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Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 29689

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Special Issue Editors

Dr. Maher Ibrahim Sameen

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Guest Editor

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: road assessment and monitoring; traffic accident analysis and modelling; GIS; remote sensing; natural hazards; machine learning

Prof. Dr. Biswajeet Pradhan

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Guest Editor

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: radar image processing remote sensing and GIS applications GIS for engineers forecasting disaster hazard; stochastic analysis and modelling; natural hazards; environmental engineering modelling; geospatial information systems; photogrammetry and remote sensing; unmanned aerial vehicles (UAVs).
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Special Issue Information

Dear Colleagues,

Vehicle traffic assessment and monitoring contribute significantly to safer roads. For example, systems, such as automatic traffic counting, and congestion detection help to improve traffic flow planning. Reducing congestion and developing traffic accident prediction models help to avoid collisions and severe injuries. Tools for pavement condition assessment provide rapid access to road condition information, which can help traffic planners to build better assessment and monitoring systems. Vehicle emission detection and traffic noise prediction are also important tools for reducing pollution.

Real-time and near-real-time information on traffic counts, road conditions, and road environment attributes are crucial for traffic assessment and monitoring. Ground-based data acquisition sensors (e.g., pneumatic tubes, inductive loop detectors, magnetic sensors, video detection systems, vehicle emission meters, and noise level meters) can be prone to failure and are too costly to install and maintain in some countries. Alternative technologies, such as GPS and remote sensing, can provide cheaper solutions for road traffic data acquisition. However, in some cases, both methods should be combined to perform validated assessment and monitoring.

Remote sensing provides aerial photos, high-resolution satellite images, and laser scanning measurements, which can be used to extract a range of road- and traffic-related attributes. Especially in areas that are difficult to access, for example, due to a disaster or conflict, remote sensing can be an important tool for the assessment and monitoring of vehicle traffic. However, remote sensing has a weakness: traffic fluctuations on small time scales can distort the accuracy of the estimated road and traffic attributes. This method also requires advanced analytical and computational resources.

As a result, this Special Issue aims to gather advances in the research on vehicle traffic assessment and monitoring by developing better methodologies of remote sensing data analysis and spatial models, contributing to improving the safety and sustainability of our road transport systems.

Dr. Maher Ibrahim Sameen|
Prof. Dr. Biswajeet Pradhan
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

Vehicle traffic
Traffic monitoring
Traffic counting
Traffic flow planning
Congestion detection
Traffic accidents
Pavement condition
Vehicle emission
Traffic noise
Machine learning
Deep learning
Object detection
Object tracking

Published Papers (6 papers)

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Research

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21 pages, 4258 KiB

Open AccessArticle

MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

by Li Zhuo, Bin Liu, Hui Zhang, Shiyu Zhang and Jiafeng Li

Remote Sens. 2021, 13(14), 2772; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142772 - 14 Jul 2021

Cited by 5 | Viewed by 1973

Abstract

Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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38 pages, 14313 KiB

Open AccessArticle

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

by Elżbieta Macioszek and Agata Kurek

Remote Sens. 2021, 13(12), 2329; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122329 - 14 Jun 2021

Cited by 43 | Viewed by 4164

Abstract

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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26 pages, 57029 KiB

Open AccessArticle

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

by Deqi Chen, Xuedong Yan, Xiaobing Liu, Liwei Wang, Fengxiao Li and Shurong Li

Remote Sens. 2021, 13(10), 1919; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101919 - 14 May 2021

Cited by 3 | Viewed by 1949

Abstract

Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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25 pages, 8700 KiB

Open AccessArticle

A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station

by Yu Kang, Jie Chen, Yang Cao and Zhenyi Xu

Remote Sens. 2021, 13(8), 1600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081600 - 20 Apr 2021

Cited by 4 | Viewed by 2515

Abstract

The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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20 pages, 6148 KiB

Open AccessArticle

An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos

by Ivan Brkić, Mario Miler, Marko Ševrović and Damir Medak

Remote Sens. 2020, 12(22), 3844; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223844 - 23 Nov 2020

Cited by 14 | Viewed by 3378

Abstract

Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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Review

Jump to: Research

22 pages, 3392 KiB

Open AccessEditor’s ChoiceReview

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

by Abolfazl Abdollahi, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty and Abdullah Alamri

Remote Sens. 2020, 12(9), 1444; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091444 - 02 May 2020

Cited by 172 | Viewed by 12872

Abstract

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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