Traffic Generation for Studies of Gap Acceptance Joseph Kearney Timofey Grechkin James Cremer Department of Computer Science Jodie Plumert Department of Psychology University of Iowa

Bicycling Injuries Bicycle crashes are a common cause of severe injury in childhood ERs treat 500,000 bicycle-related injuries a year Highest injury rate is among children 5-15 Motor Vehicles involved in 90% of bicycling fatalities Prevention Need to understand why car-bicycle collisions occur How do immature cognitive and perceptual skills put children at risk for car-bicycle collisions?

Gap Acceptance in the Hank Bicycle Simulator

Bicycle Simulator Video

Research Questions Are there age differences gaps selection? How are gap choices related to crossing behavior? Do gap choices change in dense traffic? How is temperament related to risk in road crossing?

Prior Work on Gap Acceptance Critical Gap Estimation Important component of flow computations Focus on gap selection Crossing behavior not examined Gap Affordance How is gap selection related time to cross? Converging evidence that people do not account for diminished skill Child pedestrians Alcohol impaired pedestrians Elderly with attentional deficits Child bicyclists

Virtual Environment Three 10X8 ft screens (rear projection) Projection Design Projectors -1280x1024 pixels/screen Square (Cave-like) configuration Seven networked PCs Dynamic pedal torque

Road Crossing Experiment (Plumert, Kearney, Cremer, Child Development 7(4), 2004) Subjects: Sixty 10- and 12 year olds and adults Procedure Warmup: 3 blocks with no traffic Gap crossing 6 intersections with steady traffic at 25 or 35 MPH Random gaps (1.5, 2.0, 2.5, 3.0, 3.5, 4.0 seconds) Subjects instructed to stop at each intersection and safely cross Measures Gap choice Time to spare Wait time Stopping

Coding: The “Data Visualizer”

Summary of Road Crossing Results Children and adults chose the same size gaps Average size of 3.5-3.6 seconds Children had less time to spare when cleared path of car, on average 10-year olds had 1.13 sec to spare 12-year olds had 1.49 sec to spare Adults had 1.98 sec to spare Why did children have less time to spare? Started later Took more time to get going

Long Wait Experiment Subjects: 120 10- and 12 year olds and adults Procedure Warmup Gap crossing 4 intersections with random gaps 4 long wait intersections Long Wait Traffic 8-10 uncrossable gaps (1.5 and 2 s) Stair-step increase in gap size Alternating: (two crossable gaps; four uncrossable gaps) 1.5, 2.0, 1.5, 1.5, 2.0, 1.5, 1.5, 2.0, 3.0, 3.0, 1.5, 2.0, 2.0, 1.5, 3.5, 3.5, 1.5, 1.5, 2.0, 1.5, 4.0, 4.0,…

Long Wait Gap Choice 1.0 0.9 Middle four intersections Last four intersections 0.8 0.7 Gaps Taken 0.6 Gaps Seen 0.5 0.4 first four intersections 0.3 0.2 0.1 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Gap Size

Summary of Long Wait Results Children and adults chose the same size gaps Children had less time to spare when they cleared path of oncoming car Both children and adults accept much smaller gaps at intersections with dense traffic This risky behavior carries over to intersections with “normal” traffic density

Analysis of Gap Acceptance Data Average gap selected is biased Over-estimates critical gap Cautious drivers are over-represented (Gattis and Low, 1999) Logistic Regression Estimates critical gap Cautious drivers still over-represented Stair-step presentation Long waits may influence response criteria Savvy drivers may wait

How Do Children and Adults Cross Mutli-Lane Traffic? Requires passage through two overlapping gaps More difficult perceptual task spatially and temporally Greater payoff for anticipation Greater overall distance to be crossed Staged crossing through rolling gaps Improving Pedestrian Safety at Unsignalized Crossings TCRP Report 112, 2006

Gap Acceptance with One-Lane Traffic Tail Lead current time time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane current time time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near gap Near lane time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far gap Far lane Near lane time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Overlap Near lane time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Gap Acceptance with Two-way Traffic Lead far lane Tail far lane Tail near lane Lead near lane Far lane Near lane crossing interval time current time

Generation of Two-way Traffic Specify Between Lane Gaps Randomly generate gaps (time between successive arrivals irrespective of lane) Randomly assign lane Produces natural clusters and breaks May reduce problem to one-lane crossing Specify Within Lane Gaps Independent (randomized) streams on two lanes Produces steady stream of two-way traffic Requires synchronization of gaps Increased gap size

Future Work: Young Drivers How are young drivers’ gap choices related to their crossing behavior? Little is known about road-crossing behavior in young drivers Studies of road-crossing require a wide FOV Scenarios apply to driving

Summary Gap Crossing Critical events vs. prosaic driving tasks Essential skill for safe driving Complex perceptual task Detect temporal relations in spatially disparite dynamic streams Complex motor task Synchronize movement to multiple temporal intervals Anticipate arrival times Probe for investigating skill of driving populations Critical events vs. prosaic driving tasks Understand perceptual and motor skills needed for safe driving Investigate differences in driving populations Identify risky behaviors Improve Training

Acknowledgments Contributing students, staff, faculty NSF Support: INT-9724746, EIA-0130864, and IIS-0002535; National Center for Injury Prevention and Control: R49/CCR721682; National Institutes of Health: 1 R01 HD052875-01 Contributing students, staff, faculty Hongling Wang Geb Thomas David Schwebel Pete Willemsen Penney Nichols-Whitehead Scott Davis Jennifer Lee Steffan Munteanu Sarah Rains Joan Severson Sara Koschmeder Tom Drewes Ben Fraga Forrest Meggers Kim Schroeder Paul Debbins Stephanie Dawes Bohong Zhang Lloyd Frei Zhi-hong Wang Keith Miller Xiao-Qian Jiang Timofey Grechkin Christine Ziemer