Impact of in-vehicle navigation information on lane-change behavior in urban expressway diverge segments

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics

Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers' vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers' visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles' lateral stability and driving comfort, shorten reaction time, and relieve the drivers' nervousness. By integrating the drivers' visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers.

Drivers’ Decelerating Behaviors in Expressway Accident Segments under Different Speed Limit Schemes

Traffic accidents occurring on expressways tend to give rise to traffic bottlenecks. To ensure the vehicles safely and smoothly pass through the accident segments, speed limits are generally taken to regulate the vehicles’ movements. This study aims to explore the decelerating behaviors of drivers under different speed limit schemes. We designed traffic accident scenarios under four speed limit schemes using the driving simulator. A total of 60 subjects drove the simulator passing the accident segments according to their habits. The vehicles’ kinematic data and the subjects’ operating data were recorded. To further analyze the drivers’ decelerating behaviors in different speed limit scenarios, driving experience was also taken into account. The results show that the speed limit schemes have significant effects on drivers’ decelerating behaviors. The more speed limit signs there are, the smoother the decelerating process will be. Driving experience significantly affects some of the decelerating parameters, including the location of deceleration starting point, average deceleration, and locations of decelerating to the initial and final speed limits. These results provide a theoretical basis for traffic safety and driving behavior management.

Analysis of Traffic Operation Characteristics and Calculation Model of the Length of the Connecting Section between Ramp and Intersection

Many cities have built expressways to alleviate traffic congestion, among which elevated expressways are the most common form. However, traffic congestion still occurs frequently in the connecting section between the ramp of expressway and the ground intersection. Based on the field traffic survey data, the traffic operation characteristics of vehicles in the connecting section and the main factors affecting the length of the connecting section are analyzed. A combined model for calculating the length of the connecting section between ramp of urban expressway and intersection is proposed. VISSIM is used to simulate the traffic flow under the current and calculated length of the connecting section. The comparison results show that under the calculation length, the travel time, average delay, parking time and queue length are reduced to varying degrees, which verifies the rationality of the calculation model.

Influence of prompt timing and messages of an audio navigation system on driver behavior on an urban expressway with five exits

An audio navigation system is a very useful tool for driving path guidance with less distraction. However, the influence of the audio navigation system on driver behavior, especially in complicated road environments, is still not entirely clear. This study aims to investigate navigation prompt timing (NPT), navigation prompt message (NPM), and their combination in an audio navigation system on driving behavior on an urban expressway with five exits. Driving simulator technology was used to reproduce the experimental environment and obtain driver behavior data. Four indicators-speed, standard deviation of speed, absolute values of acceleration, and depth of accelerator-were selected to examine the main and interactive effects of NPT, NPM, and their combination based on repeated measures analysis of variance. The results show that the driver's psychological state and operation of the vehicle on the urban expressway were affected by the prompt timing and messages of the audio navigation system. An interaction effect existed between prompt timing and prompt messages of the system, and this effect adjusted the effect on the driver's psychological state and vehicle operation caused by these two important factors. This study contributes to our understanding of the influence of audio navigation systems on driver behavior in complex road environments and thus lays a foundation for developing standard audio navigation broadcast guidelines to improve drivers' acceptance of navigation systems, reduce drivers' cognitive workload, and improve the level of vehicle operation safety.

Effectiveness of yellow color guardrail belt at freeway exits

If the information on freeway exits is not effective or driver vigilance is not adequate, the driver may not be able to obtain the information in time, resulting in missing the exit or making a forcible lane-change that could cause an accident. To allow the driver to obtain sufficient exit information in time and get off the freeway safely, this study proposes the creation of a guardrail painted with a yellow color and located prior to the exit. The yellow color guardrail belt (YCB) aims at informing the drivers that there is an exit ahead and to pay attention to the exit information, reminding them to adjust vehicle state and driving behavior in time. A driving simulator experiment with two different scenarios (YCB scenario and baseline scenario with no YCB) were used to explore the effectiveness of the YCB. Data on eye movement, electroencephalograph, and driving behavior of the participants were obtained. The results showed that compared with the baseline scenario, in the YCB scenario, the fixation points were mainly distributed in front of the road and the fixation duration on the guide signs was relatively longer; the EEG ratio (θ + α) / β was smaller; the driver decelerated more smoothly; and the steering wheel angle was smaller. In addition, the statistical analysis showed that there were significant differences in the fixation duration, the EEG ratio (θ + α) / β, and steering wheel angle between the two scenarios. This indicated that participants' vigilance in the YCB scenario was significantly improved, where the participants paid more attention to the guide signs and had better control of the vehicle. This study recommends a new device for reminding drivers to pay attention to freeway exits, which would greatly stimulate driver's sense of space on the exit and improve traffic safety on freeways.

Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways

Congestion has become a significant issue in recent years and has greatly affected the efficiency of urban traffic operation. Random and disorderly lane-changing behavior greatly reduces traffic capacity and safety. This paper is mainly concerned with the relationship of lane-changing spacing intervals provided by off-ramp facilities and traffic flow conditions. Through field investigations in Beijing, several typical lane-changing behaviors at off-ramp areas are analyzed. By using field traffic data and actual road geometry parameters, VISSIM-based micro-behavior simulations at off-ramp areas are implemented to obtain traffic flow conditions with different lane-changing spacing intervals and other model parameters, such as traffic volume and ratio of off-ramp vehicles. Then, the numerical relationships between traffic flow state and model parameters can be shown. The results show that with increasing traffic volume and the ratio of off-ramp vehicles, the lane-changing spacing interval required by vehicles should be increased. For the same ratio of off-ramp vehicles, if the traffic volume increases by 100 pcu/h/lane (pcu is a unit to stand for a standard passenger car), the corresponding lane-changing spacing interval should be increased by a spacing of 50–100 m to avoid increasing congestion. Based on the results of this paper, smart lane management can be implemented by optimizing lane-changing spacing intervals and lane-changing behaviors to improve traffic capacity.

Exploring the impacts of speed variances on safety performance of urban elevated expressways using GPS data

Speed variation on urban expressways has been frequently noted as a key factor associated with high crash risk. However, it was often difficult to capture the safety impact of speed variance with spaced sensor measurements. As an alternative, this paper aims to leverage the use of the floating car data (FCD) to capture the speed variance in a morning rush hour on urban elevated expressways and examine its effect on safety. A semi-automatic filtering process was introduced to distinguish taxi GPS data points on the elevated expressways from the ones on the surface roads under the expressways. Subsequently, the standard deviation of the cross-sectional speed mean (SDCSM) and the cross-section speed standard deviation (MCSSD) were derived to capture the spatial and temporal speed variances, respectively. Together with other explanatory variables, both hierarchical and non-hierarchical Poisson-gamma measurement error models were developed to model the crash frequencies of the expressways. The modeling results showed that the hierarchical model performed better and both SDCSM and MCSSD were found to be positively related to the crash occurrence. This secures the need for addressing the impact of speed variation when modeling crashes occurred on the elevated expressways.