By Saumya Jain
Researchers from the Texas A&M Transportation Institute recently published a paper that discusses the top sources for individually-acquired pedestrian and bicycle travel data assimilated from a variety of sources. This could help us understand how to solve the complexities of incorporating active transportation modes into traditional planning practices. Although, there are many platforms and companies offering bike-ped travel data acquired through smartphone apps, location-based services, fitness apps, etc., the choice can be very confusing and at times expensive. And a recent study from Sweden also noted how complex it is to incorporate bicycle trips into a traditional travel demand model, stressing the need to move toward alternate methods like Big Data for understanding non-motorized travel patterns.
In their paper, the TTI researchers compared different sources of “emerging data” collected through mobile devices for monitoring bicyclist and pedestrian activities, compared the quality of data based on the mode of collection, its availability and accessibility, level of detail, application, and key challenges and limitations attached to the data. Though the researchers emphasize that most of the data sources are not yet entirely ready for application by themselves, a combination of sources can be used to study and answer pressing non-motorized transportation questions. Out of the different sources studied, the researchers find Strava Metro data and StreetLight Data to be most reliable and closest to application, among the available sources.
Most companies offering these datasets are willing to work and partner with organizations to provide personalized and improved data quality. Although there is need for further investigation into what sources are best suited for specific transportation studies, this paper gives a good starting point to organizations that want to venture into the field of Big Data for solving active transportation issues.