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Erschienen in:
Traffic Monitoring and Reconstruction Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

7. Data Analytics and Models for Understanding and Predicting Travel Patterns in Urban Scenarios

verfasst von : Jaume Barceló, Xavier Ros-Roca, Lidia Montero

Erschienen in: The Evolution of Travel Time Information Systems

Verlag: Springer International Publishing

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Abstract

The estimation of the network traffic state, its likely short-term evolution and the prediction of the expected travel times in a network are key steps of traffic management and information systems, especially in urban areas and in real-time applications. To perform such functions, most systems have at their core engine specific dynamic traffic models whose main input is a dynamic OD-matrix describing the time dependency of travel patterns in urban scenarios. This chapter provides an overview of the main concepts supporting these dynamic traffic models and their practical implementations in some software platforms, as well as an outline on the main approaches for the estimation of dynamic OD-matrices. Additionally, this chapter provides a basic discussion on one of the main emerging trends: strategies aimed at using the unprecedented amount of new traffic data made available by “new” mobile technologies.

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Vorheriges Kapitel Dynamic Traffic Management: A Bird’s Eye View
Nächstes Kapitel Overall Conclusions and Further Research
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Metadaten
Titel
Data Analytics and Models for Understanding and Predicting Travel Patterns in Urban Scenarios
verfasst von
Jaume Barceló
Xavier Ros-Roca
Lidia Montero
Copyright-Jahr
2022
Buch
The Evolution of Travel Time Information Systems

Print ISBN: 978-3-030-89671-3

Electronic ISBN: 978-3-030-89672-0

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-89672-0_7

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