Crash Rates Research Articles

Estimating the crash reducing effect of Advanced Driver Assistance Systems (ADAS) for vulnerable road users

Vulnerable road users (VRUs) are frequently involved in road traffic crashes worldwide, and the most serious consequences often occur in collisions with motor vehicles. Countermeasures such as advanced driver assistance systems (ADASs) for have been developed and implemented in vehicles to reduce these crashes. Estimations of the effectiveness of these systems are so far dominated by prospective studies; only a few analyze real-world crash data. The objective of this study was to estimate the crash-reducing effects of the VRU ADAS systems in car-to-pedestrian and car-to-bicycle crashes involving Volvo cars in Sweden using real-world crash data. Car-to-pedestrian and car-to-bicycle collisions across Sweden from 2015–2020 (in which the car was moving forward) were identified using insurance claims at If P&C insurance. Volvo car models with ADAS VRU and comparable Volvo models without the system were selected. Poisson regression was used to estimate the effect of the VRU ADAS on the crash rate, calculated per insured vehicle-years. For the second generation of VRU ADAS, the straight crossing path (SCP) and parallel (Longitudinal) crash rate was found to be 12% less in car-to-pedestrian/bicycle crashes—23% less in car-to-pedestrian crashes and 6% less in car-to-bicycle crashes. However, no results were statistically significant. There was no difference in crash rates when evaluating all crash situations where the car was moving forward. The limitation of this study is the low number of crashes. Reducing crashes and injuries in vehicle-to-VRU conflicts is of utmost importance for fulfilling the aims highlighted in Agenda 2030. Improving ADAS to better avoid collisions with pedestrians and cyclists has the potential to greatly improve traffic safety; future generations of the system are expected to increase VRU safety benefits. Measuring ADAS effects in real-world traffic and interpreting and relating the results are essential to ensure that future systems improve VRU safety.

Demographic adjustments to driver death rates by vehicle type and size

Objective Driver death rates per million registered vehicles are often used to compare the real-world crash experiences of vehicles of different types and sizes. However, these rates are affected by the risky behavior of drivers (e.g., speeding, impaired driving, lack of restraint use) that differs by age and gender. This paper presents a method for adjusting driver death rates to account for differences in driver age and gender. Methods Driver death rates per million registered vehicles per year were calculated for passenger vehicles of model year 2020. To account for potential differences in driver exposure by age and gender, which can affect motor vehicle crash and injury experience, an algebraic method was used to standardize these rates to a common distribution of female drivers ages 25–64. The standardization depends upon an independent estimate of the relative fatality risk for female drivers ages 25–64. Results The smallest vehicles tended to have higher driver death rates compared with larger vehicles. However, for sports cars, this trend was reversed; larger sports cars had higher driver death rates. Vehicle types popular with male drivers, such as sports cars and pickups, had standardized death rates that were much lower than the raw rates. Conclusions The adjustment for driver age and gender greatly reduced the variability of driver death rates among vehicle types. The procedure used here to adjust driver death rates for driver age and gender can be extended to any situation where it is desired to compare the crash rates of two or more subgroups of a population.

A comparison of licensed driver counts from the Federal Highway Administration and driver licensing agencies in 11 states

Objective: Those who study motor vehicle crashes may rely on counts of licensed drivers to estimate crash, injury, or fatality rates. These counts may be obtained from the U.S. Department of Transportation Federal Highway Administration’s (FHWA) annual Highway Statistics Series or directly from state driver licensing agencies. However, previous studies have questioned the accuracy of these counts provided by the FHWA.Methods: To investigate this issue, we compared counts of licensed drivers from the FHWA and state licensing agencies in 11 states, categorized by sex and age group, from 2013 through 2017. We then assessed the impact of any potential differences by fitting two sets of Poisson regression models to estimate age- and sex-based driver fatality rate ratios. One set of models used counts from the FHWA as the offset and the other used counts from state licensing agencies.Results: Our analysis found that the differences between FHWA and state counts varied markedly. Seven states had substantial differences for at least one age group that spanned the entire study period. In several cases, these differences in license counts were large enough to produce directly contradictory driver fatality rate ratio estimates when comparing age groups.Conclusions: These findings highlight the continued concern regarding the accuracy of licensed driver counts from the FHWA and extend previous studies by illustrating the impact of using FHWA counts on statistical inference. We recommend against using these data for traffic safety research or policy evaluation. Nevertheless, we acknowledge the need for a centralized, easily accessible database for licensed driver data.

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.

WebGIS Based Road Crash Information System: A Case Study

Road crashes in India is showing progressive growth since COVID time, despite many road safety measures and program the rate of crashes are not declining. Many a times the road safety measures are not implemented in proper geographical locations owing to lack of proper crash information, which include crash information of the past. Road crash information is thus a vital support for the road safety assessment programs that eyes for a reduction in the road crashes. In India, as in other developing countries, very little effort is taken to providing enough road crash information. The identification of road crash location, analysis and treatment of road accident black spots are widely regarded as one of the most effective approaches to road accident prevention. A user friendly web Geographic Information System (GIS) based Road Crash Information System (RCIS) is developed in the present study for Kerala State, India. An online platform to add, update and maintain the database of road accident black spots is offered by the system, including analysis functionalities. Database maintains a standard guideline for road crash reporting thereby reducing data redundancy. Integration of all crash data from accident locations and to filter data based on different criteria are the core objectives of this study. The study also focused on systematically sharing the accident black spot details to the public user through an online platform.

Risks that induce bicycle accidents: Measurement and analysis of bicyclist behavior while going straight and turning right using a bicycle simulator

In 2021, 69 % of those who died in Japan while riding a bicycle were of an older age. Preventing such accidents is an important issue because older bicyclists are more likely to be fatally injured in an accident. We developed a bicycle simulator that can safely simulate dangerous traffic environments by combining VR technology, motion capture technology, and various sensors to investigate the driving behavior of bicyclists who are involved in accidents. The simulator measures operating data such as floor reaction force, wheel rotation angle, brake lever displacement, position and posture of motion capture devices, and position and posture of the bicycle and vehicles at 60 Hz. We projected images of the virtual traffic environment to reflect the driving behavior in real-time onto three screens installed at the front, right side, and rear of the bicycle using three projectors. The constructed simulator was implemented for a “crossing inspection” where a rider checks for safety in front and behind while riding down a straight road with one lane and crossing this roadway and compared this to a “tracing inspection” where a model route is traced using a previously measured safe route taken by a younger rider. We measured and analyzed the bicyclist behaviors of nine younger and six older participants during two types of inspections as a usability study. In the crossing inspection, the crash rate for the older riders in the near lane was 5.3 times higher than that for the young. Riders collided with vehicles exhibited minimal rearward checking angles before entering a roadway, extended durations from the end of the last rearward check to roadway entry, and considerable standard deviations during road crossings. Moreover, in the tracing inspection, younger participants demonstrated superior trajectory passage rates and maintained closer proximity to the model, whereas older participants manifested diminished trajectory passage rates and remained at more extended distances from the model. Notably, a declining trajectory passage rate corresponded to an increase in the standard deviation of the crossing route and the time taken to enter the roadway.

Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers' crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.

Determination of Optimal Spatial Sample Sizes for Fitting Negative Binomial-Based Crash Prediction Models with Consideration of Statistical Modeling Assumptions

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on negative binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating the assumption of violation identification. This study, part of an FDOT-sponsored project, investigates the established statistical diagnostic tests to identify model violations and proposes a novel approach to determine the optimal spatial regions for empirical Bayes adjustment. This adjustment aligns HSM SPFs with regression assumptions. This study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe-injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM SPFs with intended assumptions and determining appropriate region sizes.

Crashworthiness analysis based on vehicle color and driver perception

Road safety has been of increasing concern in recent years, and vehicle crashworthiness has been highlighted as a significant challenge. While known factors such as improper driving, road conditions and driving under the influence of substances have contributed to this problem, a new perspective has emerged regarding the color of cars and its impact on crash rates. Recent research has indicated that the color of a vehicle can play an important role in road safety. The colors red, silver, white and black have been identified as those that drivers perceive most clearly in different scenarios, such as time of day, distance and traffic location.

COVID-19 and transport safety

ABSTRACT Transportation safety has substantially been impacted by the COVID-19 Pandemic. To contain the spread of the virus, governments around the world released guidelines (e.g. stay-at-home orders), which reduced or eliminated peoples’ daily commutes. These travel restrictions introduced new safety challenges for roadways and drivers, transit riders, and pedestrians and cyclists. Many cities, states, and countries around the world reported more severe crashes despite fewer drivers on the road. Transit ridership reduced as the risk of infection with COVID-19 kept transit users away, and walking and biking became more popular as they could be done individually in open air. This paper conducts a structured critical review to summarise and discuss studies around the world on roadway, transit, and pedestrian and bicycle safety since the start of the COVID-19 pandemic. For roadway safety, we further review studies that examine the direct measures of safety (e.g. crash statistics and models) as well as indirect or surrogate measures (e.g. speeding, aggressive driving, and reduced seatbelt usage). Surrogate measures, although indirectly, can affect the risk of crashes and contribute to the increase in frequency and severity of crashes. Findings are documented and discussed. We then provide suggestions on potential avenues for future research. Particularly, research studies show that the rate of severe injury crashes, as well as aggressive driving (e.g. speeding) increased in most places during the travel restrictions, and continued even after these restrictions were lifted. The safety of pedestrians and bicycles has also become increasingly important due to the increase in the number of users of these modes.

How do people perceive driving risks in small towns? A case study in Central Texas

The number of studies investigating the relationship between perceived and objective traffic risk from drivers’ perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment – small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users’ perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people’s sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns.

Safety Evaluation for Highway Geometric Design Based on Spatial Path Properties

Safety is an important aspect of road design. In highway geometric design, road engineers use a two-stage design method due to its convenience, but the available two-dimensional design tools on the market are believed to impose limitations. Horizontal and vertical alignment parameters are considered and determined at the designer’s discretion, potentially downplaying the three-dimensional characters of spatial curves. This study focuses on the three-dimensional characteristics of highway alignments and investigates a safety evaluation method to establish the relationship between crash rate and spatial curve properties. This will necessitate an analytical investigation into the effects of higher-order properties, such as curvature and torsion, on the geometry of spatial Cartesian curves. First, these combinations of horizontal and vertical alignments were categorized into six classifications, each with its own spatial expression in mathematical form. After manipulating the curvature and torsion of the spatial curve algebraically, the correlation between geometric design variables and crash rate was ultimately established. A few cases involving geometric design data and crash facts were utilized for verification. The results revealed a considerable positive correlation between curvature or torsion variance and crashes per million vehicles kilometers, as a slight difference between curvature and torsion could also be spotted. And curvature distribution is correlated with collision frequency more closely than torsion spatial variation.

Crash Risk Among Young Drivers Diagnosed with Psychopathology

Young drivers’ crash rates are highly elevated. Despite the small age range, this population is extremely heterogeneous, with subgroups at greater risk than others. Real-world driving data were used to examine risks of young drivers diagnosed with psychiatric psychopathologies. 654 young drivers were assessed for up to 2yrs. Relative to healthy controls, drivers with any psychiatric diagnoses had higher crash/near-crash (CNC) rates (aIRR=1.49). Young drivers diagnosed with internalizing psychopathology (depression and/or anxiety) had 41% higher CNC rates compared to healthy controls. Young drivers with comorbid externalizing (ADD/ADHD/Tourette’s Syndrome) and internalizing psychopathologies had the highest crash and CNC rates compared to healthy controls (aIRR=2.60; aIRR=2.51, respectively). In conclusion, psychiatric disorders represent a specific vulnerability for crashes where young drivers with comorbid internalizing and externalizing disorders are at much greater risk to crash. Future research should examine possible variability or attenuation of risk associated with psychiatric treatment (e.g., medication or behavioral interventions).

Modeling the Impact of COVID-19 Interventions on Interstate Crash Rates Using Comparative Interrupted Time Series

In the early stage of the severe acute respiratory syndrome (SARS)-CoV-2 coronavirus (COVID-19) outbreak in March 2020, a series of restrictions were released to minimize the exposure and spread of the communicable virus. These restrictions had significant impacts on the mobility and safety of transportation networks. The objective of this study is to assess the causal effects of the COVID-19 restrictions on traffic crashes on interstate highways. Specifically, compared with previous years, the study is to understand whether the COVID-19 outbreak has increased the rate of traffic crashes on Interstate 80 in Nebraska. A comparative interrupted time series (CITS) model was established to examine the week-to-week differences in traffic crashes before, during, and after the implementation of the COVID restrictive measures in Nebraska. Traffic crash data and traffic count data on Interstate 80 for 33 weeks, from Day 1 to Day 183, of 2020 were used. Meanwhile, the same periods of data in 2018 and 2019 were also included as a control group in the models. Results show that the COVID-19 restrictive measures were associated with significant, immediate, positive effects on crashes (i.e., crash reduction or mitigation) with no or minor injuries, crashes with single or two vehicle(s), and crashes with one injury involved. The findings contribute to the understanding of the impact of COVID-19 interventions on interstate highway safety when future outbreaks of COVID-19 or other pandemics become possible.

Evaluation of the Pavement Distress and its Impact on the Sustainability of the Traffic Operation for Selected Roads in Al-Diwaniyah City

Roads and transportation infrastructure are critical assets for supporting political stability as well as economic and sustainable expansion in developing countries. Yet, pavement maintenance backlogs and the high capital expenditures of road rehabilitation need the adoption of pavement evaluation methods to ensure the best value for the investment. road maintenance is considered one of the main factors for preserving the road structurally and functionally, and before carrying out maintenance, pavement defects must be identified by evaluating the condition of the pavement to perform the appropriate maintenance. In this study, the Pavement Condition Index (PCI) method was relied upon to evaluate roads in Al-Diwaniyah City. Where several data were collected for the study, including the number of crashes for each region, the number of lanes, street width, the presence of a median, and the pavement condition evaluated for the regions with the highest crash rate, which are Al-Askari District with 707 crashes, Al-Urouba District with 379 crashes, Al-Wahda District with 271 crashes, Al-Jamhouri District with 235 crashes, and Al-Jazaer District with 212 crashes. This study is considered the first study to evaluate roads in Al-Diwaniyah city. The results of the study showed that the condition of the roads ranged from poor to satisfactory and there is no road reach to serious and failed. The results showed that the worst road is Al-Askari District, which lacks a lot of maintenance, at a rate of PCI 52.1. It is followed by Al-Wahda District with a PCI rate of 57.6, Al-Jamhouri District with a PCI rate of 67.2, Al-Urouba District with a PCI rate of 70.7, and Al-Jazaer District with a PCI rate of 73.7. The deterioration of the pavement surface condition affects the rash rate, and it is more dangerous when it is correlated to other road factors. Additionally, it was noted that the most prominent defects in the pavement surface of the five evaluated roads are: potholes, rutting, longitudinal and transverse cracks, corrugation, patching, raveling, depressions, and alligator cracks. The study recommended conducting more research on evaluating more roads in Al-Diwaniyah City on an annual basis and conducting periodic maintenance in order to improve the status of this city, which suffers from neglect in all respects, measures must be taken to enhance the environment and economy, sustain the streets, reduce maintenance costs, ensure traffic safety, and protect the lives of citizens.

Effects of tougher school zone laws on road traffic safety in school zones for children in South Korea

IntroductionNew school zone laws in Korea were enforced in March 2020 to increase penalties for traffic violations and preferentially install road safety equipment including automated traffic enforcement devices in school zones. This study aimed to evaluate the effectiveness of tougher school zone laws on school-zone-related road traffic outcome (RTO) rates for children aged 0–12 years in Korea. MethodsMonthly child RTO rates in school zones were calculated per million vehicles and per million children using data on police-reported road traffic crashes, registered motor vehicles, and registered resident population between 2015 and 2021: the intervention series involved school-zone-related rates and the non-equivalent control series involved total and non-school-zone-related rates. The effectiveness of the laws on RTO rates in school zones was assessed using the Bayesian structural time-series models under the controlled interrupted time-series design. ResultsThe results found that considering the trends in the overall and non-school-zone-related RTO rates, enforcement of the laws did not significantly reduce any school-zone-related RTO rates for children aged 0–12 years, both per million vehicles and per million children: equivalent fatality, road traffic injury (RTI), severe RTI, minor RTI, and road traffic crash rates. ConclusionsThe ineffectiveness of Korea's new school zone laws on child RTO rates in school zones implies that other effective measures need to be accompanied to improve children's road traffic safety in school zones, rather than just focusing on raising the severity of punishment. Further research is needed to ensure the effectiveness of school zone laws and measures to mitigate child road traffic casualties in school zones.

Learning With a Supervisor Who has Traffic Offences and Young Driver Crashes: The DRIVE Study 13-Year Follow-Up

PurposeYoung learner drivers commonly must record substantial supervised practice driving before independent licensure. Supervisory driver requirements can be limited or highly regulated, yet research is lacking on the effectiveness of different approaches. The current objective was to explore whether young drivers who were mostly supervised by someone who they perceived had traffic offences versus no offences had different crash records over a period of 13 years postlicensing. MethodsDRIVE is an Australian prospective cohort study of more than 20,000 drivers who were aged 17–24 years and newly licensed during 2003–2004. They completed detailed baseline questionnaires, including whether the person they identified as supervising their learner driving the most had perceived traffic offences in the past 12 months. Responses were linked to their state crash, hospitalization, and death records to 2016. A parametric survival model was created to calculate hazard ratios of time to crash for those reporting that their supervisor had 0 versus 1 and 0 versus 2+ perceived offences, adjusting for the participants' prior crash history and other covariates. ResultsAfter adjusting for covariates, 369 participants reporting supervisory drivers with 2+ perceived offences, compared to 15,451 participants reporting no such offences, had up to 1.67 (95% confidence interval 1.10–2.53 at 6 months) times the rate of any crash for the first 2.5 years and up to 2.01 (95% confidence interval 1.26–3.19 at 3.5 years) times the rate of crashes resulting in injury for 5.5 years. DiscussionAlthough overall supervision by a driver with two or more perceived offences was low, further attention is needed to ensure improved supervised driving experiences, with mentoring programs and professional instructor partnerships worthy of exploration.

Assessing the effects of geometric layout and signing on drivers’ behavior through work zones

Work zones are commonly deployed within or near the traveled way during roadway maintenance, repair, and construction works. Work zones could translate into hazardous spots requiring higher driver attention and possibly leading to increased crash rates and crash severity. This research addresses the effects of various design elements on drivers' behavior through work zones. Chekka diversion, currently implemented along the main Coastal Highway M-51 in Lebanon, is analyzed as it currently presents numerous non-standard design elements. A proposed standard diversion design is compared to the current non-standard diversion layout. A driving simulator has been used to model the current and the proposed diversions. Four driving scenarios involving the combination of the two diversion designs under different visibility conditions are used to evaluate drivers' behavior and responses through the diversion zones. The simulator study results showed that the proposed work zone geometry significantly improved the drivers' behavior compared to the exiting diversion. The visibility conditions have proven to have a more profound effect on driver behavior through the current non-standard diversion design. The pavement markings used along the proposed diversion reduce the vehicles' lateral fluctuations by 50% compared to the current design. The recorded driving patterns prove useful for work zone design to minimize the Lebanese drivers' risky behavior and enhance road safety.

How Do Young Provisional Drivers Who Crash Early Compare With Those Who Only Crash Later?

Much attention has been given to the elevated crash rates of young novice drivers when first driving unsupervised, particularly in the initial six months, but less to the question of whether those who crash during that period are in any way different from those who only crash subsequently. This question was examined in a large cohort of New South Wales novices aged under 25 years at their first licensing, which occurred in the financial year 2007/08. Only post-learner drivers were considered, and divided into three driver groups, early crashers within the first six months, late crashers whose first crash was later, and non-crashers. All factors derivable from the routine records provided were analysed including metropolitan residence and aggregate socio-economic status as determined by post-code of residence. Overall, the largest differences were between those who crashed and those who did not. Females were under-represented among both early and late crashers. Age, aggregate socio-economic status, and metropolitan residence at first licensing did not differ between early and late crashers, but late crashers had spent less time driving supervised. Early crashers had crashed and incurred traffic citations, including for serious offences, more frequently than late crashers, with more crashes judged at-fault. However, exclusion of offences occurring on the same day as a crash reduced the difference between early and late crashers. The difference between crashers and non-crashers was reduced but remained significant.

Mixed-Effects Zero-Inflated Negative Binomial Crash Predictive Models for Unsignalized Intersections along Two-Lane Highways with Minor Roads Operating with Very Low Traffic Volumes

Current and past investigations into road accidents have mainly focused on high-volume roads which have experienced a significant number of collisions. However, low-volume roads also record crash data which merit analysis to understand the causes of accidents and find potential countermeasures. This study focuses on unsignalized intersections linking major highways and minor stop-controlled local roads that operate with very low traffic volumes. In this scenario, an over-abundance of zero observations has to be accounted for. To address this issue, zero-inflated Poisson and negative binomial regression models have been calibrated and analyzed to find the geometric and operational factors which contribute most to the crash rate. Random effects have also been considered to account for unobserved heterogeneity inherent in the grouping of intersections within the database. The results indicate that the mixed-effect negative binomial model had the best prediction performance. Developing models for subgroups of crashes, for example, failure-to-yield and other types of crash, resulted in better prediction performance. The major road’s average annual daily traffic and the surface type (i.e., paved or unpaved) of minor road approaches had a significant effect on the frequency of all crash types. The type of intersection (three- or four-legs), the skew angle of intersection, deviation angle of near curves, and operating speed differences also had statistically significant effects on the frequency of crashes that do not include the failure-to-yield.