investigating the service quality of cycle infrastructure using big data · 2019-07-17 ·...
TRANSCRIPT
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VELO-CITY 2019:INVESTIGATING THE SERVICE QUALITY OF CYCLE
INFRASTRUCTURE USING BIG DATA
Joe Seymour & John Humphreys; AECOM Ireland Ltd
Irene McAleese; See.Sense
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Overview
See.Sense and AECOM and have collaborated over recent years to see if the data collected from See.Sense bicycle lights can be used to generate useful information for use in the planning and maintenance of bicycle networks.
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Challenge
Theme 1 : Technology, Intelligent Transport Systems and Data Analytics
Can bicycles become part of the connected world providing useful information for users and planners of networks?
Is Cycling data any good?
Is it useful data or is it just more data to fill the Data Lake
This presentation looks at how data collected from a “not so simple” bicycle light can be used to provide useful data that can improve cycling infrastructure in cities.
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Useful Data?
Some aspects of the data that AECOM thought worth looking at are available from a few sources:
Routing
Speeds
Delays
While the quality of the above data would vary from product to product the real attraction of the See.Sense product to AECOM was the unique data from the accelerometer within the lights which provides:
Vibration for road surface condition; and
Collision and Near-Miss Events.
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Quality of Service
The National Cycle Manual in Ireland uses Quality of Service ratings as a measure of the degree to which the 5 needs of a cyclists are met. The 5 needs are:
Road Safety
Coherence
Directness
Attractiveness
Comfort
Quality of Service rating:
Level D C B A A+
Poor Good
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Why is it important?
Poor Good
Confident Cyclist Utility/Vulnerable Cyclists
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Why is it important?
Poor Good
Eastbound from Rathmines Road to Baggot Street, off peak.
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596
2026
1529
2719
0
875
1750
2625
3500
Female Male
524
850
0
250
500
750
1000
Cycle lane Cycle track
Volume split of cycle routes between 8-10am Gender split of each route
Why is it important?
Res
earc
h b
y Eo
in O
’Mah
on
y an
d M
atth
ew
Ric
har
dso
n (
AEC
OM
/TC
D)
36%23% 77% 64%
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Res
earc
h b
y Eo
in O
’Mah
on
y an
d M
atth
ew
Ric
har
dso
n (
AEC
OM
/TC
D)
Why is it important?
User-type profile for each route
76
585
396
13
37
685
756
27
0 200 400 600 800
Sports
Commuter
Utility
Vulnerable
Lane (n=1,070) Track (n=1,505)
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Quality of Service
The QoS is measured through the following 5 characteristics of infrastructure which can be related back to the 5 Needs of Cyclists:
Pavement Condition
Number of adjacent Cyclists
Number of conflicts per 100m of Routes
Journey Time Delay
HGV Influence
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QoS ParametersPavement Condition
Poor Good
Severe undulations, very poor ride quality, cyclists experiences jolts, due to concrete aprons and/or very poorly maintained surface.
High quality, well maintained surface. No manholes, gullies or other iron works
Can the accelerometer be used to provide reliable data to classify road surfaces condition?
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QoS ParametersNumber of Adjacent Cyclists
Poor Good
Mixed traffic without appropriate traffic regime and heavy traffic
Facility with sufficient width to allow side by side cycling and overtaking
Rating D C B A A+
Width <1.5m 1.5 to 1.8m 1.8 to 2.0m 2 to 2.5m >2.5m
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QoS ParametersNumber of Conflicts
Poor Good
Rating D C B A A+
Conflicts (per 100m)
>10 4-10 2-3 <1
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QoS ParametersJunction Delays
Poor Good
Cyclists stopped at regular intervals with significant wait times
Cyclist experience little or no delays at junctions, crossings etc.
Can the location data be used to estimate delays at junctions along a route so as junction delay can be estimated?
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QoS ParametersHGV Influence
Poor Good
Rating D C B A A+
Width >10% 6 to 10% 2 to 5% 0 to 1% <1%
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Quality of Service
Therefore See.Sense bicycle light might be able to provide information on the more challenging, and time consuming, aspects of assessing the QoS for an urban area.
AECOM used available information for Dublin, gathered as part of the Smart Dublin programme to look at the following areas :
Pavement Condition
Journey Time Delay
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Pavement Quality
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ADELAIDE ROAD
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Source: Google Maps
ADELAIDE ROAD
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SHELBOURNE ROAD
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BAGGOT STREET
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Source: Google Maps
BAGGOT STREET
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GRAND CANAL QUAY
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GRAND CANAL QUAY
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Journey Time Delay
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Co-ordinated Points
AVERAGE SPEED
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10 Metre Buffer Applied Around Stop Lines
JUNCTION ANALYSIS
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0.
7.5
15.
22.5
30.
Trimelstown Av Strand Rd Nutley Ln Sandymount Av Ballsbridge Park Herbert Park Landsdowne Rd Canal
Ave
rage
Sp
eed
(km
/h)
Speed Profile at Junctions
JUNCTION ANALYSIS (Inbound)
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Delay at Junctions (% results with speed <5kph)
0%
23%
45%
68%
90%
Trimelstown Av Strand Rd Nutley Ln Sandymount Av Ballsbridge Park Herbert Park Landsdowne Rd Canal
JUNCTION ANALYSIS
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0.0
7.5
15.0
22.5
30.0
37.5
Trimelstown Av Strand Rd Nutley Ln Sandymount Av Ballsbridge Park Herbert Park Landsdowne Rd CanalAM IP NP PM
Inbound – Average Speed Throughout the Day
Ave
rage
Sp
eed
km
/h
JUNCTION ANALYSIS
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Journey Time Delay
0.0
6.5
13.0
19.5
26.0
32.5
0.38399 1.15197 1.91995 2.68793 3.45591 4.22389 4.99187 5.75985
KP
H
Towards City Centre – AM PEAK Hour (KM)
Cyclist - Average Speed KPH Car - Average Speed KPH
0.0
10.0
20.0
30.0
40.0
0.38399 1.15197 1.91995 2.68793 3.45591 4.22389 4.99187 5.75985
KP
H
Away From City Centre – PM PEAK Hour (KM)
Cyclist - Average Speed KPH Car - Average Speed KPH
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Routing
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Routing
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Routing
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Routing
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Conclusion
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Conclusion
Data recorded by the See.Sense bicycle lights can be used to get a better understanding of a cities cycle network in terms of:
Pavement Condition;
Junction Delay; and
Desire Lines.
While not looked at in detail for this paper there is also good quantitative and qualitative information in regard to Conflicts.
The data provided by the lights is a useful tool for planners to develop cycle networks and also identify maintenance priorities. However its usefulness and accuracy will be dependent on the number of lights in use and the demographic of the users.
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