Influence of Dockless Shared E-Scooters on Urban Mobility: WTP and Modal Shift

Abstract

Land use largely depends on the traffic policy of a city. The appearance of e-scooters can greatly affect the visual distribution of transportation, and thus the occupation of land, primarily in the central areas of cities. E-scooters as a shared micro-mobility service have become widespread worldwide since 2017. The advent of e-scooters has made changes in travel habits, especially in the central parts of big cities. However, many issues are focused on e-scooter shared mobility management policies. One of the important issues is the price of renting an e-scooter, on which the percentage of users who use e-scooters largely depend. In order to determine willingness to pay for e-scooter dockless shared mobility, a survey was conducted in the city of Belgrade (Serbia, Europe) on the willingness of participants to use this mode of transport for commuting and other travel purposes depending on the price of renting an e-scooter. The results showed that price plays an important role in the willingness of participants to use an e-scooter. The paper presents mathematical models, which include the cost of renting an e-scooter and the percentage of participants who would accept this type of transport. These mathematical models can help a decision maker to determine the pricing policy in order to maximize the profit from renting an e-scooter, as well as to influence modal shift in order to reduce car-dependent trips.

Determinants of shared e-scooter usage and their policy implications. findings from a survey in Braga, Portugal

“…After the study of the top three interventions, the one that was found to have statistical significance was the improvement in the parking spot coverage for shared e-scooters, which was also seen as an opportunity to increase usage in previous studies [55,56]. The present research also found that the gender, family size, employment status, and parish of origin of non-users have a statistical correlation with the choice of this intervention as a policy priority.…”

Parking Generating Rate Prediction Method Based on Grey Correlation Analysis and SSA-GRNN

“…(4) Land use development index [13] Land use development indicators mainly include plot ratio, building density, building layers, and green space rate [11]. The plot ratio mainly reflects the intensity of land use, which refers to the ratio of building area to land area in a piece of urban development land.…”

“…The parking generating rate and its associated data are mapped to the interval [0, 1] to remove the limitation of data units and accelerate the convergence speed and prediction accuracy of the neural network. The standardized formula is shown in Equation (13).…”

The Determinants of Electric Scooter Acceptance Among Students as a Manifestation of Adapting to the Requirements of E-Mobility, Using the Technology Acceptance Model

“…The factors influencing the use of electric scooters are perceived somewhat differently by Lee et al (2021), who do not focus on demographic characteristics, but rather on users having a higher income and being dissatisfied with public transportation. Costs associated with the use of an electric scooter are also an important factor -sometimes regarded as a decisive factor (Glavić, Milenković, Trifunović, Jokanović, Komarica, 2023).…”