Results 11 to 20 of about 2,491 (219)

Comparative Insights Into E-Scooter Usage Prediction Through Machine Learning and Deep Learning Techniques

Journal of Advanced Transportation
Shared micromobility services are experiencing rapid growth, particularly in addressing last-mile transportation needs. The most crucial questions focus on identifying the determinants of user behavior and the factors driving demand for micromobility ...
Gokhan Yurdakul, Nezir Aydin, Sukran Seker, Hao Yu   +3 more
doaj   +2 more sources

Factors influencing the usage of shared micromobility: Implications from Berlin

Journal of Cycling and Micromobility Research
The popularity of urban micromobility has steadily grown in cities worldwide. There is a lack of comparative studies investigating factors influencing the travel behavior of shared micromobility in Europe.
Maximilian Heumann, Tobias Kraschewski, Philipp Otto, Lukas Tilch, Tim Brauner, Michael H. Breitner   +5 more
doaj   +2 more sources

Identify the success factors of shared micromobility systems in European cities: How could a shared micromobility system be successful in the city of Bodø ? [PDF]

, 2022
Masteroppgave i Global ledelse (tidl.
Eminovic, Ena
core   +2 more sources

Shared micromobility, perceived accessibility, and social capital. [PDF]

Transportation (Amst)
Abstract While the impacts of shared micromobility (SMM) on the environment and transport systems are being extensively researched, its societal implications and the influence of the social environment on the use of SMM remain largely unexplored. In this research, we investigate the interrelationships between the use of SMM, perceived
An Z, Mullen C, Guan X, Ettema D, Heinen E.   +4 more
europepmc   +7 more sources

New York State Climate Impacts Assessment Chapter 09: Transportation. [PDF]

Ann N Y Acad Sci
Abstract The transportation sector's functionality depends on climate conditions. Many New York State residents, communities, businesses, and institutions have already experienced transportation‐related climate change impacts, and many more will experience impacts in the future.
Macdonald A, McDonald J, Schmitt T, Cai H, Ceponis J, Cullen C, Dutta P, Ruiz AC, Stevens A.   +8 more
europepmc   +2 more sources

What Drives Shared Micromobility Ridership?

, 2023
Shared micromobility (e.g., e-scooters, bikes, e-bikes) offers moderate-speed, space-efficient, and “carbon-light” mobility, promoting environmental sustainability and healthy travel. While the popularity and use of shared micromobility has grown significantly over the past decade, it represents a small share of total trips in urban areas.
Hyland, Michael, PhD, Saphores, Jean-Daniel, PhD, Ghaffar, Arash   +2 more
openaire   +3 more sources

Gender perspectives on electric micromobility use

Human Factors and Ergonomics in Manufacturing &Service Industries, Volume 33, Issue 6, Page 476-489, November 2023., 2023
Abstract Electric micromobility (e‐micromobility) offers the potential to enhance the sustainability of first‐ and last‐mile journeys in urban areas by reducing the number of private vehicle trips. As a new mode of transport, it is imperative that it is not subject to the same male bias that has been evidenced across our existing transport networks. An
Katie J. Parnell, Siobhan E. Merriman, Katherine L. Plant   +2 more
wiley   +1 more source

Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships [PDF]

, 2019
Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century.
6t, American Public Transit Association, Berrebi, Bösch, Call a Bike, Campbell, Campbell, Cervero, Cervero, Cervero, Chen, Clewlow, Cohen, Dickey, Dobush, Etherington, Feigon, Firnkorn, Franco, Gehrke, Hampshire, Henao, Henao, Kim, Lane, Lazarus, Lazarus, Martin, Martin, Massachusetts Governor's Office, McFarland, NASA, National Association of City Transportation Officials, Olson, Rayle, SAE International, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Shaheen, Sochor, Yvkoff   +53 more
core   +1 more source

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Geographical Analysis, Volume 55, Issue 4, Page 651-684, October 2023., 2023
Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike‐sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to ...
Katja Schimohr, Philipp Doebler, Joachim Scheiner   +2 more
wiley   +1 more source

On the simulation of shared autonomous micro-mobility

Communications in Transportation Research, 2022
Fast urbanization and climate change require innovative systems for an efficient movement of people and goods in cities. As trends towards vehicle-sharing, autonomous vehicles, and the use of micro-mobility systems gain strength, the intersection of ...
Naroa Coretti Sanchez, Iñigo Martinez, Luis Alonso Pastor, Kent Larson   +3 more
doaj   +1 more source