presentation (with text) european transport conference 2015
TRANSCRIPT
Dirk Bussche
NHTV / DAT.Mobility
Paul van de Coevering
NHTV / University Delft
European Transport
Conference, Frankfurt
29 september 2015
This presentation contains fotos from Wikipedia
The presentation is published under http://creativecommons.org/licenses/by-sa/3.0/
2
Video
• Morning peak hour in Brabant
• Each moving point = one cyclist
• Color = speed
Our vision: to understand the diversity of different
kind of cyclists, translate data into relevant policy
information and so supporting bicycle-friendly
transport planning
Datasources Bicycle
Counts
Questionaires
Transport Model
Number of cyclists on links and nodes
Experience, Motivation, Opinion
Synthetical (multimodal) description of present situation and future scenario’s
Routes, time on destination, travel time on relation
Origin-Destination relations
WIFI/Bluethooth/NFC
BiKE PRINTRoutes, speed,delays, detours
GSM
Source of GPS-data?
Smartphone or tracker
Recruite in public Interest groups Random or panel Paid fieldworkers
Heat Map
Speed
Routes
privacy
representative No (self-selection) Only for that group
Potential cycleaccessibility
Isochrones using empiricaltravel times
Delays and Speed
Origin / Destination,Actual cycle routes
Heat maps
Online maps
Foto: Wikipedia
Main Cycle Network
Not all cycle pathes can be of excelent quality.
This is why many local governments define a main
cycle network.
But we do not know if cyclists make use of altenative
routes, since counting points will be situatuated on
the main cycle network only…
With BikePRINT heatmaps we know where
cyclists use different routes.
Policy-makers should consider
• To improve quality of main cycle routes so
cyclists will again start using it, or
• Make the actually used routes „main cycle
network“ with priority at traffic lights, winter
service etc
Foto: FietsersbondBicycle Traffic Jam
Bicycle traffic jams are a hot item in discussion, such
as here nearby Utrecht Central Station.
Real Problem or rare exceptions?
The answer should be given by the data…
Cycle congestion as peak / freeflow ratio?
For car traffic, speed ratio peek / freeflow (=night) is a
common indicator for congestion.
For bicycle this makes no sense: often, cycle speed
is even slower by night (no hurry, tired, somtimes
alcohol)
Measure of SpeedContinously 10 km/h.
Red = slow?
Or no problem at all if poeple want to
cycle slow?
Continously 20 km/h.
Green = fast?
Also 20 km/h
Same color as track 2?
No: this cyclist would like to cycle 30
km/h but has to brake down to 10
km/h from time to time.
This is a traffic problem.
Measure for speed has to be delay in
relation to individually desired speed.
In order to be able to find alternatives for links
with bicycle congestion, we analise origin-
destination relations.
With a simple click in the map all routes
meeting the selected link will show up in red.
In this example, many for users of the western
tunnel under the railway, the eastern tunnel
would be a good alternative, if a cycle path
north of the railway track would exist.
Such a cycle path is much cheaper to realize
than making the western tunnel wider…
Connections that cannot be matched to existing
network.
Except for network- and GPS-errors, we get
• Informal paths
• Crossing of parking places etc
• Crossing of two-lane-roads where this is not allowed
The policy maker now has the choice
• To make these (desired!) connections possible,
• Or to close down the route and create alternatives
Foto: WikipediaDelays
Traditionally, waiting times at crossings are
calculated as average time one has to wait for
green light or free.
But doing so forgets
• Braking already earlyer, when red light is
seen,
• Time to get back on cruise speed,
• Delay by cyclists waiting in front of you
Delay at junctions
10 seconds
30 seconds
delay: 20 seconds
For BikePRINT, we consider the
travel time from 50 meters ahead
of until 50 meters after each
junction, and compare that time
with the time which would be
needed when travelling at the
same speed as 50-100 meters
away.
These calculations result in a map where each
junction is colored with the average waiting
time. The size of the dots is determined by the
number of cyclists passing by.
So red junctions with a small amount of cyclists
are no problem because only a few cyclists
have a problem…
… except if the long delay is the very reason
why cyclists avoid that junction.
Few cyclists =
low potential?Foto: Wikipedia
Are roads with few cyclists not worth an
investment in better cycle infrastructure
because „nobody“ is cycling there?
Or would more poeple want to cycle if quality
would be better?
… this map is the answer.
For this analysis we compare the actual
perceived number of cyclists with the fictive
number of cyclists if everybody would choose
the shortest path.
Red means that we perceive less cyclists than
if people choose the shortest path, simplistically
the road can be called „less attractive“.
Blue means inversely, that we perceive „too
much“ cyclists – it could be interpreted as
„attractive“.
There are many different reasons for this
perceived behaviour.
From link- and junctiondelays we calculate
empirical travel times on route level, and from
these travel times isochrones.
By clicking on the map all areas are colored
based on the cycle time to there.
The circels serve as reference: this travel time
can approximately be expected based of the
distance as the crow flies.
Yellow areas outside the yellow circle are good:
little delay.
Oranje areas inside the yellow circel are bad:
more delay than average.
From isochrones we calculate potential
accessibility maps: For each area we count the
number of inhabitants and jobs that are
accessible in cycle distance.
Firstly, this is important for spatial planning:
crowd pullers should be situated in dark blue
areas to create a high bicycle share.
Secondly, we use it to measure effect of
investments in cycle infrastructure.
Short car trips
If the aim is to replace car trips by bicycle trips, the
planner has to know where short, thus replaceble car
trips are situated.
To achieve this, we use a normal traffic model (in this
case the move meter) and perform an assignment of
only the short car trips.
These trips can be visualized on the map as spider,
share or absolute number.
When bicyle measures are (in the model) performed,
the less-car-trips can again be assigned to the
network to get insight in the indirect effect on car.
Often, bicycle measures are the best investments in
car traffic: those car drivers staying in the car have
benefit of those who become cyclists in terms of less
traffic jam and less pollution.
Estimating results for
Bicycle investments
In BikePRINT, we van calculate simple bicycle
measures using a quick-scan model
In this example, we added a cycle highway in the region
of Eindhoven.
Right, you see the travel time benefit relatively to the
situation without cycle highway. Red means 5 minutes
faster by bicycle.
PotentialAccessibility
Total accessibility
Competion Curve
Tij = k (Oi Dj) / F(cij)
Origin-Destination relations
Gravity model
Total accessibility, travel time benefit bicycle trips,new bicycle trips
Results
Quick-scan model bicycle measures
“Everything you always wanted to know about your cycling network performance but were
afraid to ask”
Dirk Bussche, [email protected]