research-article

Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations

Authors:

Rasool Tahmasbi,

S. Mehdi HashemiAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 15, Issue 1

Pages 250 - 259

https://doi.org/10.1109/TITS.2013.2278614

Published: 01 February 2014 Publication History

Abstract

Traffic flow prediction can be used for the management of traffic control systems and can be applied toward improving traffic light split times at intersections. In this paper, we developed a methodology for the short-term prediction of traffic flow using the stochastic differential equation (SDE). Since the current volume depends on the previous short-term volume and time, we used the Hull–White model. With the proposed method, a flexible short-term prediction of volume is suggested. It is possible to simulate traffic conditions easily and also detect incidents precisely. This method is applied in Tehran's highways, and the obtained results are compared with previous artworks. Our results offered a better fit to the traffic volume.

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St-Onge CBenmakrelouf SKara NTout HEdstrom CRabipour R(2021)Generic SDE and GA-based workload modeling for cloud systemsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-020-00223-510:1Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1186/s13677-020-00223-5
Wang PLai JHuang ZTan QLin T(2021)Estimating Traffic Flow in Large Road Networks Based on Multi-Source Traffic DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298880122:9(5672-5683)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1109/TITS.2020.2988801
Ma DSong XZhu JMa W(2021)Input data selection for daily traffic flow forecasting through contextual mining and intra-day pattern recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114902176:COnline publication date: 15-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114902
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Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations
1. Mathematics of computing
  1. Probability and statistics
    1. Stochastic processes

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 15, Issue 1

February 2014

456 pages

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IEEE Press

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Published: 01 February 2014

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St-Onge CBenmakrelouf SKara NTout HEdstrom CRabipour R(2021)Generic SDE and GA-based workload modeling for cloud systemsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-020-00223-510:1Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1186/s13677-020-00223-5
Wang PLai JHuang ZTan QLin T(2021)Estimating Traffic Flow in Large Road Networks Based on Multi-Source Traffic DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298880122:9(5672-5683)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1109/TITS.2020.2988801
Ma DSong XZhu JMa W(2021)Input data selection for daily traffic flow forecasting through contextual mining and intra-day pattern recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114902176:COnline publication date: 15-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114902
Song XLi WMa DWang DQu LWang Y(2018)A Match‐Then‐Predict Method for Daily Traffic Flow Forecasting Based on Group Method of Data HandlingComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1238133:11(982-998)Online publication date: 19-Jun-2018
https://dl.acm.org/doi/10.1111/mice.12381
Wang WBai YYu CGu YFeng PWang XWang R(2018)A network traffic flow prediction with deep learning approach for large-scale metropolitan area networkNOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS.2018.8406252(1-9)Online publication date: 23-Apr-2018
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Yisheng Lv Yanjie Duan Wenwen Kang Zhengxi Li Fei-Yue Wang (2015)Traffic Flow Prediction With Big Data: A Deep Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2014.234566316:2(865-873)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1109/TITS.2014.2345663

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