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2024 | OriginalPaper | Buchkapitel

8. Spatiotemporal and Intelligent Transportation Forecasting

verfasst von : K. Maithili, S. Leelavathy, G. Karthi, M. Adimoolam

Erschienen in: Spatiotemporal Data Analytics and Modeling

Verlag: Springer Nature Singapore

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Abstract

Spatiotemporal-based intelligent transportation systems are increasingly being integrated into various surveillance systems. To enhance the efficiency of these systems, automated forecasting was introduced to identify and penalize non-compliant behaviors. This chapter explores a range of location-based transportation forecasting systems and the necessary adaptations for smart cities. Additionally, the frameworks of transportation systems using intelligent methods have been evaluated to analyze their merits and demerits. Subsequently, route-based prediction was examined for its real-time application efficacy. Building upon spatial forecasting methods, their essential techniques and adaptation potentials have been explored for the transportation application of spatiotemporal data. In conclusion, analysis and forecasting-based performance metrics are presented, focusing on intelligent transportation systems across various countries, along with their challenges. As a key focus, the applications of spatiotemporal-based intelligent transportation forecasting are discussed in detail.

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Titel: Spatiotemporal and Intelligent Transportation Forecasting
verfasst von: K. Maithili
S. Leelavathy
G. Karthi
M. Adimoolam
Verlag: Springer Nature Singapore
Buch: Spatiotemporal Data Analytics and Modeling
Print ISBN: 978-981-9996-50-6

Electronic ISBN: 978-981-9996-51-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-9651-3_8

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