Robert Schönauer, mobimera Fairkehrstechnologien,Vienna,Austria.Gerald Richter,Austrian Institute ofTechnology,Vienna,Aust...
2funded by
3Background• Cyclists modal share is high in urban areas• Car traffic is often over the capacity limits Traffic control f...
4Information about thesignal program• Generic sequenceof a single signal• Communicationand interface tocyclists• Separate ...
5Estimation of signalpattern by GPS tracksProcessing flowin this paper
6Filters for a specfic signal1. Spatial filter:Only close measurements are considered. For each signal at a intersection ...
7Distance / time plot700 800 900 1000 1100 1200 1300 1400300400500600700800900100011001200time [s]distance[m]Example of cy...
8Estimating cycle time1. Cumulative histogram aftermodulo division (cycle time)2. Identifying “empty”neighboring bins no ...
9Green and Red1. Green: Steepest fallingslope in histogram2. Red: When cyclists startto wait again0 10 20 30 40 50 60 70 8...
10CDC2013 ApplicationLocation ALocation B
112750 2800 2850 2900 2950 3000 3050 3100 3150 32002.62.652.72.752.82.852.92.95x 104path-time diagramt(after 8h in the mor...
12Results: Location A30 40 50 60 70 80 90 100 110 12000.050.10.150.20.250.30.350.40.450.5rg, Fit of signal cyclestcy[s]rg[...
13Results: Location B30 40 50 60 70 80 90 100 110 12000.050.10.150.20.250.30.350.40.450.5rg, Fit of signal cyclestcy[s]rg[...
14Verification issue No available information about real signalprograms Relatively low data density and non typicalwaiti...
15 Virtual path Fixed signalprograms Stochastic powerinput (Watts) andideal physicalconditionsVerification withsimulation
16 ~25 tracks at a specificsignal: +/- 5 sec. GPS noise, adaptive controland redlight runners demanda higher number of t...
17Conclusion &Future ResearchFeasibility to find cycle period and green timeWith limited number of tracksPlausible nume...
18ContactRobert Schönauerschoenauer@mobimera.atGerald Richter, AITgerald.richter@ait.ac.athttp://www.bikecityguide.org/
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Cyclist's waiting: identifying road signal patterns

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Robert Schönauer, Gerald Richter, Markus Straub
Technical University of Graz, Austria
Topic: “Cyclist's Waiting: Identifying Road Signal Patterns”

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Cyclist's waiting: identifying road signal patterns

  1. 1. Robert Schönauer, mobimera Fairkehrstechnologien,Vienna,Austria.Gerald Richter,Austrian Institute ofTechnology,Vienna,Austria.Markus Straub,Austrian Institute ofTechnology.Vienna,Austria.Cyclists Waiting:Identifying Road Signal PatternsRobert Schönauer, 14.05.2013.Presented at the CDC2013 Workshop,@ AGILE 2013 – Leuven, May 14-17, 2013
  2. 2. 2funded by
  3. 3. 3Background• Cyclists modal share is high in urban areas• Car traffic is often over the capacity limits Traffic control focuses on car driving speeds Cyclists might lose thegreen wave.• Own experience: Knowing a route like the daily route towork helps to avoid waiting times!Green wave forbicycles inCopenhagen.
  4. 4. 4Information about thesignal program• Generic sequenceof a single signal• Communicationand interface tocyclists• Separate signals • Smartphone©i-Level
  5. 5. 5Estimation of signalpattern by GPS tracksProcessing flowin this paper
  6. 6. 6Filters for a specfic signal1. Spatial filter:Only close measurements are considered. For each signal at a intersection for full information.2. Velocity filter:Only points with speed below a certain threshold arerelevant.
  7. 7. 7Distance / time plot700 800 900 1000 1100 1200 1300 1400300400500600700800900100011001200time [s]distance[m]Example of cyling tracks influenced by traffic signals.
  8. 8. 8Estimating cycle time1. Cumulative histogram aftermodulo division (cycle time)2. Identifying “empty”neighboring bins no waiting3. Largest “empty” group green phase Relative green time4. Varying cycle time maximise relative green time 0 10 20 30 40 50 60 70 80 90 100050100150200250300Waiting time histogram hb*at tcy* = 100n* tb[s]hb*[-]
  9. 9. 9Green and Red1. Green: Steepest fallingslope in histogram2. Red: When cyclists startto wait again0 10 20 30 40 50 60 70 80 90 100050100150200250300Waiting time histogram hb*at tcy* = 100n* tb[s]hb*[-]Cumulative waiting times
  10. 10. 10CDC2013 ApplicationLocation ALocation B
  11. 11. 112750 2800 2850 2900 2950 3000 3050 3100 3150 32002.62.652.72.752.82.852.92.95x 104path-time diagramt(after 8h in the morning) [s]Travelleddistance[m]CDC2013: BicyclesTrajecories2 selected tracksat location AThe colorsrepresent thedistances tointersectionsLegend:d < 25 md < 50 mdA < 25m
  12. 12. 12Results: Location A30 40 50 60 70 80 90 100 110 12000.050.10.150.20.250.30.350.40.450.5rg, Fit of signal cyclestcy[s]rg[-]0 10 20 30 40 50 60 70 80 90 1000510152025Waiting time histogram hb*at tcy* = 100n* tb[s]hb*[-]Cumulative waiting timesRelative green time
  13. 13. 13Results: Location B30 40 50 60 70 80 90 100 110 12000.050.10.150.20.250.30.350.40.450.5rg, Fit of signal cyclestcy[s]rg[-]0 10 20 30 40 50 60 70 80 90 1000510152025Waiting time histogram hb*at tcy* = 100n* tb[s]hb*[-]Cumulative waiting timesRelative green time
  14. 14. 14Verification issue No available information about real signalprograms Relatively low data density and non typicalwaiting time pattern. At both location public transport (PT) ispresent  prioritizing of PT changes greenduration (if not cycle time).
  15. 15. 15 Virtual path Fixed signalprograms Stochastic powerinput (Watts) andideal physicalconditionsVerification withsimulation
  16. 16. 16 ~25 tracks at a specificsignal: +/- 5 sec. GPS noise, adaptive controland redlight runners demanda higher number of tracksResults of thesimulation0 10 20 30 40 50 60 70 80050100150200250300350400450500Number of tracksCummulativeerror(at8signals)[s]Cummulative error in the estimation of tgreen&toffset / number of stochastic tracksResults in a simulationy=1845/xDependency of number of tracks and error in estimation:
  17. 17. 17Conclusion &Future ResearchFeasibility to find cycle period and green timeWith limited number of tracksPlausible numeric results at example junctions! Redlight runners seem to disturbe the estimation.! Adaptive traffic controls interferes the patterns periodicity. Verification issue Complexity of intersections and its handling Estimate the impact of dynamic traffic control.
  18. 18. 18ContactRobert Schönauerschoenauer@mobimera.atGerald Richter, AITgerald.richter@ait.ac.athttp://www.bikecityguide.org/
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