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Cited by 316

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory

Koukoumidis, Emmanouil, Peh, Li-Shiuan, Martonosi, Margaret Rose·2011·Proceedings of the 9th international conference on Mobile systems, applications, and services

Abstract

While traffic signals are necessary to safely control competing flows of traffic, they inevitably enforce a stop-and-go movement pattern that increases fuel consumption, reduces traffic flow and causes traffic jams. These side effects can be alleviated by providing drivers and their onboard computational devices (e.g., vehicle computer, smartphone) with information about the schedule of the traffic signals ahead. Based on when the signal ahead will turn green, drivers can then adjust speed so as to avoid coming to a complete halt. Such information is called Green Light Optimal Speed Advisory (GLOSA). Alternatively, the onboard computational device may suggest an efficient detour that will save the driver from stops and long waits at red lights ahead.

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Crossref · 10.1145/1999995.2000008

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Metadata

Authors
Koukoumidis, E., Peh, L.-S., & Martonosi, M. R.
Venue
Proceedings of the 9th international conference on Mobile systems, applications, and services
Publisher
ACM
Published
2011-06-28
Pages
127-140
OpenAlex
W2152918251
Cited by
316
References
17
FWCI
44.55
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. https://app.cochat.ai/papers/10.1145/1999995.2000008
The paper
Koukoumidis, E., Peh, L.-S., & Martonosi, M. R. (2011). SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. Proceedings of the 9th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/1999995.2000008

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.