Period Division-Based Markov Models for Short-Term Traffic Flow Prediction
Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this article proposes a membership degree-based Markov (MM) model and two period division-...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9210061/ |
| _version_ | 1811274371468099584 |
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| author | Ronghan Yao Wensong Zhang Dong Zhang |
| author_facet | Ronghan Yao Wensong Zhang Dong Zhang |
| author_sort | Ronghan Yao |
| collection | DOAJ |
| description | Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this article proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degree-based Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance. |
| first_indexed | 2024-04-12T23:17:48Z |
| format | Article |
| id | doaj.art-f8b23cccdda949e79def9e2b66485716 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:17:48Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f8b23cccdda949e79def9e2b664857162022-12-22T03:12:38ZengIEEEIEEE Access2169-35362020-01-01817834517835910.1109/ACCESS.2020.30278669210061Period Division-Based Markov Models for Short-Term Traffic Flow PredictionRonghan Yao0https://orcid.org/0000-0002-6614-1960Wensong Zhang1https://orcid.org/0000-0002-7894-9289Dong Zhang2https://orcid.org/0000-0002-0993-207XSchool of Transportation and Logistics, Dalian University of Technology, Dalian, ChinaSchool of Transportation and Logistics, Dalian University of Technology, Dalian, ChinaSchool of Transportation and Logistics, Dalian University of Technology, Dalian, ChinaShort-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this article proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degree-based Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance.https://ieeexplore.ieee.org/document/9210061/Short-term traffic flowpredictionMarkov modelsperiod divisionordered clusteringtraffic flow pattern |
| spellingShingle | Ronghan Yao Wensong Zhang Dong Zhang Period Division-Based Markov Models for Short-Term Traffic Flow Prediction IEEE Access Short-term traffic flow prediction Markov models period division ordered clustering traffic flow pattern |
| title | Period Division-Based Markov Models for Short-Term Traffic Flow Prediction |
| title_full | Period Division-Based Markov Models for Short-Term Traffic Flow Prediction |
| title_fullStr | Period Division-Based Markov Models for Short-Term Traffic Flow Prediction |
| title_full_unstemmed | Period Division-Based Markov Models for Short-Term Traffic Flow Prediction |
| title_short | Period Division-Based Markov Models for Short-Term Traffic Flow Prediction |
| title_sort | period division based markov models for short term traffic flow prediction |
| topic | Short-term traffic flow prediction Markov models period division ordered clustering traffic flow pattern |
| url | https://ieeexplore.ieee.org/document/9210061/ |
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