Exploring the effects of the location of the lane-end sign and traffic volume on multistage lane-changing behaviors in work zone areas: A driving simulator-based study

2018 ◽  
Vol 58 ◽  
pp. 980-993 ◽  
Author(s):  
Junyu Hang ◽  
Xuedong Yan ◽  
Lu Ma ◽  
Ke Duan ◽  
Yuting Zhang
Keyword(s):  
Driving Simulator ◽  
Traffic Volume ◽  
Work Zone ◽  
Lane Changing

scholarly journals Improved Road Safety at Work Zones using Advanced Traveler Information Systems

2020 ◽  
Author(s):  
Mustafa Suhail Almallah ◽  
Qinaat Hussain ◽  
Wael K. M Alhajyaseen ◽  
Tom Brijs
Keyword(s):  
Traffic Safety ◽  
Driving Simulator ◽  
Work Zone ◽  
Speed Limit ◽  
Work Zones ◽  
Treatment Scenario ◽  
Lane Changing ◽  
Variable Message Signs ◽  
Study Results ◽  
Message Signs

Work zones are road sections where road construction or maintenance activities take place. These work zones usually have different alignment and furniture than the original road and thus temporary lower speeds are adopted at these locations. However, drivers usually face difficulty in adopting the new speed limit and maneuvering safely due to the change in alignment. Therefore, work zones are commonly considered as hazardous locations with higher crash rates and severities as reported in the literature. This study aims to investigate the effectiveness of a variable message signs (VMSs) based system for work zone advance warning area. The proposed system aims at enhancing driver adaptation of the reduced speed limit, encourage early lane changing maneuvers and improve the cooperative driving behavior in the pre-work zone road section. The study was conducted using a driving simulator at the College of Engineering of Qatar University. Seventy volunteers holding a valid Qatari passenger car driving license participated in this study. In the simulator experiment, we have two scenarios (control and treatment). The control scenario was designed based on the Qatar Work Zone Traffic Management Guide (QWZTMG), where the length of the advance warning area is 1000 m. Meanwhile, the treatment scenario contains six newly designed variable message signs where two of them were animation-based. The VMSs were placed at the same locations of the static signs in the control scenario. Both scenarios were tested for two situations. In the first situation, the participants were asked to drive on the left lane while in the second situation, they were instructed to drive on the second lane. The study results showed that the proposed system was effective in motivating drivers to reduce their traveling speed in advance. Compared to the control scenario, drivers’ mean speed was significantly 6.3 and 11.1 kph lower in the VMS scenario in the first and second situations, respectively. Furthermore, the VMS scenario encouraged early lane changing maneuvers. In the VMS scenario, drivers changed their lanes in advance by 150 m compared to the control scenario. In addition, the proposed system was effective in motivating drivers to keep larger headways with the frontal merging vehicle. Taking into account the results from this study, we recommend the proposed VMS based system as a potentially effective treatment to improve traffic safety at work zones.

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

2021 ◽  
pp. 036119812110186
Author(s):  
Samira Ahangari ◽  
Mansoureh Jeihani ◽  
Anam Ardeshiri ◽  
Md Mahmudur Rahman ◽  
Abdollah Dehzangi
Keyword(s):  
Random Forest ◽  
Driving Simulator ◽  
Situational Awareness ◽  
True Positive Rate ◽  
The United States ◽  
Driving Performance ◽  
Lane Changing ◽  
Wide Range ◽  
Positive Rate ◽  
Better Than

Distracted driving is known to be one of the main causes of crashes in the United States, accounting for about 40% of all crashes. Drivers’ situational awareness, decision-making, and driving performance are impaired as a result of temporarily diverting their attention from the primary task of driving to other unrelated tasks. Detecting driver distraction would help in adapting the most effective countermeasures. To tackle this problem, we employed a random forest (RF) classifier, one of the best classifiers that has attained promising results for a wide range of problems. Here, we trained RF using the data collected from a driving simulator, in which 92 participants drove under six different distraction scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performance measures such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Using the RF method, we achieved 76.5% prediction accuracy on the independent test set, which is over 8.2% better than results reported in previous studies. We also obtained a 76.6% true positive rate, which is 14% better than those reported in previous studies. Such results demonstrate the preference of RF over other machine learning methods to identify driving distractions.

Development of Operation Guidelines for Leader–Follower Autonomous Maintenance Vehicles at Work Zone Locations

2021 ◽  
pp. 036119812110566
Author(s):  
Qing Tang ◽  
Xianbiao Hu ◽  
Ruwen Qin
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Work Zone ◽  
Lane Changing ◽  
The Public ◽  
Car Following ◽  
Technical Requirements ◽  
Car Following Model ◽  
Niche Area

The rapid advancement of connected and autonomous vehicle (CAV) technologies, although possibly years away from wide application to the general public travel, are receiving attention from many state Departments of Transportation (DOT) in the niche area of using autonomous maintenance technology (AMT) to reduce fatalities of DOT workers in work zone locations. Although promising results are shown in testing and deployments in several states, current autonomous truck mounted attenuator (ATMA) system operators are not provided with much practical driving guidance on how to drive these new vehicle systems in a way that is safe to both the public and themselves. To this end, this manuscript aims to model and develop a set of rules and instructions for ATMA system operators, particularly when it comes to critical locations where essential decision making is needed. Specifically, three technical requirements are investigated: car-following distance, critical lane-changing gap distance, and intersection clearance time. Newell’s simplified car-following model, and the classic lane-changing behavior model are modified, with roll-ahead distance taken into account, to model the driving behaviors of the ATMA vehicles at those critical decision-making locations. Data are collected from real-world field testing to calibrate and validate the developed models. The modeling outputs suggest important thresholds for ATMA system operators to follow. For example, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, car-following distance should be no less than 75 ft for the lead truck and 100 ft for the follower truck, the critical lane-changing gap distance is 912 ft, and a minimum intersection clearance is 15 s, which are all much higher than the requirements for a general vehicle.

scholarly journals The Impact of Emergency Parking Bays on Quasi-Eight-Lane Expressway Traffic Operations

2020 ◽  
Vol 12 (8) ◽  
pp. 3432
Author(s):  
Zhen Yang ◽  
Xiaocan Chen ◽  
Dazhi Sun
Keyword(s):  
High Volume ◽  
Traffic Volume ◽  
Simulation Software ◽  
Traffic Operations ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Traffic Demand ◽  
The Road ◽  
Traffic Operation ◽  
The Impact

Recently, with the discrepancy between increasing traffic demand and limited land resources, more and more expressways are choosing to use hard shoulders to expand into quasi-six-lane or quasi-eight-lane roads. Therefore, more emergency parking bays are used in place of traditional parking belts. However, there are no standards defining clear and unified specifications for the design of parking bays. This paper aimed to investigate the impact of emergency parking bays on expressway traffic operations with various traffic volumes and setting conditions. Based on the Monte Carlo method, VISSIM (Verkehr in Städten Simulation, a microscopic simulation software) simulation experiments were conducted using measured traffic operation data from one expressway in Zhejiang province. The probability of unsafe deceleration, lane-changing maneuvers and delay times were considered as the safety and efficiency indexes in this simulation study. The simulation results indicated that the emergency parking vehicle had an increasing impact on the following vehicle as the traffic volume increased. However, the impact pattern was found to be insensitive to the changing of the bay taper length. For low traffic volume, compared with the arrival vehicle, the departure vehicle had more impact on the traffic operation of the mainline. However, the impact of the arrival vehicle became more remarkable as the traffic volume increased. After parking, the waiting time for merging into the mainline was reduced as the volume decreased or as the bay taper increased. Furthermore, reductions caused by varying bay tapers were more significant under high volume conditions. Finally, this study suggests that parking bays are inapplicable when the occupancy of the road space exceeds 20% (about 3000 veh/h), because they would cause significant impact on the safety and efficiency of the expressway. The results of this paper are useful for the design and implementation of emergency parking bays.

Integrated Strategy for Automatic Lane-Changing Decision and Trajectory Planning in Dynamic Scenario

2020 ◽  
Author(s):  
Meng Ren ◽  
Guangqiang Wu
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Driving Simulator ◽  
Back Propagation ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Lane Changing ◽  
Planning Strategy ◽  
Integrated Strategy ◽  
Dynamic Scenario

Abstract Automatic lane change is a necessary part for autonomous driving. This paper proposes an integrated strategy for automatic lane-changing decision and trajectory planning in dynamic scenario. The Back Propagation Neural Network (BPNN) is used in decision-making layer, whose prediction accuracy of the discretionary lane-changing is 94.22%. The planning layer determines the adjustable range of the average vehicle speed based on the size of the “lane-changing demand”, which is obtained based on the data of hidden layer in neural network, and then dynamically optimizes the lane-changing curve according to the vehicle speed and the current scenario. In order to verify the rationality of the proposed lane-changing architecture, simulation experiments based on a driving simulator is performed. The experiments show that the vehicle’s maximum lateral acceleration under the proposed lane-changing trajectory at a speed of 70km/h is about 0.1g, which means the vehicle has better comfort during lane-changing. At the same time, the proposed lane-changing trajectory is more in line with the human driver’s lane-changing trajectory compared with that of other planning strategy. Meanwhile, the planning strategy can also support the lane-changing trajectory planning on a curved road.

AN OVERVIEW OF VEHICLES LANE CHANGING MODEL DEVELOPMENT IN APPROACHING AT U-TURN FACILITY ROAD SEGMENT

2016 ◽  
Vol 78 (7-2) ◽  
Author(s):  
Nemmang, Mohd Shafie ◽  
Raha Rahman
Keyword(s):  
Driving Simulator ◽  
Video Recording ◽  
Model Development ◽  
Road Segment ◽  
Road User ◽  
Area Network ◽  
Safe Distance ◽  
Lane Changing ◽  
The Road ◽  
Fast Lane

Accidents are in rising mode and became the main problem in all over the world, especially in Malaysia. Many reasons have contributed to an accident including the condition of the road, driver’s behavior and the environment of the road that may lead the drivers to make a lane changing. Lane changing is a process that experienced by all drivers such as in U-turn segment. In approaching U-turn segment, drivers need to make a decision whenever any disruption in front of them such as diverge vehicle because they have their own perspective and desire. However, the lane changing model in approaching U-turn road segment yet to develop. Therefore, this study will develop a model to determine the relationship between the reaction time (RT), speed (V) and distance from the behind vehicle to the front vehicle due to the changing lane at U-turn facility road segment. For that purpose, this study is focusing on the safe distance entry of a vehicle to the fast lane with the fast lane and slow lane vehicles before make a decision to change the lane in this U-turn facility. The data will be taken from the field and driving simulator. The equipment to be used to collect the field data is automatic traffic counting (ATC), controller area network-bus (CAN-bus), radar gun and video recording. The video recording will be used to simulate the driving simulator. Furthermore, driving simulator will be used to achieve the objective of the study. Regression analysis will be done for final model for estimating the safe distance entry of a vehicle to the fast lane with the fast lane and slow lane vehicles before making a decision to change the lane in this U-turn facility road segment to make sure that the model development is valid. Finally, the model can be used to estimate the safe distance for the road user to slow down their rate of speed while approaching the U-turn facility road segment and can be used to estimate the speed and safe distance in lane changing process. 

Speed behaviour in work zone crossovers. A driving simulator study

2017 ◽  
Vol 98 ◽  
pp. 10-24 ◽  
Author(s):  
Lorenzo Domenichini ◽  
Francesca La Torre ◽  
Valentina Branzi ◽  
Alessandro Nocentini
Keyword(s):  
Driving Simulator ◽  
Work Zone ◽  
Simulator Study

scholarly journals Investigation of smart work zone technologies using mixed simulator and field studies

2020 ◽  
Author(s):  
◽  
Siyang Zhang
Keyword(s):  
Data Analysis ◽  
Field Study ◽  
Model Building ◽  
New Technologies ◽  
Driving Simulator ◽  
Survey Design ◽  
Green Light ◽  
Field Studies ◽  
Work Zone ◽  
Work Zones

Safety is the top concern in transportation, especially in work zones, as work zones deviate from regular driving environment and driver behavior is very different. In order to protect workers and create a safer work zone environment, new technologies are proposed by agencies and deployed to work zones, however, some are without scientific study before deployment. Therefore, quantitative studies need to be conducted to show the effectiveness of technologies. Driving simulator is a safe and cost-effective way to test effectiveness of new designs and compare different configurations. Field study is another scientific way of testing, as it provides absolute validity, while simulator study provides relative validity. The synergy of field and simulator studies construct a precise experiment as field study calibrates simulator design and validates simulator results. Two main projects, Evaluation of Automated Flagger Assistance Devices (AFADs), and Evaluation of Green Lights on Truck-Mounted Attenuator (TMA), are discussed in this dissertation to illustrate the investigation of smart work zone technologies using mixed simulator and field studies, along with one simulator project investigating interaction between human driven car and autonomous truck platoon in work zones. Both field and simulator studies indicated that AFADs improved stationary work zone safety by enhancing visibility, isolating workers from immediate traffic, and conveying clear guidance message to traffic. The results of green light on TMAs implied an inverse relationship between visibility/awareness of work zone and arrow board recognition/easy on eyes, but did not show if any of the light configurations is superior. Results anticipated for autonomous truck platoon in work zones are drivers behave more uniformly after being educated about the meaning of signage displayed on the back of truck, and performance measured with signage would be more preferable than those without signage. Applications of statistics are extension of studies, including experimental design, survey design, and data analysis. Data obtained from AFAD and Green Light projects were utilized to illustrate the methodologies of data analysis and model building, which incorporated simulator data, biofeedback and survey response to interpret the relationship among driver perspective and mental status, and driving behavior. From the studies conducted, it could be concluded that mixed simulator and field study is a good fit for smart work zone technologies investigation. Simulators provide a safe environment, flexibility and cost-effectiveness, while field studies calibrate and validate simulator setup and its results. The collaboration of two forms of study generates legitimate and convincing results for investigations. Applying statistical methodologies into transportation simulator and field studies is a good way to make experiment and survey design more rational, and the statistical methods are applicable for further data analysis.

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