Towards smart cities: crowdsensing-based monitoring of transportation infrastructure using in-traffic vehicles

Mei, Qipei; Gül, Mustafa; Shirzad-Ghaleroudkhani, Nima

doi:10.1007/s13349-020-00411-6

Cited by 44 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• GAPs384: the German Asphalt Pavement distresS [15] • EdmCrack600 [35,34,33]: this dataset was created by capturing images on the streets of Edmonton, Canada and includes 600 pixel-level annotated images of road cracks. Although in the paper the adopted split is random and with a proportion of 420/60/120 pairs (70/10/20 in percent- age), the dataset that can be downloaded from the GitHub repository has not been split.…”

Section: Dataset and Task Proposedmentioning

confidence: 99%

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

Thompson¹,

Ranieri²,

Biasotti³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/masks pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.

show abstract

Section: Dataset and Task Proposedmentioning

confidence: 99%

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

Thompson¹,

Ranieri²,

Biasotti³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Akbar et al [64] work on a study to analyze IoT data by integrating them (traffic, weather, and Twitter data) to predict real-time congestion. Mei et al [157] make full use of crowd-sensing traffic vehicle data to provide a city monitoring mechanism. Mai-Tan et al [158] propose an architecture of crowdsourcing data for traffic estimation, which consists of various data from monitoring systems, public websites, and mobile data collection.…”

Section: ) Processing Complexity Issuesmentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

View full text Add to dashboard Cite

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

show abstract

“…A localization scheme using infrared technology [77] is developed, and it relies on the information gathered by the inactive infrared sensor nodes connected with the poles of light. An algorithm [78] localizes Wi-Fi admittance points and structures to regulate the urban noise sources. It does not require anchor nodes but gradually trusts crowdsourcing information to enhance the localization outcomes, aiming for improved precision.…”

Section: Smart City Servicesmentioning

confidence: 99%

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021

View full text Add to dashboard Cite

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

show abstract

Towards smart cities: crowdsensing-based monitoring of transportation infrastructure using in-traffic vehicles

Cited by 44 publications

References 32 publications

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

Contact Info

Product

Resources

About