Traffic flow phenomena in large sporting events – empirical analysis and macroscopic simulation of the Vasaloppet

Abstract

In large sporting events, traffic flow phenomena similar to car traffic can be observed. The Vasaloppet, a long-distance cross-country skiing race in Sweden, exemplifies this. Athletes use classical techniques and race in tracks akin to small lanes, where athlete traffic jams can impede performance. This study empirically analyzes these phenomena using eight years of split-time data and GPS tracks. We identified critical bottlenecks, jams, and stress points where athletes must wait while the race clock continues to tick. The nature of movement under free-flow conditions is largely dispersive due to varying abilities and grouping by skill level. Using comprehensive data and observations from the Vasaloppet, we developed a two-phase macroscopic simulation model to predict and understand athlete flow during races. One phase models free-flow conditions, and the other congested conditions. A multi-class approach was implemented, with each class having different speed distributions based on official split time data. Using this model, we simulated the race under status-quo conditions, as well as with two potential measures to reduce waiting time for participants: a staggered start and a widening of the race course. Both measures result in massively reduced congestion and waiting times compared to the status quo. While these measures are unlikely to be implemented in future Vasaloppet races due to tradition and nature conservation respectively, our model could be applied to various mass sporting events, such as other cross-country skiing, running, cycling, and inline skating events. It can aid organizers who can test different measures to resolve athlete traffic jams.