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    Markov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions

    Source: Journal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 006
    Author:
    Tao Li
    ,
    Jiaqi Ma
    ,
    Changju Lee
    DOI: 10.1061/JTEPBS.0000347
    Publisher: ASCE
    Abstract: In this paper, a Markov-based time series model (MTSM) framework is developed to predict traffic network conditions by integrating archived and real-time data under various external conditions, including weather, work zones, incidents, and special events. The model considers a Markov process to explicitly characterize the probabilistic transitions between traffic states with external conditions. Environmental observations (e.g., weather) and external events (e.g., incidents and work zones) are grouped into different scenarios based on archived traffic data, and Markov transition matrices for different scenarios are calculated to predict traffic state evolutions under different scenarios. In the proposed MTSM framework, short-term and long-term time series models are combined to forecast the traffic conditions of normal cases to consider weekly/daily trends, and the outputs are further adjusted with the Markov processes for external conditions. The evaluation results show that the error rates under normal cases are about 6%, and the error rates under external conditions are around 10%. The prediction results of the proposed model are compared with the benchmark (i.e., single short-term series model and weighted time series model without the Markov process). The performances of the proposed framework are superior to the benchmark models for both cases with and without external conditions. The proposed model is able to accurately capture recurring and nonrecurring traffic congestion. It provides a reliable prediction of traffic speeds for traffic management centers to efficiently deploy proactive traffic management strategies.
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      Markov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4264989
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorTao Li
    contributor authorJiaqi Ma
    contributor authorChangju Lee
    date accessioned2022-01-30T19:16:55Z
    date available2022-01-30T19:16:55Z
    date issued2020
    identifier otherJTEPBS.0000347.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264989
    description abstractIn this paper, a Markov-based time series model (MTSM) framework is developed to predict traffic network conditions by integrating archived and real-time data under various external conditions, including weather, work zones, incidents, and special events. The model considers a Markov process to explicitly characterize the probabilistic transitions between traffic states with external conditions. Environmental observations (e.g., weather) and external events (e.g., incidents and work zones) are grouped into different scenarios based on archived traffic data, and Markov transition matrices for different scenarios are calculated to predict traffic state evolutions under different scenarios. In the proposed MTSM framework, short-term and long-term time series models are combined to forecast the traffic conditions of normal cases to consider weekly/daily trends, and the outputs are further adjusted with the Markov processes for external conditions. The evaluation results show that the error rates under normal cases are about 6%, and the error rates under external conditions are around 10%. The prediction results of the proposed model are compared with the benchmark (i.e., single short-term series model and weighted time series model without the Markov process). The performances of the proposed framework are superior to the benchmark models for both cases with and without external conditions. The proposed model is able to accurately capture recurring and nonrecurring traffic congestion. It provides a reliable prediction of traffic speeds for traffic management centers to efficiently deploy proactive traffic management strategies.
    publisherASCE
    titleMarkov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions
    typeJournal Paper
    journal volume146
    journal issue6
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000347
    page04020042
    treeJournal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 006
    contenttypeFulltext
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