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.
In the context of road safety, risk-taking is undoubtedly one of the main contributory factors in road accidents. The actual forces which influence individuals to take such risks, nevertheless, are still not fully understood. To address this, this study was therefore conducted to investigate the relationship of the demographic, personal, and social factors of motorcyclists, with a specific focus on their risk-taking behavior at signalized intersections in Malaysia. This study adopted the quantitative method using cross-sectional questionnaire surveys and involved 251 respondents. The demographic factors were analyzed using the t-test and an ANOVA Scheffe Post-Hoc test, while the motorcyclists' personal and social characteristics were analyzed with multiple linear regression. The findings indicate that the individuals who were greater risk takers at signalized intersections were teenage motorcyclists (16-25 years old) who had finished their education before taking their high school diploma, and who also received a lower than average monthly income from private sector firms. The actual experience of accidents was also shown to be positively related to this risk-taking behavior. In addition, in term of personal and social factors, results showed that, for these individuals, there was a significant difference between the strength of peer influence and that of parental and spouse guidance. However, there was no significant difference in the risk-taking behavior of Malaysian motorcyclists riding at signalized intersections for the following factors: between genders, in terms of accident involvement, in terms of enforcement of traffic regulations, and prevention steps and confidence level after being involved in an accident.
The existing literature in road safety revealed that the relationship between motorcycle deaths and per-head income follows a Kuznets or reverse U-curve pattern, whereby motorcycle deaths incline at lower income levels but decline once the per-head income has exceeded a threshold level. The same reverse U-curve relationship was also observed between per-head income and other road injury-related variables, including road deaths, road injuries, as well as road deaths to road injuries ratio. Evidence showed that motorcycles and passenger cars are the dominant vehicle modes and contributed significantly to global road deaths. The main objective of this study is to examine the relationship between the motorcycle deaths to passenger car deaths (MDC) ratio and per-head Gross Domestic Product (GDP). Examining the relationship between the MDC ratio and GDP per capita can be effective in understanding the relative change between motorcycle and passenger car deaths at different economic development stages, as well as identifying appropriate preventive measures. We apply a panel linear regression analysis on a panel of 38 countries over the period 1965-2013. Result demonstrated that there is a reverse U-curve relationship between the MDC ratio and the logarithm of GDP per capita. This implies that, at lower levels of GDP per capita, motorcycle deaths were more prevalent than passenger car deaths, whereas as the level of GDP per capita rises, passenger car deaths became relatively prevalent than motorcycle deaths. Moreover, there is a reverse U-shaped relationship between motorcycle ownership to passenger car ownership ratio (MPC) and the MDC ratio, while a U-shaped relationship exists between relative growth in higher mobility roads as compared to higher accessibility roads (MPA) and the MDC ratio. Based on our results, policies and interventions to reduce motorcycle and passenger car deaths were suggested in the conclusion of the paper.
Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data.
Construction of exclusive motorcycle lanes is one of the measures to reduce motorcycle fatalities. Previous studies highlighted the risk of crashes with roadside objects and the tendency of motorcyclists to ride with excessive speed on exclusive motorcycle lanes. However, the risk of same-direction crashes on exclusive motorcycle lanes was not explored in much detail, especially on the impact of lane geometry and roadside configurations. This study used naturalistic riding data to determine the effects of lane width and roadside configurations on overtaking speed, lateral position and likelihood of comfortable overtaking on tangential sections of an exclusive motorcycle lane. Twenty-nine recruited motorcyclists rode the instrumented motorcycles along a 20km stretch of an exclusive motorcycle lane along a major urban road. Results revealed that both the roadside configurations and lane width significantly affect the participants' lateral position, while the roadside configurations only affects the overtaking speed. Participants' overtaking speeds and the front motorcycles' lateral position contribute significantly to the likelihood of comfortable overtaking in exclusive motorcycle lanes. The findings highlight the importance of micro-level behavior indicators in improving the design and overall safety of the exclusive motorcycle facility.
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.
This study had developed a passenger safety perception model specifically for buses taking into consideration the various factors, namely driver characteristics, environmental conditions, and bus characteristics using Bayesian Network. The behaviour of bus driver is observed through the bus motion profile, measured in longitudinal, lateral, and vertical accelerations. The road geometry is recorded using GPS and is computed with the aid of the Google map while the perceived bus safety is rated by the passengers in the bus in real time. A total of 13 variables were derived and used in the model development. The developed Bayesian Network model shows that the type of bus and the experience of the driver on the investigated route could have an influence on passenger's perception of their safety on buses. Road geometry is an indirect influencing factor through the driver's behavior. The findings of this model are useful for the authorities to structure an effective strategy to improve the level of perceived bus safety. A high level of bus safety will definitely boost passenger usage confidence which will subsequently increase ridership.
Facilitating proactive pedestrian safety management, the application of extreme value theory (EVT) models has gained popularity due to its extrapolation capabilities of estimating crashes from their precursors (i.e., conflicts). However, past studies either applied EVT models for crash risk analysis of autonomous vehicle-pedestrian interactions or human-driven vehicle-pedestrian interactions at signalised intersections. However, our understanding of human-driven vehicle-pedestrian interactions remains elusive because of scant evidence of (i) EVT models' application for heterogeneous traffic conditions, (ii) appropriate set of determinants, (iii) which EVT approach to be used, and (iv) which conflict measure is appropriate. Addressing these issues, the objective of this study is to investigate pedestrian crash risk analysis in heterogeneous and disordered traffic conditions, where drivers do not follow lane disciplines. Eleven-hour video recording was collected from a busy pedestrian crossing at a midblock location in India and processed using artificial intelligence techniques. Vehicle-pedestrian interactions are characterised by two conflict measures (i.e., post encroachment time and gap time) and modelled using block maxima and peak over threshold approaches. To handle the non-stationarity of pedestrian conflict extremes, several explanatory variables are included in the models, which are estimated using the maximum likelihood estimation procedure. Modelling results indicate that the EVT models provide reasonable estimates of historical crash records at the study location. From the EVT models, a few key insights related to vehicle-pedestrian interactions are as follows. Firstly, a comparison of EVT models shows that the peak over threshold model outperforms the block maxima model. Secondly, post encroachment time conflict measure is found to be appropriate for modelling vehicle-pedestrian interactions compared to gap time. Thirdly, pedestrian crash risk significantly increases when they interact with two-wheelers in contrast with interactions involving buses where the crash risk decreases. Fourthly, pedestrian crash risk decreases when they cross in groups compared to crossing individually. Finally, pedestrian crash risk is positively related to average vehicle speed, pedestrian speed, and five-minute post encroachment time counts less than 1.5 s. Further, different block sizes are tested for the block maxima model, and the five-minute block size yields the most accurate and precise pedestrian crash estimates. These findings demonstrate the applicability of extreme value analysis for heterogeneous and disordered traffic conditions, thereby facilitating proactive safety management in disordered and undisciplined lane conditions.
Novice drivers are at a greatly inflated risk of crashing. This led in the 20th century to numerous attempts to develop training programs that could reduce their crash risk. Yet, none proved effective. Novice drivers were largely considered careless, not clueless. This article is a case study in the United States of how a better understanding of the causes of novice driver crashes led to training countermeasures targeting teen driving behaviors with known associations with crashes. These effects on behaviors were large enough and long-lasting enough to convince insurance companies to develop training programs that they offered around the country to teen drivers. The success of the training programs at reducing the frequency of behaviors linked to crashes also led to several large-scale evaluations of the effect of the training programs on actual crashes. A reduction in crashes was observed. The cumulative effect has now led to state driver licensing agencies considering as a matter of policy both to include items testing the behaviors linked to crashes on licensing exams and to require training on safety critical behaviors. The effort has been ongoing for over a quarter century and is continuing. The case study highlights the critical elements that made it possible to move from a paradigm shift in the understanding of crash causes to the development and evaluation of crash countermeasures, to the implementation of those crash countermeasures, and to subsequent policy changes at the state and federal level. Key among these elements is the development of simple, scalable solutions.
Ensuring the reliability and trustworthiness of connected and automated vehicle (CAV) technologies is crucial before their widespread implementation. Instead of focusing solely on the automation levels of individual vehicles, it is essential to consider the autonomous operations of the entire autonomous transportation system (ATS) to achieve automated traffic. However, designing and generating scenarios that unify the diverse properties of CAV testing and establish mutual trust among stakeholders pose significant challenges. Previous studies have predominantly focused on the automation levels of CAVs when characterizing scenarios, neglecting the autonomous level of the entire scenario from an ATS perspective. Moreover, there remains research potential in evaluating whether the testing scenario libraries can be effectively integrated into the CAV testing process. In this paper, we propose a grading framework for traffic scenarios based on autonomous levels in the ATS. We also classify and summarize the traffic scenarios used in CAV safety testing. Through a comprehensive literature review, we identify prevailing issues and patterns in scenario design and provide insights and directions for future research in this field.