scholarly journals Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data

2021 ◽  
Vol 13 (16) ◽  
pp. 9278
Author(s):  
Ruoxi Jiang ◽  
Shunying Zhu ◽  
Hongguang Chang ◽  
Jingan Wu ◽  
Naikan Ding ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Traffic Safety ◽  
Pearson Correlation ◽  
Highway Safety ◽  
Lane Change ◽  
Trajectory Data ◽  
Average Value ◽  
Traffic Conflict ◽  
Vehicle Trajectory ◽  
Safety Estimation

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

scholarly journals Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Cheng Zhang ◽  
Jiawen Wang ◽  
Jintao Lai ◽  
Xiaoguang Yang ◽  
Yuelong Su ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Real Life ◽  
Ramp Metering ◽  
Sensor Data ◽  
Trajectory Data ◽  
Effective Measure ◽  
Traffic Demand ◽  
Vehicle Trajectory ◽  
Historical Trajectory ◽  
Freeway Congestion

Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.

Research on Traffic Safety on Freeway Merging Sections Based on TTC and PET

2014 ◽  
Vol 587-589 ◽  
pp. 2224-2229
Author(s):  
Xiang Hai Meng ◽  
Zhi Zhao Zhang ◽  
Yong Yi Shi
Keyword(s):  
Statistical Analysis ◽  
Traffic Safety ◽  
Conflict Situation ◽  
Lane Change ◽  
Traffic Data ◽  
Time To Collision ◽  
Time Headway ◽  
Vehicle Type ◽  
Traffic Conflict ◽  
Surrogate Measures

Since the traffic safety of freeway interchange merging sections and the accidents occurred in this areas can not meet the requirement of statistical analysis, this paper employed traffic conflict technique to analyze the safety situation of freeway merging sections. The traffic data of vehicles through the merging sections are collected and analyzed. These data include the vehicle type, speed, time headway and others based on the features of individual vehicle. Then two methodologies are developed, the first is based on time to collision (TTC), which can calculate the rear-end conflict number, while the second is based on post encroachment time (PET), which can calculate the lane-change conflict number. The results show that these surrogate measures can quantitatively describe the rear-end conflict situation and lane-change conflict situation.

Modeling Integrated Lane-Changing Behavior

2003 ◽  
Vol 1857 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Tomer Toledo ◽  
Haris N. Koutsopoulos ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Empirical Work ◽  
Flow Characteristics ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Simulation Tools ◽  
Lane Changes ◽  
Trade Offs ◽  
Safety Studies ◽  
Vehicle Trajectory

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: ( a) the decision to consider a lane change, and ( b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

scholarly journals Literature review on the conflict analysis of vehicle-pedestrian interactions

2021 ◽  
Author(s):  
Ahmad Kizawi ◽  
Attila Borsos
Keyword(s):  
Traffic Safety ◽  
Rapid Improvement ◽  
Crash Data ◽  
The Road ◽  
Critical Elements ◽  
Traffic Conflict ◽  
On The Road ◽  
Vehicle Trajectory ◽  
Art Research ◽  
Surrogate Measures

An alternative to traffic safety analysis based on historical crash data the use of non-crash events is becoming more popular thanks to the rapid improvement in video-based vehicle trajectory processing. By means of Surrogate Measures of Safety (SMoS) in traffic conflict studies, the most critical elements on the road network can be identified and the probability of accidents can be proactively determined. This paper aims to summarize the state-of-the-art research regarding the analysis of pedestrian-vehicle interactions at unsignalized crossings, to synthetize the previous studies using Surrogate Measures of Safety (SMoS), and to identify the research gaps.

scholarly journals In Search of the Consequence Severity of Traffic Conflict

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Ruoxi Jiang ◽  
Shunying Zhu ◽  
Pan Wang ◽  
QiuCheng Chen ◽  
He Zou ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Evaluation Method ◽  
Prediction Models ◽  
Safety Evaluation ◽  
Threshold Value ◽  
Two Dimensions ◽  
Trajectory Data ◽  
Traffic Conflicts ◽  
Traffic Conflict ◽  
Accident Data

Currently, many studies on the severity of traffic conflicts only considered the possibility of potential collisions but ignored the consequences severity of potential collisions. Aiming toward this defect, this study establishes a potential collision (serious conflict) consequences severity model on the basis of vehicle collision theory. Regional vehicles trajectory data and historical traffic accident data were obtained. The field data were brought into the conflict consequences severity model to calculate the conflict severity rate of each section under different TTC thresholds. For comparison, the traditional conflict rate of each section under different TTC thresholds that considered only the number of conflicts was also calculated. Results showed that the relationship between conflict severity rate and influencing factors was somehow different. The conflict severity rate seemed to have a higher correlation with accident rate and accident severity rate than conflict rate did. The TTC threshold value also affected the correlation between conflicts and accidents, with high and low TTC threshold indicating a lower correlation. The results showed that conflict severity rate that considered each single conflict consequence severity was a little better than the traditional conflict rate that considered only the numbers of conflicts in reflecting real risks as a new conflict evaluation indicator. The severity of traffic conflicts should consider two dimensions: the possibility and consequence of potential collisions. Based on this, we propose a new traffic safety evaluation method that takes into account the severity of the consequences of the conflict. More data and prediction models are needed to conduct more realistic and complex research in the future to ensure reliability of this new method.

Development of a lane change risk index using vehicle trajectory data

2018 ◽  
Vol 110 ◽  
pp. 1-8 ◽  
Author(s):  
Hyunjin Park ◽  
Cheol Oh ◽  
Jaepil Moon ◽  
Seongho Kim
Keyword(s):  
Risk Index ◽  
Lane Change ◽  
Trajectory Data ◽  
Vehicle Trajectory

scholarly journals Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qianxia Cao ◽  
Zhongxing Zhao ◽  
Qiaoqiong Zeng ◽  
Zhengwu Wang ◽  
Kejun Long
Keyword(s):  
Real Time ◽  
Deep Neural Networks ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Target Vehicle ◽  
Time Prediction ◽  
Unsignalized Intersections ◽  
Vehicle Trajectories ◽  
Traffic Conflict ◽  
Vehicle Trajectory

Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.

A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments

2018 ◽  
Vol 2672 (42) ◽  
pp. 268-279 ◽  
Author(s):  
Saleh R. Mousa ◽  
Peter R. Bakhit ◽  
Osama A. Osman ◽  
Sherif Ishak
Keyword(s):  
Gradient Boosting ◽  
Lane Change ◽  
Connected Vehicle ◽  
Classification Problems ◽  
Trajectory Data ◽  
Modeling Tools ◽  
Lane Changing ◽  
Extreme Gradient Boosting ◽  
Car Crashes ◽  
Vehicle Trajectory

Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving classification problems due to its accuracy, scalability, and speed. This study implements the XGB in predicting the onset of lane changing maneuvers using CV trajectory data. The performance of XGB is compared to three other tree-based algorithms namely, decision trees, gradient boosting, and random forests. The Next Generation SIMulation trajectory data are used to represent the high-resolution CV data. The results indicate that XGB is superior to the other algorithms with a high accuracy value of 99.7%. This outstanding accuracy is achieved when considering vehicle trajectory data two seconds prior to a potential lane change maneuver. The findings of this study are promising for detection of lane change maneuvers in CV environments.

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