An event detection method using floating car data

Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved.

An event detection method using floating car data

Osamu Masutani, Hirotoshi IwasakiDenso IT Laboratory, Inc.

Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved. 2/17

SummaryBackgroundConceptMethodology- Factorization- Detection- Prediction

Conclusions


Ex. Festivals, sports eventAs a destination- Attractive POI- Drivers would like to know “fresh”

information about events. As an obstacle- Repellent POI.- Drivers would like to know risk of

congestion caused by an event. Event information- Place and time

Background: Event

!

Event

Our aspect


Background: Avoidance of event congestionTraffic information service- Watch and avoid→

Congestion-aware route guidance

→

Traffic prediction

Problem- Most of prediction method based on

stationery situation of traffic.- Event is irregular and unpredictable.

Event “plan” information can improve prediction accuracy.- Event-aware traffic prediction

Event ahead

18:00

Google maps


Background: Event databaseManually 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 (ex. school event)

Not real-time- Re-schedule isn’t always tracked

Fireworks in Stockholm


Concept: Event detection method with FCDFully 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


Concept: Relationship between FCD and an event

Definition- Zone : gridded areas- Attraction : Incoming cars to the zone- Generation : Outgoing cars from the

zone

Event specific pattern: Attractionincrease before event and generation increase after event

GenerationAttraction

Generation

Attraction

Stadium

Event period

Assumption

Count of attraction and generation of cars to/from an event venue indicates event-specific pattern.


Concept: 3D view of attraction and generationSpatio-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

Events


Factorization: For accurate detection

An event venue isn’t identical with a grid zone- Some sets of zones are related

with one event

Event specific pattern might be hidden in stationery traffic- Various factors of fluctuation are

coincident in a zone- Event related factor should be

properly separated

Stadium

Stationary fluctuation

Event fluctuation


Factorization: Spatio-temporal factorizationPCA 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 factorizationOliver, N. M., Rosario, B., Pentland, A. P., (2000).A Bayesian Computer Vision System for Modeling Human Interactions


Factorization: Factors for density of attractionCategorization of factors- Single zone / set of zones- Periodic / irregular /

transitory fluctuation

Factor 1 : Weekday morning peaks on central station and business area

Factor 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


Factorization: Event related factorZones: nearby area of the stadiumTime series: corresponding to the dates of baseball games

Weighted combination of zones

Stadium

: baseball games


Detection: Event pattern detection by HMM

Event “state” definition- 4 states (non, pre, in, post)- Pre- and post- event states defined as

2 hours before and after the event period.

Detection of pre-event can be regarded as prediction of event4 state HMM- Trained by pair of actual event data and

FCD

In-event

Pre-event Post-event

Non-event


Detection: Detection results

Evaluation on the stadium events- 1.5 month, near a stadium, 17

events (baseball games)

Results confirms our method works well- Event states detection:

precision = 86%- Event occurrence detection:

precision = 100%Recall = 100%

Event states and raw data

Detected events


Normal

Prediction: Event aware traffic predictionBenefit of event information- Based on statistical prediction- Enhanced by adding a prediction pattern for event

Holiday Holiday

Event

WeekdayWeekday

Event aware


Prediction: Evaluation resultEvaluation - Comparison event aware and normal (baseline) prediction- 1.5 month, near a stadium, 17 events (baseball games)

Event information improves prediction accuracy- Overall accuracy of event-aware prediction outperforms 38% over

normal prediction

Event period

Stadium and traffic

38%


ConclusionsFCD attraction and generation statistics is good clue to event occurrenceFactorization and HMM can detect relatively large scale eventsEvent can refine traffic prediction abilityFuture works- Smaller events- Event classification

An event detection method using floating car data

Documents

An event detection method using floating car data