Advanced data collection methods: Smartphone based travel surveys and Open APIs

Taking advantage of open data and APIs, a methodology is developed to incorporate transport operators’ APIs and other open data sources into smartphone-based travel survey tools to decrease the respondent burden and collect automatically high quality and accurate data about travel behaviour and the conditions on the transport network in real time

Overview

Availability of rich and high quality data is a key driver in every sector. Transportation is no exception, where the way data is created, collected, analysed and shared is immensely evolving. Technological progress has allowed us to shift away from historically manual and spatially fixed data collection to dynamic methods. In the field of travel behaviour, traditional face-to-face paper based surveys have developed to exploit the advancements in information and communication technologies (ICT). The rapid penetration of location-aware smartphones has made researchers realise the potential of these devices to collect individual level travel data.  These computerised systems also have huge untapped potential, as they can be used to harness information from any source. One of these sources is the vast amount of open data and APIs (Application Programming Interfaces) available for developers. The quality and quantity of such open datasets are constantly improving, expanding and becoming more dynamic through live feeds. They can provide information about transport mode routes, times, infrastructure, ticketing, prices – most of these in real time.

Given the wealth of open transport API data, the aim of this project is to develop a methodology to link open APIs to smartphone based travel survey tools to improve the quantity and the quality of the collected data and to further reduce response burden. Such APIs include public transport operator-, car sharing vehicle-, taxi-, traffic condition-, weather-APIs among others. Interoperability of these APIs is also examined, with the aim of determining how they can be linked together to harness the maximum amount of information. Greater London is taken as a case study where the Future Mobility Sensing tool that has developed by MIT and SMART, is connected with the open APIs available for London.

Key findings

• Development of methodology for linking open data and APIs to smartphone based travel survey tools

Impact, Influence & Outreach

• Advance travel surveys / data collection methods