Vehicle Trajectory Data Research Articles

Predicting the duration of reduced driver performance during the automated driving takeover process

This study carried out a simulator test to determine and predict the duration of reduced driver performance during the automated driving takeover process. Vehicle trajectory and driver behavior data were collected in critical and non-critical takeover scenarios. The earth mover’s distance was then adopted to identify the data with the optimal combination of indicators by comparing it to the reference data. The Gaussian mixture model was employed to classify the driving state as either stable or unstable, and the duration of reduced driver performance was derived for each participant based on these results. Subsequently, a generalized linear mixed model was developed to predict the duration of reduced driver performance and examine the impact of various factors on it. Results uncovered a recovery of the reduced driver performance state after drivers took over the automated vehicle. In the non-critical and critical takeover scenarios, the mean duration of reduced driver performance was 17.48 and 27.25 s, respectively. Additionally, the developed model demonstrated good overall prediction accuracy, with the duration of reduced driver performance showing a positive correlation with the lead vehicle’s speed, duration of automated driving, and takeover request lead time. Furthermore, timid drivers exhibited a longer recovery duration than aggressive drivers. These research findings offer valuable insights into understanding the recovery of reduced driver performance during the takeover process, serving as a theoretical foundation for designing safer automated driving systems.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

To increase the efficiency and safety of expressway, this paper constructed a new intelligent connected cooperative vehicle infrastructure system and its effectiveness was verifid from both data and practical applications. Firstly, considering the convenience of using intelligent networking systems for public transportation, a new intelligent connected cooperative vehicle infrastructure system architecture was proposed by incorporating mobile communication methods. Then, the new system was illustrated from road side unit (RSU), on board unit (OBU) and data interaction. Additionally, to verify the effectiveness of the system, this paper proposes a two-stage model named Transformer Embedded Clustering- Hierarchical Density-Based Spatial Clustering of Applications with Noise (TEC-HDBSCAN) model to identify outliers in the trajectory data of vehicles collected by the system and obtain the speed sequence of the vehicle. Finally, data from actual testing scenarios was collected and a Best Worst Method-Improved Gray Relational (BWM-IGR) model was built to verify the effectiveness of the system. The results show that the established intelligent networked transportation system can effectively guide vehicles and collect data with high accuracy.

GeoPM-DMEIRL: A deep inverse reinforcement learning security trajectory generation framework with serverless computing

Vehicle trajectory data is essential for traffic management and location-based services. However, the release of trajectories raises serious privacy concerns because they contain sensitive information, such as home addresses and workplaces, etc, making it indispensable to consider privacy protection when releasing trajectory data. It is urgent to reconcile data utility with privacy in trajectory generation calls for advanced methods that adhere to real-world traffic rules. Leveraging serverless computing for deep learning provides an efficient solution, bypassing infrastructure complexities to handle the scale trajectory data. In this paper, a Geo-Piecewise Mechanism Deep Maximum Entropy Inverse Reinforcement Learning (GeoPM-DMEIRL) is proposed for secure trajectory generation, which consists of three key components, namely GeoPM, A2C reinforcement learning and DMEIRL. Firstly, GeoPM is a novel local differential privacy mechanism that incorporates Geo-aware gridding techniques. The Piecewise Mechanism is used to perturb the user trajectory while ensuring that the perturbed trajectory conforms to real-world traffic rules. Secondly, an A2C reinforcement learning network is refined to train the optimal trajectory generation strategy. Thirdly, Deep Maximum Entropy Inverse Reinforcement Learning is improved to train the weights of the A2C reward function. Finally, real-world data are collected in the experiments and the serverless computing platform AWS Lambda is used for training the reinforcement learning models. Experimental results show that our proposed GeoPM-DMEIRL framework can effectively resist user re-identification attacks, which can improve the utility by an average of 54.605% and enhance the privacy by an average of 30.678%. Meanwhile, GeoPM-DMEIRL is able to maintain the utility of the data while protecting the privacy of the user’s trajectories.

Surrogate safety evaluation of curve sections on an interurban highway under heterogeneous traffic conditions in India

<p class="H1">Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (<em>ΔV</em>), as well as conflicting vehicle speed (Max <em>S</em>), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s<sup>2</sup>, implying that any conflict with more than this value may lead to a fatal crash. Further, the Delta<em>V</em> values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.</p>

Revealing the impact of stochastic driving characteristics on car-following behavior with locally collected vehicle trajectory data

This study investigates the impact of Chinese drivers’ stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow.

Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model

In order to accurately predict the lane‐changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane‐changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short‐term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN‐BiLSTM model is constructed to identify the sample set of lane‐changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US‐101 dataset in NGSIM is selected to verify the performance of the CNN‐BiLSTM model, and at the same time, it is compared with models such as CNN‐LSTM, long short‐term memory (LSTM), BiLSTM, and CNN‐GRU‐Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.

PUTS: Privacy-Preserving and Utility-Enhancing Framework for Trajectory Synthesization

Vehicle trajectory data is essential for traffic management and location-based services. However, publishing real-life trajectory data has been challenging because vehicle trajectories contain users' sensitive information. Differential privacy addresses such problems by publishing a synthetic version of the input dataset, but existing works always assume the real-world data is absolutely accurate. This assumption no longer holds in trajectory data because it typically contains errors due to inaccurate positioning services, which leads to poor performance of data synthesized by such trajectories. Even worse, existing works may generate unrealistic trajectories due to their coarse data synthesis methods, resulting in low practical utility or even inability to handle complex tasks. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy-preserving and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u> tility-enhancing framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> rajectory <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ynthesization ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> ). Our framework mitigates the impact of data errors in trajectories on differential privacy mechanisms, by exploiting map-matching techniques and real-world road network structure. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> , a two-layer approach from path to trajectory synthesis is proposed to not only guarantee the reality of synthetic trajectories, but also scale up <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> in real-world applications. Extensive experiments on real-world datasets show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> significantly outperforms existing methods in terms of utility in a range of real-world applications.

Classification and Prediction of Vehicle Lane‐Changing Crash Risk Levels Based on Video Trajectory Data

To investigate the potential lane‐changing collision risks that may arise between vehicles during lane changes and those in the original lane, a model for vehicle lane‐changing collision risk is constructed specifically for this scenario, and a research analysis is conducted. First, based on vehicle trajectory data, a sample set capturing the relationships between vehicles traveling in a straight line and those changing lanes laterally is extracted and built. Interpolation methods are then applied to fill in missing values, outliers are eliminated, and data noise is smoothed during preprocessing. After preprocessing, a total of 468 vehicle pairs and 265,392 data points are obtained. Second, a real‐time collision time model is established based on the preprocessed data, and collision risk probabilities are calculated accordingly. Then, the collision risks are classified into four levels based on whether the vehicle on the side actually changes lanes and the severity of the collision risks. Finally, a light gradient boosting machine (LightGBM) learning method is adopted to predict the risk levels and analyze the main factors that significantly impact the severity of collision risks. The results indicate that the longitudinal distance between the target vehicle and the preceding vehicle is the most critical influencing factor, followed by the speed of the target vehicle itself, and then the speed difference between the target vehicle and the preceding vehicle. The influence of other factors is relatively similar and does not have a significant impact.

Assessing the Impacts of Merge and Speed Control Strategies on Highway Work Zone Safety and Operations Using Artificial Intelligence and Advanced Sensors

Speed management and merge control are two critical factors influencing highway work zone safety and operations. This study adopts artificial intelligence (AI) and advanced radar and thermal camera sensors to generate detailed vehicle trajectory and speed data, which are used to quantify the impacts of various Traffic control strategies on driver behavior as well as on work zone safety and operations. The strategies considered include transverse rumble strips, flashing speed limit signs, dynamic message signs, and different lane closure taper lengths. Among these strategies, flashing speed limit signs are found to significantly reduce vehicle speeds when approaching the lane closure point.

Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding

Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding

Evaluating the environmental impact on connected vehicles during freeway accidents using VISSIM with probe vehicle data

&lt;abstract&gt; &lt;p&gt;We evaluated emissions as an environmental effect resulting from connected vehicles (CVs) during freeway accidents. The CVs were used to determine the CV driving characteristics, whose values were used to implement the CV driving pattern using a microscopic traffic simulation. The environmental effect of implementation of CV was evaluated using the vehicle trajectory data derived from the simulation results. Implementation of CV effectively minimized the vehicle emissions regardless of the market penetration rate (MPR). In terms of vehicle type, the emissions reduction rate of passenger cars was the highest at a maximum of 33.4%. In the case of pollutants, the reduction rate of CO based on all vehicles was the highest at a maximum of 28.8%. Overall, we found that the implementation of CV positively affected vehicle emissions reductions, and an MPR of 60% could maximize the vehicle emissions reduction effect.&lt;/p&gt; &lt;/abstract&gt;

A similarity-based data-driven car-following model considering driver heterogeneity

Human drivers usually have distinct driving patterns and preferences. Driver heterogeneity is crucial for modeling driving behaviors. This paper incorporates driver heterogeneity with data-driven approaches to predict car-following behaviors. A bi-level similarity-based car-following model is proposed to predict the vehicle's moving distance. In the upper level, drivers with similar driving patterns as the ego vehicle are identified using k-nearest neighboring (kNN) search. In the lower level, leveraging kNN model, candidate records are selected from the identified vehicles’ trajectories and applied to predict the ego vehicle's moving distance, combining the driving pattern similarity measured in the upper level. By taking into account the driver heterogeneity, the proposed model is capable of identifying the most relevant driving situations, which leads to an improvement of prediction accuracy. Furthermore, the established bi-level structure largely shrinks the searching space of candidate records, which reduces the searching complexity and enhances computational efficiency. We quantitatively evaluate and compare the performance of the proposed model in terms of both prediction accuracy and computational efficiency using real-world vehicle trajectory data.

Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.

Characteristics identification and evolution patterns analyses of road chain conflicts

Chain conflicts would cause chain-reaction crashes, which might result in elevated fatality rates. Chain conflicts describe a phenomenon wherein evasive actions taken by a following vehicle's driver after a conflict impact nearby vehicles, which occur frequently but are reported less often. To effectively reduce conflict risk, comprehending the evolution patterns of chain conflicts under varied traffic conditions and road segments is crucial, in order to make chain conflicts management strategies. Initially, rear-end or sideswipe conflicts between two vehicles are identified based on vehicle trajectory data captured by an unmanned aerial vehicle group. Subsequently, a chain conflict identification algorithm is proposed, considering the randomness of occurrence time and fluctuation of impact duration, to link individual conflicts. Chain conflict rates exhibit significant variations across different road segments under diverse traffic conditions. Multiple risk and propagation indicators are extracted to unveil latent characteristics of chain conflicts from a high-level perspective. Based on prominent characteristic disparities, three evolution patterns are identified, i.e., Longitudinal Risk Decrease Pattern, Longitudinal Risk Increase Pattern, and Comprehensive High-risk Persistent Pattern. Spatial-temporal high-risk areas associated with each pattern are determined, and transition probabilities between patterns are calculated. The results indicate that these patterns tend to remain stable, with transitions mainly occurring from low-risk to high-risk patterns. Moreover, strategies to reduce conflict risk are proposed based on the characteristics of different patterns. This study holds great significance in understanding chain conflict evolution patterns and preventing chain-reaction crashes.

Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

Probabilistic Traffic State Prediction Based on Vehicle Trajectory Data

Probabilistic Traffic State Prediction Based on Vehicle Trajectory Data

Modeling and Optimization of Connected and Automated Vehicle Platooning Cooperative Control with Measurement Errors.

This paper presents a cooperative control method for connected and automated vehicle (CAV) platooning, thus specifically addressing the challenge of sensor measurement errors that can disrupt the stability of the CAV platoon. Initially, the state-space equation of the CAV platooning system was formulated, thereby taking into account the measurement error of onboard sensors. The superposition effect of the sensor measurement errors was statistically analyzed, thereby elucidating its impact on cooperative control in CAV platooning. Subsequently, the application of a Kalman filter was proposed as a means to mitigate the adverse effects of measurement errors. Additionally, the CAV formation control problem was transformed into an optimal control decision problem by introducing an optimal control decision strategy that does not impose pure state variable inequality constraints. The proposed method was evaluated through simulation experiments utilizing real vehicle trajectory data from the Next Generation Simulation (NGSIM). The results demonstrate that the method presented in this study effectively mitigates the influence of measurement errors, thereby enabling coordinated vehicle-following behavior, achieving smooth acceleration and deceleration throughout the platoon, and eliminating traffic oscillations. Overall, the proposed method ensures the stability and comfort of the CAV platooning formation.

Vehicle Trajectory Data Mining for Artificial Intelligence and Real-Time Traffic Information Extraction

It aims to improve the efficiency of information collection and extraction in the current intelligent transportation system, and accurately mine the vehicle trajectory data By using Artificial Intelligence (AI) and Deep Learning methods, the trajectory data generated during vehicle driving are deeply mined and analyzed, and the characteristics of driving behavior of vehicle drivers are modeled and analyzed in detail. Then, a method of mining driving behavior characteristics based on Convolutional Neural Network (CNN) and vehicle trajectory is proposed. Based on the mathematical principle of wavelet packet and Least Square Support Vector Machine (LSSVM), a combined model of trajectory mining is constructed and applied to the short-term prediction of traffic flow. The traffic flow of Binjiang Road and Renmin Road in Guangzhou, Guangdong Province from August 19 to August 21, 2021 is predicted to verify the accuracy of the trajectory mining combined model. The results show that the combination model of data mining has good fitting effect, and the average accuracy is above 0.8. Besides, the effectiveness of the Deep Learning model in driver behavior classification is verified. The accuracy of the classification model is 75.2% for trajectory, and that is 76.8% for driver behavior characteristics. It is of great significance to effectively utilize the knowledge data in Intelligent Transportation System (ITS) and extract valuable information from it, which has certain reference value for the subsequent refined prediction of vehicle behavior.

Comparative Analysis of Machine-Learning Models for Recognizing Lane-Change Intention Using Vehicle Trajectory Data

Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using vehicle trajectory data to select a model for identifying vehicle LC intentions. Considering longitudinal and lateral dimensions, the information extracted from vehicle trajectory data includes the interactive effects among target and adjacent vehicles (54 indicators) as input parameters. The LC intention of the target vehicle serves as the output metric. This study compares three widely recognized machine-learning models: support vector machines (SVM), ensemble methods (EM), and long short-term memory (LSTM) networks. The ten-fold cross-validated method was used for model training and evaluation. Classification accuracy and training complexity were used as critical metrics for evaluating model performance. A total of 1023 vehicle trajectories were extracted from the CitySim dataset. The results indicate that, with an input length of 150 frames, the XGBoost and LightGBM models achieve an impressive overall classification performance of 98.4% and 98.3%, respectively. Compared to the LSTM and SVM models, the results show that the two ensemble models reduce the impact of Types I and III errors, with an improved accuracy of approximately 3.0%. Without sacrificing recognition accuracy, the LightGBM model exhibits a sixfold improvement in training efficiency compared to the XGBoost model.

Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques

Due to complex traffic conditions, transition areas in highway work zones are associated with a higher crash risk than other highway areas. Understanding risk-contributing features in transition areas is essential for ensuring traffic safety on highways. However, conventional surrogate safety measures (SSMs) are quite limited in identifying the crash risk in transition areas due to the complex traffic environment. To this end, this study proposes an improved safety potential field, named the Work-Zone Crash Risk Field (WCRF). The WCRF force can be used to measure the crash risk of individual vehicles that enter a work zone considering the influence of multiple features, upon which the overall crash risk of the road segment in a specific time window can be estimated. With the overall crash risk used as a label, the time-window-based traffic data are used to train and validate an eXtreme Gradient Boosting (XGBoost) classifier, and the Shapley Additive Explanations (SHAP) method is integrated with the XGBoost classifier to identify the key risk-contributing traffic features. To assess the proposed approach, a case study is conducted using real-time vehicle trajectory data collected in two work zones along a highway in China. The results demonstrate that the WCRF-based SSM outperforms conventional SSMs in identifying crash risks in work zone transition areas on highways. In addition, we perform lane-based analysis regarding the impact of setting up work zones on highway safety and investigate the heterogeneity in risk-contributing features across different work zones. Several interesting findings from the analysis are reported in this paper. Compared to existing SSMs, the WCRF-based SSM offers a more practical and comprehensive way to describe the crash risk in work zones. The approach using the developed WCRF technique offers improved capabilities in identifying key risk-contributing features, which is expected to facilitate the development of safety management strategies for work zones.