SHRP 2. Strategic Highway Research Program. Accelerating solutions for highway safety and performance. Charles Fay, Sr.

Transcription

1 Strategic Highway Research Program SHRP 2 Accelerating solutions for highway safety and performance Charles Fay, Sr. Program Officer GIS-T Boise, ID 2013

2 Second Strategic Highway Research Program ( SHRP 2) Naturalistic Driving Study C Fay Roadway Information Database O Smadi Q &A

3 Fact: drivers cause or contribute to over 90% of crashes Study goal: Improve traffic safety by obtaining objective information on driver behavior and driver interaction with the vehicle and the roadway(context) Collect data on the system: driver, vehicle, context Build the Naturalistic Driving Study (NDS) and Roadway Information (RID) databases Use the data: study key safety issues that can t be studied with other databases; use results to develop new countermeasures and improve existing countermeasures 3 pilot analysis projects underway

4 Existing Data characterize the environment in which the participant/ DAS operates: ~ 1950 DAS ~3000 participants ~ 5 million trips Passenger Car, Van, SUV, Pickup NDS Data (DAS GPS is Link) RID (GIS) roadway, crash, safety campaigns & laws, traffic, weather, work zones and infrastructure changes... New Roadway Data Collected and QA Application of Results: Safety Countermeasures

5 Method: continuous observation of volunteer drivers in ordinary driving for months or years What do drivers really do? Speeding, tailgating, cell phone, alcohol How do these actions affect crash risk? What were they doing just before they crashed? Usual crash studies can only guess. How do drivers react to cues and countermeasures from the vehicle and the roadway environment? Smaller previous naturalistic driving studies SHRP 2 NDS: 40 times larger, national scale; only one with roadway information (context).

6 ~ 4 petabytes of data ~ 1.4 million hrs of driving video ~ 3000 subjects (teens-elderly) ~ 5 million trips (continuously recorded) ~ 32 million miles driven ~ 4 billion GPS points Linked to the Roadway Information Database Provides context for each of the 5 million trips GIS: Roadway characteristics & features and other contextual information (crash histories, traffic, weather, safety campaigns )

7 NDS RID

8 Six NDS Data Collection Sites across the U.S. One Coordinator WA Data Collection NY Data Collection IN Data Collection NDS Data PA Data Collection NC Data Collection FL Data Collection

9 9

10 DAS Overview Multiple Videos Machine Vision Head Pose Monitor Lane Tracker Accelerometer Data (3 axis) Rate Sensors (3 axis) GPS Latitude, Longitude, Elevation, Time, Velocity Forward Radar X and Y positions X and Y Velocities Cell Phone ACN, health checks, location notification Health checks, remote upgrades Illuminance sensor Infrared illumination Passive alcohol sensor Incident push button Audio (only on incident push button) Turn signals Vehicle network data Accelerator Brake pedal activation ABS Gear position Steering wheel angle Speed Horn Seat Belt Information Airbag deployment Many more variables

11 Camera Image Samples Video 15Hz; H 264 compression 1.4 million hours Forward View - color Driver Face Rotated for max pixel efficiency Right-Rear View Center stack Pedal Interactions; hands Periodic still cabin image, permanently blurred for passenger anonymity (child safety seat use?)

12 Data so far as of April 1, participants, active or completed 4 M trips(5m), 23 M (32M) miles of driving 310 known crashes (more in database not yet identified) Near crashes estimated at 10x crashes 7 more months of collection; end 11/2013

13 13

14 Existing Data characterize the environment in which the participant/ DAS operates: ~ 1950 DAS ~3000 participants ~ 5 million trips Passenger Car, Van, SUV, Pickup NDS Data (DAS GPS is Link) RID (GIS) roadway, crash, safety campaigns & laws, traffic, weather, work zones and infrastructure changes... New Roadway Data Collected and QA Application of Results: Safety Countermeasures

15

16 Run-off-road crashes on rural 2-lane curves Effects of signs, markers, chevrons CTRE, Iowa State University Offset left-turn lanes Effect on safety, design guidance MRIGlobal Driver glance behavior What glance patterns are safer SAFER Status Phase 1 proof of concept completed Phase 2 contract awards spring 2013 Final reports July

17 Size: the file is huge 4 petabytes = 4 million 1 gig flash drives 5 M trips; 32 M miles of driving Complexity: different data types Categorical data constant over a trip: driver age, vehicle type Sampled data: collected at different rates (once a trip up to 640 Hz during a crash): speed, acceleration, GPS position, radar, Video data from 4 cameras; must be interpreted and reduced Automated reduction: lane tracker, head tracker Manual reduction: all other items for specific analyses Roadway data linked to trip data via GPS Privacy considerations: personallyidentifying data 17

18 Trip summary files Categorical data on each trip to help users identify trips of interest Trip, roadway, vehicle, and driver variables Reduced data Trips or trip segments for specific research areas: trips with drivers who peel out from a stop; trips on rural 2-lane curves Retain only NDS and RID variables needed for research area Event files: crashes, near-crashes, baseline Brief (30 sec.) data segments at events of interest Near-crashes defined by sudden braking, steering Baseline events defined in various ways: random; match same driver, time, and road Public data De-identified data in various formats 18

19 Trip summary files Initial files: summer 2013 Enhanced files, updates: through spring 2014, as resources permit Reduced data files For 3 SHRP 2 analysis contractors and FHWA researcher: spring 2013 For other users: fall 2013, as resources permit Event files: crashes, near-crashes, baseline Crash and some near-crash files: spring 2013; updated thereafter Expanded near-crash and exposure files: fall 2013, as resources permit Public data files Initial files: summer 2013 Complete quality-controlled data files available spring

20 Benefits of the Study (sample safety related) These data are not available one of a kind database(s): decades of use Value not limited to Highway Safety Asset Management Highway Operations Baseline for autonomous/ connected vehicle and infrastructure? Model for how the system operates now? Improved understanding of baseline driving behaviors: Trip characteristics Driver performance profiles Adherence to laws and basic safety practices Improved understanding of unsafe behaviors and traffic events: Assess circumstances and motivations for speeding, red light running, etc. Deconstruct crashes and near-misses and examine causality How do driver, vehicle, roadway, and environmental factors influence behavior and impact crash risk? Improved ability to develop safety countermeasures for: Education and training Roadway design and traffic engineering Vehicle design Regulation and enforcement Ability to direct countermeasures at driver subgroups

21 GIS-T Meeting May 5 th, 2013 SHRP 2 Roadway Information Database (RID)

22 Project Goal Design, Build, and Populate Roadway Information Database Linked to Support Safety Analysis NDS Database

23 Roadway Data (RID) Naturalistic Driving Data (NDS) Analyses

24 Data Sources/Database Design Mobile Van Data State DOT Data Database Design Private Data (ESRI) Supplemental Data Analysis Needs User Needs GIS Experience Long Term Management

25 Database Design and Specifications Objective: Develop the technical specifications and supporting management components for the SHRP2 Roadway Information Database Key Issues: Provide consistency across the six study area sites Account for inherent differences in available data across sites Facilitate user access to data Ensure compatibility with data from Mobile Data Ensure compatibility with Naturalistic Driving Database

26 Database Design and Specifications Locations Design Assumptions: Data will be linked to a geospatial roadway network (i.e., Geodatabase) Roadway features and attributes will be locationally referenced as linear or point events along the roadway network All linear and point event data will be stored as tables in a relational database management system (RDBMS) Route System Roadway Attributes Table LRMEquivalency Line feature class Roads Line feature class Routes Table LRMPosition Attributed relationship class LRMPositionHasGeoPosition Table Alignment Table Aspect Table Intersection Table Lane Table AspectType Many to Point feature class GeoPosition many Table SignAssembly Table Shoulder Table Sign

27 RID Data Sources (6 Sites) Existing (readily available DOT information) Supplemental Information Critical in further characterizing or analyzing operations of a roadway segment Mobile Van Data (~ 25,000 collection miles)

28 Supplemental Data Crash data (5 years before NDS and during) Traffic information Weather data State laws: Cell phone use Texting GDL Seat belt Aerial imagery Changes to infrastructure Work zone

29 FHWA Recent Efforts RDIP (Roadway Data Improvement Program) MIRE (Model Inventory Roadway Elements) FDE (Fundamental Data Elements) MAP-21

30 Mobile Data Data Element Curvature Length Curvature Radius Begin point and end point of curvature Grade (+ or -) and Cross Slope/Super Elevation Lane Width/Type Paved Shoulder Width/Type All MUTCD signs and Barriers Intersection Information Presence of Lighting/Medians/Rumble Strips Front Video Log

31 Evaluation: Roadway Section Horizontal Curves Grade Cross Slope Lane Width Shoulder Signs

32 Number of Vendors by Performance Positive Neutral Negative Roadway Feature: Curve: PC/PT Location Curve: Length Curve: Radius Roadway: Longitudinal Grade Roadway: Cross Slope Roadway: Lane Width Roadway: Paved Shoulder Width Sign: Location Sign: Message (MUTCD only) Positive: The majority of data met the minimum accuracy requirement. Neutral: Roughly half of the data met the minimum accuracy requirement. Negative: The majority of data did not meet the minimum accuracy requirement.

33 Mobile Data QA - Values Data Element Accuracy Requirement Curvature Length 100 feet (curves less than 1500 ft radius) Curvature Radius 100 feet (curves less than 1500 ft radius) PC 50 feet PT 50 feet Grade (+ or -) 1.0% Cross Slope/Super Elevation 1.0% Lane Width 1 foot Paved Shoulder Width 1 foot Inventory Features (signs) Location 7 feet Control sites (2 per location-urban and rural) Random checks

34 Mobile Data Collection Communications Plan Communication Model NAS Program Officer Project Team Project Team S04A Project Manager Project Team S04B Project Manager Project Team Ensures full compliance by all parties Provides consistency Identifies roles and responsibilities throughout the project

35 Mobile Data Collection Data Collection Manual

36 Mobile Data Collection NY: 520 collection miles NC: 615 collection miles FL: 705 collection miles A total of about 1,850 miles

37 Mobile Data Collection Completed data collection for: NC: 3,200 miles FL: 3,600 miles WA: 4,160 miles NY: 3,200 miles IN: 2,900 miles Total for 2012: 16,000 miles

38 Mobile Data Collection Proposed data collection for: IN: 1,700 miles PA: 3,800 miles Additional miles based on crashes

39

40 Lighting: N/A Rumble Strips: N/A Unpaved Shoulder: N/A Grade, Cross Slope Paved Shoulder 3 Mix/Combo N/A 3 Mix/Combo 4 Mix/Combo Median Flush Paint. N/A Flush (Painted) N/A Flush (Painted) N/A Flush Paint. Lanes Thru Lane: 1 (12 ) Thru Lane: 1 (11 ) Right Turn: 1 Thru Lane: 1 (12 ) Flush Paint. Lanes Median Thru Lane: 1 (12 ) Left Turn Lane: 1 Thru Lane: 1 (14 ) Deccel. Lane: 1 Thru Lane: 2 (11 ) Deccel. Lane: 1 Thru Lane: 1 (12 ) Accel. Lane: 1 N/A Flush (Painted) N/A Flush (Painted) N/A Thru Lane: 1 (21 ) Flush Paint. Paved Shoulder 2 Mix/Combo 0 Mix/Combo 3 Mix/Combo 2 Mix/Combo Grade, Cross Slope Unpaved Shoulder: N/A Rumble Strips: N/A Lighting: N/A

41 Linking Vehicle Driver Roadway

42

43 Questions? SHRP 2 Roadway Information Database