Dirk Bussche NHTV / DAT.Mobility Paul van de Coevering NHTV / University Delft. European Transport Conference, Frankfurt 29 september 2015

Transcription

1 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

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 2

3 Datasources Bicycle Counts Number of cyclists on links and nodes Questionaires Experience, Motivation, Opinion WIFI/Bluethooth/ NFC Transport Model GSM Routes, time on destination, travel time on relation Synthetical (multimodal) description of present situation and future scenario s Origin-Destination relations BiKE PRINT Routes, speed, delays, detours

4 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

5 Online maps Heat maps Isochrones using empirical travel times Origin / Destination, Actual cycle routes Potential cycle accessibility Delays andspeed

6 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 Main Cycle Network Foto: Wikipedia

7 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

8 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 Bicycle Traffic Jam Foto: Fietsersbond

9 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)

10 Measure of Speed Continously 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.

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12 In order to be able to find alternatives for links with bicycle congestion, we analise origindestination 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

13 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

14 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 Delays Foto: Wikipedia

15 Delay at junctions 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 meters away. 30 seconds 10 seconds delay: 20 seconds

16 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.

17 Few cyclists = 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? low potential? Foto: Wikipedia

18 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.

19 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.

20 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.

21 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.

22 Estimating results for Bicycle investments

23 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.

24 Quick-scan model bicycle measures Potential Accessibility Total accessibility Gravity model T ij = k (O i D j ) / F(c ij ) Competion Curve Origin-Destination relations Results Total accessibility, travel time benefit bicycle trips, new bicycle trips

25 Everything you always wanted to know about your cycling network performance but were afraid to ask Dirk Bussche,