions for increasing the prevention of road traffic injuries and implement universal public finance of trauma care.

Citizens’ Perceptions in Relation to Transport Systems and Infrastructures: A Nationwide Study in the Dominican Republic

One of the challenges currently faced by emerging countries is to get their citizens to decide to use sustainable transport for their regular trips, in order to reduce the current vehicular pollution rates. The objective of this descriptive research is to examine the perceptions of Dominicans regarding the state of the country’s transport systems and road infrastructure. For this purpose, a nationwide survey procedure was performed. This cross-sectional research used the data retrieved from a sample of 1260 citizens aged over 18, proportional in gender, age, habitat, and province of the Dominican Republic. The results showed how Dominicans believe that, compared to other road features, pedestrian roads and public transport vehicles remain in a very poor condition. Further, citizens report to be more interested about the improvement of road infrastructures than in the implementation of any other set of measures performed to promote sustainable road mobility, including those related with alternative transport means. Finally, this study claims for the need of fostering educational, communicative and participative actions and measures aimed at increasing the value given to sustainable transportation, and the relevance of integrate potential structural and vehicular improvements with those related to human behavior in mobility.

Vulnerable road users' crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification

This research develops safety performance functions and identifies the crash hotspots based on estimated vulnerable road users' exposure at intersections and along the roadway segments. The study utilized big data including Automated Traffic Signal Performance Measures (ATSPM) data, crowdsourced data (Strava), Closed Circuit Television (CCTV) surveillance camera videos, crash data, traffic information, roadway features, land use attributes, and socio-demographic characteristics. It comprises an extensive comparison between a wide array of statistical and machine learning models that were developed to estimate pedestrian and bike exposure. The results indicated that the XGBoost approach was the best to estimate vulnerable road users' exposure at intersections as well as bike exposure along the roadway segments. Afterwards, the estimated exposure was utilized as input variables to develop crash prediction models that relate different crash types to potential explanatory variables. Negative Binomial approach was followed to develop crash prediction models to be consistent with the Highway Safety Manual. The results show that the exposure variables (i.e., AADT, bike exposure, and the interaction between them) have significant influences on the two types of crashes (i.e., crashes of vulnerable road users at intersections and bike crashes along the segments). Further, the results indicated that the context classification is significantly related to crashes. Based on the developed models, the PSIs were calculated and the hotspots were identified for the two crash types. It was found that hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Further, C4 roadway segments were found to be significantly related to the increase of vulnerable road users' crashes at intersections and bike crashes along the segments.

Development of pedestrian- and vehicle-related safety performance functions using Bayesian bivariate hierarchical models with mode-specific covariates

INTRODUCTION

Pedestrian safety is a major concern as traffic crashes are the leading cause of fatalities and injuries for commuters. Traffic safety research in the past has developed various strategies to counteract traffic crashes, including the safety performance function (SPF). However, there is still a need for research dedicated to enhancing the SPF for pedestrians from perspectives of methodological framework and data input. To fill this gap, this study aims to add to the current SPF development practice literature by focusing on pedestrian-involved collisions, while considering the typical vehicle ones as well.

METHODS

First, bivariate models are used to account for the common unobserved heterogeneity shared by the pedestrian- and vehicle-related crashes at the same intersections. Second, variable importance ranking technique is used, along with correlation analysis, to determine mode-specific feature input. Third, the exposure information for both modes, annual pedestrian count, and annual daily vehicles traveled are used for model development. Fourth, a recent Bayesian inference approach (integrated nested Laplace approximation (INLA)) was adopted for bivariate setting. Finally, different evaluation criteria are used to facilitate comprehensive model assessment.

RESULTS

The results reveal different statistically significant factors contributing to each of the modes. The offset intersection provides better safety performance for both pedestrians and drivers as compared to other intersection designs. The model findings also corroborate the sensibility of using the bivariate models, rather than the separate univariate ones. Practical Applications: The study shows that pedestrians are more vulnerable to various intersection features such as left-turn channelization, intersection control, urban and rural population group, presence of signal mastarm on the cross-street, and mainline average daily traffic. Greater focus should be directed toward such intersection features to improve pedestrian safety.

Modeling Frequency of Injuries per Vehicle Crash in Gurage Zone, Southern Ethiopia

BACKGROUND

Traffic accident is the most serious problem in developing countries like Ethiopia, which is among the leading cause of death with the highest increasing rate from year to year in Ethiopia. This research aimed to identify the associated factors on the frequency of injuries per vehicle crash in Gurage zone.

METHODS

A retrospective study was conducted to identify the contributing factors of a number of injuries per accident. The data were collected from all traffic control and investigation office of 13 Woredas (Districts) for the past five consecutive years from 2013 to 2017. Negative Binomial Regression model was employed to identify the associated factors that affect the number of injuries per accident.

RESULTS

A total of 334 accidents recorded in the last five years from 2013 to 2017 in Gurage zone. Two hundred eight three (84.73%) of the accidents were caused 610 number of injuries. The significantly associated factors of frequency of injuries per road traffic accidents were Drivers' Age (IR: 0.9813; CI: 0.9664 - 0.9962), Drivers' Sex: Female (IR : 1.6386; CI : 1.2176 - 2.0596), Drivers' vehicles ownership: Hired (IR: 1.4216; CI: 1.1697 - 1.6735) and non-drivers' related variables, like weather condition: Rainy (IR: 1.6041; CI: 1.2552 - 1.9529), road shape: street-square (IR: 1.7421 ; CI: 1.1908 - 2.2934) and vehicle type: Isuzu (load)(IR: 1.6845; CI : 1.2592 - 2.1098) Minibus (IR: 2.7253; CI 2.3129 - 3.1377).

CONCLUSIONS

This study found that, Driver's related factors: Driver's Age, Sex, Drivers' vehicle ownership, and non-drivers' related variables: Weather condition, Road shape, and Vehicle type were identified as significantly associated factors on the frequency of injuries per vehicle crash in Gurage Zone.

Urban road space allocation incorporating the safety and construction cost impacts of lane and footpath widths

INTRODUCTION

Walkability continues to attract great attention from urban planners, designers, and engineers as they recognize not only the merits of pedestrian facilities in terms of the health benefits but also their demerits in terms of accident risk to pedestrians. Wide footpaths improve the pedestrian environment and experience, and thereby motivate travelers to walk as much as possible. However, if footpaths are too wide, they may leave a smaller space for the roadway. On the other hand, wide road lanes may lead to higher road vehicle safety but are costly to construct and maintain and also may leave little space for the footpath. Evidently, for a fixed urban space, what is needed is an optimal balance between the vehicle lane and pedestrian path. This problem is encountered routinely in dense cities including Hong Kong where land availability is severely limited.

METHOD

To address the issue, this paper first establishes safety performance functions (SPFs) for the pedestrian space and the road space, using the random-parameter negative binomial regression. The results indicate the extent to which road lane and footpath width changes are associated with changes in in-vehicle occupant and pedestrian casualties. Then the paper uses the SPFs to develop a methodology for optimizing the width allocations to the road lanes and footpaths, duly considering the user (safety) costs and agency (construction) costs associated with each candidate allocation of the widths. Finally, the paper analyzes the sensitivity of the optimal solution to the relative weights of user cost and agency cost.

RESULTS

When user and agency costs are considered equally important, the optimal lane width is 5.4 m.

CONCLUSION

It is observed that the road space allocation ratio used by the Hong Kong road agency suggests that the agency places a higher weight to user cost compared to agency cost. Practical Application: The findings can help incorporate design-safety relationships, and the stakeholders (agency and users) perspectives in urban road and footpath design.

Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China

The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.

Exploring the factors contribute to the injury severities of vulnerable roadway user involved crashes

The vehicle to pedestrian (V2P) applications will enable safety, mobility, and environmental advancements for the vulnerable roadway user (VRU) that current technologies are unable to provide. The present research aims to explore the use of random parameters in logit models to examine factors that significantly influence injury severity of VRU involved crashes. Two types of logit models, the mixed generalized ordered logit (MGOL) models and mixed logit models are proposed to provide insights on reducing injury severities of pedestrian and bicyclist involved crashes and benefit amending current V2P applications to address the special safety needs and challenges of these VRUs. Based on 9180 pedestrian involved crashes and 1402 bicyclist involved crashes from the Fatality Analysis Reporting System (FARS), the measure of injury severities - time-to-death is considered as the independent variables to capture a more comprehensive picture of events after a crash occurs. By comparing to the ordered logit models and the multinomial logit models, the effectiveness and appropriateness of the proposed models are verified through two perspectives - goodness of fit and predictive power. The modelling results show that the injury severity of VRU involved crashes is significantly associated with involved non-motorist characteristics (age and police reported alcohol involvement), involved motorist characteristics (drunk drivers, previous recorded crashes, number of occupants), involved vehicle characteristics (vehicle body type, vehicle model year, travel speed), roadway characteristics (interstate, junction, roadway profile), and environmental characteristics (light and weather condition). Among these significant factors, the number of occupants, vehicle body type, interstate, and junction result in random parameters, which capture and reflect the unobserved heterogeneity across sampled observations. The analyses of under-researched aspects of VRU involved crashes, that is time-to-death, help us develop a deeper understanding of the consequences of injury and ultimately health and social costs. The findings indicate that the proposed MGOL models and mixed logit models can account for the heterogeneity issues in crash data due to the unobserved factors. In addition, the injury severity models that incorporate the random parameter features can reveal new insights and have superior goodness of fit.

Diagnostic analysis of the effects of weather condition on pedestrian crash severity

Pedestrians are vulnerable to severe injury and mortality in road crashes. Numerous studies have attempted to identify factors contributing to crashes and pedestrian injury risks. As an active transport mode, the act of walking is sensitive to changes in weather conditions. However, comprehensive real-time weather data are often unavailable for road safety analysis. In this study, we used a geographical information system approach to integrate high-resolution weather data, as well as their corresponding temporal and spatial distributions, with crash data. Then, we established a mixed logit model to determine the association between pedestrian crash severity and possible risk factors. The results indicate that high temperature and the presence of rain were associated with a higher likelihood of Killed and Severe Injury (KSI) crashes. Also, we found the interaction effects of weather condition (hot weather and presence of rain) on the association between pedestrian crash severity and pedestrian and driver behaviors to be significant. For instance, the effects of jaywalking and risky driving behavior on crash severity were more prevalent under rainy conditions. In addition, the effects of driver inattention and reckless crossing were more significant in hot weather conditions. This has critical policy implications for the development and implementation of proactive traffic management systems. For instance, real-time weather and traffic data should be incorporated into dynamic message signs and in-vehicle warning systems. Doing so will enhance the levels of safety awareness of drivers and pedestrians, especially in adverse weather conditions. As a result, pedestrian safety can be improved over the long term.

Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Hierarchical ordered model for injury severity of pedestrian crashes in South Korea

INTRODUCTION

The high percentage of fatalities in pedestrian-involved crashes is a critical social problem. The purpose of this study is to investigate factors influencing injury severity in pedestrian crashes by examining the demographic and socioeconomic characteristics of the regions where crashes occurred.

METHOD

To understand the correlation between the unobserved characteristics of pedestrian crashes in a defined region, we apply a hierarchical ordered model, in which we set crash characteristics as lower-level variables and municipality characteristics as upper-level. Pedestrian crash data were collected and analyzed for a three-year period from 2011 to 2013. The estimation results show the statistically significant factors that increase injury severity of pedestrian crashes.

RESULTS

At the crash level, the factors associated with increased severity of pedestrian injury include intoxicated drivers, road-crossing pedestrians, elderly pedestrians, heavy vehicles, wide roads, darkness, and fog. At the municipality level, municipalities with low population density, lower level of financial independence, fewer doctors, and a higher percentage of elderly residents experience more severe pedestrian crashes. Municipalities ranked as having the top 10% pedestrian fatality rate (fatalities per 100,000 residents) have rates 7.4 times higher than municipalities with the lowest 10% rate of fatalities. Their demographic and socioeconomic characteristics also have significant differences. The proposed model accounts for a 7% unexplained variation in injury severity outcomes between the municipalities where crashes occurred.

CONCLUSION

To enhance the safety of vulnerable pedestrians, considerable investments of time and effort in pedestrian safety facilities and zones should be made. More certain and severe punishments should be also given for the traffic violations that increase injury severity of pedestrian crashes. Furthermore, central and local governments should play a cooperative role to reduce pedestrian fatalities. Practical applications: Based on our study results, we suggest policy directions to enhance pedestrian safety.

Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model

Mountainous highways generally associate with complex driving environment because of constrained road geometries, limited cross-section elements, inappropriate roadside features, and adverse weather conditions. As a result, single-vehicle (SV) crashes are overrepresented along mountainous roads, particularly in developing countries, but little attention is known about the roadway geometric, traffic and weather factors contributing to these SV crashes. As such, the main objective of the present study is to investigate SV crashes using detailed data obtained from a rigorous site survey and existing databases. The final dataset included a total of 56 variables representing road geometries including horizontal and vertical alignment, traffic characteristics, real-time weather condition, cross-sectional elements, roadside features, and spatial characteristics. To account for structured heterogeneities resulting from multiple observations within a site and other unobserved heterogeneities, the study applied a random parameters negative binomial model. Results suggest that rainfall during the crash is positively associated with SV crashes, but real-time visibility is negatively associated. The presence of a road shoulder, particularly a bitumen shoulder or wider shoulders, along mountainous highways is associated with less SV crashes. While speeding along downgrade slopes increases the likelihood of SV crashes, proper delineation decreases the likelihood. Findings of this study have significant implications for designing safer highways in mountainous areas, particularly in the context of a developing country.

The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana

Despite the benefits of walking as a means of travelling, walking can be quite hazardous. Pedestrian-vehicle crashes remain a major concern in Ghana as they account for the highest percentage of fatalities. The objective of this study is to determine the effect of both natural and built environmental features on pedestrian-vehicle crash severity in Ghana. The study is based on an extensive pedestrian-vehicle crash dataset extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR), Ghana. Using a multinomial logit modelling framework, possible determinants of pedestrian-vehicle crash severity were identified. The study found that fatal crashes are likely to occur during unclear weather conditions, on weekends, at night time where there are no lights, on curved and inclined roads, on untarred roads, at mid-blocks and on wider roads. The developed model and its interpretations will make important contributions to road crash analysis and prevention in Ghana with the possibility of extension to other developing countries. These contributing factors could inform policy makers on road design and operational improvements.

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.

Differences in Factors Affecting Various Crash Types with High Numbers of Fatalities and Injuries in China

OBJECTIVES

Road traffic crashes that involve very high numbers of fatalities and injuries arouse public concern wherever they occur. In China, there are two categories of such crashes: a crash that results in 10-30 fatalities, 50-100 serious injuries or a total cost of 50-100 million RMB ($US8-16m) is a "serious road traffic crash" (SRTC), while a crash that is even more severe or costly is a "particularly serious road traffic crash" (PSRTC). The aim of this study is to identify the main factors affecting different types of these crashes (single-vehicle, head-on, rear-end and side impact) with the ultimate goal of informing prevention activities and policies.

METHODS

Detailed descriptions of the SRTCs and PSRTCs that occurred from 2007 to 2014 were collected from the database "In-depth Investigation and Analysis System for Major Road Traffic Crashes" (IIASMRTC), which is maintained by the Traffic Management Research Institute of the Ministry of Public Security of China (TMRI). 18 main risk factors, which were categorized into four areas (participant, vehicle, road and environment-related) were chosen as potential independent variables for the multinomial logistic regression analysis. Comparisons were made among the single-vehicle, head-on, rear-end and side impact crashes in terms of factors affecting crash occurrence.

FINDINGS

Five risk factors were significant for the six multinomial logistic regression models, which were location, vertical alignment, roadside safety rating, driver distraction and overloading of cargo. It was indicated that intersections were more likely to have side impact SRTCs and PSRTCs, especially with poor visibility at night. Overloaded freight vehicles were more likely to be involved in a rear-end crash than other freight vehicles. Driver distraction is an important risk factor for head-on crashes, while vertical alignment and roadside safety rating are positively associated with single-vehicle crashes.

CONCLUSION

Based on the findings, promising measures were proposed to prevent each type of SRTC and PSRTC, which governmental or regulatory agencies could employ to plan strategies to reduce SRTCs and PSRTCs and support lifesaving policies.