SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory

By Emmanouil Koukoumidis, Li-Shiuan Peh, Margaret Martonosi. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2011

401 citations recorded by the source corpus.

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. This paper introduces and evaluates SignalGuru, a novel software service that relies solely on a collection of mobile phones to detect and predict the traffic signal schedule, enabling GLOSA and other novel applications. Our SignalGuru leverages windshield-mounted phones to opportunistically detect current traffic signals with their cameras, collaboratively communicate and learn traffic signal schedule patterns, and predict their future schedule. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66s, for pre-timed traffic signals and within 2.45s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3%, on average.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/473247bd26f674bc0db05c0c5c1639236d3b75e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Ardalan Vahidi
2. Xiangyang Li
3. Yu Zheng

In Transportation

1. Bin Guo
2. Yanmin Zhu
3. Yunhao Liu
4. Zhu Wang
5. Tong Liu
6. Zhiwen Yu
7. Ardalan Vahidi
8. Zheng Yang

Citations in the corpus

• Eco-Cooperative Adaptive Cruise Control at Signalized Intersections Considering Queue Effects (2017, background): “phone applications have been developed to realize eco-driving assistant systems for vehicles in the vicinity of signalized intersections [29], [30].”
• Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, methodology): “SignalGuru [12] utilizes a collection of mobile phones to detect and predict the traffic signal schedule.”
• How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint (2014, background): “…collect data, enabling numerous mobile crowdsourced sensing (MCS) applications, such as Sensorly [2] for constructing cellular/WiFi network coverage maps, SignalGuru [3], Nericell [4] and VTrack [5] for providing traffic information, Ear-Phone [6] and Noise-Tube [7] for making noise maps.”
• Mobile Crowd Sensing and Computing (2015, methodology): “For example, in SignalGuru [Koukoumidis et al. 2011], vehicles that are passing through an intersection can sense and share the traffic signal information and adjust their driving speed.”
• Incentives for Mobile Crowd Sensing: A Survey (2016, methodology): “Pioneer sensing systems include NoiseTube [6] for noise monitoring, SignalGuru [7] and VTrack [8] for traffic monitoring, CityExplorer [9], SmartTrace [10], Sensorly [11] for 3G/WiFi discovery, and LiFS [12] and TrMCD [13] for indoor localization.”
• Energy saving potentials of connected and automated vehicles (2018, background): “2008 Nissan Altima, 2 complying drivers [63] E, real city traffic +25 2 fixed time lights camera SPaT estimation- V2V comm.”
• A Crowdsourcing Framework for On-Device Federated Learning (2019, neutral): “, for indoor localization, traffic monitoring, navigation [3], [4], [5], [6]).”
• Crowdsourcing with Smartphones (2012, background): “SignalGuru [7] × opportunistic collective homogeneous ethical/service × camera Ear-Phone [3] × opportunistic collective homogeneous ethical × audio NoiseTube [4] × opportunistic collective homogeneous ethical × audio PotHole [5] × opportunistic collective homogeneous ethical × vibration AirPlace [8]”
• Mobile computing: the next decade (2010, background): “The importance of crowd sourcing as a fundamental technique in mobile computing is already well-recognized, as shown by a sampling of recent papers in mobile computing conferences and workshops [4, 11, 18, 34, 35, 36].”