Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Ride-pooling demand prediction: A spatiotemporal assessment in Germany
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%.
Ride-pooling demand prediction: A spatiotemporal assessment in Germany
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%.
Ride-pooling demand prediction: A spatiotemporal assessment in Germany
Zwick, Felix (Autor:in) / Axhausen, Kay W. (Autor:in)
18.02.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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
The shareability potential of ride-pooling under alternative spatial demand patterns
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