Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

Understanding speeding behavior on interstate horizontal curves and ramps using networkwide probe data

INTRODUCTION

Lane departure collisions account for many roadway fatalities across the United States. Many of these crashes occur on horizontal curves or ramps and are due to speeding. This research investigates factors that impact the odds of speeding on Interstate horizontal curves and ramps.

METHOD

We collected and combined two unique sources of data. The first database involves comprehensive curve and ramp characteristics collected by an automatic road analyzer (ARAN) vehicle; the second database includes volume, average speed, and speed distribution gathered from probe data provided by StreetLight Insight®. We evaluated the impacts of level of service (LOS), which reflects traffic density or level of congestion, time of the day (morning, evening, and off-peak hours), time of the week (weekdays and weekends), and month of the year (Jan-Dec), and various information about geometric characteristics, such as curve radius, arc angle, and superelevation, on odds of speeding.

RESULTS

The results show that the odds of speeding increases at horizontal curves with improved levels of service, as well as those with larger radii and superelevation. The odds of speeding decreases on curves with larger arc angles and during the winter months of the year. The findings indicate a reduction in odds of speeding at diagonal/loop ramps with larger arc angles and narrower lane widths.

CONCLUSION

The results show the importance of using speed enforcement and other countermeasures to reduce speeding on curves with low traffic volumes, high speed limits, and large radius and superelevation, especially for those in rural areas.

PRACTICAL APPLICATION

The results could be used to prioritize locations for the installation of speed countermeasures or dispatch enforcement resources to high-priority locations and times.

Crash risk estimation of Heavy Commercial vehicles on horizontal curves in mountainous terrain using proactive safety method

Heavy commercial vehicles (HCVs) face elevated crash risks in mountainous terrains due to the challenging topography and intricate geometry, posing a significant challenge for transportation agencies in mitigating these risks. While safety studies in such terrains traditionally rely on historical crash data, the inherent issues associated with crash data have led to a shift towards proactive safety studies using surrogate safety measures (SSM) in recent years. However, the scarcity of accurate microscopic data related to HCV drivers has limited the application of proactive safety studies in mountainous terrains. This study addresses this gap by employing an SSM known as anticipated collision time (ACT) to explore the impact of horizontal curves on the crash risk of HCVs in mountainous terrain. To perform the crash risk analysis, a collection of videos was gathered from horizontal curves in the mountainous terrain along the Guwahati-Shillong bypass in the Northeastern region of India. Subsequently, trajectories were extracted from these videos using semi-automated image processing software. Traffic conflicts were identified using ACT, and the crash risk was estimated through the Peak-Over Threshold (POT) approach of the Extreme Value Theory (EVT). The findings indicate that Run-Off-Road (ROR) traffic events happen more frequently on or near the horizontal curves falling in mountainous terrain. However, the frequency of severe ROR traffic events is lower, indicating the lower propensity for such collisions on the selected curves. The threshold for the safety margin of ROR traffic events involving HCVs was 2 s. The study revealed that stationary models exhibit an overestimation of crash frequency (0, 6) compared to the observed crash frequency (0, 2). Consequently, non-stationary crash risk models were developed, incorporating road geometry and the braking and yaw rates of HCVs as covariates. The results demonstrate that the estimated confidence bounds (1, 2) align with the observed crash frequency (0, 2), emphasizing the applicability of POT models for safety analysis in mountainous terrains in India. The study identified curve radius, length of the approach tangent, and the distance between the center points of horizontal and vertical curves as influential factors affecting the Run-Off-Road (ROR) crash risk of HCVs. Notably, sharp curves with radii less than 200 m or more are associated with a significantly higher crash risk. Additionally, an increased distance between the midpoints of horizontal and vertical curves beyond 1 m was found to escalate the ROR crash risk of HCVs. To mitigate these risks, it is recommended to reduce the length of the approach tangent to prevent high-speed travel on sharp curves. Furthermore, proper signage should be strategically placed to warn drivers and avert potential hazards.

Interaction between benzodiazepines and prescription opioids on incidence of hard braking events in older drivers

BACKGROUND

Polypharmacy use among older adults is of increasing concern for driving safety. This study assesses the individual and joint effects of benzodiazepines and prescription opioids on the incidence of hard braking events in older drivers.

METHODS

Data for this study came from the Longitudinal Research on Aging Drivers project-a multisite, prospective cohort study of 2990 drivers aged 65-79 years at enrollment (2015-2017). Adjusted incidence rate ratios (aIRRs) and 95% confidence intervals (CIs) of hard braking events (defined as maneuvers with deceleration rates ≥0.4 g and commonly known as near-crashes) were estimated through multivariable negative binominal modeling.

RESULTS

Of the 2929 drivers studied, 167 (5.7%) were taking benzodiazepines,  163 (5.6%) prescription opioids, and 23 (0.8%) both drugs at baseline. The incidence rates of hard braking events per 1000 miles driven were 1.14 (95% CI 1.10-1.18) for drivers using neither benzodiazepines nor prescription opioids, 1.25 (95% CI 1.07-1.43) for those using benzodiazepines only, 1.55 (95% CI 1.35-1.76) for those using prescription opioids only, and 1.63 (95% CI 1.11-2.16) for those using both medications. Multivariable modeling revealed that the use of prescription opioids was associated with a 19% increased risk of hard braking events (aIRR 1.19, 95% CI 1.03-1.36). There existed a positive interaction between the two drugs on the additive scale but not on the multiplicative scale.

CONCLUSION

Concurrent use of benzodiazepines and prescription opioids by older drivers appears to affect driving safety through increased incidence of hard braking events.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Exploring the stop sign running at all-way stop-controlled intersections with the SHRP2 naturalistic driving data

INTRODUCTION

All-way stop control (AWSC) has been widely used at unsignalized intersections in the United States for its safety effects. However, many drivers do not make a complete stop before stop signs in practice (i.e., stop sign running), which presents safety concerns.

METHOD

This study explores driver behaviors at AWSC intersections with the SHRP2 naturalistic driving data.

RESULTS

First, it is found that the full-stop rate is only 20.2% at AWSC intersections. Then, the study quantitatively analyzes what factors might influence the stop sign running decisions at AWSC intersections, where driver, vehicle, intersection geometry, maneuver, and environmental features are taken into account. In addition, considering the possible unobserved heterogeneities across drivers and intersections, a logistic regression model with both driver and intersection random effects is adopted. The results show that young and older drivers are less likely to fully stop, but there is no gender difference found. SUVs and vans are less likely to fully stop, drivers are less likely to fully stop at 3-leg intersections, and drivers are more likely to fully stop in daytime and weekdays. In terms of maneuvers, left-turn traversals are more likely to make a complete stop. In addition, both the driver and intersection random effects are found to be significant, vary greatly by individuals, and can be used to identify the few but critical high-risk drivers/intersections.

PRACTICAL APPLICATIONS

The findings are expected to provide new insights for transportation agencies to formulate effective measures to deter stop sign running.

Potentially Inappropriate Medication Use and Hard Braking Events in Older Drivers

Potentially inappropriate medications (PIMs) identified by the American Geriatrics Society should generally be avoided by older adults because of ineffectiveness or excess risk of adverse effects. Few studies have examined the effects of PIMs on driving safety measured by prospectively and objectively collected driving data. Data for this study came from the Longitudinal Research on Aging Drivers study, a multisite naturalistic driving study of older adults. Multivariable negative binominal modeling was used to estimate incidence rate ratios and 95% confidence intervals of hard braking events (proxies for unsafe driving behavior defined as events with a deceleration rate ≥0.4 g) associated with PIM use among older drivers. The study sample consisted of 2932 drivers aged 65-79 years at baseline, including 542 (18.5%) who used at least one PIM. These drivers were followed through an in-vehicle recording device for up to 44 months. The overall incidence of hard braking events was 1.16 per 1000 miles. Use of PIMs was associated with a 10% increased risk of hard braking events. Compared to drivers who were not using PIMs, the risk of hard braking events increased 6% for those using one PIM, and 24% for those using two or more PIMs. Use of PIMs by older adult drivers is associated in a dose-response fashion with elevated risks of hard braking events. Reducing PIM use in older adults might help improve driving safety as well as health outcomes.

Assessment of temporal stability in risk factors of crashes at horizontal curves on rural two-lane undivided highways

INTRODUCTION

Safety of horizontal curves on rural two-lane, two-way undivided roadways is not fully explored. This study investigates factors that impact injury severity of such crashes.

METHOD

To achieve the aim of this paper, issues associated with police-reported crash data such as unobserved heterogeneity and temporal stability need to be accounted for. Hence, a mixed logit model was estimated, while heterogeneity in means and variances is investigated by considering four injury severity outcomes for drivers: severe injury, moderate injury, possible injury, and no injury. Crash data for the period between 2011 and 2016 for crashes that occurred in the state of Oregon was analyzed. Temporal stability in factors determining the injury severity was investigated by identifying three time periods through splitting crash data into 2011-2012, 2013-2014, and 2015-2016.

RESULTS

Despite some factors affecting injuries in all specified time periods, the values of the marginal effects showed relative differences. The estimation results revealed that some factors increased the risk of being involved in severe injury crashes, including head-on collisions, drunk drivers, failure to negotiate curves, older drivers, and exceeding the speed limits.

CONCLUSIONS

The hypothesis that attributes of injury severity are temporally stable is rejected. For example, young drivers (30 years old and younger) and middle-aged drivers were found to be temporally instable over time. Practical applications: The findings could help transportation authorities and safety professionals to enhance the safety of horizontal curves through appropriate and effective countermeasures.

Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process

Road transportation poses one of the significant public health risks. Several contributors and factors strongly link public health and road safety. The design and advancement of higher-quality roads can significantly contribute to safer roads and save lives. In this article, the safety aspect of the roads’ horizontal curves under the standard of the American Association of State Highway Transportation Officials (AASHTO) is evaluated. Several factors, including vehicle weight, vehicle dimensions, longitudinal grades, and vehicle speed in the geometric design of the horizontal curves, are investigated through a multi-body dynamic simulation process. According to the AASHTO, a combination of simple circular and clothoid transition curves with various longitudinal upgrades and downgrades was designed. Three vehicles were used in this simulation, including a sedan, a bus, and a 3-axle truck. The analysis was based on the lateral friction between the tire and the pavement and also the safety margin parameter. The results showed that designers must differentiate between light and heavy vehicles, especially in curves with a high radius. Evaluation of longitudinal grade impacts indicated that the safety margin decreases when the vehicle is entering the curve. Safety margin reduction on the clothoid curve takes place with a lower grade toward the simple circular curve. By increasing the speed, the difference between lateral friction demand obtained from simulation and lateral friction demand proposed by AASHTO grows. The proposed novel methodology can be used for evaluating road safety.

The Influence of Road Geometry on Vehicle Rollover and Skidding

This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations.

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

Road infrastructure sustainability is directly associated with the safety of human beings. As a transportation engineer and policymaker, it is necessary to optimize the funding mechanism for road safety improvement by identifying problematic road segments. Infrastructure improvement is one of the key targets for efficient road safety management. In this study, data envelopment analysis (DEA) technique has been applied in combination with a geographical information system (GIS) to evaluate the risk level of problematic segments of a 100 km-long motorway (M-2) section. Secondly, the cross efficient method has been used to rank the risky segments for prioritization and distribution of funding to improve the road safety situation. This study will help in efficiently identifying the risky segments for safety improvement and budget allocation prioritization. GIS map will further improve the visualization and visibility of problematic segments to easily locate the riskiest segments of the motorway.

Relationship between road traffic features and accidents: An application of two-stage decision-making approach for transportation engineers

INTRODUCTION

An efficient decision-making process is one of the major necessities of road safety performance analysis for human safety and budget allocation procedure.

METHOD

During the road safety analysis procedure, data envelopment analysis (DEA) supports policymakers in differentiating between risky and safe segments of a homogeneous highway. Cross-risk, an extension of the DEA models, provides more information about risky segments for ranking purpose. After identification of risky segments, the next goal is to identify the factors that are major contributors in making that segment risky.

RESULTS

This research proposes a methodology to analyze road safety performance by using a combination of DEA with the decision tree (DT) technique. The proposed methodology not only provides a facility to identify problematic road segments with the help of DEA but also identifies contributing factors with the help of DT. Practical applications: The applicability of the proposed model will help policymakers to identify the major factors contributing to road accidents and analysis of safety performance of road infrastructure to allocate the budget during the decision-making process.

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.