In short Identifying hazardous locations at intersections by - PDF document

DLR.de • Chart 1 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 2 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 In short Identifying hazardous locations at intersections by • Measurement campaign at known hot spot • Observe traffic, conflicts with cyclists involved automatic traffic surveillance Hagen Saul, Marek Junghans and Andreas Leich German Aerospace Center (DLR) • Are conflicts (and which conflicts) detected automatically? ICTCT 2015, 29.-31.10.2015, Ashdod, Israel • Can they be clustered, thus indicating hot spots? DLR.de • Chart 3 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 4 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Overview Motivation—in general Automatic traffic surveillance: • Motivation • Data acquisition / measurement campaign 1.provides densely sampled data in time and space 1. trajectories (traffic safety) 2. Also: traffic volumes, velocities etc. • Results of analysis 2.Allows to collect all incidents (unreported ones!) 3.Pre-selection of critical incidents • Conclusion 4.Enables pre-conflict analysis 5.Enables long-term evaluation of traffic safety without accidents (even at • Outlook locations with few accidents!) 6.Cheap, once established But requires sufficient accuracy… DLR.de • Chart 5 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 6 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Motivation—for field study Official statistics • Goal of the measurement campaign is test our system (fully automatic) and • Provided by police Berlin for 2014: continue the research regarding traffic safety of cyclists. • crashes with cyclists involved: 7699 (6952 in 2013) – rise of 10% • share of crashes with cyclists involved: 5.8% • Can tendencies/correlations be shown or revealed? • 2001: 4.06%, 2002: 4.37%, …, 2013: 5.31%) (number of total crashes , all modalities , had been increasing since 2006!) • On average 21 crashes a day • Vulnerable: every 4th fatality is a cyclist • Main cause (not bicycle): wrong behavior when turning

DLR.de • Chart 7 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 8 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Current situation Measurement campaign • Number of crashs with involvement of cyclist in 2014 in dependence of • 15 crashes recorded at Prinzenstraße/Moritzplatz in 2014 daytime • Conducted on July 10th 2014, 6am-6pm DLR.de • Chart 9 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 10 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 • MUSE vorstellen DLR.de • Chart 11 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 12 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Analysis—cyclist trajectories Analysis—Traffic Parameters • Trajectory-based analysis • Traffic intensities • Virtual / optical loops of cyclists (groundtruth) 8-9am 6am – 6pm

DLR.de • Chart 13 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 14 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Analysis—Velocities Analysis—Velocities • Velocity of cyclists in free flow (6am-7am) • Velocity of cyclists at max. traffic intensity (14pm-15pm) • Peak: 5-6 m/s² (18-22 km/h) • Peak: 4-5 m/s² (14-18 km/h) DLR.de • Chart 15 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 16 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Conflicts Conflicts • if collision predicted , 1s forecast period • if collision predicted , 1s forecast period • 252 conflicts in total • 252 conflicts in total DLR.de • Chart 17 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 18 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 TTC TTC • 2-3pm

DLR.de • Chart 19 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 20 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DRAC DRAC DLR.de • Chart 21 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 22 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DRAC Braking • 2-3pm • Determined positions of max. deceleration for every cyclist • Originally: try to determine braking entry point, in order to get a clue, when possible hazard is recognized and reaction (braking) is triggered • Also possible: point of max. jerk when decelerating (earlier than point of max. deceleration) • Further research necessary, no knowledge, if and how good this entry point can be determined from outside (i.e. by camera) DLR.de • Chart 23 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 24 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Braking Braking • Tbd ViewCar-speed and velocity during braking maneuver • 11-12am

DLR.de • Chart 25 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 26 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Braking Incidents DLR.de • Chart 27 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 28 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Incidents - 1 Incident 1: trajectories recorded DLR.de • Chart 29 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 DLR.de • Chart 30 > : Identifying hazardous locations at intersections by automatic traffic surveillance > Hagen Saul • ICTCT'15 > 30.10.2015 Incidents - 2 Summary and Conclusions • Three hot-spots identified • Maybe stronger correlation traffic volume of vehicles leaving roundabout to number of conflicts with cyclists involved