Cyclist's waiting: identifying road signal patterns
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Robert Schönauer, mobimera Fairkehrstechnologien, Vienna, Austria. Gerald Richter, Austrian Institute of Technology, Vienna, Austria. Markus Straub, Austrian Institute of Technology. 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
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Robert Schönauer, Gerald Richter, Markus Straub Technical University of Graz, Austria Topic: “Cyclist's Waiting: Identifying Road Signal Patterns”
Transcript of Cyclist's waiting: identifying road signal patterns
Robert Schönauer, mobimera Fairkehrstechnologien, Vienna, Austria.
Gerald Richter, Austrian Institute of Technology, Vienna, Austria.
Markus Straub, Austrian Institute of Technology. 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
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funded by
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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.
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Information about the signal program
• Generic sequence of a single signal
• Communication and interface to cyclists • Separate signals
• Smartphone
© i-L
evel
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Estimation of signal pattern by GPS tracks
Processing flow in this paper
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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.
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Distance / time plot
700 800 900 1000 1100 1200 1300 1400300
400
500
600
700
800
900
1000
1100
1200
time [s]
dis
tance [
m]
Example of cyling tracks influenced by traffic signals.
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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 1000
50
100
150
200
250
300
Waiting time histogram hb* at tcy* = 100
n* tb [s]
hb* [
-]
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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 1000
50
100
150
200
250
300
Waiting time histogram hb* at tcy* = 100
n* tb [s]
hb* [
-]
Cumulative waiting times
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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 104 path-time diagram
t(after 8h in the morning) [s]
Tra
velle
d d
ista
nce [
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
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Results: Location A
30 40 50 60 70 80 90 100 110 1200
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5rg, Fit of signal cycles
tcy [s]
rg [
-]
0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
Waiting time histogram hb* at tcy* = 100
n* tb [s]
hb* [
-]
Cumulative waiting times Relative green time
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Results: Location B
30 40 50 60 70 80 90 100 110 1200
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5rg, Fit of signal cycles
tcy [s]
rg [
-]
0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
Waiting time histogram hb* at tcy* = 100
n* tb [s]
hb* [
-]
Cumulative waiting times Relative green time
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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).
Red: When cyclists start to wait again
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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 70 800
50
100
150
200
250
300
350
400
450
500
Number of tracks
Cum
mula
tive e
rror
(at
8 s
ignals
) [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.
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Contact
Robert Schönauer [email protected] Gerald Richter, AIT [email protected]
http://www.bikecityguide.org/
