EconPapers    
Economics at your fingertips  
 

Stage Prediction of Traffic Lights Using Machine Learning

Kevin Heckmann (), Lena Elisa Schneegans () and Robert Hoyer ()
Additional contact information
Kevin Heckmann: Universität Kassel
Lena Elisa Schneegans: Universität Kassel
Robert Hoyer: Universität Kassel

A chapter in Towards the New Normal in Mobility, 2023, pp 635-653 from Springer

Abstract: Abstract Motorized road traffic is the dominant source of greenhouse gas (GHG) emissions in the German and the pan-European transport sectors. Traffic jams and stops in front of traffic lights are causing avoidable increased emissions from motorized traffic. As various research has shown, the usage of Green Light Optimal Speed Advisory (GLOSA) systems promises to reduce the fuel consumption of motorized vehicles, with a corresponding reduction in GHG emissions in front of signalized intersections.Click or tap here to enter text.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-39438-7_36

Ordering information: This item can be ordered from
http://www.springer.com/9783658394387

DOI: 10.1007/978-3-658-39438-7_36

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-658-39438-7_36