An event detection method using floating car data
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An event detection method using floating car data

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This paper presents a method which detects events from floating car data (FCD). In traffic prediction, an event is one of unexpected factors which deteriorates the prediction performance. If occurrences of events are provided to prediction system beforehand, the prediction will be improved. Firstly, we confirmed contribution of event information to a traffic prediction accuracy. Then the paper will show that spatio-temporal pattern of zonal traffic attraction and generation is an intelligible trail of an event. Finally, the paper will propose an event detection and prediction method using PCA and HMM. The result shows feasibility of event prediction in high accuracy in certain condition.

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An event detection method using floating car data An event detection method using floating car data Presentation Transcript

  • An event detection method usingfloating car data Osamu Masutani, Hirotoshi Iwasaki Denso IT Laboratory, Inc. Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved.
  • Summary Background Concept Methodology - Factorization - Detection - Prediction Conclusions Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 2/17
  • Background: Event Ex. Festivals, sports event As a destination - Attractive POI - Drivers would like to know “fresh” information about events. Our aspect As an obstacle - Repellent POI. - Drivers would like to know risk of Event congestion caused by an event. Event information - Place and time ! Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 3/17
  • Background: Avoidance of event congestion Traffic information service - Watch and avoid → Congestion-aware route guidance → Traffic prediction Problem - Most of prediction method based on Google maps stationery situation of traffic. - Event is irregular and unpredictable. Event ahead Event “plan” information can 18:00 improve prediction accuracy. - Event-aware traffic prediction Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 4/17
  • Background: Event database Manually collected database - For web, telematics services Not integrated - Most of services are dedicated to certain genre (ex. business event, leisure event) - Private event information isn’t provided Fireworks in Stockholm (ex. school event) Not real-time - Re-schedule isn’t always tracked Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 5/17
  • Concept: Event detection method with FCD Fully automatic detection - Extracted from floating car data (FCD) Integrated - Can detect any type of event. - Private event also be detected. Real-time - Based on real-time fluctuation of FCD Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 6/17
  • Concept: Relationship between FCD and an event AttractionAssumptionCount of attraction and generation of cars to/froman event venue indicates event-specific pattern. Stadium Definition - Zone : gridded areas - Attraction : Incoming cars to the zone - Generation : Outgoing cars from the zone Generation Event period Event specific pattern: Attraction increase before event and generation increase after event Attraction Generation Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 7/17
  • Concept: 3D view of attraction and generation Events Spatio-temporal density view reveals the event-specific pattern - Attraction peak (blue chunk) followed by generation peak (red chunk) Density mapping by iso-surface - More interpretable than point scatter Stadium Similar method with “crime mapping” Nakaya, T., Yano, K., (2008). Spatio-temporal three-dimensional mapping of crime events: visualizing spatio-temporal clusters of snatch-and-run offences Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 8/17
  • Factorization: For accurate detection An event venue isn’t identical with a grid zone - Some sets of zones are related with one event Stadium Event specific pattern might be hidden in stationery traffic Event fluctuation - Various factors of fluctuation are coincident in a zone - Stationary fluctuation Event related factor should be properly separated Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 9/17
  • Factorization: Spatio-temporal factorization PCA on spatio-temporal space - Factor is a pair of zone and time series. Factored zones are zones which have coincident traffic. Factored time series are separated according to its pattern. Similar method with image factorization Oliver, N. M., Rosario, B., Pentland, A. P., (2000). A Bayesian Computer Vision System for Modeling Human Interactions Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 10/17
  • Factorization: Factors for density of attraction Categorization of factors - Single zone / set of zones - Periodic / irregular / transitory fluctuationFactor 1 : Weekday morning peaks on central station and business areaFactor 2 : Evening peaks on night spotFactor 3 : Transitory peaks on a sports centerFactor 4 : Weekday midnight peaks on central stationFactor 5 : Transitory peak on a spot Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 11/17
  • Factorization: Event related factor Zones: nearby area of the stadium Time series: corresponding to the dates of baseball games Stadium Weighted combination of zones : baseball games Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 12/17
  • Detection: Event pattern detection by HMM In-event Non-event Event “state” definition - 4 states (non, pre, in, post) - Pre- and post- event states defined as 2 hours before and after the event period. Pre-event Post-event Detection of pre-event can be regarded as prediction of event 4 state HMM - Trained by pair of actual event data and FCD Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 13/17
  • Detection: Detection results Evaluation on the stadium events - 1.5 month, near a stadium, 17 events (baseball games) Event states and raw data Results confirms our method works well - Event states detection: precision = 86% - Event occurrence detection: precision = 100% Recall = 100% Detected events Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 14/17
  • Prediction: Event aware traffic prediction Benefit of event information - Based on statistical prediction - Enhanced by adding a prediction pattern for event Normal Event aware Weekday Weekday Holiday Holiday Event Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 15/17
  • Prediction: Evaluation result Evaluation - Comparison event aware and normal (baseline) prediction - 1.5 month, near a stadium, 17 events (baseball games) Stadium and traffic Event information improves prediction accuracy - Overall accuracy of event-aware prediction outperforms 38% over normal prediction Event period 38% Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 16/17
  • Conclusions FCD attraction and generation statistics is good clue to event occurrence Factorization and HMM can detect relatively large scale events Event can refine traffic prediction ability Future works - Smaller events - Event classification Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 17/17