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.

1. An event detection method using
floating car data
Osamu Masutani, Hirotoshi Iwasaki
Denso IT Laboratory, Inc.
Copyright (C) 2009 DENSO IT LABORATORY, INC. All Rights Reserved.

2. Summary
Background
Concept
Methodology
- Factorization
- Detection
- Prediction
Conclusions
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3. 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
!
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4. 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
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5. 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
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6. 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
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7. Concept: Relationship between FCD and an event
Attraction
Assumption
Count of attraction and generation of cars to/from
an 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
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8. 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
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9. 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
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10. 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
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11. Factorization: Factors for density of attraction
Categorization 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 spot
Factor 3 : Transitory peaks on a sports center
Factor 4 : Weekday midnight peaks on central
station
Factor 5 : Transitory peak on a spot
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12. 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
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13. 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
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14. 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
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15. 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
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16. 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%
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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
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