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Driving avoidance performance on Sand-Covered roads during sand and dust storms under different visibility conditions
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Every spring, a large part of China is confronted with sand and dust storms (SDS) – mainly originating in the Gobi (including Chinese and Mongolian Gobi) and Taklamakan deserts. In March-April 2023, most of northern, northwestern and northeastern China was struck by three sandstorms that affected an area with more than 500 million people. In this study, aerosol optical, microphysical and radiative properties were studied during these SDS events using an integrated approach that combines satellite, terrestrial and re-analysis data. The results showed that dusty conditions were observed in most areas north of the Yangtze River (Chang Jiang) with daily average PM10 concentrations exceeding 1000 µg/m3 in many cities. VIIRS aerosol optical depth (AOD) at 550 nm during three SDS events exceeded a value of 1 throughout nearly the entire northern part of the country. The AERONET data obtained from the AOE_Baotou site showed a significant increase in total AOD and a corresponding decrease in AE during the SDS. The single scattering albedo (SSA), asymmetry parameter (ASY), real refractive index (RRI) and imaginary refractive index (IRI) values indicate an abundance of scattering coarse-mode particles. Aerosol radiative forcing (ARF) at top of the atmosphere and at the earth's surface was nearly always negative during the period and ranged from −48.5 to +2.7 Wm−2 and from −180.8 to −66.6 Wm−2, resulting in high positive ARF values at ATM (from +63.8 to +132.3 Wm−2). Each of these affects the heating of the atmosphere and cooling on the earth's surface. The atmospheric heating rates ranged from 1.8 to 3.7 K day−1. The formation of these SDS mainly resulted from the passage of cold fronts associated with low pressure systems in the Gobi and Taklamakan deserts, creating conditions for dust to rise into the atmosphere and move further downwind.
Overtaking on two-lane roads can lead to increased collision risks due to drivers' errors in evaluating whether or not to accept the gap to the vehicle in the opposite lane. Understanding these gap acceptance decisions can help mitigate the risks associated with overtaking. Previous research on overtaking has focused on the factors influencing gap acceptance decisions. However, the cognitive processes underlying gap acceptance decisions remain poorly understood. Previous studies have shown that response time (i.e. the time it takes the driver to evaluate the gap and make a decision) can provide valuable insights into the cognitive processes during gap acceptance decisions, in particular in pedestrian crossing and left turn decisions. However, the more complex nature of the overtaking maneuver renders it difficult to measure response times in overtaking. As a result, response times in overtaking have not been investigated, thereby limiting our understanding of overtaking behavior. To address this gap, in this paper we propose a method to measure response time in drivers' overtaking decisions and demonstrate this method in a driving simulator experiment (N=25). Specifically, we analyzed the effect of distance to the oncoming vehicle and speed of the ego vehicle on response time in accepted and rejected gaps. We found that response times for rejected gaps were on average longer than for accepted gaps. The response times increased with the distance gap and decreased with the initial velocity of the ego vehicle. We conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. These results provide basis for cognitive process models that can help further understand overtaking decisions.
Although a remarkable reduction in the frequency of sand and dust storms (SDSs) in the past several decades has been reported over northern China (NC), two unexpected mega SDSs occurred on 15–20 and 27–29 March 2021 (abbreviated as the “3.15” and “3.27” SDS events), which has reawakened widespread concern. This study characterizes the optical, microphysical, and radiative properties of aerosols and their meteorological drivers during these two SDS events using the Sun photometer observations in Beijing and a comprehensive set of multiple satellite (including MODIS, VIIRS, CALIOP, and Himawari-8) and ground-based observations (including the CMA visibility network and AD-Net) combined with atmospheric reanalysis data. Moreover, a long-term (2000–2021) dust optical depth (DOD) dataset retrieved from MODIS measurements was also utilized to evaluate the historical ranking of the dust loading in NC during dust events. During the 3.15 and 3.27 events, the invasion of dust plumes greatly degraded the visibility over large areas of NC, with extreme low visibility of 50 and 500 m recorded at most sites on 15 and 28 March, respectively. Despite the shorter duration of the 3.27 event relative to the 3.15 event, sun photometer and satellite observations in Beijing recorded a larger peak AOD (∼2.5) in the former than in the latter (∼2.0), which was mainly attributed to the short-term intrusion of coarse-mode dust particles with larger effective radii (∼1.9 µm) and volume concentrations (∼2.0 µm3µm-2) during the 3.27 event. The shortwave direct aerosol radiative forcing induced by dust was estimated to be -92.1 and -111.4 Wm-2 at the top of the atmosphere, -184.7 and -296.2 Wm-2 at the surface, and +92.6 and +184.8 Wm-2 in the atmosphere in Beijing during the 3.15 and 3.27 events, respectively. CALIOP observations show that during the 3.15 event the dust plume was lifted to an altitude of 4–8 km, and its range of impact extended from the dust source to the eastern coast of China. In contrast, the lifting height of the dust plume during the 3.27 event was lower than that during the 3.15 event, which was also confirmed by ground-based lidar observations. The MODIS-retrieved DOD data registered these two massive SDS events as the most intense episode in the same period in history over the past 2 decades. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favored enhanced dust emissions in the Gobi Desert (GD) across southern Mongolia and NC. Meteorological analysis revealed that both SDS events were triggered by an exceptionally strong Mongolian cyclone generated at nearly the same location (along the central and eastern plateau of Inner Mongolia) in conjunction with a surface-level cold high-pressure system at the rear, albeit with differences in magnitude and spatial extent of impact. In the GD, the early melting of spring snow caused by near-surface temperature anomalies over dust source regions, together with negative soil moisture anomalies induced by decreased precipitation, formed drier and barer soil surfaces, which allowed for increased emissions of dust into the atmosphere by strongly enhanced surface winds generated by the Mongolian cyclone.
It has been a controversial issue for the effect of ageing population on driving safety. Apparently, drivers’ physiological and cognitive performances deteriorate with age. However, older drivers may compensate for the elevated risk by adjusting their behaviors, known as compensatory strategy. Despite the extensive research on this topic, the compensatory strategy of older professional drivers is not well understood since many studies focused on the differences in compensatory behavior between older and young drivers. Professional drivers tend to be more skillful and able to cope with the unfavorable driving environments, thus presenting a higher capability to mitigate the risk. This study attempts to examine the compensatory behavior and its safety effect amongst older professional drivers, as compared to those of older non-professional drivers, using the driving simulator approach. In the driving simulator experiment, participants were asked to follow a leading vehicle for one hour, and two sudden brake events were presented. 41 (mid-aged and older) drivers completed the driving tests. Each participant was required to complete a car-following test, either under high or low traffic flow conditions. Performance indicators include driving capability (i.e. lateral control, longitudinal control, and brake reaction time) and compensatory behavior (i.e. average speed, and time headway). Additionally, two modified traffic conflict measures: time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) are applied to indicate the traffic conflict risk. The random parameter Tobit models were estimated to measure the association between conflict risk and driver attributes, and random intercept models were used to assess other driving performance indicators. Results show that despite the impaired lateral control performance and longer brake reaction time of older drivers, the likelihood of severe traffic conflict of older drivers is lower than that of mid-aged drivers. Furthermore, though both older professional and older non-professional drivers adopted longer time headway, the reduction in the risk of severe traffic conflict is more profound among the older professional drivers. Such findings suggest that older professional drivers are more capable of mitigating the possible collision risk by adopting the compensatory strategy, as compared to older non-professional drivers. This justifies the existence of compound effect by the compensatory strategy of older driver and better driving skills of professional driver. This research provides useful insights into driver training and management strategies for employers, as older drivers would become a major cohort in the transportation industry.
Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM2.5, PM10, SO2, NO2, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM2.5 and PM10 are 38.2 ± 19.5 and 101 ± 80.7 μg/m³ exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m³, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM2.5 and PM10 ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM2.5 and PM10 content exceeded 200 and 1000 µg/m³, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.
Of the road crashes involving personal injury in Hong Kong, over 70% involved at least one commercial vehicle. Besides, the proportion of older drivers in the transport sector has been increasing due to the shortage of labor and aging population. It is believed that increase in age can have adverse impact on driving performance, even that professional drivers may possess better driving skill than non-professional drivers and the age-related impairment can be offset by task familiarity of professional drivers. In this paper, a driving simulator experiment was conducted to address this question. Additionally, possible factors that affect the driving performance of professional drivers and that of non-professional drivers were examined. A total of 50 participants were recruited and 94 tests were completed. Driving performance was assessed in terms of standard deviation of lateral position (SDLP), standard deviation of heading error (SDHE), mean heading error (MeanHE) and standard deviation of speed (SDspeed). Results of random intercept models indicate that lateral and speed control performances of mid-aged drivers were better than those of older drivers. Then, disaggregated models were established for professional and non-professional drivers respectively based on the results of market segmentation analysis. It was found that lateral and speed control performance of mid-aged professional drivers were better than that of older professional drivers. In contrast, older non-professional drivers were more likely to have degraded steering performance under the high traffic condition. Results of this study are indicative to the driver management strategies of the transport operators for sustained safety improvement of commercial vehicle fleet.
The potential costs of road traffic accidents (RTAs) to society are immense. Yet, no study has attempted to examine the impact of climate change on RTAs in Saudi Arabia, though RTA-leading deaths are very high, and the occurrence of climatic events is very frequent. Therefore, this study aims to assess the impact of climate change on RTAs in Saudi Arabia and to recommend some climate change mitigation and adaptation policies to make roads safe for all. This study employed annual data from 13 regions of Saudi Arabia, from 2003 to 2013. The data were analyzed on the basis of panel regression models—fixed effect, random effect, and the pooled ordinary least square. The findings show that temperature, rainfall, sandstorms, and number of vehicles were statistically and significantly responsible for RTAs in Saudi Arabia in the study period. This study also found that RTAs both inside and outside cities significantly caused injuries, but only RTAs inside cities significantly caused death. Furthermore, the death from RTAs injuries was found to be statistically significant only for motor vehicle accidents. The findings will assist policymakers in taking the right courses of action to mitigate the negative impacts of climate change through understanding climate influence on RTAs.
There is a lack of published research on the economic effect and the risk associated with sand and dust storms (SDS) worldwide. The objectives of this study are to estimate the economic impact of SDS on the oil and gas industry in Kuwait, to estimate a risk index for each loss, and to recommend a sustainable system for the mitigation of the damaging effects and economic losses of infrastructures. Hot spots of wind erosion, wind corridors, and dust frequency and severity formed the basis to locate the most susceptible oil and gas fields and operations. Ten sectors with potential loss vulnerabilities were evaluated: exploration, drilling, production, gas, marine, soil remediation, project management, water handling, maintenance, and research and development. Sand encroachment, although not a sector per se, was also considered. The results indicate that sand, and to lesser extent dust, are damaging and costly to the oil and gas infrastructure of Kuwait, with an economic cost estimation of US$9.36 million, a total of 5159 nonproductive lost hours, and 347,310 m³ of annual sand removal. A risk assessment identified three sectors with the highest risk indices (RI): drilling (RI = 25), project management (RI = 20), and maintenance (RI = 16). Sand encroachment also constituted a high risk (RI = 25). Mitigation of sand storms using a hybrid biological–mechanical system was shown to be cost-effective with an equivalent saving of 4.6 years of sand encroachment. The hazard implications of sand storm events continue to be a major concern for policy-makers given their detrimental economic impacts, and require that government officials wisely allocate investment budgets to effectively control and mitigate their damaging effects.
Low visibility is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. However, unlike the other inclement weather conditions that have attracted extensive research interests, only a few studies have been conducted to investigate the impacts of risk factors on driver injury severity outcomes in low visibility related crashes. A three-year crash dataset including all low visibility related crashes from 2010 to 2012 in four South Central states, i.e., Arkansas, Louisiana, Texas, and Oklahoma, is adopted in this study. A finite mixture random parameters approach is developed to interpret both within-class and between-class unobserved heterogeneity among crash data. After a careful comparison, a two-class finite mixture random parameter model with normal distribution assumptions is selected as the final model. Estimation results show that three variables, including young (specific to injury, I), male (specific to serious injury and fatality, F), and large truck (specific to serious injury and fatality, F), are found to be normally distributed and have significant impacts on driver injury severities. Variables with fixed effects including rural, wet, 60 mph or higher, no statutory limit, dark, Sunday, curve, rollover, light truck, old, and drug/alcohol impaired also have significant influences on driver injury severities. This study provides an insightful understanding of the impacts of these variables on driver injury severity outcomes in low visibility related crashes, and a beneficial reference for developing countermeasures and strategies to mitigate driver injury severities under these conditions.
Sand and dust storms (SDS) are global phenomena that significantly impact the socio-economy, human health, and the environment. The characterization of SDS intensity is a fundamental aspect of SDS issues and studies. In this study, a sand and dust storms index (SDSI) is developed to characterize SDS intensity by addressing the potential impacts of sand and dust storms on sensitive elements. Compared with other indices, SDSI includes four SDS-related components: SDS frequency, SDS visibility, SDS duration, and SDS wind speed. Using SDSI, this study characterizes the SDS intensity in the Three-North Forest Shelterbelt Program (TNFSP) region of China. The SDSI results show that high values of SDSI are mostly concentrated in southern Xinjiang, western and central Inner Mongolia, western and central Gansu, and northern Ningxia. By analyzing the SDSI components, over half of the stations experienced sand and dust storms no more than once per year on average. Most of the SDS events reduced horizontal visibility to less than 500 m, one-third of SDS events last more than two hours, and the wind speed of over half of the SDS events varied between 10–17 m/s. In comparison with SDS frequency, SDSI performs better in reflecting the spatial and temporal variation of SDS events. Therefore, instead of SDS frequency, SDSI can be applied to studies relevant to SDS intensity. Finally, five major SDS transportation routes were identified based on the surface prevailing wind direction, SDSI, and the existing literature. The SDS routes, combined with SDSI, could help governments and policy-makers cooperate on a regional level to combat SDS events more effectively.
The aim of this simulator study was to determine whether the effects of fog on driver behaviour were identical for a given road type and whether they could explain fog-related crashes according to road type. Thirty-three participants drove on both two-lane rural roads and motorways according to three visibility conditions (clear weather, 60 m-visibility and 30 m-visibility) and two driving situations (non-free driving and free driving). The variables were: Speeds (Ss), Headway Distances (HDs) and Headway Times (HTs). Fog was simulated using special software designed as part of the French Predit project VOIR, allowing both realistic vehicle headlights and halos to be displayed.
The results showed that the drivers decreased their speed with decreasing visibility distance, i.e., speeds were slower in the 30 m-visibility conditions than in clear conditions; but, speeds on the two-lane motorway remained faster than on the two-lane rural road, even for the denser fog. In the 30 m-visibility condition, the faster speeds driven on motorways than on two-lane rural sections violated those advocated by the French Highway Code. The distances travelled in conjunction with the speeds driven according to the two-second rule revealed that HTs less than 2 s and small HDs do not necessarily match with hazardous driving.
Intelligent transport systems have a huge importance during adverse weather conditions. These systems call the drivers' attention to possible dangers by the use of variable message signs installed along the motorways. Several researchers have dealt with the connection of weather and traffic safety in the last decades, but they have not investigated the effects of weather related messages. This paper examines the impact of weather-related warning messages on traffic in adverse weather circumstances on the Hungarian motorways. Three independent databases were analyzed in order to compare the speed-reducing effect of specific signs during different weather events and precipitate intensities.
The low-pressure (and low-oxygen) environment in plateau regions significantly impacts the driver’s perception-reaction time (PRT) and, in turn, road safety. However, drivers faced increased workloads at interchange exit areas while the Tibet region experienced a significant improvement in high-grade highway construction. Therefore, this study aimed to quantitatively analyze the influence of the plateau environment on PRT. Using a UC/win-road simulator, 6 scenarios of expressway exit ramps were constructed. Then, a total of 50 participants (35 males, 15 females) in Nanjing (altitude of 50 m) and 50 participants (36 males, 14 females) in Lhasa (altitude of 3650 m) were recruited for the simulation experiments. The driver’s PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. The no-difference in PRT for various scenarios was validated using the one-way-ANOVA test. Then, the Spearman bivariate correlation and generalized linear regression model were used to analyze the influence of driver’s age, driving experience, gender, and acclimation period on PRT. In addition, the difference in PRT of different groups of plain and plateau drivers was further analyzed using the Mann-Whiteney U and Kruskal-Wallis tests. Although driving experience and gender affected the PRT of the plain and plateau drivers, the effect was greater in the plain areas. The low-pressure environment at high altitude had a more significant impact on young novice male drivers. These findings serve as valuable references for refining the design of expressway exit ramps and improving road safety in the plateau areas.
The prediction of the likelihood of vehicle crashes constitutes an indispensable component of freeway safety management. Due to data collection limitations, studies have used mainly traffic flow-related variables to develop freeway crash prediction models but rarely have considered the effect of risky driving behavior on the likelihood of crashes. This study employed navigation software to collect driving behavior data and integrated multi-source data that include vehicle speed, traffic volume, and congestion index values. The study also employed the 'synthesizing minority oversampling technique and edited nearest neighbor' (SMOTE + ENN) coupled method for data balance processing. Three freeway crash likelihood prediction models were built based on the binomial logit, eXtreme Gradient Boosting (XGBoost), and support vector machine algorithms, respectively. The Shapley additive explanation (SHAP) algorithm was utilized to explore the effect of each feature variable on the likelihood of crashes. The results show that the prediction accuracy of the XGBoost model is the best of the three compared models. Under the optimal control-to-case ratio (1:1), the prediction accuracy of the XGBoost model reached 0.96 in this study, and the recall rate, specificity, and area-under-the-curve values were 0.86, 0.96, and 0.907, respectively. Comparative test results demonstrate that ranking risky driving behavior into three levels of intensity can effectively enhance the predictive accuracy of the XGBoost model. Moreover, the XGBoost model with its ten-minute time step outperformed the XGBoost model with its five-minute time step in terms of prediction accuracy. The results of the SHAP-based analysis show that the likelihood of highway crashes is high when the traffic congestion level is high and the distribution of the vehicle speed in the upstream roadway section is significant. Also, both sharp acceleration and sharp deceleration lead to greater likelihood of crashes. This paper aims to provide an effective framework for predicting and interpreting the likelihood of freeway crashes, thereby providing guidance for crash prevention, driver training, and the development of traffic regulations.
Traffic accidents are likely to occur on sharp curves under poor driving conditions, and the severity level of such accidents is high. Therefore, predicting the risk associated with driving on curved roadways in real time can effectively improve driving safety. This paper aims to develop a dynamic real-time method that fuses multiple data sources to predict risk when driving on sharp curves in the context of the connected vehicle environment. Six curves with three small radii (60 m, 100 m, 150 m) and two driving directions (left and right) were designed for a driving simulation experiment. Driver maneuvering data, vehicle kinematic data, and physiological data of 55 drivers were collected for this study. The data were combined and spatially and dynamically segmented. The mean value of the critical lateral acceleration of the vehicle was set as the risk assessment index. K-means clustering was used to classify the driving risk into three levels: low, medium, and high. Then, the risk level was predicted using the maneuvering data, vehicle kinematic data, and physiological data as well as road alignment characteristics as input features for the proposed model that employs the long short-term memory (LSTM) network algorithm. Models with different combinations of observation window (lookback) and interval window (delay) were compared to derive the best window combination. The algorithms selected for comparison against the LSTM algorithm are random forest, XGBoost, and LightGBM. The results show that the proposed LSTM-based method can effectively predict dangerous driving behavior on sharp curves. The optimal window combination derived using the LSTM algorithm is lookback = 20 m and delay = 20 m. The prediction performance of the proposed model is significantly better than that of the other three compared algorithms, with F1-scores of 84.8% and 86.0% for the medium and high risk categories, respectively. In addition, the proposed LSTM-based model that fuses multiple data sources is proven to outperform the model that uses only vehicle kinematics data. The dynamic prediction method proposed in this paper can contribute to the development of a real-time prediction and warning system for driving risks at vehicle terminals in the intelligent connected vehicle environment.
To understand the effect of particulate contaminants on pavement skid resistance, the friction numbers of three surfaces under different pollution conditions (particle size, coverage fraction, and shape factor) were measured using a British Pendulum Tester (BPT). The results show that less than 45% coverage fraction of the particles above 0.3 mm even results in almost 50% friction loss at a low speed of 10 km/h. In this situation, the rougher surface has a stronger holding particles capacity. Moreover, the particle size determining the lowest friction level gradually increases as the surface roughness increases. The particle size of 1.18-2.36 mm is the most typical size range leading to a low friction level. In addition, the friction first drops sharply and then increases slowly with the coverage fraction increases, which also conforms to the Stribeck curve. Highlight • The skid resistance of road surfaces covered with particles was tested. • Effects of particle size, coverage fraction, and shape on friction were analysed. • Potential hazard scenarios with particulate contaminants were discussed. ARTICLE HISTORY
Sand and dust have significant impacts on air quality, climate, and human health. To investigate the influences of dust storms on chemical characterization and source contributions of fine particulate matter (PM2.5) in areas with different distances from dust source regions, PM2.5, and associated chemical composition were measured in two industrial cities with one near sand sources (i.e., Wuhai) and the other far from sand sources (i.e., Jinan) in northern China in March 2021. Results showed that PM mass concentrations significantly increased and exceeded the Chinese National Ambient Air Quality standard during the dust events, with absolute concentrations and fractional contributions of PM2.5-bound crustal and trace elements increased while secondary inorganic ions decreased at both sites. Crustal materials dominated the increased PM2.5 mass from non-dust period to dust period in both cities. These were further evidenced by PM2.5 source apportionment results from positive matrix factorization model. During the dust events, dust sources contributed up to 88% of PM2.5 mass in Wuhai and ∼38% of PM2.5 mass in Jinan, a city about thousands of kilometers away from the sand source. Besides, the measurement data indicated that dust from northwest China may also bring along with high abundance of organic matter and vanadium. Secondary and traffic sources were two of the most important source contributors to PM2.5 in both cities during the non-dust periods. However, the near sand source city was more susceptible to the aggravating effects of dust and minerals, with much higher contributions by crustal materials (∼47%, from the aspect of chemical components) and dust-related sources (∼26%, from the aspect of sources) to PM2.5 mass even during non-dust periods. This study highlighted the urgent need for more action and effective control of sand sources to reduce the impact on air quality in downstream regions.
Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.
Particulate pollutants have a significant lubricating and abrasive effect on asphalt pavements. In this research, an experimental evaluation using an indoor accelerated wearing apparatus was used to simulate the wearing of Aeolian sand on asphalt pavements in a desert area. The British Pendulum Tester and a 3D scanner have also been used to examine the changes in skid resistance and texture morphology over time. Then, to evaluate the wearing process of sand particles on pavements, the wearing rate was proposed, and ultimately, a prediction model for the pavement with aeolian sand was constructed. The results shows that the wear rates of clean pavement, 50%Ψ and 100%Ψ of sand are 25.6%, 28.6% and 33.8% respectively. In terms of particle lubrication, the pavement is in a mixed lubrication state at 50%Ψ of sand; and in a fluid lubrication state when aeolian sand completely covers asperities of the pavement. From the wearing point of view, tire wearing on clean road is two-body wear; when there is aeolian sand between the tire/road contact interface, it is abrasive wear. The skid resistance prediction model developed by integrating the Pearson Correlation and Ridge Regression methods to identify-five texture indexes (ZMPD, ZRa, λq, ZRs, Sdr) and the British Pendulum Number (BPN) can effectively predict the lubricating and wearing effects of aeolian sand on the pavement.
Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers' performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle's brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers' speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies.
Automated Commercial Motor Vehicles (CMVs) have the potential to reduce the occurrence of crashes, enhance traffic flow, and reduce the stress of driving to a larger extent. Since fully automated driving (SAE Level 5) is not yet available, automated driving systems cannot perform all driving tasks under all road conditions. Drivers need to regain the vehicle’s control when the system reaches its maximum operational capabilities. This transition from automated to manual is referred to as Take-Over Request (TOR). Evaluating driver’s performance after TORs and assessing effective parameters have gained much attention in recent years. However, few studies have addressed CMV drivers’ driving behavior after TOR and the effect of long-automated driving and repeated TORs. This paper aims to address this gap and gain behavioral insights into CMV drivers’ driving behavior after TOR and assess the effect of the duration of automated operation before TOR, repeated TORs, and driver characteristics (e.g., age, gender, education, and driving history). To accomplish this, we designed a 40-minutes experiment on a driving simulator and assessed the responses of certified CMV drivers to TORs. Drivers’ reaction time and driving behavior indices (e.g., acceleration, velocity, and headway) are compared to continuous manual driving to measure driving behavior differences. Results showed that CMV drivers’ driving behavior changes significantly after the transition to manual regardless of the number of TORs and the duration of automated driving. Findings suggest that 30 min of automated operation intensifies the effect of TOR on driving behaviors. In addition, repeated TOR improves reaction times to TOR and reduces drivers' maximum and minimum speed after TORs. Driver’s age and driving history showed significant effects on reaction time and some driving behavior indices. The findings of this paper provide valuable information to automotive companies and transportation planners on the nature of driver behavior changes due to the carryover effects of manual driving right after automated driving episodes in highly automated vehicles.
Particulate contaminants that adhere to the surface have a major impact on asphalt pavement skid resistance. In this investigation, the skid resistance analysis system for the contaminated road was used to look at the decay characteristics of the pavement friction and the movement behavior of sand particles. To analyze the effect of aeolian sand on the skid resistance, the handheld laser scanner was used to collect the road surface macrotexture and then rebuilt the 3D model. The surface roughness of the contaminated road can be jointly characterized via the centreline average (Sa), the profile root-mean-square deviation (Sq), the height distribution symmetry (Ssk), and the height distribution kurtosis (Sku). The results show that aeolian sand has shear lubrication and rolling lubrication at the contact interface between the tire and the road. It fills the macrotexture and weakens the road's skid resistance. When there is aeolian sand adhering to the pavement, sliding and rolling friction provides the skid resistance of the asphalt road. Excessive aeolian sand has adverse long-term effects on skid resistance. It is recommended to routinely clean the road surface to maintain its original skid resistance when the amount of sand reaches to 0.625 kg/m².
Work zones are established to provide a safe environment for all road users and road workers. However, based on the statistics, they can be considered as crash prone zones due to changes in the road alignments and the posted speed limits. In this driving simulator study, we aimed at investigating the safety impacts of a newly proposed system composed of graphical and animation-based variable message signs (VMSs) in the state of Qatar. The proposed VMS condition was compared with a control condition that was designed following the Qatar Work Zone Traffic Management Guide. A total of seventy subjects were invited to participate in the experiment voluntarily. Study results showed that in the VMS condition, drivers reduced their traveling speeds in advanced compared to the control condition. Drivers’ traveling speed in the VMS condition was significantly reduced by 6.3 and 11.1 km/h on the leftmost and the second leftmost lanes, respectively. Next, the results uncovered that the proposed system motivated drivers to initiate early lane changing maneuvers, i.e., 150 m earlier than the control condition. Finally, the VMS condition was effective in stimulating drivers to keep larger headways with a merging vehicle. In sum, the proposed VMS system outperformed the control condition in terms of speed reduction, early merging, and higher headways between the through and the merging vehicle.
Car manufacturers expect driving simulators to be reliable research and development tools. Questions arise, however, as to whether drivers’ behavior on simulators exactly matches that observed when they are driving real cars. Drivers’ performances and their subjective feelings about their driving were compared between two groups during a 40-min driving test on the same circuit in a real car (n = 20) and a high-fidelity dynamic simulator (n = 27). Their speed and its variability, the braking force and the engine revolutions per minute (rpm) were recorded five times on a straight line and three times on a curve. The differences observed in these measurements between circuit driving (CD) and simulator driving (SD) from the 6th to 40th minute showed no significant changes during the drive. The drivers also completed the NASA Raw Task Load Index (NASA RTLX) questionnaire and the Simulator Sickness Questionnaire (SSQ) and estimated the ease and standard of their own driving performances. These subjective feelings differed significantly between the two groups throughout the experiment. The SD group’s scores on the NASA RTLX and SSQ questionnaires increased with time and the CD group’s perceived driving quality and ease increased with time, reaching non-significantly different levels from their usual car driving standards by the end of the drive. These findings show the existence of a fairly good match between real-life and simulated driving, which stabilized six minutes after the start of the test, regardless of whether the road was straight or curved. These objective findings and subjective assessments suggest possible ways of improving the match between drivers’ performances on simulators and their real-life driving behavior.
As a product of the shared economy, online car-hailing platforms can be used effectively to help maximize resources and alleviate traffic congestion. The driver’s behavior is characterized by his or her driving style and plays an important role in traffic safety. This paper proposes a novel framework to classify driving styles (defined as aggressive, normal, and cautious) based on online car-hailing data to investigate the distinct characteristics of drivers when performing various driving tasks (defined as cruising, ride requests, and drop-off) and undergoing certain maneuvers (defined as turning, acceleration, and deceleration). The proposed model is constructed based on the detection and classification of driving maneuvers using a threshold-based endpoint detection approach, principal component analysis, and k-means clustering. The driving styles that the driver exhibits for the different driving tasks are compared and analyzed based on the classified maneuvers. The empirical results for Nanjing, China demonstrate that the proposed framework can detect driving maneuvers and classify driving styles accurately. Moreover, according to this framework, driving tasks lead to variations in driving style, and the variations in driving style during the different driving tasks differ significantly for turning, acceleration, and deceleration maneuvers.
Problem: Evolving sandstorms on rural expressways in desert countries impair drivers' contrast vision and increase the risk of serious crashes due to delayed speed adjustments. Intelligent Transport Systems (ITS) such as Variable Message Signs (VMS) conveying warnings can be activated to address drivers’ speed adaptation before entering a low visibility zone. To improve drivers’ understanding of the hazard, a sandstorm animation visualizing turbulent sand and its consequences was designed and compared with a general warning pictogram, which is applied if no specific weather pictogram is available. Moreover, minimum warning distances of the VMS to the low visibility zone were tested (e.g., 300 m or 500 m).
Method
Sixty-three participants from the State of Qatar drove in a driving simulator through clear, transition, and low visibility conditions on a rural expressway. A repeated analysis of variances was conducted to examine the impact of the two on-road warning displays on driving behavior.
Results
The results showed that the sandstorm animation was similarly effective as a generic warning pictogram in reducing driving speeds before entering the transition and low visibility zone, irrespective of being displayed 500 m or 300 m away. However, the sandstorm animation resulted in consistent similar speed reductions within the low visibility zone, whereas the generic warning pictogram did either perform better or worse after several encounters with a sandstorm. Drivers did strongly agree that the animation is clearly referring to the issue of low visibility, which can be beneficial for recurring low visibility conditions.
Practical applications: 1.) Displaying a sandstorm animation is beneficial for rural expressway sections with recurring degrading visibility and low traffic densities, whereas a warning pictogram can be more effective in speed reductions if drivers expect additional traffic hazards. 2.) Roadway authorities have the flexibility to activate a VMS sandstorm warning even for minimum warning distances.
To determine a reasonable speed limit and ensure traffic safety in a dynamic low-visibility environment with fog, a driving simulator study was conducted. A total of 31 young participants were recruited, and each completed 5 driving simulator trials under varying visibility conditions and speed levels during the daytime. The combined coupling effect of the visibility and driving speed on drivers’ recognition times was explored, and a quantitative model of the recognition time, visibility, and driving speed was established. A determination method and suggested value of a reasonable driving speed limit in dynamic low-visibility conditions were proposed based on the stopping sight distance model. The results show that there are significant differences in the recognition times of drivers under different visibility and speed conditions. The reasonable driving speed limit values in dynamic low-visibility conditions should be based on visibility changes. When the stopping sight distance is 75 m and the visibility is less than 35 m, the speed limit should be 20 km/h. When the visibility is between 35 m and 60 m, the speed limit should be 30 km/h. When the visibility is between 60 m and 140 m, the speed limit should be 50 km/h. When the visibility is greater than 140 m, the speed limit should be 60 km/h. These research results can provide a theoretical reference for the formulation of a VSL in a dynamic low-visibility environment related to fog and reduce crash risk in conditions of inadequate visibility in fog.
Gibson and Crooks (1938) argued that a 'field of safe travel' could qualitatively explain drivers' steering behavior on straights, curved roads, and while avoiding obstacles. This study aims to quantitatively explain driver behavior while avoiding obstacles on a straight road, and quantify the 'Driver's Risk Field' (DRF). In a fixed-based driving simulator, 77 (7 longitudinal and 11 lateral) positions of the obstacles were used to quantify the subjectively perceived and objectively (maximum absolute steering angle) measured DRF for eight participants. The subjective response was a numerical answer to the question "How much steering do you think you need at this moment in time?" The results show that the propagation of the width of the DRF, along the longitudinal distance, resembled an hourglass shape, and all participants responded to obstacles that were placed beyond the width of the car. This implies that the Driver's Risk Field is wider than the car width.
Previous studies have focused on the impact of visibility level on drivers' behavior and their safety in foggy weather. However, other important environmental factors such as road alignment have not been considered. This paper aims to propose a methodology in investigating rear-end collision avoidance behavior under varied foggy conditions, with focusing on changes in visibility and road alignment in this study. A driving simulator experiment with a mixed 2 × 4 × 6 factor design was conducted using an advanced high-fidelity driving simulator. The design matrix includes two safety-critical conditions, four visibility conditions, and six road alignment situations (in terms of the road curve and slope). Behavior variables from different dimensions were identified and compared under varied conditions. To estimate the safety of drivers, a time-based measurement, speed reduction time, is selected among the variables as a measure of safety. The survival analysis approach was introduced to model the relationship between environmental factors and driver safety, using speed reduction time as the survival time. Both the Kaplan-Meier method and the COX model were applied and compared. Results generally suggest that reduced visibility leads to more dangerous rear-end collision avoidance behavior from different aspects. Though findings are mixed regarding the road alignment, the impact of the road alignment was found to be significant. Interestingly, conditions of downward slope were found to be safer. Overall, the COX model outperformed the Kaplan-Meier method in understanding the impact of environmental factors, and it can be applied to investigate other contributing factors for freeway safety under foggy weather conditions.
Aggressive driving, amongst all driving behaviors, is largely responsible for leading to traffic accidents. With the objective to improve road safety, this paper develops an on-line approach for vehicle running state monitoring and aggressive driving identification, using kinematic parameters captured by the in-vehicle recorder under naturalistic driving conditions. To characterize the roads in reality, a novel road conceptual model is proposed. It accounts for not only the curve on the horizontal plane but also the slope on the vertical plane, as well as the cross slope. For each position where the vehicle is driving, the vehicle motion is decomposed into two circular motions on the horizontal and vertical planes. On each plane, the vehicle maneuver is first identified. Then, aggressive driving is identified according to the limit equilibrium of driving safety or comfortability. Based on the proposed method called “three-elements”, the vehicle maneuver, radius and slope angle on the vertical plane can be solved in an on-line manner. The novel approach is an elaborate analytical model with clear physical meaning but small computation load, and therefore is potential to be implemented in the mobile devices to assist in real-time aggressive driving identification and labeling. The developed approach is applied to a real case on the curved and sloped route in Nanjing, China. Empirical results of extensive experiments, based on the kinematic parameters collected from the in-vehicle data recorder under naturalistic driving conditions, demonstrate that aggressive driving behaviors are mostly found on the pavements with curve and slope, and can be identified by the developed approach.
This paper focuses on the behaviours adopted by road users when negotiating horizontal curves with sight limitations. Experiments at a driving simulator were conducted on two-lane highways in which drivers were confronted with a range of sight conditions generated by the manipulation of variables such as curve direction, radii and distance of lateral sight obstructions along horizontal curves. It was observed that most of the drivers adopted strategies which resulted in a stopping distance shorter than the available sight distance, thereby maintaining safe driving conditions. Some drivers reduced their speed, some increased the lateral distance from any sight obstructions along the roadside, some did both, while others did neither. A preliminary analysis indicated that the safety benefits resulting from a vehicle speed reduction strategy significantly outweigh those from a lateral shift in the lane. Further analyses on the 1246 cases investigated offered further support for this proposition, while revealing that a higher proportion of drivers opted for the first strategy for safety reasons. Moreover, visibility conditions (safe, partially safe, and unsafe) played a role in the choice of driving strategies. Results provide evidence that a significant group of drivers used the two strategies under severely restricted visibility conditions (i.e., along sharp radius curves); however, the strategies selected were independent of the driver speed profile (i.e., slower, average, or faster).
In response to developing and/or diminishing foggy conditions, the variable speed limit application in a connected vehicle environment (CV-VSL) can estimate and deliver recommended travel speeds to individual drivers, which can help to reduce crashes when visibility conditions change. This study aims to quantify the effectiveness of the CV-VSL application by exploring drivers’ reactions to warnings (e.g., recommended travel speeds). In order to analyze the effectiveness of the CV-VSL application, a connected vehicle testing platform was established based on a driving simulator, and characteristics of the drivers’ speed adjustments after receiving warnings were analyzed with respect to different levels of visibility (i.e., no fog, slight fog, and heavy fog). This study also examined the effect of warnings on drivers in different impact zones (i.e., clear zone, transition zone, and fog zone). Three indicators were identified: 1) speed at the end of the clear zone, 2) maximum deceleration rate in the transition zone, and 3) average speed reduction in the fog zone. Throughout the experiment, the relationship between speed adjustments and the level of visibility was explored. The results indicated that the CV-VSL application is effective in making drivers reduce travel speeds in all three types of zones. Furthermore, it appeared that the CV-VSL application could help manage travel speeds prior to vehicles entering the transition zone, and influence drivers’ braking decisions upon encountering reduced visibility. It was also found that the CV-VSL application was more effective in heavy fog conditions than in light fog conditions. The connected vehicle testing platform based on the driving simulator provided a new method for evaluating the effectiveness of in-vehicle messaging generated by connected vehicle applications.
This paper studies the effectiveness of fog warning systems on driving performance and traffic safety in heavy fog condition. A comparison study was conducted for four scenarios in heavy fog condition. First, a series of indexes corresponding to driving speed adjustments and surrogate measures of safety was obtained to explore the impacts that fog warning systems have on driving behavior and traffic safety when approaching a fog area. This study divided the analyzed road into three different zones (clear zone, transition zone, and fog zone) according to visibility levels. Then, multivariate analysis of variance (MANOVA) was conducted, and the effects of drivers' individual characteristics on driving behavior were also investigated. Moreover, the linear mixed model with random effects was estimated to consider the contributing factors of the drivers' speed adjustment behaviors. In addition, the standard deviation of speed, TET (time exposed time-to-collision), and TIT (time integrated time-to-collision) were selected to evaluate the longitudinal safety. To obtain the driving data, an empirical driving simulator platform was established based on a real-world road in Beijing. Thirty-five drivers were recruited to participate in the driving experiment. The results showed that the cooperative vehicle-infrastructure warning systems could be beneficial to better driving behavior and safer traffic operations. The results revealed that the warning systems could be beneficial to speed reduction before entering a fog area. In addition, the On-Board Unit (OBU) had a significant impact on individual speed adjustment. Moreover, the results showed that scenarios with fog warning systems improve safety significantly over the no warning system scenario. The study results could also facilitate the selection of a proper information release format in the context of connected vehicles.
Background: Driving simulators have become an effective research tool in traffic safety, but the validity of results obtained in simulated environments remains a debated issue of high importance. Objective: The objective of this study is to validate a fixed-base driving simulator for speed perception and actual speed and to support its application in traffic safety studies. Method: The study consisted of two experiments to test the external and subjective validity of the driving simulator in absolute and relative terms. External validity was framed into two parts i.e. for speed perception and actual speed. In the first part, the external validity was assessed based on the speed perception observations from forty volunteers that participated in the study. Speed estimations for four different requested speeds (50, 70, 80 and 100 kph) were recorded under two conditions: speedometer hidden and speedometer revealed. In the second part, the external validity was assessed based on the comparison of actual speed observations from field and simulator. The subjective validity of the simu-lator setting was assessed through a questionnaire. Results: Results from both experiments showed correspondence of the driving behavior between the simulator and real-world settings. In general, the profiles for estimated speed and actual speed followed a significantly similar tendency and indicated relative validity in both experiments. Moreover, external absolute validity for speed perception was established on all the requested speeds with speedometer hidden while only for the requested speed of 80 kph with speedometer revealed. Participants' evaluation of the quality and performance of the driving simulator supported the subjective validity of the simulator setting. Conclusion: The fixed-base driving simulator used in this study can be considered as a useful tool for research on actual speed and speed perception.
Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.
Previous studies have shown the effect of a lead vehicle’s speed, deceleration rate and headway distance on drivers’ brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle’s speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ ̇ of the lead vehicle on the driver’s retina, and inverse tau τ‾¹, the ratio between θ ̇ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ‾¹ as the measure of looming fitted better than those using θ ̇, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ‾¹.
Fog warning systems can convey warning messages to drivers and help to reduce crashes that may occur due to the sudden occurrence of low visibility conditions. This study aims to assess the effectiveness of real-time fog warning systems by quantifying and characterizing drivers’ speed adjustments under different roadway types, traffic conditions, and fog levels. In order to explore how a driver perceives the fog warning systems (i.e., beacon and dynamic message signs (DMS)) when approaching a fog area, this paper divides the roads into three zones (i.e., clear zone, transition zone, fog zone) according to visibility levels and suggests a hierarchical assessment concept to explore the driver’s speed adjustment maneuvers. For the three different zones, different indexes are computed corresponding to drivers’ speed adjustments. Two linear regression models with random effects and one hurdle beta regression model are estimated for the indexes. In addition, the three models were modified by allowing the parameters to vary across the participants to account for the unobserved heterogeneity. To validate the proposed analysis framework, an empirical driving simulator study was conducted based on two real-world roads in a fog prone area in Florida. The results revealed that the proposed modeling framework is able to reflect drivers’ speed adjustment in risk perception and acceleration/deceleration maneuvering when receiving real-time warning massages. The results suggested that installing a beacon could be beneficial to speed reduction before entering the fog area. Meanwhile, DMS may affect drivers’ brake reaction at the beginning section of reduced visibility. However, no effects of warning systems for drivers’ final speed choice in the fog can be observed. It is suggested that proper warning systems should be considered for different conditions since they have different effects. It is expected that more efficient technology can be developed to enhance traffic safety under fog conditions with a better understanding of the drivers’ speed adjustments revealed in this study.
When a highly automated car reaches its operational limits, it needs to provide a takeover request (TOR) in order for the driver to resume control. The aim of this simulator-based study was to investigate the effects of TOR modality and left/right directionality on drivers' steering behaviour when facing a head-on collision without having received specific instructions regarding the directional nature of the TORs. Twenty-four participants drove three sessions in a highly automated car, each session with a different TOR modality (auditory, vibrotactile, and auditory-vibrotactile). Six TORs were provided per session, warning the participants about a stationary vehicle that had to be avoided by changing lane left or right. Two TORs were issued from the left, two from the right, and two from both the left and the right (i.e., nondirectional). The auditory stimuli were presented via speakers in the simulator (left, right, or both), and the vibrotactile stimuli via a tactile seat (with tactors activated at the left side, right side, or both). The results showed that the multimodal TORs yielded statistically significantly faster steer-touch times than the unimodal vibrotactile TOR, while no statistically significant differences were observed for brake times and lane change times. The unimodal auditory TOR yielded relatively low self-reported usefulness and satisfaction ratings. Almost all drivers overtook the stationary vehicle on the left regardless of the directionality of the TOR, and a post-experiment questionnaire revealed that most participants had not realized that some of the TORs were directional. We conclude that between the three TOR modalities tested, the multimodal approach is preferred. Moreover, our results show that directional auditory and vibrotactile stimuli do not evoke a directional response in uninstructed drivers. More salient and semantically congruent cues, as well as explicit instructions, may be needed to guide a driver into a specific direction during a takeover scenario.
Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.
For decades, the importance of highway work zone safety has increased considerably with the continual increase in the number of highway work zones present on highways for repairs and expansion. Rural work zones on two-lane highways are particularly hazardous and cause a significant safety concern due to the disruption of regular traffic flow. In this study, researchers determined motorists’ responses to warning signs in rural, two-lane highway work zones. The researchers divided vehicles into three classes (passenger car, truck, and semitrailer) and compared the mean change in speed of these classes based on three different sign setups: portable changeable message sign (PCMS) OFF, PCMS ON with the message of Slow Down, Drive Safely, and a temporary traffic sign (W20-1, “Road Work Ahead”). Field experiments were conducted on two two-lane work zones with flagger control. Statistical analyses were performed to determine whether there was a significant interaction between motorists’ responses and the sign setups. Data analysis results show that a visible PCMS, either turned on or off, was most effective in reducing truck speeds in rural, two-lane work zones. The temporary traffic sign (W20-1) was more effective in reducing the vehicle speeds of passenger car and semitrailer. Results of this research project will help traffic engineers to better design the two-lane work zone setup and take necessary safety countermeasures to prevent vehicle crashes.
Car Following Behavior of An Expressway Driver in Fog Environment
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