The CarTel ProjectLewis Girod

M.I.T. Computer Science & Artificial Intelligence Lab

cartel.csail.mit.edu

P j t PI H i B l k i h S M dd D i l

MIT/CSAIL

• MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)– Entrepreneurial approach

• Spawned over 100 technology companies• Industrial Liaison Program develops relationships with

industry

– Interdisciplinary approach• Primarily within Electrical Engineering and Computer Science

Dept• Collaboration and overlap with Mathematics, Cog Sci,

Mechanical Engineering, Civil and Environmental Eng, Media Arts and Sciences, Health Sciences, Bioinformatics

CarTel Project• Active project since 2002

– Funding from NSF CNS-0205445, CNS-0520032, CAREER-0448124, NSF CPS, Quanta Computer, and Google

• Principal investigators– Hari Balakrishnan– Sam Madden

• Project Theme: – Transportation-related applications

• Planning, traffic mitigation, end-user applications– Leverage disruptive advances in mobile

networks and embedded computing technology

Many Traffic Data Sources• Installed loop sensors• GPS sensors in service vehicles

– Transport service vehicles (taxis, busses, etc)– Commercial fleets (delivery networks)– Government vehicles

• Personal mobile phones– Smartphones with GPS– Cellular location services

• Each has benefits and liabilities– CarTel technology applies to entire spectrum– Innovation in both data collection and data analysis

CarTel Technology Projects: Collecting and Analyzing Traffic

Data • Instrumentation projects

– Taxis: Boston limo service; Singapore taxi data

– iPhone and Android applications– Phone data mapped to Chicago CTA bus

routes– Analysis of cellular signal strength + GPS

• Analysis and data mining projects– Singapore taxi data analysis and traffic

models– vTrack (WiFi access point data traffic

Application to 3 challenges

• Encourage participation, provide info to public– iCarTel smartphone app and web portal– Chicago CTA “crowdsourced” bus routes

• Identify urgent needs, support planning– Large-scale traffic data collection and mining

• VTrack, CTrack, phone applications

• Ultra low cost probe devices– Smartphones, feature phones– Ultra-low-cost dedicated probes

iCarTel2 iPhone App

Sam Madden & Hari Balakrishnan

Current traffic delays

iCarTel Web Commute Portal

VTrack: Accurate Traffic Modeling

Map matchingDetermine best trajectory

Accurate delay estimationMatch observation to segment to extract delays

Predictive model

Highly scalable and parallel cloud implementation

Maximizes accuracy per probe asset

VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones. Arvind Thiagarajan, Lenin Ravindranath, Katrina LaCurts, Sivan Toledo, Jakob Eriksson, Samuel Madden, Hari Balakrishnan, in Proc. 14th ACM SenSys, Berkeley, CA, November 2009.

CTrack: Map Matching and Delay Estimation from Raw Cellular RSSI Data

HMM fingerprints to grid sequence

HMM smooth grid to map

Raw points (placelab)

Smooth + interpolate

grid sequenceAccurate, Low-Energy Trajectory Mapping for Mobile Devices. Arvind Thiagarajan, Lenin S. Ravindranath, Hari Balakrishnan, Samuel Madden, Lewis Girod, Proc. NSDI, Boston, MA, 2011.

Technical Approach

Internet

Servers in the cloud

Smartphones in vehicles

Highly granular raw position collectionAggregation provides environmental

infoDetails enable personalized service

Modeling, prediction, analysis

Location-based vehicular services

VTrack / CTrack delay estimationTraffic-aware routing

Singapore: Delay Model: AYE (4037)

Distance: 186 meter

Sejoon Lim & Daniela Rus

Chicago CTA Bus Route Experiment

• Goal: Deduce and predict actual bus route timing based on rider phone reports

• Match rider observations against bus routes

• System based on iPhone application + server based data mining

• Made use of GPS and acceleration sensors

• Enables riders to better anticipate bus arrival

Arvind Thiagarajan, James Biagioni, Tomas Gerlich, and Jakob Eriksson. Cooperative transit tracking using gps-enabled smartphones. In SenSys, pages 85-98. ACM, 2010.

Reducing cost of probes

• Smartphones/Java phones – works “out of the box”

• Feature phones – need provider cooperation to gather RSSI data; analysis via Ctrack

• Lowering costs of dedicated embedded solutions– Probe data alternatives:

• GPS – probe costs as little as $15 in high volume• Cellular RSSI via passive monitoring of cellular signaling• WiFi RSSI + opportunistic upload

– Data upload mechanisms: • Opportunistic WiFi

I t hi l i ti WiFi Bl t th h t

Overview• Traffic mitigation technology to

– Scalably process raw data from mobile phones to produce accurate traffic models

– Produce user-specific traffic-optimized routes and schedules

– Reduce overall costs of data collection and analysis

• Enables commute optimization for consumers and tools for planners

• http://cartel.csail.mit.edu