Customisable Route Planner App Developed by BME Researchers

Typically caused by congested car traffic, transport problems in modern major cities can essentially be addressed in two ways: expensive infrastructure investments and incentives to change travel habits. An example for the latter is an app developed by researchers at the Budapest University of Technology and Economics (BME), which was recently reported on by Domokos Esztergár-Kiss, Senior Research Fellow at the Department of Transport, Technology and Economics, in Innotéka, a magazine on science, innovation and the green economy.

The main objective of the web-based, personalised route planner is to promote and support sustainable commuting. “As part of the research, we examined the extent to which travellers in Budapest are willing to change their travel habits, with the app making suggestions for selecting the most suitable mode of transport and allowing users to give feedback,” the article states.

So the key innovation of the tool lies in its ability to assess users’ travel preferences and use that information to help select the most appropriate mode of transport. 

This essentially means the optimisation of activity chains by comparing multiple transport modes and highlighting shared and electric alternatives.

In addition to parameterisation according to individual habits, the collected data can also be utilised in strategic transport planning and transport management processes. This only requires a sufficiently large number of travellers to use the app, enabling, for example, timetables to be fine-tuned and coordinated in line with mobility patterns.

“Integration in the field of transport is developing very rapidly, which has contributed, among other things, to the emergence of Mobility as a Service (MaaS),” writes Domokos Esztergár-Kiss. During the research, a method was developed that assigns package levels to travellers and incorporates several modes of transport. To achieve the project’s objective, mobility packages were created that take both personal and location-specific factors into account. This method can be applied in a wide variety of cities and locations.

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