Drivers Encounter Research Articles

Short-Term Origin-Destination Demand Prediction Based on Spatiotemporal Encoder-Decoder Network with a Residual Feature Extractor

Online ride-hailing services play a crucial role in daily transportation, However, challenges persist in certain regions with limited access, and drivers encounter difficulties in receiving orders. Accurate prediction of short-term origin-destination (OD) demand is crucial for addressing these issues. This study leverages recent advancements in artificial intelligence and big data to introduce a spatiotemporal encoder-decoder network with a residual feature extractor (RF-STED) for short-term OD demand prediction in online ride-hailing services. The RF-STED model, built on deep learning models such as graph convolutional networks and convolutional long short-term memory (Conv-LSTM), includes spatiotemporal networks, encoding layers, and a residual feature extractor. The spatiotemporal network has two branches: branch one processes multi-pattern OD data using a multi-pattern temporal feature extraction module, utilizing a multi-channel Conv-LSTM to capture temporal correlations. Branch two utilizes a multi-spatial feature extraction module to convert OD pair associations into a spatial topology, extracting multi-spatial correlations. The encoding layer captures spatiotemporal dependencies, while the residual feature extractor decodes compressed vectors back into an OD graph for forecasting future demand. Experiments with a Manhattan taxi dataset in the U.S. show the RF-STED model outperforms 10 baseline models and four ablation models. The results emphasize the model’s strength and robustness in short-term OD flow prediction.

Drivers’ Decision-Making When Experiencing a Traffic Conflict: A Scoping Review

When drivers encounter a traffic conflict, making a prompt and correct decision to take evasive actions is crucial to prevent collisions. Therefore, studying information processing in drivers’ decision-making is essential for driving safety. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews reporting guidelines, the aim of this scoping review was to map current studies on drivers’ decision-making in such situations. Our search strategy identified 2,506 peer-reviewed articles, out of which 20 were selected. The results indicated that the selected studies mainly measured drivers’ decision-making less holistically by focusing only on the perception phase instead of measuring the entire information processing phase involved in decision-making. Additionally, these studies explored interactions between drivers, vehicles, and the environment less comprehensively. There are opportunities to explore drivers’ decision-making in traffic conflict situations further, and this scoping review presents several challenges for further research and some strategies for overcoming them.

Driver drowsiness detection and smart alerting using deep learning and IoT

Drowsiness is a common problem that many drivers encounter due to long working hours, lack of sleep, and tiredness. Tired drivers are as dangerous as drunk drivers because they have slower reaction times and suffer from reduced attention, awareness, and ability to control their vehicles. Drowsy driving causes many traffic accidents, especially fatal crashes. Therefore, the best way to prevent accidents involving drowsiness is to alert the drivers ahead of time. The accuracy of the drowsiness prediction reduces if the studies only focus on facial landmarks, ignoring other fatigue features such as tilting head, blinking, and yawning. To solve these problems, we propose an approach to detect driver drowsiness efficiently and accurately using IoT and deep neural networks improved from LSTM, VGG16, InceptionV3, and DenseNet. The use of transfer learning technique combined with multiple drowsiness signs is to improve the accuracy of the drowsiness detection in various driving conditions. The time-varying factor is also taken into consideration in the models developed from LSTM and DenseNet. When the driver’s fatigue is detected, the IoT module emits a warning message along with a sound through a Jetson Nano monitoring system. The experimental results demonstrate that our approach using deep neural networks can achieve high accuracy of up to 98%. Notably, this approach has also been verified in cases with/without wearing a mask and glasses. This has a practical meaning in the Covid-19 pandemic situation when everyone needs to comply with the wearing of masks in public places.

Driving Risk Analysis Based on Driving Experience at Hook-Turn Intersection Using the Emerging Virtual Reality Technology

The hook turn, which is rarely seen outside of Melbourne, Australia, reduces congestion in narrow road spaces shared with trams. Australia allows people from 44 nations to convert their home country driver’s license to an Australian driver’s license without a driving test. Visitors who have never heard of the hook-turn experience difficulty driving following the new traffic rule. From this aspect, investigating how inexperienced drivers encounter the hook-turn intersection is valuable for safety reasons. A driving simulator including virtual reality technology is developed to evaluate the level of safety of human driving behavior. The simulator in this research was developed by integrating Vissim and Unity3D embedded head-mounted display and driving devices to ensure a better driving experience. This research presented the development of a robust virtual reality driving simulator. It investigated how nonexperienced drivers respond to a completely new road condition. The results were compared with microsimulation outcomes (here, Vissim). The results showed that a human-driven car had a higher collision risk than a computer-driven car. The trajectories of the driver type were statistically different (t = 6.03, p 0.01, in the case of time-to collision ≤1.5 between experienced and computerized drivers). Participant responses to a postexperiment survey found that the simulator was realistic (4.31 out of 5.00), which could help beginner drivers (4.00 out of 5.00). Therefore, the simulator can be utilized for safety-related research as well as drivers’ training.

Using social media to understand challenges faced by US urban parcel delivery drivers: Reports from the curb

While several studies have begun to quantify the problems presented by curb conflicts surrounding delivery vehicles, especially unauthorized parking behavior, fewer have specifically examined these issues through the lens of driver behavior. To gain on-the-ground insight into competition for curb space, what issues drivers face, and the circumstances that inform their parking choices and related behaviors, we use data from Reddit to examine US driver perceptions and behavior, posing the following questions: What are some key challenges parcel delivery drivers encounter when delivering in urban areas? What strategies do drivers employ when parking their vehicles to make deliveries? What reasons do drivers cite for engaging in unauthorized or questionable parking practices?We find that parking is among the largest challenges drivers face while delivering in urban areas, largely because parking difficulties extend the time required to complete many routes. Drivers in our sample preferred to park in authorized spaces, but generally accepted the practice of unauthorized parking to complete their routes. Often, they did so due to lack of available parking, but also for safety and/or expedience. Moreover, drivers reported that parking enforcement personnel rarely issued tickets or other reprimands, acknowledging that for delivery vehicles, unauthorized parking is often necessary. Finally, drivers also described concerns surrounding interactions with other road users while making deliveries, especially in terms of conflict and safety. Curb management policies and freight providers’ practices alike will need to adapt in the face of the changing landscape of the curb.

Digital Fuel Measuring Techniques: A Review

Abstract: There have been significant advancements in gasoline metering technology during the last few years. In any event, a key difficulty that all drivers encounter is the inability to obtain volumetric data for the remaining gasoline level, primarily while refuelling the car or driving at reserved fuel levels. As a result, customers find up paying for fuel they don't have. This paper gives an overview of several approaches involving numerous sensors that have been designed to generate a consistent indication of the amount of fuel available. In this study, capacitive, potentiometric, fibre optic, electromagnetic, ultrasonic, and load cell sensors are explored. The aim of this study is to present a report on fuel monitoring strategies that rely on these six sensors. This investigation will aid other researchers working in this field in selecting a sensor for their project. Keywords: fuel measuring, capacitive, electromagnetic, potentiometric, fibre optic, ultrasonic, and load cell.

REAR SIDE CONSPICUITY OF HEAVY GOOD VEHICLES BASED ON LIGHTING COMPONENTS

In Malaysia, accidents that involve collisions with rigid and articulated trucks are mainly related to the conspicuity issue i.e. the issue of the conspicuity of the lighting components on heavy goods vehicles (HGVs). Thus, visibility and conspicuity must be improved because such an improvement can help drivers encounter safety measures on the psychological phenomenon. Conspicuity is the ability of a vehicle to be noticed without observers intentionally searching for it. In Malaysia, HGVs must be equipped with lighting components for improved visibility. The study aims to analyse the percentages of lighting components that comply with the MS ISO 303:2004. This research is conducted at Jawi Toll Plaza, Nibong Tebal, Pulau Pinang from 9:00 a.m. to 5:00 p.m. Results show that 93% of two-axle box trucks have installed rear lighting components that adhere to the standard. Meanwhile, other categories are between 38% and 91%. In conclusion, even though Heavy Goods Vehicles recorded as having high compliance levels of the rear lighting components, the accident still occurred. The types of crashes are based on the inadequate visibility involved when the moving vehicles are approaching the rear or sides of slow-moving or stationary vehicles at night.

Does Music Help to Stay Focus on the Road?

Attention in the physiological definition in one of several simultaneous occurrences of ideas takes ownership of the mind which means removing certain stuff to cope efficiently with others. Particular attention is required in driving in order to maintain track of the driver's vigilance to prevent the road accident. In this study, the driver's attention is analyzed by their driving performance from both the amplitude and accident rating of Electroencephalographic (EEG). The subjects were stimulated with radio talk, instrumental music and no sound (silent) in three separate recordings. It was found that drivers encounter much fewer accidents while listening to the radio talk compared to the music and worse driving score when driving in silence. This finding could be used as a foundation to develop tips or methodology to avoid the attention deficit while driving.

3D Printing Conformal K-band Lens Antenna for a Smart Parking Space Detection System

With the widespread use of vehicles, finding an empty parking space in a parking lot has become a problem that most drivers encounter daily, particularly in urban environments. In this article, a 3-D printing high-gain dielectric lens $K$ -band antenna is designed for a smart parking radar system that performs dual-mode detection by using a magnetic sensor to improve the accuracy of parking space detection. The proposed antenna uses a wideband high-efficiency electromagnetic structure (WHEMS) as the primary feed antenna. The primary antenna is etched on a substrate and can be easily integrated with a radio frequency (RF) circuit. The WHEMS antenna has a wide bandwidth that is not influenced by instability in the substrate permittivity. Benefiting from low-cost, fast prototyping 3-D printing technology, a conformal flat semiclosed perforation dielectric lens is designed to achieve a high gain; this lens avoids the measurement unreliability caused by large penetration losses in the $K$ -band. The $K$ -band radar operates at 24–24.25 GHz, with a measured gain of 18.4 dBi. Measurements obtained from the developed system reveal that the proposed high-gain, low-cost, lightweight, durable, integrated design approach is well-suited for a $K$ -band radar smart parking system.

Urban Traffic Coulomb’s Law: A New Approach for Taxi Route Recommendation

Recently, an increased amount of effort has been focused on optimizing the selection of routes for taxis, as part of the development of smart urban environments, and the increase of the accumulated trajectory data sets. One challenging issue is to match and recommend appropriate cruising routes to taxis, as most taxis cruise on streets aimlessly looking for passengers. Drivers encounter lots of difficulty in optimizing their cruise routes and hence increasing their incomes, and such inability not only decreases their profit but also increases the traffic load in urban cities. In this paper, the concept of urban traffic Coulomb’s law is coined to model the relationship between taxis and passengers in urban cities, based on which a route recommendation scheme is proposed. Taxis and passengers are viewed as positive and negative charges. It first collects useful information such as the density of passengers and taxis from trajectories, then calculates the traffic forces for cruising taxis, based on which taxis are routed to optimal road segments to pick up desired passengers. Different from existing route recommendation methods, the relationship among taxis and passengers are fully taken into account in the proposed algorithm, e.g., the attractiveness between taxis and passengers, and the competition among taxis. Moreover, real-time dynamics and geodesic distances in road networks are also considered to make more accurate and effective route recommendations. Extensive experiments are conducted on the road network using the trajectories generated by approximately 5,000 taxis to verify the effectiveness, and evaluations demonstrate that the proposed method outperforms existing methods and can increase the drivers’ income more than 8%.

Delivery truck drivers\u2019 work outside the cab: psychosocial discomforts and risks based on participatory video analyses

PurposeDelivery truck drivers face various physical and psychosocial discomforts and risks in their work. Psychosocial perceptions are linked to physiological and psychological loads—strain and stress—affecting drivers throughout various mechanisms within activities and conditions. In this study, participatory video-assisted analyses were utilised for identifying psychosocially demanding work situations that delivery truck drivers encounter outside the cab.MethodsIdentifications were made by the drivers from previously recorded videos of their own work in their daily work environments. In addition, other stakeholders, such as managers and designers, also identified situations. The video identification data were further processed by the researchers, showing differences between the perceptions of the drivers and stakeholders on the causal conditions and intervening conditions behind the discomfort identifications.ResultsAll together 99 identified situations—over half (53%) of which included a fear of causing different types of undesired events with risks of losses, such as human injuries or material damages. The results showed not only do risks and discomforts exist in demanding work situations, which seemed relevant, but they also indicated the importance of involving different stakeholders.ConclusionsThis study provides a unique methodological approach, as video observations and analyses and qualitative data analysis are combined to provide more in-depth data with visualizations into risk management processes.

A questionnaire survey on road rage and anger-provoking situations in China

This paper surveys the reactions of Chinese drivers when encountering anger-provoking situations, including traffic congestion, pedestrians crossing the street illegally, being flashed by the high beams of cars traveling in the opposite direction, aberrant overtaking by other cars and when the car ahead drives slowly. A questionnaire survey found that 69.4% of participants wait when encountering traffic congestion and that 71% of drivers tolerate pedestrians crossing the street illegally; moreover, 61.3% of drivers are “angry but tolerant” when encountering aberrant overtaking. However, 51.3% of drivers become enraged when flashed by the high beams of cars traveling in the opposite direction, and 34.1% of participants turn on their own high beams to fight back. Moreover, 61.4% of participants are dissatisfied when the car ahead drives slowly or fails to move when a traffic light turns green, and 53% of participants honk or flash their lights to prompt the driver of the car ahead. The results show that males become irritated more easily than females in all situations, except those in which pedestrians cross the street illegally. Age is a factor only when drivers are flashed by high beams or overtaken by other cars illegally. Driving experience has an effect when drivers encounter traffic congestion, are flashed by high beams, or are overtaken by other cars illegally or when the car ahead drives slowly; novices with fewer than two years of driving experience display greater tolerance for these events. The occupation of a driver acts on his/her responses when he/she is overtaken by other cars illegally or flashed by high beams or when pedestrians cross the street illegally. For the most effective measures to prevent road rage, 53.64% of participants chose “plan the trip in advance”, 57.14% chose “strengthen law enforcement”, and 71.5% chose “improve public transportation”. Females, young people, and novices pay more attention to these measures.

Analysis of drivers’ errors at roundabouts in Morocco

Roundabouts have increased in a significant way in recent years in Moroccan cities as they have been seen as a solution to congestion problems and traffic safety issues. However, roundabouts are relatively new to Moroccan drivers and the rules of priority to which the users are accustomed are reversed in roundabouts. This can lead to confusion and misleading. The overall objective of this study is to identify roundabout features that appear to play a role in the difficulties that drivers encounter when crossing roundabouts. This paper will present a comprehensive understanding of what is required of drivers when crossing roundabouts. We then predict failures that can occur at roundabout using our driver error taxonomy. A case study of two roundabouts in Morocco is analyzed as illustration. The paper concludes with implications of the findings to future design.

Empirical Analysis of Chinese Labor Supply Decisions from a Dynamic Perspective: Evidence from Hangzhou Cabdrivers

According to the life-cycle theory of labor supply, labor supply of workers responds positively to short-term wage volatility. However, many researches find the elasticity of foreign cabdrivers' labor supply to wages is negative. How about Chinese cabdrivers? This paper uses GPS trip information of Hangzhou cabs to investigate the endotaxy-based and extensionality-based labor supply of drivers. To address the endophytism problem caused by measurement error and peer effect, it uses the number of fares with long trips the drivers encounter that very day as the instrument variable for earnings per hour to analyze the influence of earnings per hour on drivers' labor supply. It argues that the elasticity of endotaxy-based and extensionality-based labor supply of Hangzhou cabdrivers to wages is significantly positive, showing that government policies such as the increase in oil subsidies are actually effective incentives to raise cabdrivers' labor supply. Meanwhile, it also implies that the release from the price regulation of taxi market can help drivers to optimize their work decisions better according to the market demands.

Implications of advanced warning messages on eliminating sun glare disturbances at signalized intersections

Abstract Due to sun glare disturbances, drivers encounter fatal threats on roadways, particularly at signalized intersections. Many studies have attempted to develop applicable solutions, such as avoiding sun positions, applying road geometric re-directions, and wearing anti-glare glasses. None of these strategies have fully solved the problem. As one of the “Connected Vehicle” practices proposed by the U.S. Department of Transportation, advanced warning messages (AWMs) are capable of providing wireless information about traffic controls. AWM acts as a supplement to conventional signs and signals, which can be blocked by obstacles or natural disturbances, such as sun glare. The drivers' smart advisory system (DSAS) can provide drivers with AWM. Using a driving simulator this research explores the effects of DSAS messages on driving behaviors under sun glare disturbance. Statistical analyses were applied to assess (1) the negative impacts of sun glare, (2) the compensation of the DSAS AWM to sun glare effects, and (3) the improvement in driving performance due to DSAS AWM. Four performance indexes were measured, including (1) half kinetic energy speed, (2) mean approach speed, (3) brake response time, and (4) braking distance. The effects of the socio-demographic factors, such as gender, age, educational background, and driving experience were also studied. The analytical results illustrate that the DSAS can compensate for reduced visibility due to sun glare and improve driving performance to a normal visual situation, particularly for left turn and through movement.

Does assisted driving behavior lead to safety-critical encounters with unequipped vehicles’ drivers?

With Intelligent Transport Systems (e.g., traffic light assistance systems) assisted drivers are able to show driving behavior in anticipation of upcoming traffic situations. In the years to come, the penetration rate of such systems will be low. Therefore, the majority of vehicles will not be equipped with these systems. Unequipped vehicles’ drivers may not expect the driving behavior of assisted drivers. However, drivers’ predictions and expectations can play a significant role in their reaction times. Thus, safety issues could arise when unequipped vehicles’ drivers encounter driving behavior of assisted drivers. This is why we tested how unequipped vehicles’ drivers (N=60) interpreted and reacted to the driving behavior of an assisted driver. We used a multi-driver simulator with three drivers. The three drivers were driving in a line. The lead driver in the line was a confederate who was followed by two unequipped vehicles’ drivers. We varied the equipment of the confederate with an Intelligent Transport System: The confederate was equipped either with or without a traffic light assistance system. The traffic light assistance system provided a start-up maneuver before a light turned green. Therefore, the assisted confederate seemed to show unusual deceleration behavior by coming to a halt at an unusual distance from the stop line at the red traffic light. The unusual distance was varied as we tested a moderate (4m distance from the stop line) and an extreme (10m distance from the stop line) parameterization of the system. Our results showed that the extreme parametrization resulted in shorter minimal time-to-collision of the unequipped vehicles’ drivers. One rear-end crash was observed. These results provided initial evidence that safety issues can arise when unequipped vehicles’ drivers encounter assisted driving behavior. We recommend that future research identifies counteractions to prevent these safety issues. Moreover, we recommend that system developers discuss the best parameterizations of their systems to ensure benefits but also the safety in encounters with unequipped vehicles’ drivers.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

Logit-Based Analysis of Drivers' Crossing Behavior at Unsignalized Intersections in China.

This study aims to investigate the crossing behavior of straight-moving drivers when they encounter other straight-moving drivers at unsignalized intersections in China. In China, when two vehicle drivers encounter at an unsignalized intersection, neither driver completely stops his or her vehicle in most cases. Instead, one driver gradually approaches the intersection and dynamically decides to either yield or preempt by gaming with the other vehicle. This process increases the probability of accidents. A total of 305 crossing cases were collected at an unsignalized intersection in Kunming City. Motion parameters were extracted from the video detection program designed by our research group. Based on a logistic regression method, we analyzed decision making moment of straight-moving drivers under crossing conditions, established crossing behavior models, and identified the main factors that affected drivers' decisions. In all, 68 cases observed at a separate intersection were used to validate the established models. For crossing processes at unsignalized intersections in China, straight-moving drivers from the right side completed preemptive/yielding decisions at 1.3 s before reaching the crossing point. However, the majority of straight-moving drivers from the left side completed decisions at 1.1 s before reaching the crossing point. The most important parameter that influenced the drivers' decisions was the difference between the speeds of the two vehicles. An effective method for preventing traffic conflict between two straight-moving drivers is to control the speeds of the vehicles before they enter the intersection. This study explores crossing behavior of straight-moving drivers and provides significant insights for controlling driver behavior.

Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections

Drivers’ indecisiveness in dilemma zones (DZ) could result in crash-prone situations at signalized intersections. DZ is to the area ahead of an intersection in which drivers encounter a dilemma regarding whether to stop or proceed through the intersection when the signal turns yellow. An improper decision to stop by the leading driver, combined with the following driver deciding to go, can result in a rear-end collision, unless the following driver recognizes a collision is imminent and adjusts his or her behavior at or shortly after the onset of yellow. Considering the significance of DZ-related crashes, a comprehensive safety measure is needed to characterize the level of safety at signalized intersections. In this study, a novel safety surrogate measure was developed utilizing real-time radar field data. This new measure, called safety surrogate histogram (SSH), captures the degree and frequency of DZ-related conflicts at each intersection approach. SSH includes detailed information regarding the possibility of crashes, because it is calculated based on the vehicles conflicts. An example illustrating the application of the new methodology at two study sites in Virginia is presented and discussed, and a comparison is provided between SSH and other DZ-related safety surrogate measures mentioned in the literature. The results of the study reveal the efficacy of the SSH as complementary to existing surrogate measures.

Traffic Safety at Road–Rail Level Crossings Using a Driving Simulator and Traffic Simulation

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic micro-simulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers' behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.