Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data

Accurately predicting pedestrian travel times is critically valuable in emergency response, wildland firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions is highly variable between individuals, making it difficult to estimate how long it will take broad populations to get from one location to another on foot. Although functions exist for predicting travel rates, they typically oversimplify the inherent variability of pedestrian travel by assuming the effects of landscapes on movement are universal. In this study, we present an approach for predicting the variability in pedestrian travel rates and times using a large, crowdsourced database of GPS tracks. Acquired from the outdoor recreation website AllTrails, these tracks represent nearly 2000 hikes on a diverse range of trails in Utah and California, USA. We model travel rates as a function of the slope of the terrain by generating a series of non-linear percentile models from the 2.5 th to the 97.5 th by 2.5 percentiles. The 50 th percentile model, representing the hiking speed of the typical individual, demonstrates marked improvement over existing slope-travel rate functions when compared to an independent test dataset. Our results demonstrate novel ca-pacity to estimate travel time variability, with modeled percentiles being able to predict actual percentiles with less than 10% error. Travel rate functions can also be applied to least cost path analysis to provide variability in travel times.

Automated summary

Accurately predicting pedestrian travel times is crucial in various fields such as emergency response, firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions varies greatly among individuals, posing challenges in estimating travel times for broad populations. This study presents an approach using a large crowdsourced GPS database to predict the variability in pedestrian travel rates and times. The results demonstrate the ability to estimate travel time variability with less than 10% error, providing valuable insights for urban planning and path analysis.