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Characteristics of Urban Road Non-recurrent Traffic Congestion Based on Floating Car Data

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Lin SunAuthors Info & Claims

EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

Pages 120 - 126

https://doi.org/10.1145/3443467.3443740

Published: 01 February 2021 Publication History

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Abstract

Non-recurrent traffic congestion caused by traffic accidents is one of the most important factors that affect the operational efficiency of urban road networks. In this paper, the floating car data (FCD) and the traffic accident data in Wuhan were adopted to analyze the characteristics of traffic flow near the road accident point. Analysis results showed that the normal traffic flow was affected by the accident obviously. In terms of space, the speed of car before the accident point decreased significantly, and the impact produced by the accident would affect the upstream road, while the car speed of downstream road increased due to the less of vehicles. In terms of time, the queuing and congestion became serious as time goes on, and the vehicle delay increased with the increase of the duration of the accident. Thus, the floating car data could be used for automatic detection of traffic accidents due to the characteristics of floating car data near the accident point.

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Li JBoonaert JDoniec ALozenguez G(2021)Multi-models machine learning methods for traffic flow estimation from Floating Car DataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2021.103389132(103389)Online publication date: Nov-2021
https://doi.org/10.1016/j.trc.2021.103389

Index Terms

Characteristics of Urban Road Non-recurrent Traffic Congestion Based on Floating Car Data
1. Applied computing
  1. Computers in other domains
  2. Operations research

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EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 2020

1202 pages

ISBN:9781450387811

DOI:10.1145/3443467

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 February 2021

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EITCE 2020

EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 6 - 8, 2020

Xiamen, China

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EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;

Overall Acceptance Rate 508 of 972 submissions, 52%

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Li JBoonaert JDoniec ALozenguez G(2021)Multi-models machine learning methods for traffic flow estimation from Floating Car DataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2021.103389132(103389)Online publication date: Nov-2021
https://doi.org/10.1016/j.trc.2021.103389

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A Novel Traffic Prediction System based on Floating Car Data and Machine Learning

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A Novel Traffic Prediction System based on Floating Car Data and Machine Learning

Study on Space-Time Distribution Characteristics of Floating Car Data Based on Large Samples

Supporting augmented floating car data through smartphone-based crowd-sensing

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