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Remote Sensing Advances in Urban Traffic Monitoring
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Remote Sensing Advances in Urban Traffic Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 14791

Special Issue Editors

Dr. Teresa Pamuła

E-Mail Website1 Website2
Guest Editor
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: neural network; deep learning; traffic flow prediction; object classifier; road traffic conditions classification; energy estimation
Dr. Wiesław Pamuła

E-Mail Website
Guest Editor
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: road traffic control systems; monitoring of road traffic using image processing methods; development of remote sensing devices using IoT technology
Prof. Dr. Zhenwei Shi

E-Mail Website
Guest Editor
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
Interests: remote sensing image processing and analysis; computer vision; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues.

The ongoing process of urban development is exacerbating the problems related to controlling and managing traffic in road networks. The basis for an efficient solution to these problems is the accurate and timely collection of traffic data. The creation of reliable traffic data banks that combine data from many sources and work in real time is of utmost importance for urban administration. The advent of sensors using IoT technology and the application of AI to fuse diverse data from many sources has inspired new approaches to finding a solution to the problem of traffic data collection and monitoring. The use of new technologies, and in particular methods involving artificial intelligence, such as deep learning, allows for large amounts of data to be processed quickly and creates new possibilities for their analysis.

This Special Issue focuses on reviewing advancements in the methods and technologies used to monitor traffic in cities. We welcome submissions that present the results of studies on the application of new technologies for remote sensing and the fusion of traffic data from diverse sources.

Original research papers or review manuscripts that focus on the following areas are invited:

  • Traffic monitoring using UAVs (Unmanned Aerial Vehicles);
  • UAVs for the collection of traffic data;
  • Data fusion from multiple traffic sensing modalities;
  • Image-based assessment of road network congestion;
  • Road infrastructure condition monitoring;
  • The application of deep learning in urban traffic monitoring systems;
  • Impact of IoT technology on traffic data collection.

Dr. Teresa Pamuła
Dr. Wiesław Pamuła
Prof. Dr. Zhenwei Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban traffic monitoring
  • traffic data fusion
  • road network congestion
  • UAV
  • image processing
  • road infrastructure
  • deep learning

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Published Papers (7 papers)

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Research

21 pages, 11309 KiB  
Article
LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model
by Yuxiao Zhang, Ming Ding, Hanting Yang, Yingjie Niu, Maoning Ge, Kento Ohtani, Chi Zhang and Kazuya Takeda
Remote Sens. 2024, 16(12), 2247; https://doi.org/10.3390/rs16122247 - 20 Jun 2024
Viewed by 857
Abstract
The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of [...] Read more.
The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model’s efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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21 pages, 4764 KiB  
Article
Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks
by Renata Żochowska and Teresa Pamuła
Remote Sens. 2024, 16(7), 1202; https://doi.org/10.3390/rs16071202 - 29 Mar 2024
Cited by 1 | Viewed by 1191
Abstract
Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques [...] Read more.
Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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14 pages, 4833 KiB  
Communication
An Improved Hybrid Beamforming Algorithm for Fast Target Tracking in Satellite and V2X Communication
by Aral Ertug Zorkun, Miguel A. Salas-Natera and Ramón Martínez Rodríguez-Osorio
Remote Sens. 2024, 16(1), 13; https://doi.org/10.3390/rs16010013 - 19 Dec 2023
Cited by 1 | Viewed by 1715
Abstract
Autonomous remote sensing systems establish communication links between nodes. Ensuring coverage and seamless communication in highly dense environments is not a trivial task as localization, separation, and tracking of targets, as well as interference suppression, are challenging. Therefore, smart antenna systems fulfill these [...] Read more.
Autonomous remote sensing systems establish communication links between nodes. Ensuring coverage and seamless communication in highly dense environments is not a trivial task as localization, separation, and tracking of targets, as well as interference suppression, are challenging. Therefore, smart antenna systems fulfill these requirements by employing beamforming algorithms and are considered a key technology for autonomous remote sensing applications. Among many beamforming algorithms, the recursive least square (RLS) algorithm has proven superior convergence and convergence rate performances. However, the tracking performance of RLS degrades in the case of dynamic targets. The forgetting factor in RLS needs to be updated constantly for fast target tracking. Additionally, multiple beamforming algorithms can be combined to increase tracking performance. An improved hybrid constant modulus RLS beamforming algorithm with an adaptive forgetting factor and a variable regularization factor is proposed. The forgetting factor is updated using the low-complexity yet robust adaptive moment estimation method (ADAM). The sliding-window technique is applied to the proposed algorithm to mitigate the steady-state noise. The proposed algorithm is compared with existing RLS-based algorithms in terms of convergence, convergence rate, and computational complexity. Based on the results, the proposed algorithm has at least 10 times better convergence (accuracy) and a convergence rate two times faster than the compared RLS-based algorithms. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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18 pages, 2775 KiB  
Article
Deployment of Remote Sensing Technologies for Effective Traffic Monitoring
by Tingting Chen, Jingwen Qi, Min Xu, Liye Zhang, Yu Guo and Shuaian Wang
Remote Sens. 2023, 15(19), 4674; https://doi.org/10.3390/rs15194674 - 23 Sep 2023
Cited by 1 | Viewed by 1906
Abstract
A rising volume of vessel traffic increases navigation density, which leads to an increasing risk of vessel collisions in navigation channels. Navigation safety issues have been widely studied with the aim of reducing such collisions. Intelligent navigation channels, which involve deploying remote-sensing radars [...] Read more.
A rising volume of vessel traffic increases navigation density, which leads to an increasing risk of vessel collisions in navigation channels. Navigation safety issues have been widely studied with the aim of reducing such collisions. Intelligent navigation channels, which involve deploying remote-sensing radars on buoys, are an effective method of tackling vessel collisions. This paper investigates the problem of radar deployment in navigation channels, aiming to expand the radar coverage area and effectively detect vessel locations. A mixed-integer linear programming model is formulated to determine the optimal deployment of radars in navigation channels under a given budget, where radars with different coverage radii and different types of buoys are introduced. Then, sensitivity analyses involving the impacts of budgets, the coverage radii of the radars, the distance between adjacent discrete locations, and the distribution of the existing buoys on the radar deployment plan are conducted. The computational results indicate that the coverage ratio of the navigation channel can be improved by reasonably deploying the different types of radars on the existing and new buoys under a given budget. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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18 pages, 3644 KiB  
Article
An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector
by Nuno Reis, José Machado da Silva and Miguel Velhote Correia
Remote Sens. 2023, 15(18), 4570; https://doi.org/10.3390/rs15184570 - 17 Sep 2023
Cited by 1 | Viewed by 1262
Abstract
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats [...] Read more.
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm’s confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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27 pages, 6786 KiB  
Article
An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems
by Payam Gouran, Mohammad H. Nadimi-Shahraki, Amir Masoud Rahmani and Seyedali Mirjalili
Remote Sens. 2023, 15(13), 3374; https://doi.org/10.3390/rs15133374 - 1 Jul 2023
Cited by 5 | Viewed by 1913
Abstract
In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. [...] Read more.
In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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Graphical abstract

22 pages, 15060 KiB  
Article
Keypoint3D: Keypoint-Based and Anchor-Free 3D Object Detection for Autonomous Driving with Monocular Vision
by Zhen Li, Yuliang Gao, Qingqing Hong, Yuren Du, Seiichi Serikawa and Lifeng Zhang
Remote Sens. 2023, 15(5), 1210; https://doi.org/10.3390/rs15051210 - 22 Feb 2023
Cited by 7 | Viewed by 4097
Abstract
Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection [...] Read more.
Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoint-based 3D object detector with monocular vision, named Keypoint3D. We creatively leveraged 2D projected points from 3D objects’ geometric centers as keypoints for object modeling. Additionally, for precise keypoints positioning, we utilized a novel self-adapting ellipse Gaussian filter (saEGF) on heatmaps, considering different objects’ shapes. We tried different variations of DLA-34 backbone and proposed a semi-aggregation DLA-34 (SADLA-34) network, which pruned the redundant aggregation branch but achieved better performance. Keypoint3D regressed the yaw angle in a Euclidean space, which resulted in a closed mathematical space avoiding singularities. Numerous experiments on the KITTI dataset for a moderate level have proven that Keypoint3D achieved the best speed-accuracy trade-off with an average precision of 39.1% at 18.9 FPS on 3D cars detection. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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