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

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

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

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  1. An incident just occured. How severe will its impact be? Mahalia Miller, HP Labs, NSF Research Associate / Stanford University *Chetan Gupta, HP Labs Date: August 12, 2012

  2. 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? Photo credit: Jim Frasier/Flickr

  3. Research combines disparate sources of data

  4. 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 Sensor map created for District 7 highways (Los Angeles)

  5. Upstream sensor, “c” Spatial graph helps link police reports, sensor records, and weather Traffic flow Traffic flow • 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 Closest upstream sensor, “b” Reported incident location Downstream sensor, “a” Diagram of relative sensor locations

  6. Results of deriving delay definitions • By integrating the delay definitions over space and time the following equations result:

  7. Algorithms track spatial and temporal spread of incidents to build baseline model Spread of sample incident on I-5 in Los Angeles 4 minutes after incident start 14 minutes after incident start 29 minutes after incident start

  8. Impact Prediction Results v-v* > 4.6 Yes No ρ > 0.22 Accident Yes No #vehicles > 0 False alarm No Yes Accident False alarm

  9. 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

  10. 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:

  11. 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

  12. Thank you • Contact: • chetan.gupta@hp.com • mahalia@stanford.edu Photo credit: ShaojingBJ/Flickr

  13. For two regions, models predict incident duration and travel delay-induced economic losses • Table: Example results from travel delay-induced economic losses

  14. Model is good predictor of incident false alarms Sample classification tree for incident delay impact < 1 veh-hr • Results • With 90%+ accuracy, within 2 minutes can determine if an incident will have a non-negligible delay or instead be a “false alarm” (v*-v)up>4.6 No Yes Accident ρ>0.22 Yes No False alarm # vehicles>0 Yes No False alarm Accident

  15. 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

  16. 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

  17. 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

  18. 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>

  19. Upstream sensor, “c” Spatial graph helps link police reports, sensor records, and weather Traffic flow Traffic flow • 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 Closest upstream sensor, “b” Reported incident location Downstream sensor, “a” Diagram of relative sensor locations