Search Results (11)

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

This research examines the utility of combined crash modification factors (CMFs) in minimizing crash severity at urban roundabouts. Conventional CMFs typically assess the influence of singular interventions on road safety. However, traffic dynamics and the interactions among multiple safety measures necessitate a more comprehensive strategy. This study introduces a novel methodology for developing integrated CMFs that accounts for the interactive effects of multiple safety measures, providing a comprehensive understanding of their collective impact on road safety. The investigation utilized data from 16 urban roundabouts in Amman, Jordan, from 2015 to 2021. Regression models have been employed to develop individual and combined CMFs for various geometric and traffic characteristics, such as entry and exit widths, speed limits, and weaving patterns. Key findings indicate that interventions, such as reducing speed limits, modifying entry and exit widths, and adjusting weaving lengths, significantly improve safety. The analysis identifies hazardous roundabouts and proposes effective single and combined treatments to mitigate crash risks. This study highlights the importance of considering multiple treatments simultaneously to improve the predictive accuracy of safety assessments and supports the development of more effective road safety strategies tailored to specific crash types and severities. The approach demonstrates a significant potential to enhance road safety analysis and decision-making, ultimately contributing to safer road environments. Full article

This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years. Full article

The combined contributions to mountain freeway safety of pavement performance, weather conditions, and traffic condition indicators have not been thoroughly investigated due to the complexity of their interactions and temporal instability. A cross-sectional analysis using a Generalized Linear Model (GLM) approach with negative binomial distribution considering time-correlation effects (TC-NB) was adopted to estimate the Crash Modification Factors (CMFs) of these indicators for different segment types, alignment types, and cross-sectional forms based on eight quarters of data from mountain freeways in China. According to the results, improving the pavement performance indexes positively impacts the safety of different freeway segments, especially for the curved segments. Quarterly Average Daily Traffic (QADT) has significantly negative safety effects on two-lane segments with relatively narrow spaces, while the proportion of large vehicles plays a decisive role in the safety impacts of tunnel segments. Small/moderate rain days in a quarter (SMR) were significantly positively correlated with crash frequency, while the percentage of torrential rain days in a quarter (TR) showed an opposite trend. The results of this study contribute to the effective coordination of traffic monitoring systems, pavement management systems, and traffic safety management systems to develop targeted improvement countermeasures for different freeway section types. Full article

The purpose of this study is to determine the safety effectiveness of an intervention on an existing road by using predictive methods. Predictive methods allow the benefit of the intervention to be quantified in terms of crash reduction. Currently, the most widely used model is reported in the Highway Safety Manual, developed in the US. The HSM model is adapted to the Italian context through a calibration procedure. The model is then applied to two future scenarios: in the absence and presence of intervention. The redesign intervention consists of rehabilitating some road sections and constructing five tunnel bypasses to avoid crossing residential areas. The comparison between the ‘with’ and ‘without’ scenario estimated an overall reduction in the number of accidents of around 45%. The variant scenario is based on reasonable assumptions that allowed the determination of the proportion of traffic that will be diverted to the variant. In addition, several alternative future scenarios are considered to assess a possible different trend in assumed traffic distribution. Moreover, a possible overall increase or reduction in total traffic affecting the road is taken into account. The results showed that the intervention provided significant benefits even with increased traffic, proving the resilience of the intervention. Full article

Statistical data indicate that trucks are more prone to rear-end crashes, making this an area of concern. The objective of this study is to create a model that analyzes the factors influencing the frequency of rear-end crashes involving trucks (TIRC). To achieve this, researchers identified the most appropriate model as Spatial Zero-Inflated Negative Binomial Regression (SZINB). This model takes into account spatial correlation, which plays a significant role in the occurrences of TIRC on different road segments supervised by each highway ward. The estimation of parameters in the SZINB model has led to key findings that shed light on the factors contributing to a higher likelihood of TIRC. These findings include the increased probability of TIRC on curved roads compared to straight ones, roads that feature open middle islands, six lanes per direction, a slope, right-of-way shoulder width, pavement type, lane width, and a post speed limit. Based on these key findings, this study developed policy recommendations and sample measures aimed at reducing the frequency of TIRC. Implementing measures such as improving the road design on curved sections, optimizing middle islands, and enhancing traffic management on wider roads can help mitigate the risk of crashes involving trucks. Full article

Driving fatigue is a serious issue for the transportation sector, decreasing the driver’s performance and increasing accident risk. This study aims to investigate how fatigue mediates the relationship between the nature of work factors and driving performance. The approach included a review of the previous studies to select the dimensional items for the data collection instrument. A pilot test to identify potential modification to the questionnaire was conducted, then structural equation modelling (SEM) was performed on a stratified sample of 307 drivers, to test the suggested hypotheses. Based on the results, five hypotheses have indirect relationships, four of which have a significant effect. Besides, the results show that driving fatigue partially mediates the relationship between the work schedule and driving performance and fully mediates in the relationship between work activities and driving performance. The nature of work and human factors is the most common reason related to road accidents. Therefore, the emphasis on driving performance and fatigue factors would thereby lead to preventing fatal crashes and life loss. Full article

The main objective of this study was to evaluate the safety effects caused by altering the lengths of deceleration and acceleration lanes at rest areas on expressways in Korea. Although general conclusions can be found through crash-based safety analysis, to examine more specific optimal conditions considering various traffic conditions, this study proposes a novel framework to explore and evaluate crash-based and simulation-based safety performances. For this purpose, the safety performance function (SPF) and crash modification factor (CMF) were developed to reflect real-world safety impacts. To consider nonlinear trends of the parameters, nonlinearizing link functions were introduced into the analysis. Two types of simulation analyses were conducted to (1) find the combination of surrogate safety measures (SSMs) that best fit with the crash-based safety performance results and (2) determine the optimal lengths of deceleration lane and acceleration lanes for different traffic conditions. The results showed that the best length of deceleration lane of a rest area is between 240 and 260 m, depending on the traffic conditions. The results also indicated that the optimal length of acceleration lane of a rest area is between 385 and 400 m, depending on the traffic parameters. The findings of this study could be used to determine the safety solutions with a micro-traffic simulator. Full article

This paper examines the transferability of the Safety Performance Function (SPF) of the Highway Safety Manual (HSM) and other 10 international SPFs for total crashes on rural multi-lane divided roads in Egypt. Four segmentation approaches are assessed in the transferability of the international SPFs, namely: (1) one-kilometer segments (S1); (2) homogenous sections (S2); (3) variable segments with respect to the presence of curvatures (S3); and (4) variable segments with respect to the presence of both curvatures and U-turns (S4). The Mean Absolute Deviation (MAD), Mean Prediction Bias (MPB), Mean Absolute Percentage Error (MAPE), Pearson χ² statistic, and Z-score parameters are used to evaluate the performance of the transferred models. The overdispersion parameter (k) for each transferred model and each segmentation approach is recalibrated using the local data by the maximum likelihood method. Before estimating the transferability calibration factor (Cr), three methods were used to adjust the local crash prediction of the transferred models, namely: (1) the HSM default crash modification factors (CMFs); (2) local CMFs; and (3) recalibrating the constant term of the transferred model. The latter method is found to outperform the first two methods. Besides, the results show that the segmentation method would affect the performance of the transferability process. Moreover, the Italian SPFs based on the S1 segmentation method outperforms the HSM and all of the investigated international SPFs for transferring their models to the Egyptian rural roads. Full article

In the summer of 2011, a change in the Kansas laws came into effect, increasing the speed limit on a selected set of freeway sections from 70 mph to 75 mph. Higher speeds were thought to have economic benefits, mostly because the travel time reduction means people reach their destinations more quickly. In this study, the sections where the speed limits remained unchanged, are compared to freeway sections that have been influenced by speed limit increase, to evaluate safety effectiveness. The study utilizes the before-and-after study with comparison group method to assess the safety effects provided in the Highway Safety Manual (HSM). Two crash datasets, obtained by considering three years before and three years after the speed limit increase, were compared in order to evaluate the safety effects of the speed limit change. The crash modification factors (CMFs) were estimated, which showed that there was a 27% increase in total crashes and a 35% increase in fatal and injury crashes across all sections after the speed limit change, and these increases were statistically significant at 95% confidence level. These confounding results show that the speed limit increase has not been beneficial for traffic safety in Kansas, and hence it is important to be cautious in such future situations. Also, additional data have been presented which would be beneficial in identifying and understanding any behavior change in drivers following a speed limit increase. Full article

Driving experiences provided by the introduction of new vehicle technologies are directly impacting the criteria for road network design. New criteria should be taken into consideration by designers, researchers and car owners in order to assure traffic safety in changed conditions that will appear with, for example, introduction of Autonomous Vehicles (AVs) in everyday traffic. In this paper, roundabout safety level is analysed on the originally developed microsimulation model in circumstances where different numbers of AVs vehicles are mixed with Conventional Vehicles (CVs). Field data about speed and traffic volumes from existing roundabouts in Croatia were used for development of the model. The simulations done with the Surrogate Safety Assessment Model (SSAM) give some relevant highlights on how the introduction of AVs could change both operational and safety parameters at roundabouts. To further explore the effects on safety of roundabouts with the introduction of different shares of AVs, hypothetical safety treatments could be tested to explore whether their effects may change, leading to the estimation of a new set of Crash Modification Factors. Full article