Traffic Estimation Research Articles

ShieldTSE: A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV

<i>ShieldTSE</i>: A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV

Toward Robust Car-Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is critical for the performance of classical car-following (CF) models. There is a vast body of literature on which target variable is optimal for classical CF models, but there is no study that evaluates the selection of optimal target variables for black-box models prevalent in machine learning, which are increasingly being used to model CF behavior. This paper evaluates different target variables, for example acceleration, velocity, and headway, for three models: Gaussian processes (GP), kernel ridge regression, and long short-term memory (LSTM). These models have different objective functions and work in different vector spaces, for example, GP work in function space, and LSTM in parameter space. Furthermore, this paper evaluates the efficacy of black box and classical CF models across datasets, including diverse traffic scenarios and human versus automated control. Finally, this paper evaluates the coupling of design choices across selection of model type, target variable during model calibration, and dataset characteristics. The experiments show that the optimal target variable recommendations for black-box models differ from classical CF models, depending on the objective function and the vector space. Statistical analysis indicates that selection of models couples greatly with target variables, while recommended target variables do not depend on the datasets used during model calibration. These results provide a path toward more robust CF models, potentially resulting in more accurate and generalizable capacity and/or traffic flow estimates for roadways, even as driving behaviors change with the introduction of new types of automation.

Research on passenger flow control at metro transfer stations based on real-time flow calculation of streamlines

PurposeThe volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.Design/methodology/approachThe synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.FindingsUtilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.Originality/valueThe construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Traffic flow parameter estimation for complex environments based on improved YOLOv8

Estimation of traffic flow parameters based on computer vision is still a very popular challenge. In particular, the detection problems such as occlusion, small targets, luminance variations, and lighting jitter, etc., are caused by dense traffic scenes and complex weather conditions. Previous studies about detecting and tracking methods focused on daytime and nighttime or single weather condition, but the performance of this model will decline dramatically due to complex conditions. In this paper, a framework for estimating traffic flow parameters in complex environments is presented, in which Ghost-YOLOv8 vehicle detection model is proposed. GhostConv is used to replace part of Conv and a new C2fGhost module is designed to replace part of C2f, reducing the number of parameters and improving the detection performance of the model; The global attention mechanism module is added to the neck network, which strengthens the semantic and location information in the features and improves the model’s ability of feature fusion; In view of the loss of semantic information caused by different scales in detecting small targets, a small target detection layer is added to enhance a combination of deep and shallow semantic information; The GIoU bounding loss function is used instead of the original loss function, which improves the performance of the network’s bounding box regression. Then, combining DeepSORT tracking algorithm and virtual line counting method, the proposed framework estimates traffic flow parameters, including volume, speed, and density in complex environments. The experimental results show that compared with the original model in the UA-DETRAC dataset, the improved YOLOv8 model’s the accuracy is increased by 1.4%, and the parameter number and model size are reduced by 0.229G and 0.2MB respectively. In addition, the framework has good robustness, reaching 97.56% accuracy when estimating average traffic flow parameters. It is sufficient to overcome complex weather conditions and effectively help control traffic for intelligent transportation systems and traffic management to provide good data support. In conclusion, it shows that the model can reduce the number and size of model parameters and improve the detection precision as well as meeting the requirements of edge computing devices and having better real-time performance, so it has practical application value.

Optimized demand-based charging networks for long-haul trucking in Europe

Abstract Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of origin-destination flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

Self-adaptive equation embedded neural networks for traffic flow state estimation with sparse data

The data-driven approach in intelligent traffic systems has achieved successive breakthroughs, thanks to the ever-increasing volume of traffic data. Nonetheless, in practical scenarios, the collected data often contain some issues, e.g., missing values, significantly impacting the accuracy and efficiency of the algorithms. To enhance the precision of traffic estimation utilizing the sparse data, we have developed a physics-informed neural network (PINN) based algorithm in the line with the traffic flow theory and deep learning principles. In contrast to the conventional PINNs, our approach uniquely incorporates a self-adaptive macro model for mixed flow into the network's architecture, serving as an embedded source of physics information. With this algorithm, we can capture the dynamic behavior of an entire traffic flow including its spatiotemporal evolution with sparse traffic data such as initial and boundary value information. To realize the model's adaptability, we have revised the macro model by inverting its parameters and incorporating a data-driven nonlinear element, which simplifies the intricate macro model structure. The network's effectiveness has been validated through the experiments conducted on a mixed traffic flow system experiencing local agglomeration and real-world data, demonstrating its capability for precise traffic simulation, efficient traffic flow prediction, and interpretability. Our study offers a novel insight for data-driven traffic flow state estimation.

Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut

Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut

An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NOx, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Urban Traffic Identification by Comparing Machine Learning Algorithms

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%.

Analysis of congestion key parameters, dynamic discharge process, and capacity estimation at urban freeway bottlenecks: a case study in Beijing, China

ABSTRACT Recurring bottlenecks significantly contribute to urban freeway congestion, making their analysis essential. This study examines six bottlenecks on Beijing’s Ring Road using multi-day data, identifying them via Dynamic Time Warping and Fuzzy C-Means Clustering (DTW+FCM). Key parameters—free-flow speed, critical speed, critical density, and jam density—are calibrated using fundamental diagram models. The Weibull distribution analyzes flow and speed patterns during congestion phases. The DTW+FCM method effectively identified bottlenecks and congestion levels. Severe congestion lasting over 10 hours on the West Second and Third Ring Roads averaged speeds of 15 km/h. The S3 model best fits data for the West Ring Roads, while the Van Aerde model suits the North Ring Roads. Different methods yield varying traffic capacity estimates, highlighting the need for nuanced approaches in urban expressway planning to maintain traffic quality and comfort. These findings offer valuable guidance for research and practical traffic management solutions.

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections

Objectives The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes. Methods Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores. Regression models were then developed to relate crashes at the same intersections to the classified conflicts based on the value of their safety indexes. Cumulative Residual plots were investigated. Finally, equations defining the boundary between consecutive anomaly score levels were developed to facilitate application in practice. Results Regression models for total and fatal plus severe (FSI) crashes utilizing classified extreme conflicts based on anomaly scores were found to outperform the models using total conflicts. The improvement is more pronounced for FSI crashes. The results also suggest that the machine learning integration method can efficiently classify conflicts accurately according to crash severity levels since the higher anomaly score is associated with a higher crash severity level (i.e., FSI). Conclusions The proposed framework represents a methodological advancement in traffic conflict-based estimation of crashes using a machine learning model to classify conflicts by their anomaly scores. For jurisdictions without the resources to develop such a model to classify conflicts for their own datasets, the simple equations defining the boundary between consecutive anomaly score levels could be used as an approximation.

High-Resolution Large-Scale Urban Traffic Speed Estimation With Multi-Source Crowd Sensing Data

High-Resolution Large-Scale Urban Traffic Speed Estimation With Multi-Source Crowd Sensing Data

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Intersection Traffic Estimation Through Integration of C-ITS and Location Intelligence Data

Intersection Traffic Estimation Through Integration of C-ITS and Location Intelligence Data

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.

Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception

Robust roadside traffic perception requires integrating the strengths of multi-source sensors under various adverse conditions, which is challenging but indispensable for formulating effective traffic management strategies. One limitation of existing radar-camera perception systems is that they focus on integrating multi-source information without directly considering scene information, leading to difficulties in achieving scene adaptive fusion. How to establish the connection between scene information and multi-source information is the key challenge to solving this problem. In this article, we propose a Scene adaptive Sensor Fusion (SSF) framework that characterizes scene information and integrates it into radar-camera fusion schemes, aiming to achieve high-quality roadside traffic perception. Specifically, we introduce a multi-source object association method that accurately associates multi-source sensor information on the roadside. We then utilize coding techniques to characterize the scene information, including visibility characterization regarding lighting and weather conditions, and road characterization regarding sensor viewpoint. By incorporating sensor and scene information into the fusion model, the SSF framework effectively establishes the connection between them. We evaluate the SSF framework on the Roadside Radar and Video Dataset (RRVD) and the Traffic flow Parameter Estimation Dataset (TPED), both collected from real-world traffic scenarios. Experiments demonstrate that SSF significantly improves vehicle detection accuracy under various adverse conditions compared to traditional single-source sensing methods and other state-of-the-art fusion techniques. Furthermore, vehicle trajectories based on SSF detection results enable accurate traffic parameter estimation, such as volume, speed, and density, in complex and dynamic environments.

A cross-comparison of different extreme value modeling techniques for traffic conflict-based crash risk estimation

Extreme Value Theory (EVT) models have recently gained increasing popularity for crash risk estimation using traffic conflict data. Extreme value modeling consists of two fundamental approaches: the block maxima approach and the peak-over-threshold approach, each with several variants. However, a comprehensive comparison of these two approaches and their variants in crash risk estimation is lacking. This study bridges this gap by comparing different extreme value modeling techniques and evaluating their performance in estimating crash frequencies. Within a non-stationary Bayesian hierarchical modeling framework, the analyzed models include the block maxima model, the r largest order statistic model, and the peak-over-threshold model with the fixed and dynamic threshold, across univariate and bivariate traffic conflict cases. The analysis utilizes modified time-to-collision and post-encroachment time conflict indicator data collected from four signalized intersections in the City of Surrey, British Columbia, Canada. The results show that incorporating additional order statistics in the r largest order statistic model improves predictive performance, particularly with limited extreme conflict samples. Moreover, employing the dynamic threshold within the peak-over-threshold model enhances model goodness-of-fit and yields more accurate crash frequency estimates compared to using the fixed threshold. While the performance of the block maxima and peak-over-threshold models varies with the selected conflict indicator in the univariate case, the bivariate peak-over-threshold model with the dynamic threshold exhibits superior overall prediction accuracy over the corresponding block maxima model. This is likely due to the effectiveness of the dynamic threshold in precisely identifying truly critical extreme conflicts.

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

Emission analysis based on mixed traffic flow and license plate recognition model

With the rising ownership of new energy vehicles (NEVs), accurate road traffic emission estimations are crucial. This study combined mixed traffic flow and license plate recognition (LPR) using improved 3D convolutional neural network (3D-CNN) and Transformer models. The 3D-CNN model is designed for vehicle localization and traffic flow data acquisition, while the Transformer-based LPR model accurately recognizes license plates, distinguishing NEVs from conventional vehicles. The overall model is validated by the training and test datasets, and then with field application along a primary arterial segment in the South 2nd Ring Road, Xi’an, China. The results demonstrate capability for the traffic emission approximation of the mixed traffic flow including new energy vehicles, revealing that mixed traffic flow identification plays an important role in road emission approximation. Results and procedures of the study may provide benchmark for the subsequent research and the verification of related transportation policies.