GreenDrive: A smartphone-based intelligent speed adaptation system with real-time trafiic signal prediction

Yiran Zhao, Shen Li, Shaohan Hu, Lu Su, Shuochao Yao, Huajie Shao, Hongwei Wang, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the design and evaluation of GreenDrive, a smartphone-based system that helps drivers save fuel by judiciously advising on driving speed to match the signal phase and timing (SPAT) of upcoming signalized traffic intersections. In the absence of such advice, the default driver behavior is usually to accelerate to (near) the maximum legally allowable speed, traffic conditions permitting. This behavior is suboptimal if the traffic light ahead will turn red just before the vehicle arrives at the intersection. GreenDrive uses collected real-time vehicle mobility data to predict exact signal timing a few tens of seconds ahead, which allows it to offer advice on speed that saves fuel by avoiding unnecessary acceleration that leads to arriving too soon and stopping at red lights. Our work differs from previous work in three respects. First and most importantly, we tackle the more challenging scenario, where some phases (such as left-turn arrows) are added or skipped dynamically, in accordance with real-time traffic demand. Second, our approach can accommodate a low system penetration rate and low vehicle density. Third, GreenDrive treats user-specified travel time requirements as soft deadlines and chooses appropriate speed adaptation strategies according to the user time budget. Using SUMO traffic simulator with real and large-scale road network, we show that GreenDrive learns phase durations with an average error below 2s, and reduces fuel consumption by up to 23.9%. Realworld experiments confirm 31.2% fuel saving and the ability to meet end-to-end travel time requirements.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages229-238
Number of pages10
ISBN (Electronic)9781450349659
DOIs
StatePublished - Apr 18 2017
Event8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)

Other

Other8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017
CountryUnited States
CityPittsburgh
Period4/18/174/20/17

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Keywords

  • Optimal speed advisory
  • Smartphone sensing
  • Traffic signal prediction

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Zhao, Y., Li, S., Hu, S., Su, L., Yao, S., Shao, H., Wang, H., & Abdelzaher, T. (2017). GreenDrive: A smartphone-based intelligent speed adaptation system with real-time trafiic signal prediction. In Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week) (pp. 229-238). (Proceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/3055004.3055009