Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian federal roads

Effect of spatial relationship between curves on crash severity at horizontal curves in a mountainous terrain

The study aimed to model the effect of spatial relationship between adjacent curves on the severity of curve-based crashes along with driver and crash causal characteristics, reflecting driver's short-term expectancy. The crash and other associated data was retrieved from the web-based Road Accident Data Management System available in Himachal Pradesh, India, and curvature attributes were extracted using GIS. Overall, the study included 1113 curve based crashes. The driver's perception of the sharpness of a curve was quantified as a single representative categorical factor based simultaneously on its length and radius using K-Medoid Clustering. Separate crash severity models catering to the two possible approach directions of the subject curve were developed reflecting its independent interaction with its corresponding adjacent curve in each direction. Partial Proportional Odds models were developed to overcome the predictive limitations of Ordinal and Multinomial logit models. Indicators of spatial relationship and the intensity of sharpness of the subject curve were found to be statistically significant. A sharp approach curve (radius:40-60 m) increased the risk of fatality by 2.16 times with a similar increase (2.5 times) observed for a short (length:30-60 m) adjacent curve. Adjacent curves turning in the same direction were 2.34 times more prone to fatalities. A very sharp subject curve with radius ≤ 40 m increased the risk of fatal crashes by 2.5 times, as did the short subject curves (30-60 m) (at least 3 times). Subject curves characterized by a short length and a very sharp curvature contributed relatively 3-4 times more to fatal crashes. The identified risk factors and their impact can help the relevant stakeholders to take appropriate actions and can further assist them in identifying high risk scenarios.

Bivariate ordered probit modelling of motorcycle riders and pillion passengers' injury severities relationship and associated risk factors

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

Network-wide road crash risk screening: A new framework

Network-wide road crash risk screening is a crucial issue for road safety authorities in governing the impact of road infrastructures over road safety worldwide. Specifically, screening methods, which also enable a proactive approach (i.e., pinpointing critical segments before crashes occur), would be extremely beneficial. Existing literature provided valuable insights on road network screening and crash prediction models. However, no research tried to quantify the risk of crash on the road network by considering its main components together (i.e., probability, vulnerability, and exposure). This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-urban road network of the Province of Brescia (Lombardy Region - Italy). This framework introduces a valid support for road safety authorities to help identify the most critical road segments on the network, prioritise interventions and, possibly, improve the safety performance. Finally, this framework can be incorporated in any safety managerial system.

Modeling injury severity of crashes involving golf carts: A case study of The Villages, Florida

OBJECTIVE

Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation.

METHODS

The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models' accuracy parameters were used to check their reliability.

RESULTS

The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model's findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity.

CONCLUSIONS

Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs.

Investigating transportation safety in disadvantaged communities by integrating crash and Environmental Justice data

Recent efforts to identify disadvantaged communities (DACs) on a census tract level have evoked possibilities of attaining transportation justice and vision zero goals in these areas. To identify DACs, the United States Department of Transportation (USDOT) has developed six comprehensive indicators: economy, environment, equity, health, resilience, and transportation access. The indicators are used to explore the associations between DACs (in 71,728 census tracts) and five years of fatal crashes, providing a comprehensive understanding of safety risks. Specifically, using data on DACs and linking it with census and crash data, this study aims to understand the complex connections between safety (captured through fatal crashes) and disadvantages that communities confront due to a convergence of multiple challenges and burdens using Zero-Hurdle Negative Binomial models. The results reveal that health, resilience, and transportation-disadvantaged tracts are associated with more fatal crashes. The study also found the presence of a higher percentage of the population with bachelor's degrees and increased use of public transportation are correlated with fewer fatal crashes. Also, a higher fatal crash rate is observed in disadvantaged census tracts where a high proportion of the Hawaiian or other Pacific Islander, and American Indian or Alaska Native populations live. This implies that targeted interventions can be explored further in tracts that show high correlations with fatal crashes. The findings contribute to traffic safety by highlighting the risks in DACs, which can help design and implement traffic safety interventions. The insights gained from this study can inform decision-making and help to guide the development of more equitable traffic safety programs in disadvantaged communities.

Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways

Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways.

Effect of urban street trees on pedestrian safety: A micro-level pedestrian casualty model using multivariate Bayesian spatial approach

In the past decades, trees were considered roadside hazard. Street trees were removed to provide clear zone and improve roadside safety. Nowadays, street trees are considered to play an important role in urban design. Also, street tree is considered a traffic calming measure. Studies have examined the effects of urban street trees on driver perception, driving behaviour, and general road safety. However, it is rare that the relationship between urban street trees and pedestrian safety is investigated. In this study, a micro-level frequency model is established to evaluate the effects of tree density and tree canopy cover on pedestrian injuries, accounting for pedestrian crash exposure based on comprehensive pedestrian count data from a state in Australia, Melbourne. In addition, effects of road geometry, traffic characteristics, and temporal distribution are also considered. Furthermore, effects of spatial dependency and correlation between pedestrian casualty counts of different injury severity levels are accounted using a multivariate Bayesian spatial approach. Results indicate that road width, bus stop, tram station, on-street parking, and 85th percentile speed are positively associated with pedestrian casualty. In contrast, pedestrian casualty decreases when there is a pedestrian crosswalk and increases in tree density and canopy. Also, time variation in pedestrian injury risk is significant. To sum up, urban street trees should have favorable effect on pedestrian safety. Findings are indicative to optimal policy strategies that can enhance the walking environment and overall pedestrian safety. Therefore, sustainable urban and transport development can be promoted.

Habitat change and biodiversity loss in South and Southeast Asian countries

South and Southeast Asia is by far the most populous region in Asia, with the greatest number of threatened species. Changes in habitat are a major contributor to biodiversity loss and are more common as a result of land-use changes. As a result, the goal of this study is to use negative binomial regression models to investigate habitat change as one of the important drivers of biodiversity loss in South and Southeast Asian countries from 2013 to 2018. According to the negative binomial estimates, the findings for the habitat change measures are quantitatively similar for the impacts of agricultural land and arable land on biodiversity threats. Agricultural and arable land both have a positive impact on biodiversity loss. We found that, contrary to our expectations, the forest area appears to have an unexpected direct influence on the number of threatened species. A higher number of threatened species is associated with rising per capita income, human population and a low level of corruption control. Finally, the empirical findings are consistent across taxonomic groups, habitat change measures and Poisson-based specifications. Some policy implications that could mitigate biodiversity loss include educating and promoting good governance among the population and increase the conservation effort to sustain green area and national forest parks in each country.

Environmental Kuznets curve for biodiversity loss: evidence from South and Southeast Asian countries

This study aims to explore the income-biodiversity loss nexus in South and Southeast Asian countries covering the period between 2013 and 2019. Negative Binomial regression models are used to deal with the count regressand variable with specific emphasis on different taxonomic groups of threatened species, namely, mammal, bird, reptile, amphibian, fish, mollusk, other invertebrate, plant, and total threatened species. We find strong support of an inverted U-shaped relationship between income and biodiversity loss in all taxonomic groups of threatened species examined. Additionally, agricultural land has a significant and positive effect on biodiversity loss. Control of corruption and biodiversity loss are found to be negatively associated. The inverted U-shaped EKC suggests that South and Southeast Asian countries are required to identify policy priority areas that could achieve robust economic growth while reducing biodiversity loss. Our findings also provide valuable policy insights to assist the policy makers to better cope with the problem of biodiversity loss via corruption control and agricultural land use.

Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of Madrid

Introduction

The transport and mobility sector is experiencing profound transformations. These changes are mainly due to: environmental awareness, the increase in the population of large urban areas and the size of cities, the aging of the population and the emergence of relevant technological innovations that have changed consumption habits, such as electronic commerce or the sharing economy. The introduction of new services such as Uber or Cabify is transforming urban and metropolitan mobility, which has to adapt to this new scenario and the very concept of mobility.

Objective

Thus, the purpose of this study was to evaluate whether ride-hailing platforms substitute or complement public transport to reduce accident rates, considering the two basic transport zones of Madrid: “The Central Almond” and the periphery.

Methods

The data were collected from the 21 districts of Madrid for the period 2013–2019, and they were analyzed by a Random Effects Negative Binominal model.

Results

The results obtained in this study suggest that since the arrival of Uber and Cabify to the municipality of Madrid the number of fatalities and serious injuries in traffic accidents has been reduced. Traffic accidents on weekends and holidays, with at least one serious injury or death, have also been reduced. However, the number of minor injuries has increased in the central districts of Madrid.

Conclusion

Overall, what was found in this study supports the hypothesis that these services replace the urban buses. However, these services improve the supply to users with greater difficulties to access taxis or public transport, constituting an alternative mode of transport for high-risk drivers. Therefore, such findings may be quite useful for policy makers to better define regulatory policies for these services.

The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level

Road infrastructure has an impact on the occurrence of road traffic crashes. The aim of this study was to analyze the impact of road geometric formation on road traffic crashes. Based on the nature, convenience, and availability of data, the study used Budapest city road traffic crash data from 2017 to 2021. For organizing, analysis, and modeling, the study used Microsoft-Excel, the Statistical Package for Social Science, and Quantum Geographic Information System. Relative frequency distribution, Multinomial Logistic Regression, Multilayer Perceptron Artificial Neural Network, and Severity Index were used for the analysis. Both inferential and descriptive statistics are used to describe and summarize the study outcome. Multicollinearity tests, p-value, overdispersion, percent of incorrect error, and other statistical model testes were undertaken to analyze the significance of the data and variable for modeling and analysis. A large number of crashes were observed in straight and one-lane road geometric formationsr890. However, the severity level was high at the horizontal curve and in all three lanes of the road. The regression model indicated that light conditions, collision type, road geometry, and speed had a significant effect on traffic accidents at a p-value of 0.05. A collision between the vehicle (rear end collision), and a vehicle with a pedestrian was the probable cause of the crash. The Multilayer Perceptron Artificial Neural Network indicated that horizontally curved geometry has a positive and strong relationship with road traffic fatalities. The primary reasons for the occurrences of a road traffic crash at an intersection, horizontal curve, and straight road geometric formation were the improper use of road traffic signs, road pavement condition, and stopping sight distance problems, respectively. The hourly distribution showed that from 16:01 to 17:00 time interval was a peak hour for the occurrences of road traffic crashes. Whereas, driver plays vital role and responsible body for the occurrences of crashes at all geometric formations.

Accident Frequency Prediction Model for Flat Rural Roads in Serbia

Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.

Comparison of Statistical and Machine-Learning Models on Road Traffic Accident Severity Classification

Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.

Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

In recent years, changes have occurred in consumption, ownership, and social relations, giving rise to new economic models in which technology enables new ways of connecting, creating, and sharing value. The nature of transport has transformed with the emergence of mobile applications, such as Uber and Cabify, which offer an alternative to the services traditionally provided by the taxi and chauffeur-driven hire vehicle (CDV) sectors. These services have developed within a context of market regulation of the taxi and CDV which are subject to considerable unjustified restrictions for entering and operating in the market, including the numerus clausus of licenses, the limited geographical scope of the license and, in the case of taxis, the regulation of prices as inflexible public rates. Bearing in mind the latest legislative changes affecting mostly the provision of the services of these platforms, this study analyzes whether the number of traffic accident victims has fallen since the introduction of these services in the city of Madrid using a Random Effects Negative Binominal model. The results show that the deployment of these platforms is associated with a reduction of 25% in the number of serious injuries and deaths.

Analysis of Crash Severity of Texas Two Lane Rural Roads Using Solar Altitude Angle Based Lighting Condition

Many studies have examined the impact of factors affecting accident severity in rural areas; however, little attention has been paid to different lighting conditions (LCs), and less to the detailed categories and precise determining of twilight. In this paper, solar altitude angle (SAA), as a basis for differentiating and categorizing LCs, is proposed to investigate explanatory variables in much greater detail. For each LC, namely, dark, twilight, dark lit (dark with street lights) and daylight, separate random parameter models are developed to investigate the impacts of some factors on crash injury severity data of 2017 and 2018 in two lane rural roads of Texas. The model estimation results indicated that different LCs have various contributing factors, indeed, to each injury severity, further stressing the significance of investigating crashes based on SAA. The key differences include crash location, marked lane, grade direction, no passing zone, shoulder width, weekday and collision type. The important findings were that developing artificial lighting at intersections and LED raised pavement markers on two lane rural roads could lead to enhanced road safety under dark LCs. Furthermore, increasing shoulder width in straight segments of two lane rural roads is important for decreasing severe injury in daylight conditions.

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

INTRODUCTION

With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted.

METHOD

Utilizing DI-related crash data collected from North Carolina for the period 2013-2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset.

RESULTS

The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.

Studies of the effectiveness of transport sector interventions in low- and middle-income countries: An evidence and gap map

BACKGROUND

There are great disparities in the quantity and quality of infrastructure. European countries such as Denmark, Germany, Switzerland, and the UK have close to 200 km of road per 100 km², and the Netherlands over 300 km per 100 km². By contrast, Kenya and Indonesia have <30, Laos and Morocco <20, Tanzania and Bolivia <10, and Mauritania only 1 km per 100 km². As these figures show, there is a significant backlog of transport infrastructure investment in both rural and urban areas, especially in sub-Saharan Africa. This situation is often exacerbated by weak governance and an inadequate regulatory framework with poor enforcement which lead to high costs and defective construction.The wellbeing of many poor people is constrained by lack of transport, which is called "transport poverty". Lucas et al. suggest that up to 90% of the world's population are transport poor when defined as meeting at least one of the following criteria: (1) lack of available suitable transport, (2) lack of transport to necessary destinations, (3) cost of necessary transport puts household below the income poverty line, (4) excessive travel time, or (5) unsafe or unhealthy travel conditions.

OBJECTIVES

The aim of this evidence and gap map (EGM) is to identify, map, and describe existing evidence from studies reporting the quantitative effects of transport sector interventions related to all means of transport (roads, rail, trams and monorail, ports, shipping, and inland waterways, and air transport).

METHODS

The intervention framework of this EGM reframes Berg et al's three categories (infrastructure, prices, and regulations) broadly as infrastructure, incentives, and institutions as subcategories for each intervention category which are each mode of transport (road, rail trams and monorail, ports, shipping, and inlands waterways, and air transport). This EGM identifies the area where intervention studies have been conducted as well as the current gaps in the evidence base.This EGM includes ongoing and completed impact evaluations and systematic reviews (SRs) of the effectiveness of transport sector interventions. This is a map of effectiveness studies (impact evaluations). The impact evaluations include experimental designs, nonexperimental designs, and regression designs. We have not included the before versus after studies and qualitative studies in this map. The search strategies included both academic and grey literature search on organisational websites, bibliographic searches and hand search of journals.An EGM is a table or matrix which provides a visual presentation of the evidence in a particular sector or a subsector. The map is presented as a matrix in which rows are intervention categories (e.g., roads) and subcategories (e.g., infrastructure) and the column outcome domains (e.g., environment) and subcategories as (e.g., air quality). Each cell contains studies of the corresponding intervention for the relevant outcome, with links to the available studies. Included studies were coded according to the intervention and outcomes assessed and additional filters as region, population, and study design. Critical appraisal of included SR was done using A Measurement Tool to Assess Systematic Reviews (AMSTAR -2) rating scale.

SELECTION CRITERIA

The search included both academic and grey literature available online. We included impact evaluations and SRs that assessed the effectiveness of transport sector interventions in low- and middle-income countries.

RESULTS

This EGM on the transport sector includes 466 studies from low- and middle-income countries, of which 34 are SRs and 432 impact evaluations. There are many studies of the effects of roads intervention in all three subcategories-infrastructure, incentives, and institutions, with the most studies in the infrastructure subcategories. There are no or fewer studies on the interventions category ports, shipping, and waterways and for civil aviation (Air Transport).In the outcomes, the evidence is most concentrated on transport infrastructure, services, and use, with the greatest concentration of evidence on transport time and cost (193 studies) and transport modality (160 studies). There is also a concentration of evidence on economic development and health and education outcomes. There are 139 studies on economic development, 90 studies on household income and poverty, and 101 studies on health outcomes.The major gaps in evidence are from all sectors except roads in the intervention. And there is a lack of evidence on outcome categories such as cultural heritage and cultural diversity and very little evidence on displacement (three studies), noise pollution (four studies), and transport equity (2). There is a moderate amount of evidence on infrastructure quantity (32 studies), location, land use and prices (49 studies), market access (29 studies), access to education facilities (23 studies), air quality (50 studies), and cost analysis including ex post CBA (21 studies).The evidence is mostly from East Asia and the Pacific Region (223 studies (40%), then the evidence is from the sub-Saharan Africa (108 studies), South Asia (96 studies), Latin America & Caribbean (79 studies). The least evidence is from Middle East & North Africa (30 studies) and Europe & Central Asia (20 studies). The most used study design is other regression design in all regions, with largest number from East Asia and Pacific (274). There is total 33 completed SRs identified and one ongoing, around 85% of the SR are rated low confidence, and 12% rated as medium confidence. Only one review was rated as high confidence. This EGM contains the available evidence in English.

CONCLUSION

This map shows the available evidence and gaps on the effectiveness of transport sector intervention in low- and middle-income countries. The evidence is highly concentrated on the outcome of transport infrastructure (especially roads), service, and use (351 studies). It is also concentrated in a specific region-East Asia and Pacific (223 studies)-and more urban populations (261 studies). Sectors with great development potential, such as waterways, are under-examined reflecting also under-investment.The available evidence can guide the policymakers, and government-related to transport sector intervention and its effects on many outcomes across sectors. There is a need to conduct experimental studies and quality SRs in this area. Environment, gender equity, culture, and education in low- and middle-income countries are under-researched areas in the transport sector.

Examining crash injury severity and barrier-hit outcomes from cable barriers and strong-post guardrails on Alabama's interstate highways

INTRODUCTION

This study investigates the impact of several risk factors (i.e., roadway, driver, vehicle, environmental, and barrier-specific characteristics) on the injury severity resulting from barrier-related crashes and also on barrier-hit outcomes (i.e., vehicle containment, vehicle redirection, and barrier penetration). A total of 1,685 barrier-related crashes, which occurred on three major interstate highways (I-65, I-85, and I-20) in the state of Alabama, were collected for a seven-year period (2010-2016), and all relevant information from the police reports was reviewed. Features that were rarely explored before (e.g., median width, barrier length, barrier offset or lateral position, left shoulder width, blockout type, and number of cables) were also collected and examined. Two types of longitudinal barriers were analyzed: high-tension cable barriers installed on medians and strong-post guardrails installed on medians and/or roadsides.

METHOD

Two separate mixed logit (MXL) models were used to analyze crash injury severity in median and roadside barrier-related crashes. Two additional MXL models were separately adopted for median and roadside barrier-related crashes to estimate the probability of three barrier-hit outcomes (vehicle containment, vehicle redirection, and barrier penetration).

RESULTS

The results of crash injury severity MXL models showed that, for both median and roadside barrier crashes, barrier penetration, female drivers, and driver fatigue were associated with a higher probability of injury or fatal crashes. The results of barrier-hit MXL models showed that longer barrier length, Brifen cable barrier system, and barrier lateral position were significant predictors of median barrier-hit outcomes, whereas dark lighting condition, driving under the influence (DUI), presence of curved freeway sections, and right shoulder width significantly contributed to roadside barrier-hit outcomes.

CONCLUSIONS

The MXL model succeeded in identifying several contributing factors of crash severity and barrier-hit outcomes along Alabama's interstate highways. Practical applications: One study application is to design longer barrier run length (greater than 1230 feet or 0.2 miles) to reduce the barrier penetration likelihood.

Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

This study aims to characterize locations on two-way rural roads where head-on crashes are more likely to occur, attending to geometric road design factors. For this purpose, a case-control study was carried out using multiple logistic regression models with variables related to road design parameters, considering several scenarios. The dataset corresponding to cases (places where crashes have occurred) was collected on Spanish “1+1” rural roads over a four-year period. The controls (places where no crashes have occurred in the period) where randomly selected through a specific ad hoc designed method. The obtained model identifies risk factors and allows the computation of the odds of a head-on collision on any specific road section: width of the pavement (when it exceeds 6 m), width of the lanes (for intermediate widths between 3.25 and 3.75 m) and tight curves (less than 250 m of radius) are identified as factors significantly increasing the odds of a crash, whereas a paved shoulder is a protective factor. The identified configurations on two-way rural roads may be susceptible to transformation into “2+1” roads to decrease the odds of a head-on crash, thus preventing possible serious injuries and enhancing transportation safety.

Investigation of road infrastructure and traffic density attributes at high-risk locations for motorcycle-related injuries using multiple correspondence and cluster analysis in urban Tanzania

Rapid growth in use of motorcycles combined with limited road infrastructures has increased the burden of road traffic crashes and injuries in low-and middle-income countries. The aim of this study was to assess whether high-risk locations for motorcycle-related injuries identified from police crash data registers for the period 2016 to 2017 share similar road infrastructure and traffic density attributes in Dar es Salaam city. Analysis was performed using multiple correspondence and hierarchical cluster analysis. Three distinct clusters for motorcycle injury hotspots were identified. Clusters 1 and 2 were associated with more fatal and severe injuries and were characterized by overrepresentation of trunk roads, unseparated two-way roads, mixture of road users and commercial and residential areas compared to Cluster 3. Cluster3 was associated with less severe injuries compared to clusters 1 and 2 (p < 0.001). Cluster 3 was characterized by overrepresentation of feeder/street roads, separated two-way roads and presence of traffic control measures. The clusters of hotspots differed by road infrastructure and traffic density attributes. Clusters 1 and 2 were characterized by more dangerous road environments, while cluster 3 was characterized by road environments with less severe outcomes. These findings can assist in prioritizing preventive strategies for motorcycle- related injuries.

Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid

In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the municipality of Madrid from 2014 to 2018. To do this, a regression analysis has been carried out using a random effects negative binomial regression (RENB). The results of the model show that Uber and Cabify services are associated with a decrease in fatal and serious accidents in Madrid.

An exploratory analysis of factors associated with traffic crashes severity in Cartagena, Colombia

Traffic fatalities are the second cause of violent deaths in Colombia. However, due to the signing of the peace agreement and the growing number of fatalities in road crashes, it is possible that soon traffic fatalities will be the primary cause of violent deaths in the country, particularly in urban areas. This study is an exploratory analysis focused on identifying the main factors associated with the severity of traffic crashes in urban areas, using Cartagena as a case study. We analyzed three levels of crash severity, namely fatal, injury, and property-damage-only, considering factors in several different dimensions: victim, vehicle, road infrastructure, traffic and control, day and time, and environmental factors. A modeling approach based on multinomial ordered discrete models was used to properly identify the main factors associated with the severity levels. We found that the probability of fatal accidents is higher on streets with speed limits over 40 km/h, and that males and people aged 60 years or older are the victims with the most significant risk of fatal crashes. Motorcycles were also identified as vehicles with the highest probability of fatal crashes in the city. We showed that the probability of fatal crashes occurring is higher on streets where pedestrian bridges, traffic lights, and crosswalks are present. These findings are worthy because, in Colombia and other developing countries, the authorities normally expect to reduce the probability of fatal accidents through investments in pedestrian bridges, signaling devices, and crosswalk markings. However, according to our results, it possibly will not occur unless further countermeasures are taken. Based on these findings, reducing speed limits, operational improvements at signalized intersections, zero tolerance for traffic violations related to pedestrians, an awareness campaign on pedestrian safety focused on males and people aged 60 or older, and improving motorcycle safety are the countermeasures we proposed. Furthermore, as the authorities make significant efforts to investing in pedestrian bridges, we propose a further investigation into the traffic crashes in streets where there is this infrastructure since more severe events occur near them.

Identifying the Factors That Increase the Probability of an Injury or Fatal Traffic Crash in an Urban Context in Jordan

The lack of robust studies carried out on urban roads in developing countries makes it difficult to enhance traffic safety, ensuring sustainable roads and cities. This study analyzes the contribution of a number of explanatory variables behind crashes involving injuries on arterial roads in Irbid (Jordan). Five binary logistic regression models were calibrated for a crash dataset from 2014–2018: one for the full database, and the others for the four main crash causes identified by Jordanian Traffic Police reports. The models show that whatever the crash cause, the three most significant factors linked to an injury or fatality lie in urban road sections that are in large-scale neighborhood areas, have fewer than six accesses per kilometer, and have a low traffic volume (under 500 veh/h/ln). Some of these results agree with previous studies in other countries. Jordan’s governmental agencies concerned with urban road safety might use these results to develop appropriate plans and implement priority actions for each crash cause, in addition to undertaking further research for comparative purposes.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis

Traffic accident statistics have shown the necessity of risk assessment when driving in the dynamic traffic environment. If the risk associated with different traffic elements (i.e., road, environment and vehicles) could be evaluated accurately, potential accidents could be significantly avoided or mitigated. This paper proposes a driving risk assessment model that can quantitatively evaluate the driving risk associated with intelligent vehicles via the coupled analysis of different traffic elements. First, we present a concept of the internal field and external field for establishing the driving risk coupling model, through employing the internal field to define the risk range of driver's perspective and the external field to calculate the risk coefficients of those traffic elements. Then, the relative risk coefficients are computed by incorporating both naturalistic driving study (NDS) and driver attitude questionnaire (DAQ) using a multinomial logit model. Specifically, we perform a large-scale naturalistic driving study to investigate the objective driving risks. Typical driver behavior parameters, such as velocity, time headway, and acceleration, are analyzed. Besides, a self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks that drivers may face in various situations. Finally, validation of the model is conducted by comparing the accuracy with the typical risk assessment index, i.e., TTC and THW. Results demonstrate that the proposed approach is effective in evaluating the comprehensive driving risks by quantifying the influence factors of driving risks in dynamic environments.

Safety effects of traffic lane and shoulder widths on two-lane undivided rural roads: A matched case-control study from Norway

This study estimates the effects of lane and shoulder widths on occurrence of head-on and single-vehicle accidents on rural two-lane undivided roads in Norway while considering the differences between winter and non-winter accidents and their severity levels. A matched case-control method was applied to calculate the odds ratios for lane and shoulder width categories, while controlling for the effects of AADT and adjusting for the effects of region, speed limit, segment length, share of long vehicles in AADT and horizontal alignment. The study used a sample of 71,999 roadway segments identified in GIS and 1886 related accidents recorded by the police in five-year period. The results suggest that it is relevant to consider winter and non-winter accidents as well as severe and slight accidents separately when studying the effects of lane and shoulder widths on the occurrence of head-on and single-vehicle accidents. When examining lane and shoulder widths for all related accidents, the lane widths 1.50-2.50 m and shoulder widths 0.50-0.75 m were relatively safer than other categories on Norwegian two-lane rural undivided roads.

Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina

Although only 2 % of crashes are head-on crashes in the United States, they account for over 10 % of all crash-related fatalities. This study aims to investigate the contributing factors that affect the injury severity of head-on crashes and develop appropriate countermeasures. Due to the unobserved heterogeneity inherent in the crash data, a latent class clustering analysis is firstly conducted to segment the head-on crashes into relatively homogeneous clusters. Then, mixed logit models are developed to further explore the unobserved heterogeneity within each cluster. Analyses are performed based on the data collected from the Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The estimated parameters and associated marginal effects are combined to interpret significant variables of the developed models. The proposed method is able to uncover the heterogeneity within the whole dataset and the homogeneous clusters. Results of this research can provide more reliable and insightful information to engineers and policy makers regarding the contributing factors to head-on crashes.

Classification of traffic accidents datasets between 2003–2017 in Iraq

After the epidemic disease and the violence, the traffic injuries in Iraq has become a massive threat that menace the lives of the citizens and plagued the number of victims in Iraq after 2003. Iraq is seeing a catastrophic growth in the number of the traffic injuries reaching a high level during the previous ten years. Datasets results for the previous 10 years in Iraq were collected in this study. The data was arranged into spreadsheets creating a useful database for the prospectus studies. Classification of the traffic injuries was performed according to the number of fatalities, the number of injuries, and the number of accidents. Overall, traffic accidents were drastically growing from 2005 to 2017. In additional, the number of accidents recorded a relatively higher rate of accidents in a month with about 9%. However, the highest rates were observed during 2014, 2015, 2016 and 2017 consecutively. It may be attributed to the absence of security and safety precautions procedures. The number of injuries was as high was 12% and it increased during 2014, 2015, 2016 and 2017 respectively. whereas the number of fatalities recorded the highest number during 2017 with a ratio about 21%.

A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates

Numerous studies have previously used a variety of count-data models to investigate factors that affect the number of crashes over a certain period of time on roadway segments. Unlike past studies which deal with crash frequency, this study views the crash rates directly as a continuous variable left-censored at zero and explores the application of an alternate approach based on tobit regression. To thoroughly investigate the factors affecting freeway crash rates and the potentially temporal instability in the effects of crash factors involving traffic volume, freeway geometries and pavement conditions, a classic uncorrelated random parameters tobit (URPT) model and a correlated random parameters tobit (CRPT) model were estimated, along with a conventional fixed parameters tobit (FPT) model. The analysis revealed a large number of safety factors, including several appealing and interesting factors rarely studied in the past, such as the safety effects of climbing lanes and distance along composite descending grade. The results also showed that the CRPT model was not only able to reflect the heterogeneous effects of various factors, but also able to estimate the underlying interactions among unobserved characteristics, and therefore provide better statistical fit and offer more insights into factors contributing to freeway crashes than its model counterparts. Additionally, the results showed significant temporal instability in CRPT models across the studied time periods indicating that crash factors (including unobserved characteristics and the underlying interactions among them) and their effects on crash rates varied over time, and more attentions should be paid when interpreting crash data-analysis findings and making safety policies. The modeling technique in this study demonstrates the potential of CRPT model as an effective approach to gain new insights into safety factors, particularly when the heterogeneous effects of factors on safety are interactive. Additionally, findings from this study are also expected to assist in developing more effective countermeasures by better understanding the safety effects of factors associated with freeway design characteristics and pavement conditions.

Modeling head-on crash severity with drivers under the influence of alcohol or drugs (DUI) and non-DUI

Objective: The objective of this research is to identify and compare contributing factors to head-on crashes with drivers under and not under the influence of alcohol or drugs.Methods: The head-on crash data are collected from 2005 to 2013 in North Carolina from four aspects: vehicle, driver, roadway, and environmental characteristics. The final dataset includes 9,153 head-on crashes. A mixed logit model is developed to analyze the crash dataset involving drivers under and not under the influence of alcohol or drugs.Results: According to the obtained results, factors such as rural roadways, adverse weather, curve road, and high speed limit are among the most significant contributing factors to both head-on crashes with DUI and non-DUI. In addition, the results of this research demonstrate that high speed limit is found to be better modeled as random-parameters at specific injury severity levels for head-on crashes with DUI. Besides the factors mentioned above, dark light condition, old drivers, pickups, and motorcycles also significantly affect the severity of head-on crashes with non-DUI.Conclusions: The results of this study identify various factors that significantly affect the severity of head-on crashes with drivers under and not under the influence of alcohol or drugs. Also, the mixed logit model examines the heterogeneous effects and correlation in unobserved factors by allowing coefficients to be randomly distributed. The findings of this study call for more attention to head-on crashes and provide a reference for planners and engineers when developing and selecting countermeasures to reduce and/or mitigate head-on crashes.

Identification of differential risk hotspots for collision and vehicle type in a directed linear network

Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014-2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern. A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.

Comparison of univariate and two-stage approaches for estimating crash frequency by severity-Case study for horizontal curves on two-lane rural roads

The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models.

The effects of public private partnerships on road safety outcomes

Public Private Partnerships (PPP) have become common in providing high-quality infrastructure in many countries worldwide. One of the main reasons for PPP agreements is to improve efficiency and quality in the delivery of public services, as well as to boost investments for expensive projects. Despite PPPs having been particularly widespread in the case of the construction and rehabilitation of high-capacity road infrastructure, their impact in terms of road safety outcomes is still unexplored. This paper studies the effects of PPPs on road safety outcomes by taking advantage of the variety of production models provided in the Spanish highway network. Results based on a panel-data fixed-effects method show that the most relevant aspect influencing road safety outcomes is the quality of design of the road. However, we find evidence suggesting that privately operated highways (PPPs) are positively correlated with better road safety outcomes for roads with similar quality. This finding that should be confirmed by further research raises interest in the mechanisms that could produce this link between management models and road safety.

A weighted partial likelihood approach for zero-truncated models

Zero-truncated data arises in various disciplines where counts are observed but the zero count category cannot be observed during sampling. Maximum likelihood estimation can be used to model these data; however, due to its nonstandard form it cannot be easily implemented using well-known software packages, and additional programming is often required. Motivated by the Rao-Blackwell theorem, we develop a weighted partial likelihood approach to estimate model parameters for zero-truncated binomial and Poisson data. The resulting estimating function is equivalent to a weighted score function for standard count data models, and allows for applying readily available software. We evaluate the efficiency for this new approach and show that it performs almost as well as maximum likelihood estimation. The weighted partial likelihood approach is then extended to regression modelling and variable selection. We examine the performance of the proposed methods through simulation and present two case studies using real data.

Validating Driver Behavior and Attitude Measure for Older Italian Drivers and Investigating Their Link to Rare Collision Events

The present study aimed to: (a) validate the factor structures of three scales assessing driving behavior, attitudes toward traffic safety (ATTS) and self-regulation in driving, in a sample of Italian older adults, through confirmatory factor analyses and (b) to determine the effectiveness of these measures in predicting the likelihood and the frequency of collision involvements in the following year. A 28-item driver behavior questionnaire (DBQ), a 16-item ATTS, a 21-item extended driving mobility questionnaire (DMQ-A) were administered to 369 active Italian drivers, aged between 60 and 91 years. Results showed a four-factor structure for the DBQ, a five-factor structure for the ATTS and a two-factor structure for the Extended DMQ-A, as the best fitting models. Hurdle model analysis of count data with extra-zeros showed that all factors of DBQ predicted the likelihood of road collisions. Risky behavior, except for aggressive violations, self-regulation and attitudes toward traffic rules were associated with the frequency of collision involvement. The aforementioned three scales seemed to be a useful and concise suite of instruments assessing risky as well as protective factors of driving behavior in elderly.

The influence of shoulder characteristics on the safety level of two-lane roads: A case-study

Constructing proper shoulders may improve road safety on two-lane roads. Previous research reported crash reductions following shoulder widening. This study aimed to examine the relationship between shoulder characteristics and crash occurrences on two-lane rural roads in Israel. The study database combined information on crash numbers, traffic volumes and road infrastructure characteristics of 3594 road sections. To examine a relationship between shoulder characteristics and crashes, given other road characteristics, two types of statistical models were developed: case-control and negative-binomial regression models, for several crash types. We found that the impacts of shoulder width and other road characteristics on crashes were generally consistent across various models and crash types, where a non-monotonous link between the shoulder width and crashes was typically observed. For various crash types, the models showed an increase in crash risk with an initial extension of the total shoulder, up to 2.2 m, and a consequent decrease in crashes with a further shoulder widening, over 2.2 m, by 2-6% and 1-4%, respectively, for each 0.1 m of shoulder extension. An increase in the width of unpaved shoulders, over 0.9 m, was associated with increased crash risk, in injury and total crashes, by 5% for each 0.1 m of shoulder extension. Lowest crash risks were found for total shoulder widths of about 3 m or more, but also for narrow total shoulders, below 1 m. Conversely, medium total shoulders, of 1.8-2.4 m in width, and unpaved shoulders of over 1 m, were associated with an increase in crash risk and, hence, are not recommended for use. The tools developed in the study may assist in decision-making during the design stages of a new road or upgrading existing road sections, on two-lane local roads.

Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study

In response to the rapid economic growth in China, its freeway system has become the longest in the world and likely will continue to expand. Unfortunately, the safety issues on freeways in China have grown as well and are of great concern to Chinese transportation authorities and drivers. While many proven safety countermeasures developed and implemented by other countries are available for reference, they may be not fully transferrable to China due to the differences in driving cultures and conditions. As a result, an investigation of China's unique safety factors and effective relevant countermeasures are urgently needed. The study presented in this paper thoroughly investigated the factors contributing to freeway crashes in China based on detailed crash data, traffic characteristics, freeway geometry, pavement conditions, and weather conditions. To properly account for the over-dispersion of data and unobserved heterogeneity, a random effects negative binomial (RENB) model and a random parameters negative binomial (RPNB) model were applied, along with a negative binomial (NB) model. The analysis revealed a large number of crash frequency factors, including several interesting and important factors rarely studied in the past, such as the safety effects of climbing lanes. Moreover, the RENB and RPNB models were found to considerably outperform the NB model; however, although the RPNB exhibited better goodness-of-fit than the RENB model, the difference was rather small. The findings of this study shed more light on the factors influencing freeway crashes in China. The results will be useful to highway designers and engineers for creating, building, and operating safe freeways as well as to safety management departments for developing effective safety countermeasures. The study presented in this paper also provides additional guidance for choosing relevant methods to analyze safety and to identify safety factors.

Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model

Rollover crashes are responsible for a notable number of serious injuries and fatalities; hence, they are of great concern to transportation officials and safety researchers. However, only few published studies have analyzed the factors associated with severity outcomes of rollover crashes. This research has two objectives. The first objective is to investigate the effects of various factors, of which some have been rarely reported in the existing studies, on the injury severities of single-vehicle (SV) rollover crashes based on six-year crash data collected on the Malaysian federal roads. A random-effects generalized ordered probit (REGOP) model is employed in this study to analyze injury severity patterns caused by rollover crashes. The second objective is to examine the performance of the proposed approach, REGOP, for modeling rollover injury severity outcomes. To this end, a mixed logit (MXL) model is also fitted in this study because of its popularity in injury severity modeling. Regarding the effects of the explanatory variables on the injury severity of rollover crashes, the results reveal that factors including dark without supplemental lighting, rainy weather condition, light truck vehicles (e.g., sport utility vehicles, vans), heavy vehicles (e.g., bus, truck), improper overtaking, vehicle age, traffic volume and composition, number of travel lanes, speed limit, undulating terrain, presence of central median, and unsafe roadside conditions are positively associated with more severe SV rollover crashes. On the other hand, unpaved shoulder width, area type, driver occupation, and number of access points are found as the significant variables decreasing the probability of being killed or severely injured (i.e., KSI) in rollover crashes. Land use and side friction are significant and positively associated only with slight injury category. These findings provide valuable insights into the causes and factors affecting the injury severity patterns of rollover crashes, and thus can help develop effective countermeasures to reduce the severity of rollover crashes. The model comparison results show that the REGOP model is found to outperform the MXL model in terms of goodness-of-fit measures, and also is significantly superior to other extensions of ordered probit models, including generalized ordered probit and random-effects ordered probit (REOP) models. As a result, this research introduces REGOP as a promising tool for future research focusing on crash injury severity.

Predicting expressway crash frequency using a random effect negative binomial model: A case study in China

To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50km long expressway in China, including 567 crash records (2006-2008), were collected and analyzed. Both the fixed-length and the homogeneous longitudinal grade methods were applied to divide the study expressway section into segments. A negative binomial (NB) model and a random effect negative binomial (RENB) model were developed to predict crash frequency. The parameters of both models were determined using the maximum likelihood (ML) method, and the mixed stepwise procedure was applied to examine the significance of explanatory variables. Three explanatory variables, including longitudinal grade, road width, and ratio of longitudinal grade and curve radius (RGR), were found as significantly affecting crash frequency. The marginal effects of significant explanatory variables to the crash frequency were analyzed. The model performance was determined by the relative prediction error and the cumulative standardized residual. The results show that the RENB model outperforms the NB model. It was also found that the model performance with the fixed-length segment method is superior to that with the homogeneous longitudinal grade segment method.

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

Rural casualty crashes on the Kings Highway: A new approach for road safety studies

This paper will consider the contribution that changes in road geometry and driver visual information make to the incidence and distribution of road casualties in different driving environments. This relationship will be explored specifically for the Kings Highway, a major arterial road connecting Queanbeyan with coastal southern New South Wales, Australia. It introduces and suggests a new empirical approach of plotting crashes with road segmentation, calculating sinuosity indices and grades as key features of road geometry, and critical visual points as a behavioural component of road curvature, within a GIS context. It is an approach that might be used when detailed road geometry data is not available. The visualisation and segmentation approach in this research might be used for summarising crash rates and road geometry factors, and for comparing day/night and eastbound/westbound driving conditions. The results suggest some early interpretations for detailed road safety studies that might be considered at local or national levels. The rate of crashes increases according to changes in road geometry factors during the day and for eastbound travel. This is not the case for night driving where the incidence of crashes is similar on both straight and curved roads segments due to the headlight effect and limited background visual field. Crash clusters at day-time may be due to the stronger effect of road geometry (e.g. combination of curvature and vertical grade) on driver behaviour travelling eastbound. The outcomes suggest that it might be essential to consider the effect of environmental factors in any road safety and crash analysis studies.

Development of safety performance functions for Spanish two-lane rural highways on flat terrain

Over decades safety performance functions (SPF) have been developed as a tool for traffic safety in order to estimate the number of crashes in a specific road section. Despite the steady progression of methodological innovations in the crash analysis field, many fundamental issues have not been completely addressed. For instance: Is it better to use parsimonious or fully specified models? How should the goodness-of-fit of the models be assessed? Is it better to use a general model for the entire sample or specific models based on sample stratifications? This paper investigates the above issues by means of several SPFs developed using negative binomial regression models for two-lane rural highways in Spain. The models were based on crash data gathered over a 5-year period, using a broad number of explanatory variables related to exposure, geometry, design consistency and roadside features. Results show that the principle of parsimony could be too restrictive and that it provided simplistic models. Most previous studies apply conventional measurements (i.e., R(2), BIC, AIC, etc.) to assess the goodness-of-fit of models. Seldom do studies apply cumulative residual (CURE) analysis as a tool for model evaluation. This paper shows that CURE plots are essential tools for calibrating SPF, while also providing information for possible sample stratification. Previous authors suggest that sample segmentation increases the model accuracy. The results presented here confirm that finding, and show that the number of significant variables in the final models increases with sample stratification. This paper point out that fully models based on sample segmentation and on CURE may provide more useful insights about traffic crashes than general parsimonious models when developing SPF.

Safety evaluation of signalized intersections with left-turn waiting area in China

In recent years the metropolitans in China have seen the surging installations of the left-turn waiting area (LWA) at the signalized intersections. The design allows the left-turning vehicles to enter the intersection at the onset of the through green phase (of the same approach) and wait for the exclusive left-turn signal at the LWA. The LWA layout can effectively reduce the probability of stranded and queue overflow of the left-turn vehicles, but no study is conducted yet to assess the safety performance of the signalized intersections with LWA. The paper adopts the traffic conflict technique (represented by post-encroachment time), compares the discrepancy of conflict types between intersections with LWA and without, and develops the severity models to identify the contributing factors for the left-turn conflicts. Results demonstrate that the left-turn volume, driving outside the LWA, running red light, the presence of secondary conflicts, and the rear-end conflicts significantly increase the severities of traffic conflicts at the LWA. The findings serve to provide recommendations to revise the current design standard of the LWA (GB5768-2009) and consequently improve the safety operations of signalized intersections with LWA in China.

Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis. Additionally, spatial spillover effects are explored in the models by employing exogenous variables from neighboring zones. The dual-state models such as zero-inflated negative binomial and hurdle negative binomial models (with and without spatial effects) are compared with the conventional single-state model (i.e., negative binomial). The model comparison for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency.

Safety performance functions for crash severity on undivided rural roads

The objective of this paper is to explore the effect of the road features of two-lane rural road networks on crash severity. One of the main goals is to calibrate Safety Performance Functions (SPFs) that can predict the frequency per year of injuries and fatalities on homogeneous road segments. It was found that on more than 2000km of study-road network that annual average daily traffic, lane width, curvature change rate, length, and vertical grade are important variables in explaining the severity of crashes. A crash database covering a 5-year period was examined to achieve the goals (1295 injurious crashes that included 2089 injuries and 235 fatalities). A total of 1000km were used to calibrate SPFs and the remaining 1000km reflecting the traffic, geometric, functional features of the preceding one were used to validate their effectiveness. A negative binomial regression model was used. Reflecting the crash configurations of the dataset and maximizing the validation outcomes, four main sets of SPFs were developed as follows: (a) one equation to predict only injury frequency per year for the subset where only non-fatal injuries occurred, (b) two different equations to predict injury frequency and fatality frequency per year per sub-set where at least one fa tality occurred together with one injury, and (c) only one equation to predict the total frequency per year of total casualties correlating accurate percentages to obtain the final expected frequency of injuries and fatalities per year on homogeneous road segments. Residual analysis confirms the effectiveness of the SPFs.

Risk factors for the severity of injury incurred in crashes involving on-duty police cars

OBJECTIVE

This article explores the risk factors associated with police cars on routine patrol and/or on an emergency run and their effects on the severity of injuries in crashes.

METHODS

The binary probit model is used to examine the effects of important factors on the risk of injuries sustained in crashes involving on-duty police cars.

RESULTS

Several factors significantly increase the probability of crashes that cause severe injuries. Among those causes are police officers who drive at excessive speeds, traffic violations during emergency responses or pursuits, and driving during the evening (6 to 12 p.m.) or in rainy weather. Findings also indicate some potential issues associated with an increase in the probability of crashes that cause injuries. Younger police drivers were found to be more likely to be involved in crashes causing injuries than middle-aged drivers were. Distracted driving by on-duty police officers as well as civilian drivers who did not pull over to let a police car pass in emergency situations also caused serious crashes.

CONCLUSIONS

Police cars are exempted from certain traffic laws under emergency circumstances. However, to reduce the probability of being involved in a crash resulting in severe injuries, officers are still obligated to drive safely and follow safety procedures when responding to emergencies or pursuing a car. Enhancement of training techniques for emergency situations or driving in pursuit of an offender and following the safety procedures are essential for safety in driving during an emergency run by police.

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.