Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance

Clustering framework to identify traffic conflicts and determine thresholds based on trajectory data

Traffic conflict indicators are essential for evaluating traffic safety and analyzing trajectory data, especially in the absence of crash data. Previous studies have used traffic conflict indicators to predict and identify conflicts, including time-to-collision (TTC), proportion of stopping distance (PSD), and deceleration rate to avoid a crash (DRAC). However, limited research is conducted to understand how to set thresholds for these indicators while accounting for traffic flow characteristics at different traffic states. This paper proposes a clustering framework for determining surrogate safety measures (SSM) thresholds and identifying traffic conflicts in different traffic states using high-resolution trajectory data from the Citysim dataset. In this study, unsupervised clustering is employed to identify different traffic states and their transitions under a three-phase theory framework. The resulting clusters can then be utilized in conjunction with surrogate safety measures (SSM) to identify traffic conflicts and assess safety performance in each traffic state. From different perspectives of time, space, and deceleration, we chose three compatible conflict indicators: TTC, DRAC, and PSD, considering functional differences and empirical correlations of different SSMs. A total of three models were chosen by learning these indicators to identify traffic conflict and non-conflict clusters. It is observed that Mclust outperforms the other two. The results show that the distribution of traffic conflicts varies significantly across traffic states. A wide moving jam (J) is found to be the phase with largest amount of conflicts, followed by synchronized flow phase (S) and free flow phase(F). Meanwhile, conflict risk and thresholds exhibit similar levels across transitional states

Accident Analysis and Prevention: Course Notes 1987/88

This report consists of the notes from a series of lectures given by the authors for a course entitled Accident Analysis and Prevention. The course took place during the second term of a one year Masters degree course in Transport Planning and Engineering run by the Institute for Transport Studies and the Department of Civil Engineering at the University of Leeds. The course consisted of 18 lectures of which 16 are reported on in this document (the remaining two, on Human Factors, are not reported on in this document as no notes were provided). Each lecture represents one chapter of this document, except in two instances where two lectures are covered in one chapter (Chapters 10 and 14). The course first took place in 1988, and at the date of publication has been run for a second time. This report contains the notes for the initial version of the course. A number of changes were made in the content and emphasis of the course during its second run, mainly due to a change of personnel, with different ideas and experiences in the field of accident analysis and prevention. It is likely that each time the course is run, there will be significant changes, but that the notes provided in this document can be considered to contain a number of the core elements of any future version of the course

Estimation of Safety Performance Measures from Smartphone Sensors

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Rural expressway intersection safety treatment evaluations

A rural expressway is a high-speed, multi-lane, divided highway with partial access control which may consist of both at-grade intersections and grade separated interchanges. Many State Transportation Agencies (STAs) are converting rural two-lane undivided highways into expressways for improved safety and mobility; however, collisions at two-way stop-controlled (TWSC) intersections (particularly far-side right-angle crashes) on rural expressways are reducing the safety benefits that should be achieved through conversion. When the safety performance of these intersections begins to deteriorate, the improvement path typically begins with the application of several signing, marking, or lighting improvements, followed by signalization, and ultimately grade separation. Because signals hamper the mobility expressways are meant to provide and because interchanges are not economically feasible at all problematic intersections, there is a need for more design options at TWSC rural expressway intersections. Some STAs have experimented with innovative rural expressway intersection safety treatments to avoid signalization and grade separation; however, little is known about the safety effects of these designs. Therefore, the objective of this research was to document their experience with these treatments and to conduct nayve before-after safety evaluations where possible. The ten case studies included within this thesis investigate J-turn intersections, offset T-intersections, jughandle intersections, Intersection Decision Support (IDS) technology, static roadside markers, left-turn median acceleration lanes (MALs), offset right-turn lanes, offset left-turn lanes, enhanced intersection guide signing, and dynamic advance intersection warning systems. These case studies help to begin to understand the safety improvement potential of these countermeasures and start to set the stage for the development of a richer set of design options at TWSC rural expressway intersections

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated

Effects of Highway Geometric Features and Pavement Quality on Traffic Safety

Curbs are commonly used on roadways to serve for drainage managing, access controlling and other positive functions. However, curbs may also bring about unfavorable effects on drivers’ behavior and vehicles’ stability when hitting curbs, especially for high-speed roadways. In addition, numerous pavements have been experiencing wear off rapidly in recent decades, which significantly affected driving quality. However, previous study of pavement management factors as related to the happening and outcome of traffic-related crashes has been rare. The objective of this dissertation is to evaluate the influences of outside shoulder curbs, pavement management factors, and other tradition traffic engineering factors on the occurrence and outcome of traffic-related crashes. The Illinois Highway Safety Database from 2003 to 2007 and the Tennessee crash data from 2004 to 2009 was employed in this research. A few advantage statistics models were built to study the effects of curbs, pavement quality and other typical factors on both the occurrence and the outcome of crashes. These models include: the Zero-Inflated Negative Binomial models (ZINB), the Zero-Inflated Ordered Probit (ZIOP) model, the random effect Poisson and Negative Binomial model, as well as the Bayesian Ordered Probit (BOP) model. The findings of this study suggest that the employment of curbed outside shoulders on high-speed roadways would not pose any significantly harmful effect on the occurrence of crashes. On high-speed roadways with curbed outside shoulders in terms of the crash frequency, reducing speed limit from55 mphto45 mphwould not achieve any safety benefit. Crashes occurring on roadways with curbed outside shoulders are more likely to be no and minor injury related as compared to crashes on roadways without curbs. The increase of speed limit from 45 to 55 has relatively small effects on single vehicle crashes occurring on roadways with curbed outside shoulders. Rough pavements were associated with higher overall crash frequency but lower level of injury severity given that a two vehicle involved rear-end, head-on and angle crash has occurred. Pavements with more severe distress were related to lower crash frequency

Relationship between Road Network Characteristics and Traffic Safety

The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The high-risk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections

A NEW SIMULATION-BASED CONFLICT INDICATOR AS A SURROGATE MEASURE OF SAFETY

Traffic safety is one of the most essential aspects of transportation engineering. However, most crash prediction models are statistically-based prediction methods, which require significant efforts in crash data collection and may not be applied in particular traffic environments due to the limitation of data sources. Traditional traffic conflict studies are mostly field-based studies depending on manual counting, which is also labor-intensive and oftentimes inaccurate. Nowadays, simulation tools are widely utilized in traffic conflict studies. However, there is not a surrogate indicator that is widely accepted in conflict studies. The primary objective of this research is to develop such a reliable surrogate measure for simulation-based conflict studies. An indicator named Aggregated Crash Propensity Index (ACPI) is proposed to address this void. A Probabilistic model named Crash Propensity Model (CPM) is developed to determine the crash probability of simulated conflicts by introducing probability density functions of reaction time and maximum braking rates. The CPM is able to generate the ACPI for three different conflict types: crossing, rear-end and lane change. A series of comparative and field-based analysis efforts are undertaken to evaluate the accuracy of the proposed metric. Intersections are simulated with the VISSIM micro simulation and the output is processed through SSAM to extract useful conflict data to be used as the entry into CPM model. In the comparative analysis, three studies are conducted to evaluate the safety effect of specific changes in intersection geometry and operations. The comparisons utilize the existing Highway Safety Manual (HSM) processes to determine whether ACPI can identify the same trends as those observed in the HSM. The ACPI outperforms time-to-collision-based indicators and tracks the values suggested by the HSM in terms of identifying the relative safety among various scenarios. In field-based analysis, the Spearman’s rank tests indicate that ACPI is able to identify the relative safety among traffic facilities/treatments. Moreover, ACPI-based prediction models are well fitted, suggesting its potential to be directly link to real crash. All efforts indicate that ACPI is a promising surrogate measure of safety for simulation-based studies