an incident just occured. how severe will its impact be?

© Copyright 2011 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. HP Confidential.

Mahalia Miller, HP Labs, NSF Research Associate / Stanford University*Chetan Gupta, HP LabsDate: August 12, 2012

An incident just occured. How severe will its impact be?


Our research questions in traffic management:Understanding the past

• What was the spatial and temporal impact of a given incident on traffic congestion?

• What was the non-recurrent delay associated with a given incident?

Predicting the future

• An incident just occurred. How severe will its impact be?

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Photo credit: Jim Frasier/Flickr


Research combines disparate sources of data

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Spatial graph helps link police reports, sensor records, and weather• Graph is created from sensor

metadata–Sensors in each corridor (I-605 North, e.g.) linked by

parsing postmile and freeway for each sensor location

–Free text in sensor metadata including on-ramp/off-ramp information aids linking corridors (I-605 South with I-5 South, e.g.)

• Reported incident start locations mapped to closest upstream sensor on given corridor

• Sensors linked to closest weather station

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Sensor map created for District 7 highways (Los Angeles)








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Diagram of relative sensor locations

Traffic flow

Reported incident location

Closest upstream sensor, “b”

Downstream sensor, “a”

Upstream sensor, “c”


Results of deriving delay definitions

By integrating the delay definitions over space and time the following equations result:

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Algorithms track spatial and temporal spread of incidents to build baseline model

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Spread of sample incident on I-5 in Los Angeles

4 minutes after incident start




Impact Prediction Results

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Yes

False alarm

YesNo

No

Accident

No

AccidentFalse alarm

v-v* > 4.6

ρ > 0.22

#vehicles > 0

Yes

Experiment Classes Bounds (min) Counts Accuracy(%)

SF-1 2 5 146, 92 95.59

SF-2 2 30 188, 50 87.86

SF-3 3 5, 30 146,42, 50 81.09

LA-1 2 5 85, 87 91.40

LA-2 2 30 118, 54 88.84

LA-3 3 5, 30 85, 33, 54 82.33

Experiment Classes Bounds (in $) Counts Accuracy (%)

SF-1 2 10 114, 124 95.59

SF-2 2 100 157,81 87.86

SF-3 3 10, 250 114, 54, 70 81.09

SF-4 4 10, 250, 1000 114, 54, 34, 36 73.73

LA-1 2 10 75, 97 91.40

LA-2 2 250 94, 78 88.84

LA-3 3 1000 120, 52 82.33

LA-4 3 10, 1000 75, 45, 52 75.87


With high accuracy, model predicts which of 2 accidents will have higher impact

• Preliminary results indicate 90%+ accuracy for predicting relatively which incident will have a higher impact–Impact metric is economic losses from travel time delay

–Determination done within 2 minutes of reported times

• Future work will compare incidents with both starting in a given time and space window to simulate traffic dispatcher’s decision where to focus limited resources

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Results indicate high degree of transfer learning possible

Table: Prediction accuracy (%) by each bin selection choice for k classes of incident impact trained on the SF dataset and tested directly on LA:

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Measure Bounds Accuracy

Cost $10 90.07

Cost $10, $100 86.05

Duration 5 minutes 91.28

Duration 5, 30 minutes 73.84


Summary of key contributions

• Build baseline model for traffic conditions across time and space

• Predict the impact of an incident for an incident that just occurred using classification models as measured by incident duration and travel delay-induced economic losses

• Models show high level of transfer learning

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Thank you

Contact:

[email protected]

[email protected]

Photo credit: ShaojingBJ/Flickr


For two regions, models predict incident duration and travel delay-induced economic lossesTable: Example results from travel delay-induced economic losses

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Experiment Classes Bounds ($) Counts Accuracy f-Measure

SF-1 2 10 114,124 95.59 0.95-0.96

…

SF-3 4 10,250,1000 114,54,34,36 73.73 0.47-0.95

LA-1 2 10 75,97 91.40 0.90-0.92

…

LA-4 3 10,100 75,52,45 75.87 0.55-0.89


Model is good predictor of incident false alarmsResults

•With 90%+ accuracy, within 2 minutes can determine if an incident will have a non-negligible delay or instead be a “false alarm”

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Sample classification tree for incident delay impact < 1 veh-hr

Yes

False alarm

YesNo

No

(v*-v)up>4.6

Accident

YesNo

ρ>0.22

# vehicles>0

Accident

False alarm


State of California records highway traffic conditions• California-based system (PeMS) stores billions of traffic records

–from ~34,000 sensors across ~30,000 directional miles of highways (some offline)

–at frequencies up to every 30 seconds for over a decade

Schema: <time, station_id, district, route, direction, road_type, length, tot_samples, %observed, ave_flow, ave_occupancy, ave_speed, samples_i, flow_i, occupancy_i, speed_i, imputed_boolean_i … samples_N, flow_N, occupancy_N, speed_N, imputed_boolean_N>

Sample: <01/06/2009_00:00:00,715918,7,5,N,ML,.615,30,100,55,.015,66.8,10,8,.0048,71.9,1,

10,22,.0155,69.7,1,10,25,.0246,62.7,1,,,,,0,,,,,0,,,,,0,,,,,0,,,,,0>

• We used aggregated 5-minute inductive loop Los Angeles (D7) data–Test study has ~300 sensors

–Results from 5 minute periods for 2 months

–Approximately 5 million records

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California Highway Patrol (CHP) provides incident reports• Summarized incident reports are available

–Schema: <ID, district, area, freeway, start_time, duration, abs_postmile, state_postmile, location_description, incident_type>

• We grouped incident types into 9 categories

• Approximate location, time, and raw incident details are in free text

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Weather data gives insight into rain and wind conditions facing drivers

• California Department of Water Resources records rain, wind, temperature, etc.

• We scraped this data for our test temporal period (January 1-March 1 2009) from their website

• Schema: <date, hour, cumulative rainfall>

• Sample: 20090101 0 5.50

20090101 100 5.50

20090101 200 5.50

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Algorithms query database to access raw data• Incident summary table

–Schema: <date, day (0=Sunday…7=Saturday), holiday_boolean (0=not holiday, 1=holiday), minutes (since midnight that is reported as start time), duration (in minutes), incidentID, district, area, route, direction, abs_postmile, state_postmile, location_description, incident_type>

• Devices table–Schema: <sensorID, route, direction, district, county, city, state_postmile, abs_postmile, latitude,

longitude, length, road_type, lanes, name, user_id_1, user_id_2, user_id_3, user_id_4715897>

• Sensors table–Schema: <date, day (0=Sunday…7=Saturday), holiday_boolean (0=not holiday, 1=holiday), minutes

(since midnight), sensorID, district, route, direction, road_type, length, tot_samples, %observed, average flow, average occupancy, flow-weighted average speed>

• Recurrent speed table (created after analysis of raw data)–Schema: sensorID, minutes (since midnight), computed recurrent speed>

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Diagram of relative sensor locations

Traffic flow

Reported incident location

Closest upstream sensor, “b”

Downstream sensor, “a”

Upstream sensor, “c”

an incident just occured. how severe will its impact be?

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