Effect of Surrounding Traffic Characteristics on Lane Changing Behavior

Lane-changing maneuvers of heavy vehicles have significant influence on surrounding traffic characteristics because of the physical effects that the heavy vehicles impose on surrounding vehicles. These effects are the result of heavy vehicles’ length, size, weight, and limitations in their maneuverability. Because of the importance of heavy vehicle lane-changing maneuvers, this article presents an exclusive fuzzy logic lane-changing decision model with 2 and 3 fuzzy sets for heavy vehicle drivers on freeways. To examine the accuracy of the heavy vehicle drivers’ lane-changing decision model, the authors compared the number of estimated lane-changing maneuvers of heavy vehicles with the observed number of heavy vehicle lane-changing maneuvers. In addition, the authors compared estimated traffic flow measurements (e.g., traffic flow, average speed) with the observed traffic flow measurements through microscopic traffic simulations. Results show that the fuzzy logic heavy vehicle lane-changing model can better replicate the observed lane-changing decisions of heavy vehicle drivers than can the current lane-changing decision models. Furthermore, using an exclusive heavy vehicle lane-changing decision model can increase the accuracy of the microscopic traffic simulation software in estimating the macroscopic traffic flow measurements.

Background: Several studies indicate that truck drivers have an increased risk of lung cancer, but few studies have examined lung cancer risk in heavy equipment operators. Workers in both occupations...

Estimating crashes involving heavy vehicles in Western Australia, 1999–2000: A capture–recapture method

<div class="section abstract"><div class="htmlview paragraph">As a basic link of driving behavior in urban roads, vehicle lane changing has a significant impact on traffic flow characteristics and traffic safety, and the automation of lane change is also a key issue to be solved in the field of intelligent driving. In this paper, the research on the automatic lane change control for intelligent vehicles is carried out. The main work is to build the overall structure of the vehicle's automatic lane change behavior, of which the planning and tracking are focused. The strategy of Constant Time Headway (CTH) is used in the lane change decision. The lane change trajectory adopts the model of constant velocity offset plus sine function, and the longitudinal displacement is determined by the vehicle speed when changing lanes. Model Predictive Control (MPC) theory is used to track the trajectory, which optimizes tracking accuracy and vehicle stability and constrains the range and rate of change of vehicle speed and steering angle. By using weighted quadratic cost function, linearity matrix inequality constraints and upper and lower bound constraints, the multi-objective trajectory tracking problem is eventually transformed into a constrained online convex quadratic programming problem. The results of simulation and HIL test show that the scheme of automatic lane change can make the vehicle smoothly complete the lane changing behavior, and the errors can meet the error requirements of lane change. Compared with other controller, the method shows smaller lateral acceleration, stronger robustness and higher control precision during the test. Moreover, the computational time of the proposed MPC controller, implemented using the PXI, is 47.994ms during one sampling period, which can satisfy the real-time requirement.</div></div>

More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

Determinants of the occupational environment and heavy vehicle crashes in Western Australia: A case–control study

BackgroundSince 1995, Traumatic injuries remain as the leading cause of hospitalisation in Sri Lanka for consecutive years. Among traumatic injuries, road traffic injuries are leading. It accounts for 22% to...

This study evaluated the operational impacts of an unconventional outside left-turn lane design used at some signalized intersections in China. Using traffic data collected from 10 approaches to five signalized intersections where both traditional inside and unconventional outside left-turn lanes are installed, the research team compared turning speeds and saturation flow rates for left-turning vehicles from inside and outside left-turn lanes. The team found that vehicles turning from outside left-turn lanes had relatively higher turning speeds than those turning from inside left-turn lanes. The differences in speed were statistically significant for both passenger cars and heavy vehicles. However, the higher speeds did not result in higher left-turn lane capacity. Unconventional outside left-turn lanes provided slightly lower, but generally comparable, left-turn saturation flow rate when compared with traditional inside left-turn lanes. The differences in discharge headways from inside and outside left-turn lanes were not statistically significant. A binary logit model was developed to quantify the impacts of various factors on the probability of a driver selecting the outside left-turn lane to make a left turn. The chance of a left-turning driver choosing the outside left-turn lane would increase with an increase in the traffic volume of the major street and the queue length in the inside left-turn lane and would decrease with an increase in the distance from the upstream right-side side street to the signalized intersection. Drivers of heavy vehicles and those from upstream right-side side streets were more likely to select outside left-turn lanes.

Enhancing lane changing trajectory prediction on highways: A heuristic attention-based encoder-decoder model

To determine the association between obstructive sleep apnea (OSA), health-related factors and the likelihood of heavy vehicle crashes in Western Australia (WA). This case-control study included 100 long-haul heavy vehicle drivers who were involved in a police-reported crash in WA during the study period (cases) and 100 long-haul heavy vehicle drivers recruited from WA truck stops, who were not involved in a crash during the past year (controls). Driver demographics, health, and fatigue-related characteristics were obtained using an interviewer administered questionnaire. Drivers were tested for OSA using a diagnostic Flow Wizard. Logistic regression was used to determine health-related factors associated with crash involvement among long distance heavy vehicle drivers. Heavy vehicle drivers diagnosed with OSA through the use of the FlowWizard were over three times more likely to be involved in a crash than drivers without OSA (adjusted OR: 3.42, 95% CI: 1.34-8.72). The risk of crash was significantly increased if heavy vehicle drivers reported a diagnosis of depression (adjusted OR: 6.59, 95% CI: 1.30-33.24) or had not completed fatigue management training (adjusted OR: 6.05, 95% CI: 1.80-20.24). Crash risk was 74% lower among older drivers (> 35 years) than younger drivers (adjusted OR: 0.25, 95% CI: 0.08-0.82). The results suggest that more rigorous screening and subsequent treatment of OSA and depression by clinicians as well as compulsory fatigue management training may reduce crashes among heavy vehicle drivers. A commentary on this article appears in this issue on page 409.

The expressway traffic flow characteristics of off-ramp have an important impact on traffic congestion. By analyzing the vehicle car-following and lane-changing behavior, the cellular automata model of off-ramp area was built. When analyzing lane-changing behavior, the behavior of vehicle changing lane from main lane to off-ramp buffer lane was considered, and the information interaction process between the vehicle ready to changing lane and the rear vehicle on the target lane was considered, the new lane-changing rules were built. In the new lane-changing rules, free lane-changing and compulsory lane-changing were included. By analyzing the simulation results, when traffic flow density is lower, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane has less effect on traffic capacity of the road section, but when traffic flow density rising to a certain extent, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane will bring an adverse effect on traffic capacity of the road section.

This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data

ObjectivesTo profile sleep duration and sleep efficiency in UK long-distance heavy goods vehicle (HGV) drivers and explore demographic, occupational and lifestyle predictors of sleep.MethodsCross-sectional analyses were carried out on 329...

The use of FASTag is prevalent in modern society. However, it is clear that the number of FASTag frauds in the global integration and existing protection system is constantly increasing. This is why the issue of FASTag fraud detection is very important. Various FASTag frauds occur and there is no means to detect or prevent them. Among the many frauds, we will try to reduce the fraud where drivers of heavy vehicles issue and use FASTags that are intended for light vehicles. In this way, drivers of heavy vehicles are exempted from paying the higher tolls that are set for heavy vehicles. FAScam is used to solve this problem. The system will be able to identify the type of vehicle and the FASTag amount paid. Using this data, FAScam detects if any anomaly has occurred and alerts the system accordingly.