Detecting spatiotemporal extents of traffic congestion: a density-based moving object clustering approach
Author:
Affiliation:
1. Department of Geo-informatics, Central South University, Changsha, Hunan, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hunan Province, China
Hunan Key Research and Development Project
Independent Exploration and Innovation Project Fund Designated for Graduate Students of Central South University
Publisher
Informa UK Limited
Subject
Library and Information Sciences,Geography, Planning and Development,Information Systems
Link
https://www.tandfonline.com/doi/pdf/10.1080/13658816.2021.1905820
Reference59 articles.
1. Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories
2. Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability
3. Network Density Estimation: Analysis of Point Patterns over a Network
4. Traffic Congestion Identification and Reduction
5. Map-matching algorithm for large-scale low-frequency floating car data
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