Stay mobile with TAPAS
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| License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/12693 (DOI) | |
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00:00
Computer animation
00:23
WorkshopRailroad
00:32
BusRapid transitTrainAerial tramwayLecture/Conference
00:41
Transmission (mechanics)WorkshopHot workingMotor vehicle
00:53
Motor vehicle
01:00
Catadioptric systemMode of transportRailroad
01:07
Pattern (sewing)Moving walkwayMeeting/Interview
01:16
Motor vehicle
01:22
Motor vehicleRailroad
01:29
Belt (mechanical)Computer animation
01:38
SawMode of transportWorkshopDeutsches Zentrum für Luft- und RaumfahrtTypesettingOutsourcingMoving walkwayPattern (sewing)TrainRailroad carFord FocusMusical ensemble
04:23
Moving walkwayBusStagecoachFinger protocolBicycleMode of transportPattern (sewing)Railroad carCartridge (firearms)Hot working
Transcript: English(auto-generated)
00:15
In Berlin there are around 1.7 million jobs, over 40 universities and higher education
00:22
colleges and almost a thousand schools. Every workday offices and workshops fill up with workers, lecture rooms with students and teachers and pupils rush to school. Tens of thousands are on the move. They come from the suburbs or the inner city and take commuter trains, subway, tram or bus. They drive or cycle.
00:44
Shopping trips supplement their journey to and from college or work. Sports and cultural centres are called at, friends visited. There is not simply one way from A to B. An enormous number of potential destinations are available and there's a lot of scope for combining trips
01:03
and selecting various means of transport. It is up to each transport user to decide what is convenient for him or her. Individual travel patterns characterise human mobility. What influences those patterns? Can they be controlled? What has to happen in order to maintain and improve
01:22
our mobility and design it to be more environmentally sound? When designing tomorrow's mobility one has to know the demand, the transport users. Transport modelling can help here to examine the behaviour of various population groups in scenarios.
01:42
DLR researchers are working on such simulation models. One of these models has a particularly strong focus on individual behaviour. It's called TAPAS. Travel and activity patterns simulation.
02:04
The modelling of travel patterns is no simple task as human beings do not always act strictly rationally. Routines, norms, experience, as well as the spatial and social environment characterise the patterns and preferences.
02:22
Knowing the factors influencing decisions is though a prerequisite when making predictions about future mobility behaviour. Looking at diverse transport behaviour data helps the transport researchers. One of these data sets is a study called Mobility in Germany for which over 60,000 people
02:42
reported their travel behaviour. The statistical analysis of these records helps the modeller to understand on which factors it depends whether people own cars, how often they leave the house, which mode of transport they favour and how they react to changes in transport provision or opening hours.
03:09
The city is built as a model. With streets, commuter trains, universities, shopping and leisure facilities, homes and much more.
03:22
From various data sources an artificial population is drawn up which however correctly reflects the actual population. It represents people along with their social and economic characteristics age, gender, employment status
03:42
and family status. The DLR researchers send these various individuals on virtual journeys. The artificial individuals have the task of selecting transport modes and locations for the planned activities. This is a matter of weighing up
04:02
alternatives. How far away is a certain destination? How long do I need to get there using different means of transport? What will it cost me? Their decisions depend just as much on the purpose of their journey and their individual characteristics as on the spatial context
04:20
and the available transport options. But what precisely is happening in the model here? A working middle-aged woman will serve as an example. Let's call her Mrs. Smith. On the basis of her individual characteristics and also those of her household, the model selects an appropriate
04:41
empirical day's schedule. This schedule provides for Mrs. Smith to take her child to the nursery in the morning and subsequently travel to work. In the evening she has arranged to go to the cinema with a friend. In the next stage the destinations and means of transport must be specified
05:03
for each activity beginning with the most important one, work in our case. Mrs. Smith has a driving license and the car is out front. She decides to drive today. Next a nursery is selected. The child is taken there on the way to work.
05:24
The options for the evening are more numerous. There are many cinemas in town for her to choose from. As a rule she would more likely choose an easily reachable large cinema nearby than a small one further away. In our example she decides to go by bicycle in the evening
05:41
even though the selected cinema is a few minutes away. The travel pattern of Mrs. Smith for a particular day is thus mapped out. What would happen though if the price of petrol rose and public transport were improved?
06:03
How would Mrs. Smith's schedule be altered? Let's return to the question of what the way to work would look like. Using the car has become more expensive and is therefore less attractive. Mrs. Smith decides to take the bus to work. The nursery demands only a short detour on the way to the bus stop.
06:23
Easily managed on foot even with a child. Only in the evening due to the longer travel times by bus time gets a little tight. A cinema in the near vicinity is chosen and Mrs. Smith can get there by bike on time before the film starts.
06:46
The test of the altered schedule shows that travel times and costs are plausible and that the plan is practicable. At the end of the simulation there is a schedule with all the day's destinations,
07:02
the means of transport used, the distance covered and the time needed for the journeys not only for Mrs. Smith but for every individual of the artificial population. Every day mobility in the model as in real life
07:20
is the result of numerous individual decisions. By observing many separate individuals the tapas demand model facilitates the build up of a complete picture of future transport demand. Transport modelling enables us to assess the effect of transport policy measures, new transport options, altered prices
07:42
or even the aging of the population and only those who know the present and can foresee the consequences of change will be able to sustainably shape future mobility for a mobile tomorrow.