Intelligent Transportation Systems Research Research Articles

Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.

Utilizing YOLOv8 for enhanced traffic monitoring in intelligent transportation systems (ITS) applications

The increasing demand for artificial intelligence-based motor vehicle detection in Intelligent Transportation Systems (ITS) applications highlights the significance of advancements in this field. The introduction of YOLOv8, the latest iteration in the YOLO algorithm series, presents a new avenue for exploring the potential of this detection algorithm within the ITS domain. The algorithm has not been previously tested in applications such as vehicle detection, which highlights a gap in the existing literature. This presents an opportunity to explore its capabilities and contributions in traffic monitoring and vehicle detection. This study aims to address this gap by employing YOLOv8 for vehicle detection within the broader context of ITS applications. Distinguishing itself from its predecessors, YOLOv8 features a decoupled head structure and employs a C2f module instead of C3. Extensive testing was performed using datasets acquired through aerial monitoring with a drone. Special emphasis was placed on ensuring a diverse array of target objects during dataset creation, a detail frequently neglected in comparable studies. The algorithm's training not only facilitated an evaluation of its ability to generalize and process data proficiently but also provided initial insights into its potential for real-time applications. The model underwent a comprehensive series of performance tests, revealing both strengths and weaknesses and outlining its capabilities and limitations. In a comparative analysis, the study systematically compared the performance metrics of YOLOv8 with those of YOLOv5, a widely adopted model in ITS research. Precision assessments unveiled a significant disparity, with YOLOv8 exhibiting an 18% increase in precision compared to YOLOv5. Further investigation into the inference times of both algorithms highlighted the superior processing speed performance of YOLOv8. The study's findings shed light on the limitations of the detection process, attributing misclassifications to factors such as variations in vehicle shapes, lighting conditions, and relative sizes.

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

A survey of sustainable development of intelligent transportation system based on urban travel demand

<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

Measurement-based fading characteristics analysis and modeling of UAV to vehicles channel

With the rapid development of unmanned aerial vehicle (UAV) and autonomous driving technology, wireless communication between UAV and vehicles has become one of the hotspots in the research of intelligent transportation systems (ITS). Particularly, link-level UAV-based communication requires correlation characteristics of propagation channel. In the current channel measurement, the transmitter or receiver of the ground is fixed, which ignores the high dynamics of the vehicle and the complexity of the environment in the ITS scene. Therefore, it is necessary to conduct a dynamic channel measurement and analysis for UAV. In this paper, we carry out an UAV-to-Vehicle (U2V) measurement campaign in S- and C-band for multiple scenarios of low-altitude UAV and mobile vehicles propagation and provide a comprehensive investigation of channel fading characteristics. Based on the measurement data, the statistics of large-scale fading (path loss, shadow fading and its autocorrelation) and small-scale fading (amplitude distribution) for several typical measurement scenarios are extracted first, which are compared with other air-to-ground (A2G) and standard terrestrial propagation scenarios to analyze the U2V propagation characteristics in various scenarios. A comprehensive analysis and comparative study of all considered channel parameters extracted is then performed to reflect the physical laws behind the measurements. The analysis results reveal that the Log-distance model outperforms the considered typical models in terms of predicting the path loss, and the proposed autocorrelation model shows better performance than traditional models. The quantitative results are essential for modeling and realizing reliable communications in U2V wireless systems and analyzing the performance for UAV-enabled ITS.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords “naturalistic driving data” and “naturalistic driving study data”. As a result, a set of 393 papers, Published between January 2002–March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

AI+CASE Lab: Advanced Interdisciplinary Research and Education Lab for Connected, Autonomous, Shared, and Green Transportation Systems [Its Research Lab

Please send your proposal on profiling research activities of your or other intelligent transportation systems research groups and labs for the “ITS Research Labs” column to Yisheng Lv at yisheng.lv@ ia.ac.cn.

Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (>50%), which shows the robustness and efficiency of the proposed model.

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

Vehicle speed estimation is one of the most critical issues in intelligent transportation system (ITS) research, while defining distance and identifying direction have become an inseparable part of vehicle speed estimation. Despite the success of traditional and deep learning approaches in estimating vehicle speed, the high cost of deploying hardware devices to get all related sensor data, such as infrared/ultrasonic devices, Global Positioning Systems (GPS), Light Detection and Ranging (LiDAR systems), and magnetic devices, has become the key barrier to improvement in previous studies. In this paper, our proposed model consists of two main components: 1) a vehicle detection and tracking component – this module is designed for creating reliable detection and tracking every specific object without doing calibration; 2) homography transformation regression network – this module has a function to solve occlusion issues and estimate vehicle speed accurately and efficiently. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art methods by reducing the mean square error (MSE) metric from 14.02 to 6.56 based on deep learning approaches. We have announced our test code and model on GitHub with <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ervinyo/Speed-Estimation-Using-Homography-Transformation-and-Regression-Network</uri> .

Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}^{\mathbf {1+\alpha }}$ </tex-math></inline-formula> tracking performance. Second, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{C}^{ \boldsymbol {\infty }}$ </tex-math></inline-formula> -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

Recognition of road cracks based on multi-scale Retinex fused with wavelet transform

Cracks are the main diseases of roads and potential threats to road safety. The detection and repair of cracks is the focus of intelligent transportation system research. However, the performance of automatic crack detection is often not good enough due to the uneven illumination, low contrast between the crack and the surrounding pavement and the possible presence of shadows similar in intensity to the crack. In this paper, an improved multi-scale Retinex algorithm is proposed to enhance the crack image. The wavelet transform is integrated into the traditional multi-scale Retinex algorithm to avoid the halo generated by the Retinex algorithm, thereby reducing the image distortion. Meanwhile, the multi-scale Retinex algorithm can make up for the lack of useful information lost by wavelet transform, so the combination of the two can obtain better crack enhancement effect. In addition, the preprocessing of shadow removal is performed before crack enhancement, which effectively eliminates the interference of high-intensity shadows. Through the comparison of objective performance indicators, the newly proposed algorithm can better highlight the crack information. The method proposed in this paper can effectively realize the functions of shadow removal and crack enhancement, so that the recognition accuracy of the overall detection system reaches 95.8%, indicating that the algorithm has high research significance and engineering application value.

Short-Term Traffic Prediction of the Urban Road Network based on the Intelligent Transportation System

As an important part of the intelligent transportation system research, traffic prediction is the premise of realizing traffic guidance and can provide decision-making basis for traveler service and traffic management. To realize the macromanagement of the entire road network, it must be based on the traffic information of all road sections in the road network. In fact, short-term traffic information has certain characteristics such as real-time, high-dimensional, nonlinear, and nonstationary characteristics, but the traffic information of the same road section has stability and regularity in different periods, and the short-term traffic state has a self-similarity. This makes short-term traffic information predictable. The prediction is made by using the information of the road sections with detectors related to it, and the dynamic dissimilarity matrix is introduced to deal with the three parameters of flow, speed, and time occupancy at the same time. The quantitative relationship between the traffic information of the nondetector road segment and the known traffic information of other road segments, so as to realize the prediction of the traffic information of the nondetector road segment and obtain the complete traffic information of the regional road network. In addition, we use the actual data of the local road network in a certain area to verify the feasibility of the method.

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular Intelligent Transportation Systems research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron

Traffic volume data are the important part of research and application of intelligent transportation systems (ITS). However, data loss often happens due to various factors in the real world, which may cause large deviations in prediction or bad accuracy of optimizations. Imputation is a valid way to handle missing values. A multilayer perceptron-multivariate imputation of chain equation (MLP-MICE) regression imputation method optimized by the limit-memory-BFGS algorithm is proposed, considering the temporal and spatial characteristics of traffic volume. Also, 32 groups of simulated imputation experiments based on the detected traffic volume of road sections in the Guangdong freeway system are conducted, which take the scenarios of continuous missing and jumped missing into account. The results of the experiments show that the MLP-MICE can optimize the imputation performance in the missing value of traffic volume with the MAPE of imputation results from 6.38% to 30%. Meanwhile, the proposed model has higher imputation accuracy for the traffic volume data with a lower degree of mutation. Lastly, the performance of the proposed model of imputation in short-term traffic volume prediction is discussed using the support vector machine. The results of it show that the MAPE of prediction under the proposed model is much lower than all-zero imputation. Therefore, the proposed model in this study is positive on improving the accuracy of traffic volume prediction and intelligent traffic control and management.

Research on Vehicle Detection and Recognition Based on Infrared Image and Feature Extraction

Vehicle detection and identification and safe distance keeping technology have become the main content of current intelligent transportation system research. Among them, vehicle detection and recognition is one of the most important research contents, and it is also crucial to the safe driving of vehicles. Real-time detection and recognition of current vehicles can effectively prevent the occurrence of malignant traffic accidents such as rear-end collision. Because the infrared image has some shortcomings such as poor contrast, loud noise, and blurred edge, this paper mainly studies the color space preprocessing of the image and uses threshold segmentation method and infrared image enhancement to segment the front vehicle and background. That is to say, by analyzing the infrared image captured by infrared CCD, we use median filter to remove noise from the collected infrared image and then use the improved histogram equalization to enhance the contrast of the image. Vertical Sobel operator is selected to enhance the vertical edge of the image, and the image is segmented by binary segmentation method. Finally, vehicle detection and recognition are realized by vertical edge symmetry, aspect ratio, and gray-scale symmetry. The experimental image and experimental data analysis results show that the image processing technology studied in this paper has achieved the intended research purpose.

When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), as a main building block of modern cities, and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network

The rapid development of internet of things (IoT) and in-stream big data processing technology has brought new opportunities for the research of intelligent transportation systems. Traffic forecasting has always been a key issue in the smart transportation system. Aiming at the problem that a fixed model cannot adapt to multiple environments in traffic flow prediction and the problem of model updating for data flow, a traffic flow prediction method is proposed based on variable structure dynamic Bayesian network. Based on the complex event processing and event context, this method divides historical data through context clustering and supports cluster update through online clustering of event streams. For different clustered data, a search-scoring method is used to learn the corresponding Bayesian network structure, and a Bayesian network is approximated based on a Gaussian mixture model. When forecasting online, a suitable model or combination of models is selected according to the current context for prediction.

Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles

The era of Big Data has arrived. Recently, under the environment of intelligent transportation systems (ITS) and connected/automated vehicles (CAV), Big Data has been applied in various fields in transportation including traffic safety. In this study, we review recent research studies that employed Big Data to analyze traffic safety under the environment of ITS and CAV. The particular topics include crash detection or prediction, discovery of contributing factors to crashes, driving behavior analysis, crash hotspot identification, etc. From the reviewed studies, employing advanced analytics for Big Data has a great potential for understanding and enhancing traffic safety. Big Data application in traffic safety integrates and processes massive multi-source data, breaks through the limitations of the traditional data analytics, and discovers and solves the problems, which cannot be solved by the traditional safety analytics. Lastly, suggestions are provided for future Big Data safety analytics under the environment of ITS and CAV.

Applications of Deep Learning in Intelligent Transportation Systems

In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.