Braving the elements: A time series analysis of e-scooter ridership assessing the impact of weather and seasonality across different climate regions

The introduction of e-scooters sharing schemes represents the latest phase in the diversification of micromobility in urban environments. Municipal authorities in the United States have led the way with piloting e-scooters sharing schemes to consider how they are used and the impacts this has for the wider transport system. This paper reports a time series econometric analysis of the trip data derived from e-scooter pilots in Minneapolis, Louisville, and Austin which represent a climate gradient down the central United States. The analysis evaluates the link between ridership and prevailing weather conditions such as temperature, wind speed, precipitation alongside weather events such as thunder and fog. An autoregressive distributed lag model format is applied at both daily and hourly aggregations of e-scooter demand with specific attention paid to how consistent these models are across the three pilots.

The results indicate that demand in the subtropical climate of Austin is quite stable throughout the year while ridership in the more northern climes of Louisville and Minneapolis follows a seasonal profile of being high in summer and dropping off during winters. The parameters calculated for the meteorological elements for daily ridership are stable across the pilots, indicating that user response to weather conditions is reasonably consistent in different climates. Riders in Austin appear more willing to displace trips to attain better weather conditions, bringing trips forward when the temperatures are hotter and postponing trips when it is windy or raining. Hourly ridership models are less stable in terms of their results and require more focused research to further reveal their dynamics. Practical implications of these studies are that cities considering the introduction of e-scooter sharing schemes would benefit from examining demand patterns in similar climate regions to plan for suitable scales and seasonal operations.