Towards a Learning Incident Detection

System

ICML 06 Workshop on Machine Learning for Surveillance and Event Detection

June 29, 2006Tomas Singliar

Joint work with Dr. Milos Hauskrecht

Outline Replace traffic engineers with ML

algorithms for incident detection Traffic data collection and quality

Why, who and for what purposes Incident detection algorithms Evaluation metrics Individual feature performance Sensor fusion with SVM Noisy data problems

Attempts to model accident evolution with DBN Conclusions and future work

Noisy data: Poor onset tagging and “bootstrap”

Traffic data collection Sensor network

Volumes Speeds Occupancy

Data aggregated over 5 minutes

Incidents police camera system

Incident Annotationincident

incident no incident

Incident annotation Incident labels not necessarily correct or timely

Do not correct timing (opportunity for more ML )

Incident detection algorithms, intuition Incidents detected indirectly through caused congestion Baseline: “California 2” algorithm:

If OCC(up) – OCC(down) > T1, next step If [OCC(up) – OCC(down)]/ OCC(up) > T2, next step If [OCC(up) – OCC(down)]/ OCC(down) > T3, possible accident If previous condition persists for another time step, sound alarm

Hand-calibrated T1-T3 – very labor intensive Why so few ML applications?

nontraditional data, anomaly detection – rare positives, common sense works well

Occupancy spikes Occupancy falls

Evaluation metrics AMOC curve

Time to detection (TTD) vs False positive rate (FPR)

Don’t know when exactly incident happened

Maximal TTD (120min) AU interesting region of C

Performance envelope Detection rate (DR) vs FPR Random gets over diagonal Report ROC as a check

Sensitivity vs specificity

Low false positive region 1 false alarm/day * 150

sensors

Features

Sensor measurements Temporal derivative Spatial differences

Features Simple measurements: 3 per sensor, 6 total

Occupancy < threshold

Temporal features Capture abrupt changes

Occupancy spike – now minus previous time slice

Spatial differences “Discontinuities” in flow between sensor positions

Difference in speeds downstream - upstream

Sensor fusion

Information in all simple detectors How to combine their outputs? Linear combination – SVM

Baseline: California 2 Hand-calibrated (+brute force) Good low FAR performance, but poor detection rate

SVM Combines sensor measurements via

a linear combination

SVM Spatial relations

Sensor measurements plus ratios and differences from the neighboring sensor

SVM Temporal derivatives

Sensor measurements plus differences and ratios to previous step

Focus on low FAR California better – persistency check

A dynamic Naïve Bayes network Problem: Incidents are recorded later than they occur

True state of highway is unobservable by sensors Picture of incidents evolves in time

About 30 features: 3 readings up/down stream, differences, ratios to neighboring sensor, previous time point

speed

Occupancy(t-5)

Incident observed

…

True hidden stateH HH

I

On

O1

I

On

O1

I

On

O1

………

…

A dynamic Naïve Bayes network Evolution of an accident:

Normal traffic steady state Accident happens, effects build up Constricted steady state Recovery

Model has 4 hidden states Anchor hidden states to desired semantics: clamp p(I|H) Raise alarm if p(H=acc_state|O) > threshold

Learned hidden state transition matrix:

0.9536 0.0332 0.0000 0.01330.0050 0.9577 0.0339 0.00340.0000 0.0882 0.9033 0.00840.0957 0.0000 0.0753 0.8290

H1 H2

H4 H3

DNB Performance Poor job at low FAR

Fairly insensitive to threshold

Summary Challenges to ML in traffic incident detection

Rare class – data sparsity, unequal misclassif cost Incident annotations are noisy

Machine learning methods competitive though SVM outperforms current practice No manual tuning, readapts to data after changes

Lessons and surprises: Richer feature sets do not help much Neither does removing diurnal trends (?) SVM has very stable performance Dynamic Naïve Bayes weak

Future work Discriminate incident and benign congestion

Improve discriminative classification SVM with nonlinearities (?) Unequal misclassification cost models

Improve dynamical models SVM handles time awkwardly – Dynamic Bayes Nets Conditional random fields – discriminative + time

Improve Data Bootstrap – use even a strawman to label incident start,

learn from relabeled data (, iterate)

Supplemental materials available http://www.cs.pitt.edu/~tomas/papers/icml06w/ (AMOC curves that did not fit into the paper)

Thank you

Questions?

Suggestions?

SVM California 2 measurements

Current and past occupancies

DNB Performance