Traffic Safety

CERIN, E., B. E. SAELENS, J. F. SALLIS, and L. D. FRANK. Neighborhood Environment Walkability Scale: Validity and Development of a Short Form. Med. Sci. Sports Exerc., Vol. 38, No. 9, pp. 1682-1691, 2006. Purpose: The aim of this study was to examine the factorial and criterion validity of the Neighborhood Environment Walkability Scale (NEWS) and to develop an abbreviated

Mega elliptical roundabouts are considered a new kind of intersection and can be found on rural multilane highways. Their form is that of an elongated ellipse. They combine the best advantages of an unconventional median U-turn intersection and roundabout. The purpose of this research is analyzing safety performance for mega elliptical roundabout interchange using the VISSIM software version 4.30 and the surrogate safety assessment model (SSAM) software version 3. This research also considered eight other interchange designs to compare them with the mega elliptical roundabout interchange. In total, 20 scenarios were analyzed using VISSIM version 4.30 then tested by SSAM software version 3. To compare the different interchanges in terms of the mean values of conflicts, the one-way analyses of variance (ANOVA) were conducted. Based on the research results, a mega elliptical roundabout interchange was one of the best interchanges studied in this research in relation to the frequency of conflicts as a total and the frequency of crossing conflicts.

This paper reviews theoretical and empirical evidence relating to the effectiveness of fear (threat) appeals in improving driver safety. The results of the review highlight the mixed and inconsistent findings that have been reported in the literature. While fear arousal appears important for attracting attention, its contribution to behaviour change appears less critical than other factors, such as perceptions of vulnerability and effective coping strategies. Furthermore, threatening appeals targeting young males (a high-risk group of concern) have traditionally relied on the portrayal of physical harm. However, the available evidence questions the relevance, and hence effectiveness, of strong physical threats with this group. Consequently, further research is required to determine the optimum way to utilise fear in road safety advertising, as well as the type of threat(s) most effective with different road users.

Since the STS-114 mission in August of 2005, the FAA has partnered with NASA to protect aircraft flying in the National Airspace System from the potential hazards associated with a catastrophic failure of a reentering Space Shuttle orbiter, similar to that which occurred during ...

In 2004, the Michigan Department of Transportation (MDOT) began a comprehensive program to implement engineering countermeasures to address the needs of older drivers. The countermeasures included the use of Clearview Font on Guide Signs (freeway and non-freeway), installation of Box Span signals, installation of pedestrian countdown signals, use of Fluorescent Yellow Sheeting on warning signs, and use of arrow-per-lane on guide signs. This study aimed at evaluating safety benefits of the selected countermeasures. Evaluation was performed through a perception survey of Michigan drivers and analysis of crash data. Safety Performance Functions (SPF) for these improvements were developed as part of the study. Crash Modification Factors (CMF) for each countermeasure were developed through the before-after analysis of crash data. Finally, the benefit cost analysis of each countermeasure was performed and is documented in this report.

As use of handheld multimedia devices has exploded globally, safety experts have begun to consider the impact of distraction while talking, text-messaging, or listening to music on traffic safety. This study was designed to test how talking on the phone, texting, and listening to music may influence pedestrian safety. 138 college students crossed an interactive, semi-immersive virtual pedestrian street. They were randomly assigned to one of four groups: crossing while talking on the phone, crossing while texting, crossing while listening to a personal music device, or crossing while undistracted. Participants distracted by music or texting were more likely to be hit by a vehicle in the virtual pedestrian environment than were undistracted participants. Participants in all three distracted groups were more likely to look away from the street environment (and look toward other places, such as their telephone or music device) than were undistracted participants. Findings were maintained after controlling for demographics, walking frequency, and media use frequency. Distraction from multimedia devices has a small but meaningful impact on college students’ pedestrian safety. Future research should consider the cognitive demands of pedestrian safety, and how those processes may be impacted by distraction. Policymakers might consider ways to protect distracted pedestrians from harm and to reduce the number of individuals crossing streets while distracted.► Talking, texting, or listening to music may impact college student pedestrian safety. ► 138 students randomly assigned to cross a virtual street undistracted or distracted. ► Music or texting distraction caused greater likelihood of collision with vehicles. ► All distracted individuals looked away from the street environment more often. ► Multimedia device distraction has small but meaningful impact on pedestrian safety.

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot inte...

The use of driver behavior has been considered a complex way to solve road safety complications. Car drivers are usually involved in various risky driving factors which lead to accidents where people are fatally or seriously injured. The present study aims to dissect and rank the significant driver behavior factors related to road safety by applying an integrated multi-criteria decision-making (MCDM) model, which is structured as a hierarchy with at least one 5 5 (or bigger) pairwise comparison matrix (PCM). A real-world, complex decision-making problem was selected to evaluate the possible application of the proposed model (driver behavior preferences related to road safety problems). The application of the analytic hierarchy process (AHP) alone, by precluding layman participants, might cause a loss of reliable information in the case of the decision-making systems with big PCMs. Evading this tricky issue, we used the Best Worst Method (BWM) to make the layman's evaluator task easier and timesaving. Therefore, the AHP-BWM model was found to be a suitable integration to evaluate risky driver behavior factors within a designed three-level hierarchical structure. The model results found the most significant driver behavior factors that influence road safety for each level, based on evaluator responses on the driver behavior questionnaire (DBQ). Moreover, the output vector of weights in the integrated model is more consistent, with results for 5 5 PCMs or bigger. The proposed AHP-BWM model can be used for PCMs with scientific data organized by traditional means.

Rechtlichen Anforderungen an automatisiertes Fahren basieren unter anderem auf dem Wiener Übereinkommen über den Straßenverkehr von 1968, das einen Rechtsrahmen für nationale Straßenverkehrsgesetze vorgibt. In Deutschland erfolgte mit Wirkung vom 21. Juni 2017 die Ergänzung der Paragraphen 1a und 1b in das Straßenverkehrsgesetz (StVG). Entsprechend dem § 1a Absatz 1 StVG ist der Betrieb eines Kraftfahrzeugs „mittels hoch- oder vollautomatisierter Fahrfunktion“ grundsätzlich zulässig, unter der Bedingung, dass die Funktion bestimmungsgemäß verwendet wird. Wegen der Bedeutsamkeit des Themas hatten sich 2015, 2018 und 2019 Arbeitskreise beim deutschen Verkehrsgerichtstag für Empfehlungen zum automatisierten Fahren ausgetauscht.
Hier wurde auch auf den bereits mehrfach von dem Juristen Prof. Dr. Dr. Hilgendorf zitierten „Aschaffenburger Fall“ von Januar 2012 hingewiesen. Dabei wurde davon ausgegangen, dass ein wegen Schlaganfall bewusstloser Fahrzeugführer durch einen aktivierten Lenkassistent in einen Ort eingefahren ist und im Anschluss zwei Menschen getötet hat. Das Fahrzeug entsprach dem Stand der Technik und Hilgendorf begrenzt in diesem konkreten Fall die Sorgfaltspfl icht des Herstellers durch den Grundsatz des erlaubten Risikos. Im Rahmen der Produktbeobachtungspflicht müssen jedoch seiner Meinung nach zukünftige Neuentwicklungen Konsequenzen aus diesem nun bekannten Verkehrsunfall ziehen. Als weiteres Beispiel wurde der tödliche Unfall in Tempe im US-Staat Arizona angesprochen, bei dem ein Uber-Testfahrzeug im selbstfahrenden autonomen Modus mit einer Fußgängerin und ihrem Fahrrad kollidierte. Es handelt sich dabei um den ersten aufgezeichneten tödlichen Fußgängerunfall mit einem selbstfahrenden Uber-Testfahrzeug. Auf der Basis von veröffentlichten Fotos des Deformierten Volvo XC90, der Unfallstelle mit den Endlagen konnte vom Verfasser eine Unfallrekonstruktion mit PC-Crash angefertigt werden. Unter Annahme einer Geschwindigkeit von 64 km/h (17,8 m/s) und einer sofort wirkenden Notbremsung 1,3 Sekunden vor Kollision mit einer Verzögerung von 6,8 m/s2 wäre der Unfall vermieden worden.
Als Basis für systematische Unfallanalysen mit künstlicher Intelligenz und Risikobewertungsmethoden (beispielsweise AcciMap, das Systems-Theoretical Accident and Process Model - STAMP und die Functional Resonance Analysis Method - FRAM) im Rahmen der Produktbeobachtung, dem Freigabeprozess und der Entwicklung von automatisierten empfiehlt sich eine flächendeckende Speicherung der Unfall-, Verkehrsfluss-, Wetter- und Fahrzeugbetriebsdaten.

Legal requirements for automated driving are based, amongst others, on the Vienna Convention on Road Traffic of 1968, which provides a legal framework for national road traffic laws pretends. In Germany, paragraphs 1a and 1b were added to the Road Traffic Act (StVG) with effect from 21 June 2017. According to Section 1a, Paragraph 1 of the StVG, the operation of a motor vehicle "by means of a highly or fully automated driving function" is generally permissible, provided that the function is used as intended. Due to the importance of the topic, working groups had exchanged recommendations for automated driving at the German Traffic Court Conference in 2015, 2018 and 2019.
Here reference was also made to the "Aschaffenburg case" of January 2012, which has already been quoted several times by the lawyer Prof. Dr. Dr. Hilgendorf. This case was based on the assumption that a driver who was unconscious due to a stroke had entered a town by means of an activated steering assistant and subsequently killed two people. The vehicle was state of the art and Hilgendorf limited the manufacturer's duty of care in this specific case by the principle of permissible risk. In his opinion, however, future new developments must draw consequences from this now known traffic accident within the framework of the product monitoring obligation. As a further example, the fatal accident in Tempe in the US state of Arizona was mentioned, in which an Uber test vehicle collided with a pedestrian and her bicycle in self-propelled autonomous mode. This is the first recorded fatal pedestrian accident involving a self-propelled Uber test vehicle. On the basis of published photos of the deformed Volvo XC90, the accident site with the end positions, the author was able to make an accident reconstruction with PC-Crash. Assuming a speed of 64 km/h (17.8 m/s) and an immediately effective emergency braking 1.3 seconds before collision with a deceleration of 6.8 m/s2 the accident would have been avoided.
As a basis for systematic accident analyses and risk assessment or Artificial Intelligence methods (e.g. AcciMap, the Systems-Theoretical Accident and Process Model - STAMP and the Functional Resonance Analysis Method - FRAM) in the context of product monitoring, the release process and the development of automated ones, it is recommended that accident, traffic flow, weather and vehicle operating data be stored area-wide.

For more than five decades, wrong-way driving (WWD) has been notorious as a traffic safety issue for controlled-access highways. Numerous studies and efforts have tried to identify factors that contribute to WWD occurrences at these sites in order to delineate between WWD and non-WWD crashes. However, none of the studies investigate the effect of various confounding variables on the injury severity being sustained by the at-fault drivers in a WWD crash. This study tries to fill this gap in the existing literature by considering possible variables and taking into account the ordinal nature of injury severity using three different ordered-response models: ordered logit or proportional odds (PO), generalized ordered logit (GOL), and partial proportional odds (PPO) model. The findings of this study reveal that a set of variables, including driver's age, condition (i.e., intoxication), seatbelt use, time of day, airbag deployment, type of setting, surface condition, lighting condition, and type of crash, has a significant effect on the severity of a WWD crash. Additionally, a comparison was made between the three proposed methods. The results corroborate that the PPO model outperforms the other two models in terms of modeling injury severity using our database. Based on the findings, several countermeasures at the engineering, education, and enforcement levels are recommended.

This paper presents the results of an online survey of licensed driver comprehension of the right-turn signal displays with a focus on the flashing yellow arrow (FYA) and also including the circular green and red and red arrow. Recruitment postcards were mailed to a random sample of 9,872 residents in Oregon. The online survey yielded 399 responses. The open-ended responses were coded for comprehension and analyzed. The results suggest that FYA for right turns is well understood by Oregon drivers despite its current novelty (only two locations at the time of the research). Importantly, survey respondents were more likely to recognize the yielding requirement of the permissive movement and associate the yielding with pedestrians with the FYA over the circular green (CG) display. The research also confirmed that the expected driver response to the red arrow display for right turns is not well understood (only 52% of the respondents correctly stated the expected driver response). Binary logistic regression modeling revealed that the driver's age and their educational level were significant factors in comprehension.

This thesis describes the development of models for predicting accidents at the intersections in a road network, from the turning traffic volumes at each intersection. The accident prediction models were developed using Poisson and negative binomial regression for each of the major accident types, at each of the major intersection types. Countrywide models that predict the motor vehicle accidents (accidents involving motor vehicles only) in a five year period, from the product of the conflicting traffic volumes were developed for each accident type. Accident prediction models have also been developed to predict the number of motor vehicle accidents occurring in different periods of the day (eg. the morning peak, 7am to 9am) and in different urban centres.
This thesis also describes three case studies, where the accident prediction models have been used to predict the total number of accidents (major accident types) at the intersections in
three road networks. Case studies were performed on the Christchurch Southern Arterial network, Christchurch Central network and the Lower Hutt network. For the two latter networks the total number of intersection accidents predicted was quite similar to that observed. In the Southern Arterial network, however, the total number of intersection accidents was under-predicted considerably

Distracted driving behavior is a perennial safety concern that affects not only the vehicle's occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index is applied in this study. The coefficient of determination ( R2) and Nash–Sutcliffe model efficiency coefficient ‘ E’ values are found to be 0.92, 0.91 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under ...

The growing population and motorization generate more movements. In many cities, the increase of population and motorization is much greater than the development of the capacity of the transportation network. For unprotected road users, the risk of getting in a traffic accident increases and the risk of being more severely injured in an accident. In March 2020, a pandemic was declared because of a Coronavirus. More people started to work/study from home to prevent the virus from spreading by avoiding unnecessary trips, gatherings, and crowded areas. Therefore, travel behaviours have shifted during the pandemic compared to previous years. This project aims to get knowledge of how mobility and traffic accidents are affected by significant shifts of travel flow, predict the effect of traffic accidents based on mobility, and evaluate the risk of travelling on a particular road segment.

Mobility data has been collected from Google Mobility, Apple Mobility, the Environmental Barometer, Trafikkontoret, and traffic accident data collected from STRADA. Mobility and traffic accident data have been analysed using Excel, QGIS, and PTV Visum Safety. The accident rates have been calculated to determine if the accident rate has changed during the pandemic, and three scenarios for 2021 have been predicted. A risk analysis model has also been used to calculate the risk of being involved in an accident on particular streets using a car, cycle, or walking.

It was found that mobility has decreased, and the usage of transportation modes has shifted. During the pandemic, it has been more popular to cycle, which is also reflected in the traffic accident data, where the percentages of cyclists being involved in traffic accidents have increased. No matter the degree of injury or transportation mode, the total number of traffic accidents had decreased in 2020. However, the number of severe accidents is almost the same as in previous years. Males are overrepresented in traffic accidents, and the differences are even more significant for 2020. In 2020, the travel speed on the roads increased, which might be due to decreased traffic volume, making it possible to drive faster. The percentage of accidents involving alcohol also increased. The results from the risk analysis show whether the risk of getting into a traffic accident on a particular street using a specific transportation mode has increased or decreased depending on the street and transportation mode. Three scenarios (better, same, worse) of the risk of travelling on the particular road stretches in 2021 have also been calculated.

To make better predictions, additional years should be considered. For future work, it would also be interesting to consider the weather since it greatly impacts the transportation mode used and the risk of accidents. The infrastructure was not considered in this project, which would be interesting since the transportation mode, route used, and speed might depend on the infrastructure and current constructions.

Abstract
Knowledge of crash causes is important because it directs the mind to the consideration of potential prevention actions and  because knowledge of the frequency with which various causes arise in crashes is necessary for determining the promise of potential prevention actions. Clinical crash causation studies consistently found that in the majority of crashes the road user was the sole cause and that in almost all crashes the road user was one of the causes. This is a ‘quasi-finding’ which provides false respectability to a style of road safety management that makes the road-user the primary target of prevention actions. For the knowledge obtained by clinical crash causation studies to be useful ‘cause’ has appropriately defined.

In this chapter, a survey of the current state of research on autonomous driving is given and is set in the context of the requirements of an autonomous vehicle following the vision of an automated taxi. The overview is based on (scientific) publications and self-reports of the developing teams. Aspects of interest for this summary are approaches on environmental perception, self-perception, mission accomplishment, localization, cooperation, map usage, and functional safety. Typically, emphasis is given to reliance on global satellite systems (e.g., GPS) and map data. Only a few approaches focus on environmental perception and scene understanding. Even though impressive demonstrations of autonomous driving have been presented in recent decades, this overview concludes that many aspects still remain only partially solved or even unsolved, especially when driving autonomously in public road traffic.

In many developing countries motorcycles are used not only for personal travel (commuting, leisure, etc.) but also to deliver documents and small commodities (e.g., food) or to provide taxi services. Incomes of these commercial motorcyclists are generally based on the number of services they are able to provide within a given amount of time. Some have suggested that this incentive might induce commercial motorcyclists to engage in risky behaviors – that is they might be more likely to drive fast and break traffic laws in order to maximize income. To empirically test for differences between commercial and non-commercial motorcyclists, I estimated a logistic regression model using data from a survey conducted by the Development Bank of Latin America (CAF) between 2012 and 2013 in Bogota and Barranquilla, Colombia. My results support the hypothesis that motorcycles used for commercial activities and whose income is generally based on the number of services they are able to provide are at a higher risk of being involved in a crash. The probability of being involved in a crash is higher when using motorcycles for taxi services than when using motorcycles for non-commercial purposes or for commercial activities other than courier or taxi services (after controlling for exposure, demographic characterizes, vehicle features, and other possible explanatory variables, though this finding may be affected by a possible reporting bias by those providing (illegal) taxi services). By contrast, motorcyclists who provide courier services have neither a higher nor lower probability of being involved in a crash compared to those riding motorcycles for non-commercial purposes or for commercial activities other than courier or taxi services. Other variables associated with crash occurrence include age, owning the motorcycle, and riding in Bogota. While I see evidence that the financial incentives of being paid per job motivate risky driving behaviors among commercial motorcyclists, better data and further research are needed to fully understand the causes of differential crash risks among types of motorcyclists.

Accident is not an act of God; but it is a man-made epidemic which is amenable by rational analysis and countermeasures. There is no difference between road traffic injuries and other diseases. Road traffic crashes are fast growing relative to other diseases. Though roads are a vital part of any country’s growth and development but with more traffic, more and more traffic crash related injuries and fatalities are eminent. Apart from the human suffering, crashes cost a country a great deal of money, time and effort. Bangladesh is a very densely populated low lying country with more than 140 million inhabitants living in an area of about 147,570 square kilometers. Each year nearly 5000 crashes as reported by police occur in Bangladesh. About 10% of these reported crashes occur on national highways. Of the crashes and fatalities of known locations, the share of national highways is much higher, 56 % and 61% respectively. Among the national highways of Bangladesh, Dhaka-Chittagong highway is considered to be the major crashprone highway. The goal of the paper is to analyze the crash patterns in this highway. It was found that high incidence of crashes are closely related to the inbuilt roads and roadside hazards, not related to weather conditions and sunlight availability. Detailed field observations in two sites of this national highway were carried out to evaluate the extent of hazards and risk conditions associated with the road environment, road users and road vehicles. Most of the crashes at these locations are found to be of fatal types and pedestrians are the most vulnerable group susceptible to road traffic crashes. On the basis of field investigation and analysis, summary of important findings and recommendations (such as providing proper pedestrian facilities, dividers, widening of the highways etc.) to minimize crash numbers and severity have also been presented in this paper work.