Ride-pooling demand prediction: A spatiotemporal assessment in Germany. (April 2022)

• Record Type: Journal Article
• Authors: Zwick, Felix; Axhausen, Kay W.
• Is Part Of: Journal of transport geography. Volume 100(2022)
• Publication Date: 2022-04
• Subjects: Ride-sharing -- On-demand mobility -- Spatial regression -- Open data -- New mobility -- Demand prediction -- Ride-splitting; AIC Akaike information criterion -- BIC Bayesian information criterion -- GHG greenhouse gas -- GWR geographically weighted regression -- GWPR geographically weighted poisson regression -- MAE mean absolute error -- MTS main train station -- OSM OpenStreetMap -- POI point of interest -- RENB random-effects negative binomial -- RF random forest -- RMSE root mean square error -- SEM spatial error model -- SLX spatial lag of X -- SD standard deviation -- SDEM spatial Durbin error model -- TNC transportation network companies -- VKT vehicle kilometers traveled; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2022.103307
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: Ride-pooling has attracted considerable attention from both academia and practitioners in recent years, promising to reduce traffic volumes and its negative impacts in urban areas. Simulation studies have shown that large-scale ride-pooling has the potential to increase vehicle utilization, thereby reducing vehicle kilometers traveled (VKT) and required fleet sizes compared to single-passenger mobility options. However, in the real world, large-scale ride-pooling services are rare and not yet widely implemented, in part due to high operating costs that are expected to decrease substantially with the advent of automated vehicles. Two large-scale ride-pooling fleets are operated by MOIA in Hamburg and Hanover, Germany, and serve as testbeds for future pooled services. For this study, we analyze pre-pandemic demand data from both services from 2019 and 2020 and perform spatial and random forest regressions to understand the (spatial) characteristics of ride-pooling trip origins in both cities. We then examine how well findings from one study area (Hamburg in our case) can be generalized and transferred to other cities (Hanover in our case) to enable spatial predictions beyond areas with an existing service. The regression results are similar in both cities and show the strongest impact on ridepooling demand by the variables capturing the density of workplaces, gastronomy and culture. Given the regression results for Hamburg, we predict trip origins for Hanover and observe that demand is overestimated by all applied models. The spatial lag of X (SLX) model showed the most promising results with an overall overestimation of trip origins below 20%.