Crowdsourced Pedestrian Map Construction for Short-Term City-Scale Events

This paper targets the construction of pedestrian maps for city-scale events from GPS trajectories of visitors. Incom- plete data with a short lifetime, varying localisation accu- racy, and a high variation of walking behaviour render the extraction of a pedestrian map from crowd-sourced data a difficult task. Traditional network or map construction methods lean on accurate GPS trajectories typically ob- tained over longer time periods from vehicles at high speeds with less variation in locomotion. Not designed to oper- ate under mobility conditions of pedestrians at large scale events they cannot be directly applied. We present an al- gorithm based on a crowd-sensing scheme to construct the pedestrian network during city scale events. In a thorough evaluation, we investigate the effect of trajectory quality and quantity on the map construction. To this end, we use a real world dataset with 25M GPS points obtained from 28.000 users during a three-day public festival event. Results in- dicate that with a short observation window of 30min the estimated pedestrian network can represent previously un- seen trajectories with a median map-matching deviation in matching of only 5m and a map accuracy of more than 85%.