© 2010 IBM Corporation IBM Research - Ireland - 2014 © 2014 IBM Corporation xStream Data Fusion...

© 2010 IBM Corporation

IBM Research - Ireland - 2014IBM Research - Ireland - 2014


xStream Data Fusion for Transport

Smarter Cities Technology CentreIBM Research - Ireland

IBM Research – Ireland - 2014IBM Research – Ireland - 2014

© 2014 IBM Corporation2

Multi-Sensor Data Fusion for Travel Time Estimation

Description Intelligent Operation for Transport backend for the real time fusion of

traffic-related sensor data.

Significance Transport operators are looking to revamp their control center with focus

on• Improve situational awareness of the transport network and increased

data granularity as to its performance• Maximize accuracy, minimize computational latency.• Maximize efficiency of investment and minimize dependence on on-

street equipment Processes and disseminates information to road users and traffic

managers. Provides information on which improved decision making can be taken to positively affect the above KPIs.

Impact Achieve broader coverage, higher accuracy using combination of legacy

sensor types

Applications Deployed and evaluated over a 4 months period in London in the context

of a competitive bid organized by Transport For London.



• Why Data Fusion?

– Separately, information sources have drawbacks:

• Automated Numerical Plate Recognition (ANPR): after the fact, not real time (1h delay), availability issues, high operation costs

• Induction Loops (SCOOT): do not translate directly into desired KPI (e.g. travel times), require traffic flow models and continuous (re)calibration

• Opportunistic (e.g. smartphone etc.): lot of open questions - perenniality of the technology, social, ICO regulations, …

• All: spatiotemporally sparse, uncertain

Motivation



• Why Data Fusion?

– Together, information sources complement each other:

• E.g.: Traffic models translate SCOOT occupancy into travel-times, and Bluetooth/ANPR validate and calibrate the traffic model.

• Achieve wider coverage and finer information granularity

• Increase trust in information, eliminate error bias introduced by individual sensor types

• Increase robustness against sensor failures

Motivation



Example - Fusing Travel Times with Volumetric Information

Travel Time (Bluetooth, ANPR) Volume/Density Fundamental Curves(Induction loops, CCTV, …)

Travel time (s)Density

Vo

lum

e (

v/h

)

P(t

rave

l tim

e)



Example - Fusing Travel Times with Volumetric Information

Data fusion offers a scalable and systematic way to capture and exploit the relation between data sources of different types.



Denoising - E.g. Bluetooth using frequency of detections from same devices

• Source of noise: bluetooth mac address clones detected at mutiple locations, variable detection ranges, casual drivers who stop frequently for non-traffic related reasons

• Commuters are a more reliable source of information. They can be identified overtime from their regular travel patterns.

Hours Elapsed Between consecutive detections



xStream Data Fusion Solution

IntelligentOperation

TransportationGIS Transform &

Adapters

DataassimilationDataassimilation

Traffic FlowModelsTraffic FlowModels Data FusionData Fusion

InterpolationPredictionInterpolationPrediction

DenoisingDenoising

InfrastructureAnd data models



Generalized Additive Models

Flexible, versatile class of statistical models:

Different types of input variables: categorical (weekday), continuous (temperature), ...

Non-linear effects of input variables (covariates)

Applicable to various domains (Energy, Transport, Water, ...) Human-understandable, robust:

No “black box” → easy to validate

Representation of expert domain knowledge

Straight-forward analysis of uncertainty and outlier events Efficient learning algorithms (batch and streams)

Yt dependent output variableXi

t independent input variables (covariates)fi transfert function



Big Data Platform

DBMS

Stream computing approach

Map Reduce

KPIs

Real-time

Offline

modelmodel



TfL - Transport for London - Results

• Prediction Results: The fitted GAM explains on average 74.5% variation of the data

Scenario 5-minahead

30-minahead

True(second)

102.3 102.3

Predicted(second)

108.6 112.7

RMSE(1day)

11.8 16.6

© 2010 IBM Corporation IBM Research - Ireland - 2014 © 2014 IBM Corporation xStream Data Fusion...

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