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This research study aims to understand the impact of traffic incidents on congestion and non-recurrent delay. By combining disparate sources of data, such as police reports, sensor records, and weather information, we create a spatial graph that links these data points. Using this graph, we can predict the severity of an incident's impact on traffic flow. Preliminary results show high accuracy in predicting the impact of incidents. This research has potential applications in traffic management and resource allocation.
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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
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
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)
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
Results of deriving delay definitions • By integrating the delay definitions over space and time the following equations result:
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
Impact Prediction Results v-v* > 4.6 Yes No ρ > 0.22 Accident Yes No #vehicles > 0 False alarm No Yes Accident False alarm
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
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:
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
Thank you • Contact: • chetan.gupta@hp.com • mahalia@stanford.edu Photo credit: ShaojingBJ/Flickr
For two regions, models predict incident duration and travel delay-induced economic losses • Table: Example results from travel delay-induced economic losses
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
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
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
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
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>
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