Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model.

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver's age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

Safety climate and the distracted driving experiences of truck drivers.

BACKGROUND: For truck drivers, distracted driving is a workplace behavior that increases occupational injury risk. We propose safety climate as an appropriate lens through which researchers can examine occupational distracted driving. METHODS: Using a mixed methods study design, we surveyed truck drivers using the Safety Climate Questionnaire (SCQ) complemented by semi-structured interviews of experts on distracted driving and truck safety. Safety climate was assessed by using the entire SCQ as an overall climate score, followed by factor analysis that identified the following safety climate factors: Communications and Procedures; Management Commitment; and Work Pressure. RESULTS: In multivariate regression, the overall safety climate scale was associated with having ever experienced a crash and/or distraction-involved swerving. Interview participants described how these SCQ constructs could affect occupational distracted driving. CONCLUSION: To reduce distraction-related crashes in their organizations, management can adhere to safe policies and procedures, invest in engineering controls, and develop safer communication procedures.

Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDAR.

The proliferation of motorcycles in urban areas has raised concerns regarding traffic safety. However, traditional sensors struggle to obtain precise high-resolution trajectory data, which hinder the accurate identification and quantification of near-crash risks for takeout delivery motorcycles. To fill this gap, this study presents a novel approach utilizing roadside light detection and ranging (LiDAR) to identify and evaluate the risk of near crashes of takeout delivery motorcycles. First, a trajectory amendment method incorporating speed and steering angle was introduced to enhance the accuracy and continuity of the trajectory prediction. Second, a trajectory prediction method combining the steering intention and a repulsive force model was proposed for near-crash risk prediction. Subsequently, a near-crash identification method was developed that relied on the closest distance and risk radius. Finally, near-crash risk fields were constructed to quantify risk levels by leveraging velocity, position, and weight. The experimental results demonstrated 92.10 % accuracy in intention prediction, with mean absolute error (MAE) and root mean square error (RMSE) values of 0.53 m and 0.45 m, respectively. In addition to its higher accuracy, the proposed method makes it easier to quantify near-crash risk and supports a proactive approach for visualizing and analyzing traffic safety.

Variability in crash and near-crash risk among novice teenage drivers: a naturalistic study.

OBJECTIVE: Using video monitoring technologies, we investigated teenage driving risk variation during the first 18 months of independent driving. STUDY DESIGN: Driving data were collected on 42 teenagers whose vehicles were instrumented with sophisticated video and data recording devices. Surveys on demographic and personality characteristics were administered at baseline. Drivers were classified into 3 risk groups using a K-mean clustering method based on crash and near-crash (CNC) rate. The change in CNC rates over time was evaluated by mixed-effect Poisson models. RESULTS: Compared with the first 3 months after licensure (first quarter), the CNC rate for participants during the third, fourth, and fifth quarters decreased significantly to 59%, 62%, and 48%, respectively. Three distinct risk groups were identified with CNC rates of 21.8 (high-risk), 8.3 (moderate-risk), and 2.1 (low-risk) per 10 000 km traveled. High- and low-risk drivers showed no significant change in CNC rates throughout the 18-month study period. CNC rates for moderate-risk drivers decreased substantially from 8.8 per 10 000 km in the first quarter to 0.8 and 3.2 in the fourth and fifth quarters, respectively. The 3 groups were not distinguishable with respect to personality characteristics. CONCLUSION: Teenage CNC rates varied substantially, with distinct high-, moderate-, and low-risk groups. Risk declined over time only in the moderate-risk group. The high-risk drivers appeared to be insensitive to experience, with CNC rates consistently high throughout the 18-month study period, and the moderate-risk group appeared to learn from experience.

Differences in frequency of occurrence, event characteristics, and pre-impact vehicle kinematics between crashes, near-crashes, and single vehicle conflicts in a large-scale naturalistic driving study.

Objective: Motor vehicle crashes result in egregious personal injury, mortality, and economic cost but are relatively rare in naturalistic observations. There is, however, evidence of strong relationships between crashes and less severe (but more common) "surrogate" events (e.g., near-crashes). Despite this strong relationship, there can still be some important differences in findings when these surrogate events are investigated in lieu of, or combined with, crashes. Therefore, it is relevant to describe and quantify differences between crashes and crash-surrogate events. Consequently, the focus of this investigation was to establish how crashes and crash surrogate events in a large-scale naturalistic driving study compare in terms of frequency of occurrence, event characteristics, and pre-impact vehicle kinematics.Methods: Crashes, near-crashes, and single-vehicle conflicts (SVCs) derived from the Second Strategic Highway Research Program Naturalistic Driving Study were coded to summarize the environmental and contributing variables involved. The original coding for these events was downsized to the variables of interest, and those variables underwent recoding to simplify the coded options. Additional variables based on the kinematic characteristics for each event were also derived and analyzed. Multinomial logistic regression was used to assess the contributions of these different variables toward classification of an event as a crash, near-crash, or SVC.Results: The regression model comparing crashes with near-crashes and SVCs identified several variables that allowed differentiation between crashes and these surrogates, primarily the pre-incident maneuver of the subject vehicle and the evasive maneuver that was executed by the driver. Kinematic variables prior to event onset, however, were not predictive of event outcome.Conclusions: The results suggest that important differences exist between crashes and their near-crash surrogates, and between crashes and SVCs. These results, however, should not discourage the analysis of surrogate events, which still provide useful information in prevention and mitigation of crash circumstances. This investigation highlights how crashes are different from two types of surrogate events and provides information that may allow for more precise analysis of these surrogate events in the future.

Contributing factors to near-miss experiences of motorcyclists in Thailand: A random parameter probit model approach.

Road accidents cause a large number of deaths, especially in Thailand. When considered in depth, motorcycles account for the highest percentage of fatalities. According to Heinrich's Safety Triangle Model, a decrease in near misses will reduce the number of road accidents. There is still a lack of studies on risky behaviors contributing to near misses involving motorcycles. This study aims to comprehend the various factors that influence the frequency of near-miss experiences using a questionnaire on near-miss incidents. The contributing factors include road factors (e.g., road surface, number of traffic lanes, speed limit), environmental factors (e.g., driving at night), and driver factors (e.g., using a phone while driving). Of the 2002 respondents, a total of 1547 people have occasionally experienced a near-miss incident. A random parameter probit model (RPOP) was used for analyzing the relationship between the contributing factors and the near-miss frequency, and model statistics clearly confirm that RPOPs that import only significant variables are the most suitable models. The study found 14 factors that affect near-miss frequency, and there are 5 variables that are random parameters. Variables that increase the chance of a near-miss incident include driving at night (both with and without lights), roads with concrete road surfaces, and roads with unclear lane markings. This study provides policy recommendations for relevant agencies that were identified to reduce near-miss motorcycle accidents.

Causation analysis of crashes and near crashes using naturalistic driving data.

Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.

Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining.

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method - the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g ≤ deceleration rate ≤ -4.5 g) and non-trivial near-crash events (≤-7.5 g). From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial near-crash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events. This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.

The Impact of Commutes, Work Schedules, and Sleep on Near-Crashes during Nurses' Post Shift-Work Commutes: A Naturalistic Driving Study.

OCCUPATIONAL APPLICATIONSDriving and survey data were collected from nurses following the night-shift and analyzed with logistic regression and frequency analysis. The analyses showed that prior near-crashes and drive length contributed significantly to near-crashes. The frequency analysis showed that most near-crashes occurred on major roadways, including principal arterials, major collectors, and interstates, within the first 15 minutes of the drive. These results highlight the urgent need for countermeasures to prevent drowsy driving incidents among night-shift nurses. Specifically, nurses and hospital systems should focus on countermeasures that encourage taking a break on the post work commute and those that can intervene during the drive. This may include the use of educational programs to teach nurses the importance of adequate rest or taking a break to sleep during their drive home, or technology that can recognize drowsiness and alert nurses of their drowsiness levels, prompting them to take a break.

TECHNICAL ABSTRACTBackground Night-shift nurses are susceptible to drowsy driving crashes due to their long working hours, disrupted circadian rhythm, and reduced sleep hours. However, the extent to which work, sleep, and on-road factors impact the nurses' commutes and the occurrence of near-crash events is not well documented.Purpose A longitudinal naturalistic driving study with night-shift nurses from a large hospital in the United States was conducted to measure these factors and analyze the occurrence and location of near-crashes during post-shift commutes.Methods An on-board data recorder was used to record acceleration, speed, and GPS coordinates continuously. Nurses also completed daily surveys on their sleep, work, and commute. Near-crashes were identified from the data based on acceleration thresholds. Data from a total of 853 drives from 22 nurses and corresponding surveys were analyzed using Poisson and negative binomial regressions for swerve and hard brake near-crash events, respectively.Results Swerve events were increased by the length of the drive (RR = 2.59, LL = 1.62, UL = 4.16), and the occurrence of hard brakes (RR = 1.69, LL = 1.45, UL = 1.99), while hard brake events were increased by the occurrence of swerves (RR = 1.55, LL = 1.28, UL = 1.88). The majority of near-crashes occurred on principal arterials (n = 293), minor arterials (n = 71), and interstates (n = 51).Conclusions The results demonstrate the high risk of near-crashes during post-shift commutes, which may present danger to nurses and other drivers, and highlight the need for countermeasures that address shift structures, sleep quality, and taking breaks.

Mining patterns of near-crash events with and without secondary tasks.

The engagement of secondary tasks, like using a phone or talking to passengers while driving, could introduce considerable risks to driving safety. This study utilizes a near-crash dataset extracted from a naturalistic driving study to explore the patterns of near-crash events with or without the involvement of secondary tasks as a surrogate approach to understand the impact of these behaviors on traffic safety. The dataset contains information about driver behaviors, such as secondary tasks, vehicle maneuvers, other conflict vehicles' maneuvers before and during near-crash events, and the driving environment. The patterns for near-crashes with or without the involvement of secondary tasks are mined by adopting the apriori association rule algorithm. Finally, the mined rules for the near-crash events with or without the involvement of the secondary tasks are analyzed and compared. The results demonstrate that near-crashes with the involvement of secondary tasks often occur with drivers in a relatively stable and presumably predictable environment, such as an interstate highway with a constant speed. This type of near-crash is highly associated with the leading vehicle's sudden slowing or stopping since there is no expectation of any interruptions for these drivers performing the secondary tasks. The most common evasive maneuver in this kind of emergency is braking. Near-crashes without the involvement of secondary tasks is often associated with lane-changing behavior and sideswipe incidents. With shorter reaction time and awareness of the driving environment, the drivers in this type of near-crash can often make more complex maneuvers, like braking and steering, to avoid a collision. Understanding the patterns of these two types of near-crash incidents could help safety researchers, traffic engineers, and even vehicle designers/engineers develop countermeasures for minimizing potential collisions caused by secondary tasks or improper lane changing behaviors.

An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor.

PROBLEM: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle-pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. METHOD: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. RESULTS: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.

Assessing driver behavior using shrp2 adverse weather data.

INTRODUCTION: This study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. METHODS: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver's behavior changed from normal driving to inclement-weather driving. RESULTS: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. CONCLUSIONS: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.

A novel skateboarder-related near-crash identification method with roadside LiDAR data.

Skateboarding is being an emerging travel model, especially for young travelers. The conflict between skateboarders and the other road users has raised safety concerns for traffic engineers. Safety evaluation about skateboarder-related conflicts has not been well performed due to the low skateboarder-related crashes and the limited historical crash data. Near-crashes have been considered as surrogate data for skateboard-related safety evaluation. This paper developed a procedure to extract skateboarder-associated near-crashes automatically with the roadside Light Detection and Ranging (LiDAR). A new indicator: distance-deceleration-time profile (DDTP) which combined time, space, and deceleration information was introduced for skateboarder-pedestrian near-crash identification. The DDTP was developed for the roadside LiDAR data specially. The case studies showed that the proposed method can extract skateboarder-pedestrian safety-critical events with high accuracy. The proposed method can be also used for skateboarder-vehicle and skateboarder-bicycle near-crash identification.

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events-Concept of event-based driving volatility.

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. This study focuses on the component of "driving volatility matrix" related to specific normal and safety-critical events, named "event-based volatility." The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

Assessing the relationship between self-reported driving behaviors and driver risk using a naturalistic driving study.

The Manchester Driver Behavior Questionnaire (DBQ) identifies risky driving behaviors resulting from psychological mechanisms. Investigating the relationships between these behaviors and drivers' crash risk can provide a better understanding of the personal factors contributing to the incidence of crashes, allowing the more effective development of safety education and road management countermeasures and interventions. The objectives of this study are therefore: 1) to determine the extent to which driver involvement in both crashes and near crashes (CNCs) is related to self-reported driving behaviors, and 2) to assess the relationship between each type of risky behavior and individual driver CNC risk. Driver and crash data were acquired from the Shanghai Naturalistic Driving Study and included 45 males and 12 females, participants with the mean age of 38.7. A K-mean cluster method was adopted to classify participants into three CNC groups of high-, moderate- and low-risk drivers. Drivers completed the DBQ to self-evaluate the frequency during their daily driving of the questionnaire's 24 risky behaviors. Principal component analysis of the 24 items led to a five-component structure including aggressive violations, ordinary violations, lapses, inattention errors, and inexperience errors. Two logistic regression models were developed to investigate the correlation between the five DBQ components and drivers' CNC levels. Conclusions are as follows: 1) high-risk drivers were significantly more likely to have engaged in inattention errors (e.g., missing a "yield" sign) and ordinary violations (e.g., running a red light) than the other drivers, and, 2) aggressive violations (e.g., racing against others) and ordinary violations were positively related to the probability of being a high- or moderate-risk driver.

Near-miss crashes and other predictors of motorcycle crashes: Findings from a population-based survey.

OBJECTIVE: Crash and injury surveillance studies have identified a range of rider-related factors, including age, sex, licensure, training and experience, as being associated with motorcycle crash risk. The aim of this study was to establish whether these previously identified factors were associated with crash involvement in an Australian-based population. METHODS: Data obtained from motorcyclists recruited from road authority licensing offices in a population-based survey design were analyzed. In addition to descriptive analysis, survey logistic regression was used to examine predictors of self-reported motorcycle crashes. A statewide population prevalence study of motorcyclists in New South Wales, Australia, was conducted using a multistage stratified random sampling plan. Participants (n = 503) represented 47% of eligible riders invited to participate. The distribution of responses was weighted to represent the population based on motorcycle registrations as a proxy for active motorcyclists, adjusted for age, sex, and variations in sample size and population density between survey sites. RESULTS: This analysis investigated factors associated with having crashed in the past 12 months. The key predictors of increased crash risk included frequent near-crash experiences (6-10) in the past year (adjusted odds ratio [ORadj] = 5.3; 95% confidence interval [CI], 1.3-21.8), having 4 or more riding demerit points (ORadj = 4.1; 95% CI, 1.1-14.7), and motorcycle type and riding purpose. Sports (ORadj = 2.8; 95% CI, 1.1-7.3) and commuter motorcycles (ORadj = 4.0; 95% CI, 1.1-15.3) were associated with higher odds of crashes compared to cruiser/touring motorcycles. Those whose purpose for riding frequently involved commuting, high-speed roads, or motorcycle sports had higher odds of being involved in a crash compared to riders who rarely took part in such activities. Rider age, license type, and time holding a motorcycle license were not predictive of crash involvement when other factors were taken into account. CONCLUSIONS: These findings provide important population-level information and insights about risk exposure for motorcyclists. Taking a more tailored approach to data collection meant that factors associated with crash involvement were identified that are not commonly observed in studies relying on administrative data. In particular, the study highlights the importance of near-crash experiences as warnings to riders and the need to use such experiences as learning opportunities to improve their riding style and safety.

Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions.

Rear-end crashes are one of the most frequently occurring crash types, especially in urban networks. An understanding of the contributing factors and their significant association with rear-end crashes is of practical importance and will help in the development of effective countermeasures. The objective of this study is to assess rear-end crash potential at a microscopic level in an urban environment, by investigating vehicle-by-vehicle interactions. To do so, several traffic parameters at the individual vehicle level have been taken into consideration, for capturing car-following characteristics and vehicle interactions, and to investigate their effect on potential rear-end crashes. In this study rear-end crash potential was estimated based on stopping distance between two consecutive vehicles, and four rear-end crash potential cases were developed. The results indicated that 66.4% of the observations were estimated as rear-end crash potentials. It was also shown that rear-end crash potential was presented when traffic flow and speed standard deviation were higher. Also, locational characteristics such as lane of travel and location in the network were found to affect drivers' car following decisions and additionally, it was shown that speeds were lower and headways higher when Heavy Goods Vehicles lead. Finally, a model-based behavioral analysis based on Multinomial Logit regression was conducted to systematically identify the statistically significant variables in explaining rear-end risk potential. The modeling results highlighted the significance of the explanatory variables associated with rear-end crash potential, however it was shown that their effect varied among different model configurations. The outcome of the results can be of significant value for several purposes, such as real-time monitoring of risk potential, allocating enforcement units in urban networks and designing targeted proactive safety policies.

A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data.

Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehicle-pedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehicle-pedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.

Difference between car-to-cyclist crash and near crash in a perpendicular crash configuration based on driving recorder analysis.

Analyzing a crash using driving recorder data makes it possible to objectively examine factors contributing to the occurrence of the crash. In this study, car-to-cyclist crashes and near crashes recorded on cars equipped with advanced driving recorders were compared with each other in order to examine the factors that differentiate near crashes from crashes, as well as identify the causes of the crashes. Focusing on cases where the car and cyclist approached each other perpendicularly, the differences in the car's and cyclist's parameters such as velocity, distance and avoidance behavior were analyzed. The results show that car-to-cyclist crashes would not be avoidable when the car approaching the cyclist enters an area where the average deceleration required to stop the car is more than 0.45 G (4.4 m/s2). In order for this situation to occur, there are two types of cyclist crash scenarios. In the first scenario, the delay in the drivers' reaction in activating the brakes is the main factor responsible for the crash. In this scenario, time-to-collision when the cyclist first appears in the video is more than 2.0 s. In the second scenario, the sudden appearance of a cyclist from behind an obstacle on the street is the factor responsible for the crash. In this case, the time-to-collision is less than 1.2 s, and the crash cannot be avoided even if the driver exhibited avoidance maneuvers.

Driver behavior analysis on rural 2-lane, 2-way highways using SHRP 2 NDS data.

OBJECTIVE: Rural 2-lane, 2-way (RTLTW) highways comprise a large portion of the rural road network in the United States. Crashes on RTLTW roads have been a major concern for traffic safety and may impact traffic significantly. Driver behavior on RTLTW roads has not been analyzed thoroughly. This article analyzed the role of driver behavior fault in crashes/near-crashes on RTLTW roads. METHODS: A total of 467 crash/near-crash records and 160 baseline events (normal driving) were extracted from the Strategic Highway Research Program 2 Naturalistic Driving Study (NDS) database. The contribution of driver behavior to crash risk compared to roadway geometry and environmental factors was ranked using random forest. The influence of different factors on driver behavior fault in a crash/near-crash was further examined using a probit model. The reliability of the model was evaluated using 3 different parameters. RESULTS: It was found that driver behavior fault was the most important factor in crashes that occurred on RTLTW roads. Driver age, traffic density, alignment, presence of an intersection, and hands on wheel were found to be highly related to driver behavior fault. CONCLUSIONS: Young drivers (16-19 years old) were more likely to have driving behavior fault compared to other age groups. The presence of an intersection may also slightly increase the probability of driver behavior fault. The curve on the road can increase the risk of driver behavior fault. Drivers were more likely to have driver behavior fault when no hands were on the wheel. Sites with curves near intersections are locations where drivers are more likely to have behavior fault.