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

1. Robert Schönauer, mobimera Fairkehrstechnologien,Vienna,Austria.
Gerald Richter,Austrian Institute ofTechnology,Vienna,Austria.
Markus Straub,Austrian Institute ofTechnology.Vienna,Austria.
Cyclist's Waiting:
Identifying Road Signal Patterns
Robert Schönauer, 14.05.2013.
Presented at the CDC2013 Workshop,
@ AGILE 2013 – Leuven, May 14-17, 2013

2. 2
funded by

3. 3
Background
• 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 the
green wave.
• Own experience: Knowing a route like the daily route to
work helps to avoid waiting times!
Green wave for
bicycles in
Copenhagen.

4. 4
Information about the
signal program
• Generic sequence
of a single signal
• Communication
and interface to
cyclists
• Separate signals 
• Smartphone
©i-Level

5. 5
Estimation of signal
pattern by GPS tracks
Processing flow
in this paper

6. 6
Filters for a specfic signal
1. 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 are
relevant.

7. 7
Distance / time plot
700 800 900 1000 1100 1200 1300 1400
300
400
500
600
700
800
900
1000
1100
1200
time [s]
distance[m]
Example of cyling tracks influenced by traffic signals.

8. 8
Estimating cycle time
1. Cumulative histogram after
modulo division (cycle time)
2. Identifying “empty”
neighboring bins
 no waiting
3. Largest “empty” group
 green phase
 Relative green time
4. Varying cycle time
 maximise relative green time 0 10 20 30 40 50 60 70 80 90 100
0
50
100
150
200
250
300
Waiting time histogram hb
*
at tcy
* = 100
n* tb
[s]
h
b
*[-]

9. 9
Green and Red
1. Green: Steepest falling
slope in histogram
2. Red: When cyclists start
to wait again
0 10 20 30 40 50 60 70 80 90 100
0
50
100
150
200
250
300
Waiting time histogram hb
*
at tcy
* = 100
n* tb
[s]
h
b
*[-]
Cumulative waiting times

10. 10
CDC2013 Application
Location A
Location B

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2750 2800 2850 2900 2950 3000 3050 3100 3150 3200
2.6
2.65
2.7
2.75
2.8
2.85
2.9
2.95
x 10
4
path-time diagram
t(after 8h in the morning) [s]
Travelleddistance[m]
CDC2013: Bicycles
Trajecories
2 selected tracks
at location A
The colors
represent the
distances to
intersections
Legend:
d < 25 m
d < 50 m
dA < 25m

12. 12
Results: Location A
30 40 50 60 70 80 90 100 110 120
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
rg
, Fit of signal cycles
tcy
[s]
rg[-]
0 10 20 30 40 50 60 70 80 90 100
0
5
10
15
20
25
Waiting time histogram hb
*
at tcy
* = 100
n* tb
[s]
h
b
*[-]
Cumulative waiting timesRelative green time

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Results: Location B
30 40 50 60 70 80 90 100 110 120
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
rg
, Fit of signal cycles
tcy
[s]
rg[-]
0 10 20 30 40 50 60 70 80 90 100
0
5
10
15
20
25
Waiting time histogram hb
*
at tcy
* = 100
n* tb
[s]
h
b
*[-]
Cumulative waiting timesRelative green time

14. 14
Verification issue
 No available information about real signal
programs
 Relatively low data density and non typical
waiting time pattern.
 At both location public transport (PT) is
present  prioritizing of PT changes green
duration (if not cycle time).

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 Virtual path
 Fixed signal
programs
 Stochastic power
input (Watts) and
ideal physical
conditions
Verification with
simulation

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 ~25 tracks at a specific
signal: +/- 5 sec.
 GPS noise, adaptive control
and redlight runners demand
a higher number of tracks
Results of the
simulation
0 10 20 30 40 50 60 70 80
0
50
100
150
200
250
300
350
400
450
500
Number of tracks
Cummulativeerror(at8signals)[s]
Cummulative error in the estimation of tgreen&toffset / number of stochastic tracks
Results in a simulation
y=1845/x
Dependency of number of tracks and error in estimation:

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Conclusion &
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
Contact
Robert Schönauer
schoenauer@mobimera.at
Gerald Richter, AIT
gerald.richter@ait.ac.at
http://www.bikecityguide.org/