NDLI: GreenDrive: a smartphone-based intelligent speed adaptation system with real-time traffic signal prediction

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GreenDrive: a smartphone-based intelligent speed adaptation system with real-time traffic signal prediction

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GreenDrive: a smartphone-based intelligent speed adaptation system with real-time traffic signal prediction

Content Provider	ACM Digital Library
Author	Li, Shen Su, Lu Abdelzaher, Tarek Zhao, Yiran Hu, Shaohan Wang, Hongwei Shao, Huajie Yao, Shuochao
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%. Real-world experiments confirm 31.2% fuel saving and the ability to meet end-to-end travel time requirements.
Starting Page	229
Ending Page	238
Page Count	10
File Format	PDF
ISBN	9781450349659
DOI	10.1145/3055004.3055009
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-04-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Optimal speed advisory Traffic signal prediction Smartphone sensing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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