Case ISTmobil

The implementation of new mobility services is prepared in several steps. The concept phase, as well as the stakeholder and user involvement, are crucial factors for successful implementations. The objective of the optimisation in the process is to incorporate mobility trends and data, identify transport modes and potential mobility hubs, and simulate and evaluate MaaS and sharing concepts such as e-car sharing, car-pooling and bike sharing. This will generate a decision support for regional and urban infrastructure planning and mobility service providers for the efficient use and disposition of resources in the field of mobility.

This case example, described in more detail in the public deliverable, is about the first steps to identify success factors based on data acquisition, availability and mobility trends, and on the detailed case analysis of ISTmobil mobility service. The consideration of success factors in the design of an optimised MaaS gives a possibility to obtain a guideline and to avoid repeating mistakes. This helps to efficiently implement concepts for MaaS and also transfer them into new regions.

In general, an increase of the car ownership and vehicle miles travelled per capita has been visible for most countries in the world since 1990. Between 1990 and 2005 people tended to drive more miles per capita year after year. However, since 2005 a shift in this trend is visible at least for urban regions. Millard-Ball and Schipper (2011) investigated this trend for cities in eight different countries (Australia, Canada, France, Germany, Sweden, UK, U.S. and Japan). Based on their analysis, they conclude that travel activity has reached a plateau in all eight countries. Newmann & Kenworthy (2011) lists possible reasons for the saturation of private car usage:

  • Constant travel time budget: People are willing to travel further but not longer. This means if the travel speed has reached an upper limit, e.g., due to safety or environmental reasons, the vehicle miles travelled remains constant.
  • The growth of public transport: Due to environmental reasons, the development of public transport is an important topic for every city. As a consequence, public transport is becoming a competitive alternative to motorised individual traffic.
  • The ageing of citizens: The average age of people living in cities is rising, and people who are older tend to drive less. So, the amount of vehicle miles per capita decreases.
  • The increasing urbanisation: Urbanisation is constantly increasing and approximately half of the people live in urban regions currently. Because private car usage is more difficult (cost, space) and less necessary (public transport, walking/cycling distances) in urban regions, urbanisation leads to fewer vehicle miles travelled.
  • Rise in fuel prices: Although the elasticity associated with fuel price is high, it has reached a level where higher prices lead to a decrease in private car use.

Our case example was focused on a region in the north of Vienna, District Korneuburg (the figure below). As the service of the mobility provider (ISTmobil) has operated in the case area for some years now, different statistics about mobility behaviour were available and the recorded data could be analysed. All the collected statistics were generated by a register-based census and contain only working commuters (‘Erwerbspendler’), i.e., students are not included.

Korneuburg
Out-commuter Korneuburg 2013, Source: Statistics Austria.

ISTmobil develops and operates a shared taxi system in Austria. The system is flexible in terms of the pick-up location and time. Moreover, trips are ordered up to one week in advance by telephone or web service. All vehicles of the system are equipped with a positioning system, which enables the tracking of taxis in real time. We used this tracking data to estimate the potential of sharing taxi trips when easing current constraints such as the pick-up or delivery time. In the next figures, all the points of departures and destinations of customer trips in the sample are visualized.

ISTmobil data
Points of departures and destinations of customer trips. Please note that many destinations (green dots) are concealed by the points of departure (red dots). Blue stars are the ten most frequent points of departures or destinations of trips.

Heat 1
Heatmap for points of departures. Red indicates a higher number of trips in the corresponding region. Green lines indicate railroads.

Heat 2
Heatmap for points of destinations. Red indicates a higher number of trips in the corresponding region. Green lines indicate railroads.

The two most frequent locations for both points of departure and destinations are the railway stations of the two largest cities in the district. Therefore, the assumption is that the ISTmobil service is very often used in combination with other transport modes, especially for commuting. The lengths of the customer trips using the ISTmobil service are in 95% of the cases below 15 km and take less than 20 minutes (next two figures). Half of the trips are even below 5 km and take less than 10 minutes. From this, we conclude that the service is mostly used for short distances.

Distribution 1
Cumulative distribution of trip distance.
Distribution 2
Cumulative distribution of trip duration.

Since the number and complexity of user trips are constantly growing, a transport system has to enable a more intertwined mobility behaviour. For instance, the pricing of public transport has to be designed in a way to enable a large diversity of trips for users, e.g., different destinations. To keep pace with this development, the transport association for Eastern-Austria (VOR – Verkehrsverbund) changed its pricing from fixed zones to user-specific transport fees. Moreover, seasonal tickets in the region can be used for a personalized subset of the public transport system.

Next: AC2SG Software Oy

 


References

Millard‐Ball, A. & Schipper, L. (2011). Are we reaching peak travel? Trends in passenger transport in eight industrialized countries. Transport Reviews, 31(3), 357-378.

Newman, P. & Kenworthy, J. (2011). ‘Peak car use’: understanding the demise of automobile dependence. World Transport Policy & Practice, 17(2), 31-42.

 

 

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