© 2010 IBM Corporation IBM Research - Ireland © 2014 IBM Corporation xStream Data Fusion for Transport Smarter Cities Technology Centre IBM Research - Ireland

IBM Research – Ireland © 2014 IBM Corporation 2 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.

IBM Research – Ireland © 2014 IBM Corporation 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

IBM Research – Ireland © 2014 IBM Corporation 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

IBM Research – Ireland © 2014 IBM Corporation Example - Fusing Travel Times with Volumetric Information Travel Time (Bluetooth, ANPR) Volume/Density Fundamental Curves (Induction loops, CCTV, …) Travel time (s) Density Volume (v/h) P(travel time)

IBM Research – Ireland © 2014 IBM Corporation 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.

IBM Research – Ireland © 2014 IBM Corporation 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

IBM Research – Ireland © 2014 IBM Corporation xStream Data Fusion Solution Intelligent Operation Transportation GIS Transform & Adapters Data assimilation Traffic Flow Models Data Fusion Interpolation Prediction Denoising Infrastructure And data models

IBM Research – Ireland © 2014 IBM Corporation 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) Y t dependent output variable X i t independent input variables (covariates) f i transfert function

IBM Research – Ireland © 2014 IBM Corporation Big Data Platform DBMS Stream computing approach Map Reduce KPIs Real-time Offline model

IBM Research – Ireland © 2014 IBM Corporation TfL - Transport for London - Results Prediction Results: The fitted GAM explains on average 74.5% variation of the data Scenario5-min ahead 30-min ahead True (second) Predicted (second) RMSE (1day)