Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

TL;DR

This study investigates Munich commuters' preferences for ride-hailing services, analyzing willingness to pay, demographic influences, and potential impacts on modal split using survey data and choice modeling.

Contribution

It introduces a novel application of stated preference surveys and multinomial logit models to assess ride-hailing's impact on urban mobility in Munich.

Findings

Younger adults (18-39) prefer ride-hailing services.

Higher income groups are willing to pay more for ride-hailing.

Ride-hailing could influence modal split depending on cost and time tradeoffs.

Abstract

The growth of ridehailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services popularity among those aged 18 to 39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.