Scipediacontent: Scipediacontent moved page Draft Content 634612390 to Hassan et al 2018a

2021-02-02T07:27:23Z

Scipediacontent moved page Draft Content 634612390 to Hassan et al 2018a

Scipediacontent: Created page with " == Abstract == Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make..."

2021-02-02T07:27:17Z

Created page with " == Abstract == Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make..."

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== Abstract ==

Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make better travel decisions, avert traffic congestion, help the environment by reducing carbon emissions, and improve traffic efficiency. Travel time prediction has begun to attract more attention with the rapid development of intelligent transportation systems (ITSs), and is considered one of the more important elements required for successful ITS subsystems deployment. However, the stochastic nature of travel time makes accurate prediction a difficult task. This paper proposes travel time modeling using a mixture of linear regressions. The proposed model consists of two normal components. The first component models the congested regime while the other models the free-flow regime. The means of the two components are modeled by two linear regression equations. The predictors used in the linear regression equation are selected out of the spatiotemporal speed matrix using a random forest machine-learning algorithm. The proposed model is tested using archived data from a 74.4-mile freeway stretch of I-66 eastbound connecting I-81 and Washington, D.C. The experimental results show the ability of the model to capture the stochastic nature of travel time and to predict travel time accurately.

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.5220/0006690601130120 http://dx.doi.org/10.5220/0006690601130120]

* [https://doi.org/10.5220/0006690601130120 https://doi.org/10.5220/0006690601130120] under the license cc-by-nc-nd

* [https://eprints.qut.edu.au/203710 https://eprints.qut.edu.au/203710],
: [https://doi.org/10.5220/0006690601130120 https://doi.org/10.5220/0006690601130120],
: [http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006690601130120 http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006690601130120],
: [https://dblp.uni-trier.de/db/conf/vehits/vehits2018.html#ElhenawyHR18 https://dblp.uni-trier.de/db/conf/vehits/vehits2018.html#ElhenawyHR18],
: [https://academic.microsoft.com/#/detail/2794867229 https://academic.microsoft.com/#/detail/2794867229]

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Hassan et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 634612390 to Hassan et al 2018a

Scipediacontent: Created page with " == Abstract == Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make..."