a sustainable and needs-based way. This requires accurate in- formation about people’s mobility patterns in a particular regi- on, and herein lies the problem: valid data for transport plan- ning can only be gathered at considerable financial and per- sonnel cost. People’s actual mobility patterns and mobility needs are usually investigated through surveys. This method is a considerable chore for the participants as, for a number of days, they have to enter every single journey made in a list in- dicating the start and end point, duration and means of trans- port used, etc. Experience has shown that inaccuracies and gaps increasingly appear over the course of the survey, often necessitating further checks.


Consequently researchers in the AIT Mobility Department have developed more effective alternatives to the classic survey of mobility patterns. One of these is AIT Smart Survey, which gathers mobility information via smartphone. “This develop- ment does away with the need to laboriously record and digiti- se mobility data as AIT Smart Survey automatically records the routes taken and means of transport used by those taking part in the survey,” explains Markus Ray of AIT’s Mobility Depart- ment. This “digital mobility diary” describes the actual situati- on far more reliably than personal records. Using integrated sensors, the app tracks the smartphone and transmits the movement data to the AIT server. Here a pattern recognition method developed at the AIT automatically detects the means

of transport used, based on the transmitted GPS coordinates and acceleration data.


“While similar systems can only distinguish between public and private means of trans- port, cycle and pedestrian paths, our system can detect whether the individual is travelling by bus, tram, underground or suburban train, by car, bicycle, motorbike or on foot”, says Markus Ray describing the ability of AIT’s in- novative tool to differentiate between the transport modes. The underlying sophistica- ted methodology was developed in the Mobili- ty Department and has already been publis- hed in respected journals. Automated statisti- cal analysis reduces the costs for customers commissioning mobility studies and the sur- vey data can be exported in digital form and readily incorporated into planning software. The participants can view their own mobility data with a web browser and correct, edit or delete it as necessary. Customers can also use this method to ask additional questions, for example, about the reasons for using a particular means of transport or transport- related requests, etc.


Scientist, AIT Mobility Department

Several projects at AIT fo- cus on how existing mobi- lity patterns can be im- proved and how unfa- vourable habits can be broken.