Reprint

Urban Street Networks and Sustainable Transportation

Edited by
May 2022
200 pages
  • ISBN978-3-0365-3933-1 (Hardback)
  • ISBN978-3-0365-3934-8 (PDF)

This is a Reprint of the Special Issue Urban Street Networks and Sustainable Transportation that was published in

Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary

Urban street space is challenged with a variety of emerging usages and users, such as various vehicles with different speeds, passenger pick-up and drop-off by mobility services, increasing parking demand for a variety of private and shared vehicles, new powertrains (e.g., charging units), and new vehicles and services fueled by digitalization and vehicle automation. These new usages compete with established functions of streets such as providing space for mobility, social interactions, and cultural and recreational activities. The combination of these functions makes streets focal points of communities that do not only fulfill a functional role but also provide identity to cities. Streets are prominent parts of cities and are essential to sustainable transport plans. The main aim of the Street Networks and Sustainable Transportation collection is to focus on urban street networks and their effects on sustainable transportation. Accordingly, various street elements related to mobility, public transport, parking, design, and movement of people and goods at the street level can be included.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
consecutive signalized arterials; urban street; hierarchical longitudinal control; optimal control; connected and automated vehicles; walking; pedestrians; urban street design; pedestrian facilities; link and place functions; sidewalk; walkability; cycling; urban street design; cycling facilities; bike lanes; sustainable commute mode; walkability assessment tool; measurement quality appraisal; walking environment; walking needs; sustainable urban form; urban networks analysis; street connectivity; Arab Gulf urbanization; tolerable travel time; university students; built environment; early life-course; Bayesian network; machine learning; autonomous vehicles; vulnerable road users; public perception; machine learning; most effective variables; pedestrian fatality; road accident; Bayesian neural network; Bayesian theorem; sustainable road network development; machine learning; sustainable vehicle ownership; nonlinear relationships; built environment; XGBT; sustainable travel to public transit stations; complex relationship; Bayesian network algorithm; work trip