Despite the structure of road environments, imposed via geometry and rules, traffic flows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high ...dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euclidean spaces are often limited by their high sample complexity and the sparseness of available datasets. Our key insight is that the structure of traffic behavior could be effectively captured by lower-dimensional abstractions that emphasize critical interaction relationships. In this article, we abstract the space of behavior in traffic scenes into a discrete set of interaction modes, described in interpretable, symbolic form using topological braids. First, through a case study across real-world datasets, we show that braids can describe a wide range of complex behavior and uncover insights about the interactivity of vehicles. For instance, we find that high vehicle density does not always map to rich mixing patterns among them. Further, we show that our representation can effectively guide decision-making in traffic scenes. We describe a mechanism that probabilistically maps vehicles’ past behavior to modes of future interaction. We integrate this mechanism into a control algorithm that treats navigation as minimization of uncertainty over interaction modes, and investigate its performance on the task of traversing uncontrolled intersections in simulation. We show that our algorithm enables agents to coordinate significantly safer traversals for similar efficiency compared to baselines explicitly reasoning in the space of trajectories across a series of challenging scenarios.
Although there have been assorted car-following (CF) models for connected vehicles (CVs), studying their impacts in mixed traffic flow of human-driven vehicles (HVs) and CVs remains a challenge. ...Considering the multiple front vehicles' optimal speed changes with memory, this study proposed a new CF model (MVCM model) implemented through vehicle-to-everything (V2X) technology in CVs environment in terms of OVCM (Optimal Velocity Changes with Driving Memory) model. The stability condition of MVCM model was derived through linear stability analysis. Then, the disturbance propagation of MVCM model was compared with that of both classical FVD (Full Velocity Difference) model and MHOVA (Multiple Headway Optimal Velocity and Acceleration) model. Finally, a case study was conducted in VISSIM to analyze the impact of CV (with MVCM model) rates on traffic characteristics, including the average speed, delay time and travel time. Results show that 1) considering more front vehicles' optimal speed strengthens the stability of traffic flow and the optimal considered vehicle number is 4; 2) MVCM model shows better resistance to disturbance than FVD model; 3) and obtain the same stability with MHOVA model, but with fewer front vehicles considered (only 3); 4) larger rate of CVs leads to higher average speed, smaller average travel time and the delay time of CVs and HVs; and 5) CVs' positive effects reaches stable when CV rate approaches 0.6.
Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles (UAVs), or drones, have become increasingly popular. However, the civil applications of this technology, ...particularly for traffic data collection and analysis, still need to be thoroughly explored. For this purpose, the authors previously proposed a detailed methodological framework for the automated UAV video processing in order to extract multi-vehicle trajectories at a particular road segment. In this paper, however, the main emphasis is on the comprehensive analysis of vehicle trajectories extracted via a UAV-based video processing framework. An analytical methodology is presented for: (i) the automatic identification of flow states and shockwaves based on processed UAV trajectories, and (ii) the subsequent extraction of various traffic parameters and performance indicators in order to study flow conditions at a signalized intersection. The experimental data to analyze traffic flow conditions was obtained in the city of Sint-Truiden, Belgium. The generation of simplified trajectories, shockwaves, and fundamental diagrams help in analyzing the interrupted-flow conditions at a signalized four-legged intersection using UAV-acquired data. The analysis conducted on such data may serve as a benchmark for the actual traffic-specific applications of the UAV-acquired data. The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. The future research will mainly focus on further extensions of UAV-based traffic applications.
•Providing a more effective Weighed DTW for traffic flow data analysis.•Systematically analysing and labelling a large real traffic dataset from the City of Melbourne.•GWDTW outperforms Euclidean and ...DTW using k-medoid for clustering traffic flow data.
This paper presents a novel similarity measure to identify interesting traffic patterns on a large traffic flow time series data for the central suburbs of Melbourne city in Australia. This new measure is a weighted Dynamic Time Warping (DTW) method based on Gaussian probability function, named GWDTW, that reflects the relative importance of peak hours. We have shown its superior performance over two benchmark similarity measures, the Euclidean distance and conventional DTW measure, on the intersection clustering task using k-medoids clustering algorithm, with respect to both internal and external evaluation measures. With intensive evaluation, the results show that GWDTW is a very effective similarity measure for modelling traffic behaviours, which can provide policy makers with more valuable information for infrastructure design, and smart city development.
This paper proposes a vehicle types classification modelfrom video streams for improving Traffic Flow Analysis (TFA) systems. A Video Content-based Vehicles Classification (VC-VC) model is used to ...support optimization for traffic signal control via online identification of vehicle types.The VC-VC model extends several methods to extract TFA parameters, including the background image processing, object detection, size of the object measurement, attention to the area of interest, objects clash or overlap handling, and tracking objects. The VC-VC model undergoes the main processing phases: preprocessing, segmentation, classification, and tracks. The main video and image processing methods are the Gaussian function, active contour, bilateral filter, and Kalman filter. The model is evaluated based on a comparison between the actual classification by the model and ground truth. Four formulas are applied in this project to evaluate the VC-VC model’s performance: error, average error, accuracy, and precision. The valid classification is counted to show the overall results. The VC-VC model detects and classifies vehicles accurately. For three tested videos, it achieves a high classification accuracy of 85.94% on average. The precession for the classification of the three tested videos is 92.87%. The results show that video 1 and video 3 have the most accurate vehicle classification results compared to video 2. It is because video 2 has more difficult camera positioning and recording angle and more challenging scenarios than the other two. The results show that it is difficult to classify vehicles based on objects size measures. The object's size is adjustable based on the camera altitude and zoom setting. This adjustment is affecting the accuracy of vehicles classification.
This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic ...dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach adopted for data collection was based on mobile phone video recording at a representative signalized intersection in Marrakech, capturing traffic behaviors during four distinct time periods. Then the YOLOv8 algorithm was employed for real-time object detection and analysis, allowing precise monitoring of motorcycle positioning and examining its influence on the start-up lost time. Afterwards, VISSIM simulations were implemented, on the basis of the collected data, to explore various scenarios, such as motorcycles sharing lanes with cars or dedicated motorcycle lanes. The results reveal a compelling correlation between motorcycle proximity to cars and traffic congestion, with closer positioning leading to increased congestion, longer travel times, reduced average vehicle speeds, and extended queue lengths at intersections. On the contrary, scenarios with dedicated motorcycle lanes consistently show reduced congestion and smoother traffic flow.
Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application ...challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.
Human-machine handover of conditionally automated driving vehicles (CADVs) significantly affects traffic safety. Therefore, a simulation modeling was conducted for the traffic flow mixed with manual ...driving vehicles, fully automated driving vehicles (FADVs), and CADVs under different takeover times to unravel the impact of CADVs on traffic flow. The results showed that different takeover times significantly affected traffic flow stability. A moderate takeover time allows the driver to complete the takeover quickly with sufficient observation of the surrounding traffic conditions and mitigate the adverse effects of CADVs takeover transition on traffic flow and improve traffic flow safety. Taking moderate takeover time (7s) as the given takeover time, we developed a traffic flow model, and it is found that increasing the total penetration rates of CADVs and FADVs or that of CADVs alone will expand the traffic flow stability area. Moreover, the improving effects of traffic flow stability increase with the value of both penetration rates. This research can be a reference for the safety analysis of heterogeneous traffic flow mixed with CADVs.
Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating ...vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08.
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