Highway Drivers Research Articles

Road Safety of Highway Traffic System of Halsema Highway

Traffic accidents happen all the time. It’s a situation that can happen in any transportation network, including the one in the study area, where safety steps aren’t good enough. The study then looked at how safe the roads are on the Halsema Highway in the Philippines. In particular, it found out what the main reasons are for traffic accidents along Halsema Highway and how well safety standards are being followed. The goal is to come up with ways to lessen the bad effects of land transportation traffic accidents. A questionnaire checklist was the main tool used to collect data, with human interviews and observation also being used. T-tests and both quantitative and qualitative study methods were used to look at the data that was collected. The results show that mistakes made by people are the main reason why cars crash on the Halsema Highway. There is a high level of compliance with road safety standards (2.63) and traffic engineering (2.56), but only a modest level of compliance with traffic signs (2.46). Right now, it’s clear that some Halsema Highway drivers don’t care about safety. On the other hand, there are some issues and concerns that need to be addressed when it comes to road safety standards, especially when it comes to traffic signs that aren’t big enough.

Driving environmental inequality: the unequal harms and benefits of highways

ABSTRACT Highway construction projects have created extensive environmental and social burdens on racially segregated neighborhoods in urban areas in the U.S. However, few studies examine how some places benefit from highways and contribute to the harms in highway-adjacent communities. This paper aims to fill this gap by (1) conducting a case study of three highway projects in Omaha, Nebraska, and (2) using location-based services data to compare the neighborhood racial demographics of highway drivers to the racial demographics of highway-adjacent neighborhoods. In doing so, our paper heeds the call for more research on relative distribution environmental inequality and environmentalized urban sociology. Our historical case study elucidates how highway planning differed across three highway projects in racially segregated Black, Hispanic, and White neighborhoods. Further, the descriptive statistics show that modern-day drivers on highways in Black and Hispanic neighborhoods are from disproportionately more White neighborhoods compared to the neighborhoods bordering the highways. However, the reverse is not true: drivers using the highway in a majority White neighborhood are from neighborhoods that largely match the demographics of bordering neighborhoods. We discuss the implications of these results for future studies of transportation-related environmental and social inequality and current policy initiatives to remedy these harms.

Probabilistic modeling and simulation of drivers’ lane selection and its impact on highway traffic using fuzzy-based decision-making

Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company.

Stimulant Drug Using, Road Traffic Accidents, and Illegal Activities among Highway Drivers in Iraq in 2022: A Nation-Wide Cross-Sectional Survey

Background: Drug and stimulant abuse is especially common in low and middle- income countries, such as Iraq and certain populations, such as highway drivers. Moreover, Iraq has high rates of traffic and road accidents. Objective: Our objective was to assess the associations of using stimulants with traffic events among highway drivers in Iraq. Methods: We used a cross-sectional study design, in which a self-administered questionnaire was distributed online on different social media platforms. The study included male highway drivers who used drugs from all provinces of Iraq. The questionnaire consisted of demographic questions and an addiction questionnaire about stimulants specially validated for the Arabic language. We followed a convenience sampling method to recruit participants. Results: A total of 1700 participants from 18 provinces were included with a median (IQR) age of 34 (24:55) years. Around 58% used drugs for reasons other than medical indications, while 81% reported abusing prescription drugs. Amphetamine and cocaine were used by 52% and 20% of the participants, respectively. Around 84% of the drivers had at least 56 driving hours per week, while 87% were involved in illegal activities due to drug use. Most of the participants (78%) had accidents at least three times per month. Most of the participants had social and medical issues. Conclusion: Drug use, especially stimulant use, is a major contributor to road accidents and thus, increased morbidity and mortality rates in Iraq. Urgent policies and regulations are needed regarding the dispensing of drugs and the working hours of the drivers.

Feasibility of Modernizing the Acceptable International Roughness Index Value

The U.S. National Highway System has set a standard for acceptable pavement roughness, measured by the international roughness index (IRI) at less than 2.68 m/km (170 in./mi). This value corresponds to a present serviceability rating (PSR) of 2.5, where half of the highway drivers perceive the pavement ride quality as unacceptable with a repair need. For over 3 decades, public transportation agencies in the U.S. have used this IRI value to determine when pavement surface rehabilitation is needed. However, with advancements in vehicle suspension systems improving ride quality, the current acceptable IRI value corresponding to 2.5 PSR has been questioned. To our knowledge, studies in the literature have yet to explore this issue. This study aims to evaluate the feasibility of modernizing the acceptable IRI value through nationwide, large-scale field testing, which is typically expensive and time-consuming. The IRI-present serviceability index (PSI) equation that is capable of converting the acceptable IRI value into the estimated 2.5 PSR (i.e., the PSI) was selected and calibrated by applying test quarter-car parameters synthesized through a literature review. Then, the calibrated IRI-PSI equation was used to find a new acceptable IRI value that was evaluated for the statistical significance and the financial benefits of the new value. The evaluation results suggest a need for a nationwide field test to modernize the current acceptable IRI value. Public transportation agencies can easily adapt the technical approach we used to calibrate the IRI-PSI equation without altering their current IRI measurement systems.

The Bordeaux Sleepiness Scale (BOSS): a new questionnaire to measure sleep-related driving risk.

Sleepiness is a well-known risk factor for traffic accidents. Our study presents a new questionnaire, the Bordeaux Sleepiness Scale (BOSS), specifically designed to evaluate sleep-related driving risk in patients with sleep disorders. The BOSS was designed by gathering data on sociodemographics, sleepiness, driving items, and traffic accident exposure (kilometers driven) in the past year of 293 patients followed for sleep disorders at a French sleep clinic. It was then validated on data from a large population-based cohort of 7,296 highway drivers. Its performance was compared to the Epworth sleepiness scale and to self-reported episodes of severe sleepiness at the wheel. Receiver operating characteristic curves were computed. The sensitivity and specificity of the BOSS (cutoff = 3) to predict sleep-related near-misses or accidents was, respectively, 82% and 74%, with an area under the receiver operating characteristic curve of 0.83. In a cohort of patients and a large population-based cohort, the area under the curve of the BOSS was significantly larger than that of the Epworth sleepiness scale (P < .001). Although the areas under the curve were equivalent between the BOSS and sleepiness at the wheel, the specificity of the BOSS was higher. The BOSS scale combining exposure (kilometers driven) and self-perception of situational sleepiness provides a simple and reliable evaluation of sleep-related driving risk. This short, specific questionnaire should be promoted as a first-line tool to evaluate the risk of traffic accidents in sleepy patients. Philip P, Micoulaud-Franchi J-A, Taillard J, etal. The Bordeaux Sleepiness Scale (BOSS): a new questionnaire to measure sleep-related driving risk. J Clin Sleep Med. 2023;19(5):957-965.

Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encountering a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicles; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following vehicle and the preceding vehicle considering the initial longitudinal velocities. Results suggest that there should be no risk of a collision between the following vehicle and the preceding vehicle when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.

Value of travel time by road type

Travel time is less costly if it is comfortable or can be used productively. One could hence argue that the value of travel time (VTT) of car travellers in economic appraisal should be differentiated by road type, reflecting differences in road quality. We explain the theoretical foundation for such a differentiation, review the relevant literature and show the results of an empirical case study based on actual route choice of highway drivers in Norway. We find little existing literature discussing the link between road type and VTT, but closely related findings suggest that that the impact on VTT could be substantial. Our empirical case study also suggests that the VTT is lower on higher quality road types. Applying this to economic appraisal would imply higher user benefits of road projects that improve road quality.

Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers

For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making. It uses a net that reveals preferences from the agents' past joint trajectory, and a differentiable implicit layer that maps these preferences to local Nash equilibria, forming the modes of the predicted future trajectory. Additionally, it learns an equilibrium refinement concept. For tractability, we introduce a new class of continuous potential games and an equilibrium-separating partition of the action space. We provide theoretical results for explicit gradients and soundness. In experiments, we evaluate our approach on two real-world data sets, where we predict highway drivers' merging trajectories, and on a simple decision-making transfer task.

Exploring spatiotemporal mobilities of highway traffic flows for precise travel time estimation and prediction based on electronic toll collection data

In this paper, we propose a travel time estimation and prediction (TTEP) framework to enhance the driving efficiency on highways through the Internet of Vehicles (IoV). Highway travel time estimation and prediction are important for the drivers in a long-distance traveling. The accurate travel time information on highways is the key to improve the efficiency of transportation systems. When current flow status is collected through the IoV, TTEP can accurately estimate and predict highway travel time by the proposed weighted root-mean-square similarity (Weighted-RMSS) method. In addition, when current flow status is unavailable at the present time, we propose the multiple slope-based linear regression (Multi-SBLR) method to predict highway travel time only using historical traffic data. Furthermore, the spatiotemporal mobilities of vehicles on highways are analyzed and explored to improve the prediction accuracy of the proposed Weighted-RMSS and Multi-SBLR methods. To verify the feasibility and superiority of TTEP, we adopt the open Electronic Toll Collection data of highways in Taiwan to evaluate the prediction accuracy of our approaches. Experimental results show that our approaches outperform existing methods and can significantly reduce the prediction errors of highway travel time. In particular, we further implement the Android-based and web-based systems of TTEP to predict and compare travel time at different departure times and locations for highway drivers.

Potential trend discovery for highway drivers on spatio‐temporal data

Inter-city transportation plays an important role in modern cities, and has accumulated massive spatio-temporal data from various sensors by IoT (Internet of things) technologies. Travel characteristics and future trends of highway behind data are valuable for traffic guidance and personalized service. As a routine domain analysis, trend discovery for highway drivers faces challenges in processing efficiency and predictive accuracy. Insufficient profiles of those drivers are available directly, sensible executive latency on huge data is hard to guarantee, and inadequate features among spatio-temporal correlations hinder the analytical accuracy. In this paper, a travel-characteristic based method is proposed to discover the potential trend of payment identity for highway drivers. Considering time, space, subjective preference and objective property, travel characteristics are modeled on monthly data from highway toll stations, through which predictive errors can be reduced by gradient boosting classification. With real-world data of one Chinese provincial highway network, extensive experiments and case studies show that our method has second-level executive latency with more than 85% F1-score for trend discovery.

Research on Drivers’ Behavior Characteristics of Expressway Straight Section under Moderate Rainfall

Understanding the behavior characteristics of highway drivers in rainy weather is of great significance to traffic safety. Based on the driving test in the middle rain environment, this paper uses the method of combining the sports index and the driver’s behavior to study the characteristics of the driver’s behavior in the middle rain environment. A total of 15 drivers were recruited in this test to observe the impact of the moderate rain environment on the drivers, and to analyze the correlation of various indicators to summarize the vehicle operation rules on the straight road section under the moderate rain environment. The results of the study show that the speed on straight road sections is generally higher in moderate rain weather, and the driver’s recognition time for signage is longer than in sunny day. Finally, according to the behavior characteristics of drivers in rainy days, the corresponding traffic safety measures are put forward.

Toll roads vs. Public transportation: A study on the acceptance of congestion-calming measures in Madrid

• This article compares the acceptance of transit improvements and incentives to use an underutilized toll road. • Toll discounts are less acceptable than transit improvements, especially those related to carpooling. • Incentives to use the toll road are not enough to attract free highway drivers. • Socioeconomic factors play a smaller role compared to trip-related variables. To relieve road congestion, a variety of transport demand management measures (TDM) have been implemented all over the world. The success of these measures has been found to depend at least partially on users’ perception about them. Several articles have jointly addressed the acceptability of public transport improvement (carrot policies) and road pricing (stick policies). However, these research works have not considered discounts on existing toll roads to relieve congestion on free alternative expressways. The objective of this paper is to study the combined acceptance of different congestion-calming policies at the suburban level, including the promotion of toll roads and measures to foster the use of transit. To that end, a survey was conducted among travelers on a commuting transport corridor in the region of Madrid aimed at exploring their perceptions towards four TDM measures, and a choice modeling framework was conducted. The scenarios considered are “carrot” policies, and the results of the analysis indicate that enhancing public transportation enjoys more support among respondents than toll promotions. Motorists using the free highway are more willing to use transit improvements than to opt for a toll road as an alternative regardless of the proposed toll discounts. Among the TDM measures explored, the least supported is the promotion of toll discounts associated with non-household carpooling. Furthermore, according to the results, the adoption of these TDM measures is more influenced by trip-related factors, in particular, the trip frequency and the mode of transport, than by socio-economic characteristics of the traveler. Finally, some geodemographic attributes of the residential location are also found to be statistically significant.

Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems

Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.

Evidence from Highway Drivers in Greece Showing Toll Avoidance and Utility of Alternative Routes

Road tolling plays a significant role on highways’ financial sustainability since it consists the major revenue source. This paper aims to examine the factors that affect the drivers’ route choice and urge them to avoid toll roads when an alternative toll-free route is available. The paper presents the results of a case study that is dealing with the issue of toll avoidance at the last non-privatized highway of Greece, the Egnatia Odos (EO) road. Data from a combined revealed and stated preference survey were collected and binary choice models were built for car and truck drivers so as to determine the utility of alternative routes. The results show that travel cost and toll fees are critical route choice criteria for car drivers, while travel time is a key decision factor for truck drivers. The high safety standards for the toll route were appreciated by both categories of drivers. Additional trip and personal characteristics, such as gender, trip frequency, type of transported cargo, and total trip length also affect drivers’ choices. The elasticity of travel time and cost was estimated to shed light on drivers’ sensitiveness to the route attributes and it was found that truck drivers’ choices are greatly influenced by their working time schedules. These findings highlight the key factors that influence the utility of toll roads and therefore could assist highway authorities and concessionaires in developing successful toll pricing policies which will not act as a deterrent to the use of highways.

The Socio-Economic Benefits of Sharing Economy: Colleague-Based Carpooling Service in Korea

Carpooling—a concept of shared transportation for addressing traffic issues such as congestion and CO2 emissions—has been actively introduced, especially in developed countries. This study proposes a method to estimate the benefits of introducing carpooling for employees in public agencies that are transferring innovation cities. To overcome the shortcomings of previous carpooling services, a carpooling service for inter-company employees was designed and evaluated in our study. The traffic flow theory was used to estimate the direct benefits to carpooling users and the indirect benefits to express highway drivers. The results indicate that carpooling services have a significant socio-economic cost-saving effect on traffic congestion, environmental cost reduction, and so forth, and will therefore play an important role in traffic demand management.

Evaluation of Traffic Accident Risk in In-City Bus Drivers: The Use of Berlin Questionnaire.

Traffic accidents associated with high mortality rate may produce serious problems especially in highways. Obstructive sleep apnea (OSA) has been associated with a high risk for traffic accidents due to excessive daytime sleepiness even in in-city drivers. In the present study, it was aimed to evaluate the rate of OSA symptoms and to identify risk factors associated with traffic accidents in in-city bus drivers. A self-administered questionnaire including demographic and anthropometric features, sleep and work schedules, Berlin questionnaire, Epworth sleepiness score (ESS), and history of traffic accidents was used. The questionnaire was conducted for 1400 male bus drivers (mean age, 38.0±6.4 y, body mass index, 27.8±3.9 kg/m2). A total of 1058 (75.6%) drivers had one or more accidents while driving bus. According to the Berlin questionnaire, 176 (12.6%) drivers were found to have high OSA risk and the accident rate was 83.0% in high-risk group, whereas 74.5% of low-risk drivers had accidents (p=0.043). The drivers with a history of traffic accident were older (p=0.030), had higher ESS (p=0.019), and were more in the high-risk OSA group according to the Berlin questionnaire (p=0.015). In multivariate linear regression analysis, traffic accident was associated with only Berlin questionnaire (p=0.015). The present results support that city bus drivers with high OSA risk according to Berlin questionnaire have increased accident rates. Therefore, we suggest using Berlin questionnaire for screening sleep apnea not only in highway drivers but also in in-city bus drivers.

Knowledge and Observance of Road Communication Signs among Commercial Drivers in South-South, Nigeria

Transportation is very primary in the development of any society. The increasing loss of lives, goods and services is becoming much. Traffic signs were introduced as a means of informing drivers about the nature of the road and maintain discipline in the drivers. This study set out to evaluate the knowledge and observance of road communication signs among commercial drivers in south-south, Nigeria. Using the survey method, data were collected using the questionnaire as the instrument. Employing Taro Yamane formula, a sample of 368 was arrived at. The purposive sampling method was used to select public parks in Port Harcourt, Uyo and Benin City while systematic sampling was used to select the respondents. Data were presented in tables and percentages. The results show that a good number of commercial drivers in south-south Nigeria have fair knowledge of traffic signs, but willfully do not observe them. It was recommended that the FRSC should institute refresher programmes for highway drivers at regular intervals for the drivers to have fresh thoughts of safe driving in their minds. Key words: Road signs, Observance, Communication, Commercial drivers

Assessment of Behavioral Patterns of Food Intakes on Micro-Sleepiness, with Busy Lifestyles

The effect of food habits and attitudes on micro-sleepiness was attempted to study simultaneously with development of a food product to combat. Data were collected from Sri Lanka Police Traffic Division and from 250 respondents using a pre-tested questionnaire representing drivers (especially highway drivers), private and public sector workers (shift based) and cramming students (university and school). Questionnaires were directed to respondents to fill independently and analyzed statistically. Results exposed that 76.84, 96.39 and 80.93% out of total consumed rice for all three meals, which leads to obtain a higher glycemic meal. Having two hyper glycemic meals before 14.00h was identified as a cause for micro-sleepiness. Peak level of road accidents were witnessed at 14.00 - 20.00h (38.2%) and intensity of micro-sleepiness was also falling at the same period (37.36%). While from 14.00 to 16.00h was the peak time, 16.00 to 18.00h was the least; again it reappearing to some extent from 18.00 to 20.00h. Even though respondents expressed that peak hours of micro- sleepiness is 14.00-16.00h, according to police reports, peak hours fall in between 18.00-20.00h. Out of the interviewers, 69.27% strongly required to evade micro-sleepiness and keen to spend LKR 10-20 on a commercial product. Since age-old practices connected to suppress micro-sleepiness were time consuming, recent respondents (51.64%) like to have a quick answer via a product like rapid drink. Finally it was concluded that morning and noon food habits may reason for micro- sleepiness and dinner may affect for both, natural as well as micro sleepiness due to heavy glycemic load of food. According to the study, micro-sleepiness can be categorized in to three zones such as low-risk zone (08.00-10.00h and 18.00-20.00h), manageable zone (10.00-12.00h), and high- risk zone (14.00-16.00h).

Attention Deficit Hyperactivity Disorder Symptoms, Sleepiness and Accidental Risk in 36140 Regularly Registered Highway Drivers.

BackgroundAttention Deficit Hyperactivity Disorder (ADHD) is a frequent neurodevelopmental disorder that increases accidental risk. Recent studies show that some patients with ADHD can also suffer from excessive daytime sleepiness but there are no data assessing the role of sleepiness in road safety in patients with ADHD. We conducted an epidemiological study to explore sleep complaints, inattention and driving risks among automobile drivers.Methods and FindingsFrom August to September 2014, 491186 regular highway users were invited to participate in an Internet survey on driving habits. 36140 drivers answered a questionnaire exploring driving risks, sleep complaints, sleepiness at the wheel, ADHD symptoms (Adult ADHD Self-Report Scale) and distraction at the wheel. 1.7% of all drivers reported inattention-related driving accidents and 0.3% sleep-related driving accidents in the previous year. 1543 drivers (4.3%) reported ADHD symptoms and were more likely to report accidents than drivers without ADHD symptoms (adjusted OR = 1.24, [1.03–1.51], p < .021). 14.2% of drivers with ADHD symptoms reported severe excessive daytime sleepiness (Epworth Sleepiness Scale >15) versus 3.2% of drivers without ADHD symptoms and 20.5% reported severe sleepiness at the wheel versus 7.3%. Drivers with ADHD symptoms reported significantly more sleep-related (adjusted OR = 1.4, [1.21–1.60], p < .0001) and inattention-related (adjusted OR = 1.9, [1.71–2.14], p<0001) near misses than drivers without ADHD symptoms. The fraction of near-misses attributable to severe sleepiness at the wheel was 4.24% for drivers without ADHD symptoms versus 10,35% for drivers with ADHD symptoms.ConclusionOur study shows that drivers with ADHD symptoms have more accidents and a higher level of sleepiness at the wheel than drivers without ADHD symptoms. Drivers with ADHD symptoms report more sleep-related and inattention-related near misses, thus confirming the clinical importance of exploring both attentional deficits and sleepiness at the wheel in these drivers. Road safety campaigns should be improved to better inform drivers of these accidental risks.