Road Environmental Factors Research Articles

Study on self-healing performance of basalt fiber asphalt concrete in road environment

Load crack is one of the main failure forms of asphalt concrete pavement. Basalt fiber has become an important research direction of road fiber modifier. However, the self-healing performance research of basalt fiber asphalt mixture under different road environment factors are rarely. In this paper, the self-healing performance of asphalt pavement in different road environments is studied by combining the self-healing properties of asphalt and the crack resistance of fiber materials. The self-healing test of basalt fiber asphalt concrete was carried out after long and short aging, ultraviolet aging, snow melting and freeze-thaw cycles treatment. By using the damage rate as the evaluation index, the self-healing characteristics of basalt asphalt pavement were analyzed by the method of “fatigue-self-healing - fatigue” under the influence of different road environment. The experiment also compared the self-healing properties with SBS modified asphalt and matrix asphalt and explained the mechanism of basalt fiber mixture self - healing by electron microscope. The results show that the addition of basalt fiber can greatly improve the self-healing level of asphalt concrete under various road conditions.

Where Do Pedestrians Look When Crossing? A State of the Art of the Eye-Tracking Studies

It has been widely shown in the literature that analysing eye movements and positions can provide useful information for a better understanding of human perception and cognition. The eye-tracking technology, as a process of measuring where people look, has established itself as a widespread means of studying visual information processing in several domains, including in the study of human walking. Street-crossing can be defined as a particular form of walking. Indeed, several elements have to be considered in the decision-making process, such as the distance headway, traffic density, vehicle speed, etc. It is also a very risky aspect of walking as pedestrians are considered one of the most vulnerable road users. In this article, we present an up-to-date comprehensive review of existing eye-tracking experiments in the literature, from the pedestrian's point of view, with a view to study the effects of both internal (e.g., age) and external (e.g., road environment) factors on pedestrians' road crossing gaze behaviour. Furthermore, the current gaps in the literature are then discussed in order to open up some future perspectives in the field, such as the forthcoming introduction of automated vehicles on the roads.

Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Introduction: Digital billboards (DBs) are a competing factor for attracting drivers’ attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers’ attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers’ behavior and measure factors responsible for drivers’ distraction in Babol, Iran, as a case study. Method: Corresponding data were collected through a Naturalistic Driving Study (NDS) of 78 participants when facing DBs (1,326 samples). These data were analyzed by applying structural equation modeling (SEM) to concurrently recognize relationships between endogenous and exogenous variables. Human, environmental, and road factors were determined as exogenous latent variables in a model to evaluate their influences on drivers’ distraction as an endogenous variable. Results: The results showed that road, environmental, and human factors reciprocally interact with drivers’ distraction, although the estimated coefficient of human factors was more of a factor than that of the other groups. Furthermore, younger drivers, beginner drivers, and male drivers (as human factors); night and unclear weather like a rainy day (as environmental factors); and installing DBs at complicated traffic positions like near-intersections (as road factors) were determined to be the main factors that increase the possibility of drivers’ distraction. Finally, model assessment was suggested using the goodness-of-fit indices.

Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data

Speeding is one of the major contributors to traffic crashes. To solve this problem, speeding prediction is recognized as a critical step in a pre-warning system. While previous studies have shown that speeding is affected by road environmental design, research in predicting speeding behavior through road environment features has not yet been conducted. Furthermore, there is a large discrepancy between actual and perceived road environmental information given that a driver’s visual perception plays a crucial role as the dominant source of information in determining driver’s behavior. Thus, this paper aims to establish a speeding prediction model based on quantifying the visual road environment to improve the design of pre-waring systems, which can predict whether drivers are going to speed and provide them with visual or/and audio warnings about their current driving speed and the speed limit prior to the occurrence of speeding behavior. Twenty input variables derived from three categories including visual road environment parameters, vehicle kinematic features, and driver characteristics were considered in the proposed speeding prediction model. Especially, the road environmental design factors consisting of the visual road geometry and visual roadside environment as perceived by the driver’s eyes were quantified using a visual road environment model. Field experiments were conducted to collect naturalistic driving data concerning speeding behavior on the typical two-lane mountainous rural highways in five provinces of China. Random Forests, an ensemble learning method for regression and classification, were applied to build the speeding prediction model and variable importance was calculated. Additionally, logistic regression was used as a supplement to further investigate factors impacting on speeding behavior. A speeding criterion was defined with two levels in this study: a lower level (exceeding the posted speed limit) and a higher level (10% above the posted speed limit). Under both levels of the speeding criterion, the speeding prediction model performed well with high accuracy (over 85%). This model could use the value of the variables obtained from the current position to predict drivers’ speeding behavior at the future position located a sighting distance away. This interval was sufficient for a pre-warning system to give a speeding warning that a driver with normal perception-reaction time (around 2.5 s) could respond to. Findings in this study can be used to effectively predict speeding in advance and help to reduce speeding-related traffic accidents.

Proactive Safety Measure Using Road Environment Assessment Program (REAP)

In Malaysia, reactive crash statistics have becoming very crucial in evaluating road’s safety level and in deciding crash-prone areas known as black spot. The establishments of these statistics normally take years to complete as a result of several well-known setbacks within developing countries. Those obstacles had produced poor crash database having low accessibility, reliability and adequateness of crash data that finally brought major impact to the entire road safety system. In light of that, a proactive safety measure called Road Environment Assessment Program (REAP) has been developed to help evaluating the environment risk factors of a road, calculating the risk index and presented the results through Google Earth platform. REAP was developed based on composite risk index value aggregated from 14 road environment indicators existed in most Malaysia federal roads. Based on the local conditions of the selected roads, specific road environment risk factor were produced where trend and risk level as well as identifications of riskiest road section could be easily identified. REAP is a time-saving and cost-saving tool as it can directly recognize problematic road environment factors while planning on the best and suitable road improvement procedures for problematic sections.

Development of countermeasures to effectively improve pedestrian safety in low-income areas

Abstract In recent years, many departments of transportation in the US have invested more resources to enhance pedestrian safety. However, there is still a need to effectively and systematically address pedestrian safety issues in low-income areas. Statistics show that pedestrians are fatally injured at disproportionately higher rates in the nation's poorer neighborhoods. Low-income areas often are sectioned by high-volume/high-speed arterials, which compound the problem. In this study, pedestrian-vehicle crash data in low-income areas were analyzed from two aspects: how demographic factors, road environment, and land use types influence pedestrian crash occurrence (based on frequency data) and how demographic and other factors influence severity in a pedestrian crash (based on severity data). The crash frequency modeling results show that major influential variables for higher pedestrian crash frequency include four demographic factors (proportions of older adults, commuters using public transit or biking, people with low education level, and zero-car ownership), three road environmental factors (densities of traffic signals and bus stops and proportion of higher-speed roads), and three land use factors (densities of discount stores, convenience stores, and fast-food restaurants). The injury severity modeling results show that a dark–not lighted condition is the most influential variable for severe injury pedestrian crashes, and the number of impaired pedestrians and aggressive drivers also greatly increases the probability of severe injury. Based on the analysis results, this paper makes specific recommendations for both engineering countermeasures and pedestrian safety education/outreach plans that resonate with a given area's demographics to effectively improve pedestrian safety in low-income areas.

The Influence of Different Factors on Right-Turn Distracted Driving Behavior at Intersections Using Naturalistic Driving Study Data

High frequency of distracted driving behavior is considered a high potential risk for traffic safety. Right-turn drivers’ distracted driving behavior can dramatically increase the crash risk considering the complex procedures required by the right-turn movements at intersections. This paper analyzed the influence of several factors including road geometry, environmental factors, and traffic conditions on the occurrence of right-turn drivers’ distracted driving activities. The data were collected through the Naturalistic Driving Study (NDS). A total of 581 events including 208 events with distracted driving and 373 events without distracted driving (baseline events) were extracted from the Strategic Highway Research Program 2 (SHRP 2) NDS database. The logistic model and random forest (RF) were applied for regression analysis. It was found that Vehicle Lane Occupied and Traffic Control are significantly correlated to distracted driving behavior in both models. The analysis of odds ratios indicated that dedicated right-turn lane design and adding yield sign at intersections can reduce the probability of having distracted driving behavior. Traffic density and driving time may also play important roles in the occurrence of distracted driving activities. Countermeasures are recommended to reduce distracted driving in this paper. The findings of this paper can help engineers and researchers better understand the dominant factors affecting drivers’ distraction. This research can also provide theoretical support for the distraction detection function in the advanced driver-assistance systems (ADAS).

Analytical Study of Causes, Effects and Remedies of Traffic Congestion in Nigeria: Case Study of Lagos State

Road traffic congestion remains a global phenomenon that plagues the cities in Nigeria; especially Lagos state, resulting in massive delay, unpredicted travel times, increased fuel consumption and monetary loss. The phenomenon has arisen from the poorly planned road network and traffic management, resulting in elongated and unbearable traffic jams. Lagos – a megacity with over 12million people is chosen as a typical case study, being the fastest growing city in Nigeria. It is prevalent greatly during weekends, public holidays and periods of major activities. Major causes of the congestion include lane indiscipline, high traffic density, low road network carrying capacity, poor traffic management and support infrastructure, low response to the removal of broken down and crashed vehicles. This paper sets out to identify the diverse patterns of road traffic congestion in relation to human, road traffic environment and causative factors in a Nigeria’s major city of Lagos, being the commercial nerve center of Nigeria with a view to recommending some cost-effective and sustainable policy options for a better and enhanced intraurban mobility. The paper recommends the integration of an enduring urban traffic planning and management strategies, such as effective mass transit, strict land-use adherence, effective traffic control and enforcement and integration of traffic management institutions, the mechanism at discouraging excessive car usage

Factors associated with compliance rate at pedestrian crosswalks with Rectangular Rapid Flashing Beacon

In the recent decade, Rectangular Rapid Flashing Beacon (RRFB) has been introduced and widely installed across North America to improve pedestrians’ safety at crosswalks. While the treatment has been reported to be effective in improving safety, relatively few studies have been conducted to explore the factors associated with its effectiveness. This study investigates the effect of road characteristics, environmental factors, and device specification on vehicles’ yielding compliance, used as a surrogate measure of safety. Nineteen crosswalks within the City of Calgary were chosen for field study. An ordinary least squares regression model is applied to identify the factors that affect the compliance rate. It is found that type of road, daily traffic volume, posted speed limit, median, and rainy weather have significant impacts on the compliance rate. Besides, the result suggests using smaller size beacons and installing them above the pedestrian sign to improve safety to its fullest at RRFB-enhanced crosswalks.

Vehicle Driving Risk Prediction Based on Markov Chain Model

A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00am to 6am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7pm to 12pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70km/h speed limit and double lane line road marking.

A Review of Road Crash Prediction Models for Developed Countries

Road Crash losses have been on an growing trend for the preceding decade or so in India. consequently traffic safety organization has emerged as a topic of argument for researchers all over the world. For this reason Crash modeling on different factors causing them has to be conducted. Crash modelling helps to anybody to recognize the real causative agents behind an accident to occur. The effect of one cause can be greater than the other. And those causes can only be known from Crash modelling. In this paper it is tried try to divide this Crash modelling techniques into different categories based on the road geometrics characteristics, traffic characteristics and Environmental factors on urban roads and on rural roads of different developed countries. In both urban and rural road crash studies it can be seen that for the most part regression techniques like linear, multi-linear, logit and poisons regression were used for modelling the road crashes. It was also noticeable that frequently authors have tried to research on one reason and go profound into it to a certain extent considering all factors at a time. From the study of different researches the attention was paid to the safety effects of road environment such as traffic flow, lane width, number of accesses, speed and road connectors. In this paper it is tried to review as much papers as possible and various gaps in research along with future possibility of study in this area has been indicated. Starting from the basic models like Simple/Multiple regression model to the logistic and linear regressions to the new modeling techniques involving Negative Binomial/Zero inflated modelling, genetic mining and fuzzy logics have been discussed in the paper.

Comparative Analysis of the Tax Burden on Car Ownership and Car Use in the EU Countries and Russia

Abstract The purpose of this article is to compare the tax burden on car owners and car users in the countries of Europe and in Russia and to use the findings for identifying areas of improvement in the system of fiscal instruments under the present conditions of transport development. The article examines the European experience in the construction of fixed and variable transport taxes to regulate the behavior of the car owners. The theoretical background collection of taxes for the use of a car is considered. The article looks the size of fixed taxes in different countries depends on the technical characteristics of the car that determine its capability to have a negative impact on the road network and environmental factors. A conclusion is made that in Russia, the system of transport taxes lacks logic. The level of fixed taxes that is rather higher for Russia generates two effects: it hinders the growth of car ownership and preserves the negative structure of the car fleet. The low level of variable taxes (fuel tax and tolls) encourages the intense use of passenger cars. The approach will spur further growth of car ownership rates in Russia, making people car dependent. A reform of variable transport taxes is needed to regularly increase them. Such an increase should also be accomplished by regularly increasing fuel excise duties and introducing congestion charges and toll charges on more roads, bridges, tunnels, and conservation areas.

Analyzing injury severity of bus passengers with different movements

ABSTRACTObjective: Though public transport vehicles are rarely involved in mass casualty accidents, when they are, the number of injuries and fatalities is usually high due to the high passenger capacity. Of the few studies that have been conducted on bus safety, the majority focused on vehicle safety features, road environmental factors, as well as driver characteristics. Nevertheless, few studies have attempted to investigate the underlying risk factors related to bus occupants. This article presents an investigation aimed at identifying the risk factors affecting injury severity of bus passengers with different movements.Method: Three different passenger movement types including standing, seated, and boarding/alighting were analyzed individually using classification and regression tree (CART) method based on publicly available accident database of Great Britain.Results: According to the results of exploratory analyses, passenger age and vehicle maneuver are associated with passenger injury severity in all 3 types of accidents. Moreover, the variable “skidding and overturning” is associated with injury severity of seated passengers and driver age is correlated with injury severity of standing and boarding/alighting passengers.Conclusions: The CART method shows its ability to identify and easily explain the complicated patterns affecting passenger injury severity. Several countermeasures to reduce bus passenger injury severity are recommended.

데이터 기반의 도로구간별 운전자의 통행행태를 고려한 교통사고지표 개발

고속도로는 중 장거리 차량 통행을 담당하고 있으며, 공공기관들의 지방이전 등으로 인해 직장과 거주지 간의 거리가 멀어짐에 따라 운전자들의 통행거리는 증가 추세를 보이고 있다. 이러한 이유로 고속도로에서는 피로나 졸음으로 인한 운전자요인 사고의 비중이 매우 높다. 하지만 기존의 사고예측 연구에서는 주로 도로요인, 환경요인, 차량요인 등 외적인 조건만을 고려하였을 뿐 도로구간별 운전자의 통행행태를 고려하지 못하였다. 본 연구에서는 대용량 내비게이션 경로자료를 활용하여 통행행태기반의 사고지표(운전피로도지표)를 제안하고, 교통사고와의 관계를 살펴보았다. 분석결과 개발된 지표와 교통사고와는 높은 상관성을 보이는 것으로 나타났으며, 이를 통해 시설물 위주에서 통행행태기반으로 교통사고 예측연구의 패러다임 변화가 필요함을 확인할 수 있었다. 본 연구에서 제안한 지표는 다양한 분야에서 활용이 가능할 것이다. 교통사고(졸음, 부주의운전 등)를 사전에 방지하기 위한 정보제공, 고속도로 안전진단 핵심요소기술로의 활용, 휴게소 및 졸음쉼터 입지 우선순위 선정 시, 피로한 구간에 대하여 운전자에게 주의환기를 위한 방법(노면요철, 그루빙 등)을 적용해야하는 구간 선정 시 활용이 가능할 것이다. Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

FACTOR ANALYSIS OF MOTORCYCLE CRASHES IN MALAYSIA

This research paper focused on factor analysis of motorcycle crashes in Malaysia. Statistics have shown that Malaysia has the highest road fatality risk (per 100,000 populations) among the ASEAN countries and more than 50% of the road accident fatalities involve motorcyclists. The research literature has shown that key factors that significantly involve in the motorcycles crashes are human factors, road and vehicle conditions and environment factors. The purpose of this research is to analyse the driving attitude, behaviour and habits of Malaysian motorcyclists especially the young students. For this purpose, a research survey (N=210) has been conducted in University Technology Malaysia (UTM) in order to perceive road user perception on key factors that contribute to motorcycle crashes in Malaysia among the young students. The results show that the highest factor that contributes to the motorcycle crashes in Malaysia is the human behavior factor, follow by road and vehicle and lastly the environment factor. Human attitude on the road is the main factor that causes a motorcycle crash. Impatience, careless, selfish and dangerous driving make driver involve an accident. Evidence recommendations are suggested to reduce the number motorcycle crash in Malaysia. Implementation of these recommendations can be helpful to reduce the number of accidents and changing the attitude of young driver.

FACTOR ANALYSIS OF MOTORCYCLE CRASHES IN MALAYSIA

This research paper focused on factor analysis of motorcycle crashes in Malaysia. Statistics have shown that Malaysia has the highest road fatality risk (per 100,000 populations) among the ASEAN countries and more than 50% of the road accident fatalities involve motorcyclists. The research literature has shown that key factors that significantly involve in the motorcycles crashes are human factors, road and vehicle conditions and environment factors. The purpose of this research is to analyse the driving attitude, behaviour and habits of Malaysian motorcyclists especially the young students. For this purpose, a research survey (N=210) has been conducted in University Technology Malaysia (UTM) in order to perceive road user perception on key factors that contribute to motorcycle crashes in Malaysia among the young students. The results show that the highest factor that contributes to the motorcycle crashes in Malaysia is the human behavior factor, follow by road and vehicle and lastly the environment factor. Human attitude on the road is the main factor that causes a motorcycle crash. Impatience, careless, selfish and dangerous driving make driver involve an accident. Evidence recommendations are suggested to reduce the number motorcycle crash in Malaysia. Implementation of these recommendations can be helpful to reduce the number of accidents and changing the attitude of young driver.

Not all minds wander equally: The influence of traits, states and road environment factors on self-reported mind wandering during everyday driving

Inattention is a road safety problem, but few studies have focused specifically on mind wandering during everyday driving. This paper explores differences in self-reported mind wandering according to driver demographic characteristics (including age and gender), cognitive traits (such as tendency toward cognitive failure or mindful attention), states (such as feeling tired or stressed) and road environment factors (such as route familiarity). Five hundred and two participants (113 male, average age 44.4 years, SD=14.0years) completed a series of questionnaires (Mindful Attention and Awareness Scale (MAAS), Cognitive Failures Questionnaire (CFQ) and Driver Behaviour Questionnaire (DBQ)), as well as study-specific questions about mind wandering during different personal states and across a range of road and traffic situations. All respondents reported mind wandering during driving at least some of the time. Mind wandering was more likely to be reported on familiar roads than on unfamiliar roads and when drivers are tired. Drivers who reported relatively more mind wandering were younger, reported less mindful attention in daily life, more cognitive failures, and more driving violations and lapses. Together, the findings suggest that mind wandering is common in everyday driving, however any link with crash risk remains unclear. Future research using self-report and naturalistic methods could provide more insight into relationships between mind wandering, error and crash risk.

Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model

Current urban traffic congestion costs are increasing on account of the population growth of cities and increasing numbers of vehicles. Many cities are adopting intelligent transportation systems (ITSs) to improve traffic efficiency. ITSs can be used for monitoring traffic congestion using detectors, such as calculating an estimated time of arrival or suggesting a detour route. In this paper, we propose an urban traffic flow prediction system using a multifactor pattern recognition model, which combines Gaussian mixture model clustering with an artificial neural network. This system forecasts traffic flow by combining road geographical factors and environmental factors with traffic flow properties from ITS detectors. Experimental results demonstrate that the proposed model produces more reliable predictions compared with existing methods.

Development of evaluation criteria for curve-section safety level with visual distortions on two-lane rural highways

Curve-section traffic accidents occur due to a mixture of vehicle, human, and road environmental factors. Traffic safety research in curve sections, however, so far has been confined to only investigating the effect of highway geometry factors on traffic accidents. Accordingly, this study suggested curved part safety-level evaluation criteria by complexly considering the hazard-level, which forecasts traffic accidents utilizing geometric structure of road, and the level of visual distortion, which recognizes the geometric structure of road as different from actual. The research first presents data collected on the geometry of highways in curve sections and from field investigations of traffic accidents. Second, a rating scale for curve-section safety level is presented with a consideration of hazard levels and visual distortion as variables. Third, evaluation criteria are presented for curve-section safety level, utilizing a safety-level rating scale. The evaluation criteria are classified into three levels dependent on route alignment type and each level is again classified into six grades from A to F. In this research, the causes of accidents can be determined in advance during design of the curve sections of a highway by applying evaluation criteria for the safety level as the curve sections are developed, and thus can be utilized in selecting a priority order for projects when implementing improvements in existing roads.