VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

By Arvind Thiagarajan, Lenin Ravindranath, Katrina LaCurts, Samuel Madden, Hari Balakrishnan, Sivan Toledo, Jakob Eriksson. ACM International Conference on Embedded Networked Sensor Systems, 2009

939 citations recorded by the source corpus.

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.

Subfields: Building and Construction, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/944c1c39fe581214dcc44e8008c9901d8c5d7cfe

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

In Electrical and Electronic Engineering

In Transportation

Citations in the corpus

A survey of mobile phone sensing (2010, neutral): “Collectively, these sensors are enabling new applications across a wide variety of domains, such as healthcare [1], social networks [2], safety, environmental monitoring [3], and transportation [ 4 , 5], and give rise to a new area of research called mobile phone sensing.”
TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing (2014, background): “Although a number of mobile crowdsourcing applications [5] [6] [7] [8] have been proposed, most of them have assumed that smartphones voluntarily contribute to the mobile crowdsourcing system, which is not true in the real world.”
A Survey on Platoon-Based Vehicular Cyber-Physical Systems (2016, background): “Some typical use cases include [39] road travel times estimation by the aid of mobile phones and the traffic accident detection [40] by leveraging accelerometers and acoustic data on mobile phones.”
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing (2012, background): “Although there are many applications and systems on mo-Smartphone bile phone sensing [16, 17, 20, 23, 25], most of them are based on voluntary participation.”
ParkNet: drive-by sensing of road-side parking statistics (2010, methodology): “Although cellular signals provide much lower location precision, they have also been used for traffic monitoring applications [25,28,29].”
RoadTracer: Automatic Extraction of Road Networks from Aerial Images (2018, methodology): “We use a standard map-matching method based on the Viterbi algorithm [15].”
Crowdsourcing with Smartphones (2012, methodology): “, Waze3) and road traf c delay estimation (VTrack [2]); constructing negrained noise maps by letting users upload data captured by their smartphone microphone (Ear-Phone [3], NoiseTube [4]); identifying holes in streets by allowing users to share vibration and location data captured by their smartph”
Resilience and efficiency in transportation networks (2017, background): “Other resilience approaches apply traffic network modeling to identify locations for critical buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12).”
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory (2011, background): “Several systems have been proposed that leverage GPS, accelerometer and proximity sensors in order to estimate traffic conditions [15, 23, 27], detect road abnormalities [11], collect information for available parking spots [22] and compute fuel efficient routes [12].”
Mobile Data Offloading: How Much Can WiFi Deliver? (2013, background): “14 examines power consumed for delayed transfers of 10-and 100-MB ﬁ les with various delay deadlines, which is directly translated from their transmission times.”
T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence (2013, background): “There are a few projects [2], [16], [17] aiming to estimate realtime traffic flows and forecast future traffic conditions on some road segments in terms of floating car data [5], [18], [12], such as GPS trajectories as well as Wi-Fi signals.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “Tracks generated by humans also provide useful information for map building, traffic estimation, and ride sharing [30, 1].”
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.”
Driving with knowledge from the physical world (2011, background): “1 Traffic Estimation There are a few projects [1][2][9][11][12][25] aiming to learn historical traffic patterns, estimate real-time traffic flows and forecast future traffic conditions on some road segments in terms of floating car data [23], such as GPS trajectories as well as Wi-Fi and GSM signals”