Smart City Gnosys

Smart city article details

Title Urban Traffic State Estimation Techniques Using Probe Vehicles: A Review
ID_Doc 60232
Authors Mehta V.; Chana I.
Year 2017
Published Lecture Notes in Networks and Systems, 12
DOI http://dx.doi.org/10.1007/978-981-10-3935-5_28
Abstract Accurate and economical traffic state estimation is a challenging problem for future smart cities. To curb this problem, fixed roadside sensors are used for traffic data collection traditionally, but their high costs of installation and maintenance has led to the use of probe vehicles or mobile phones containing GPS-based sensors as an alternative cost-effective method for traffic data collection. However, the data collected by the latter method are sparse because the probe vehicles are very randomly distributed over both time and space. This survey paper presents state-of-the-art techniques prevalent in the last few years for traffic state estimation and compares them on the basis of important parameters such as accuracy, running time, and integrity of the data used. The dataset used for the implementation of techniques comes from probe vehicles such as taxis and buses of cities such as San Francisco, Shanghai, and Stockholm with different sampling rates (frequencies) of probes. Finally, it represents the challenges that need to be addressed along with the possible data processing solution. © Springer Nature Singapore Pte Ltd. 2017.
Author Keywords Data segmentation; Probe vehicles; Traffic state estimation


Similar Articles


Id Similarity Authors Title Published
35056 View0.892Mahmud S.; Day C.M.Leveraging Data-Driven Traffic Management In Smart Cities: Datasets For Highway Traffic MonitoringThe Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems (2022)
24469 View0.877Burdzik R.; Celiński I.; Ragulskis M.; Ranjan V.; Matijošius J.Estimation Of Vehicle Traffic Parameters Using An Optical Distance Sensor For Use In Smart City Road InfrastructureJournal of Sensor and Actuator Networks, 13, 4 (2024)
46591 View0.873Deveshwar P.; Singh T.; Sharma Y.; Bidwe R.V.; Hiremani V.; Devadas R.; Shah K.Revolutionizing Smart Cities: A Data-Driven Traffic Monitoring System For Real-Time Traffic Density Estimation And VisualizationLecture Notes in Networks and Systems, 1075 LNNS (2025)
5541 View0.871Lopez L.; Gacitua R.A Technological Proposal For Vehicle Detection In The Context Of Smart CitiesProceedings of the 2019 IEEE 1st Sustainable Cities Latin America Conference, SCLA 2019 (2019)
25494 View0.867Almeida A.; Brás S.; Sargento S.; Oliveira I.Exploring Bus Tracking Data To Characterize Urban Traffic CongestionJournal of Urban Mobility, 4 (2023)
24468 View0.865Salahshour B.; Nezafat R.V.; Cetin M.Estimation Of Unobserved Vehicles In Congested Traffic From Probe Vehicle SamplesInternational Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities - Selected Papers from the International Conference on Transportation and Development 2019 (2019)
11673 View0.864Sun Z.; Huang Z.; Hao P.; Ban X.J.; Huang T.Batch-Based Vehicle Tracking In Smart Cities: A Data Fusion And Information Integration ApproachInformation Fusion, 102 (2024)
44614 View0.862Zourlidou S.; Sester M.; Hu S.Recognition Of Intersection Traffic Regulations From Crowdsourced DataISPRS International Journal of Geo-Information, 12, 1 (2023)
1612 View0.861Liponhay M.; Valenzuela J.F.; Dorosan M.; Dailisan D.; Monterola C.A Dynamic Urban Mobility Index From Clustering Of Vehicle Speeds In A Tourist-Heavy CityApplied Sciences (Switzerland), 13, 23 (2023)
4996 View0.859Bidwe R.V.; Bidwe S.; Zope B.A Study Of Traffic Monitoring Systems For Smart City2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023 (2023)