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Sustainability
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Probe Vehicle Data and Sustainability
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Probe Vehicle Data and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3241

Special Issue Editors

Prof. Thomas M. Brennan Jr.

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Guest Editor
Civil Engineering, The College of New Jersey, Ewing, NJ 08628, USA
Interests: transportation; infrastructure management; sustainable design; big data
Special Issues, Collections and Topics in MDPI journals
Prof. Mohan M. Venigalla

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Guest Editor
Civil and Infrastructure Engineering, George Mason University, Fairfax, VA 220130, USA
Interests: transportation engineering; transportation air quality; transportation planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stephen M. Remias

E-Mail Website
Guest Editor
Civil and Environmental Engineering, Wayne State University, Detroit, MI 48202, USA
Interests: intelligent transportation systems; traffic operations; connected infrastructure; safety and mobility performance measures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is being assembled to publish research papers on the topic of probe vehicle data as it relates to a broad range of sustainable transportation practices. The problems to which probe vehicle data are applied can range from the evaluation of congestion reduction, regional congestion performance, land use practices, economics, policy, and other applications including multimodal transportation systems. The objective of the Special Issue is to publish papers that provide novel ideas about improved data visualization and performance metrics to support sustainable transportation practices. At a minimum, the paper must include probe vehicle type data, which comprise any of the following: speed, location, vehicle type, user information, origin–destination, etc. To derive sustainability metrics, the probe vehicle data may be associated or fused with supplemental data such as demographic, economic, land use, environmental, and cultural datasets.

Please consider submitting your original articles for publication in this Special Issue. Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications. We would greatly appreciate your sharing this information with your colleagues who might be interested in publishing in the Special Issue.

Prof. Thomas M. Brennan Jr.
Prof. Mohan M. Venigalla
Prof. Stephen M. Remias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Probe data
  • Big data
  • Transportation sustainability
  • Performance measures
  • Multimodal

Published Papers (1 paper)

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Research

13 pages, 733 KiB  
Article
An Adapted Version of the Water Wave Optimization Algorithm for the Capacitated Vehicle Routing Problem with Time Windows with Application to a Real Case Using Probe Data
by Letizia Tebaldi, Teresa Murino and Eleonora Bottani
Sustainability 2020, 12(9), 3666; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093666 - 01 May 2020
Cited by 6 | Viewed by 2511
Abstract
Customers’ habits, as far as shipping requests are concerned, have changed in the last decade, due to the fast spread of e-commerce and business to consumer (B2C) systems, thus generating more and more vehicles on the road, traffic congestion and, consequently, more pollution. [...] Read more.
Customers’ habits, as far as shipping requests are concerned, have changed in the last decade, due to the fast spread of e-commerce and business to consumer (B2C) systems, thus generating more and more vehicles on the road, traffic congestion and, consequently, more pollution. Trying to partially solve this problem, the operational research field dedicates part of its research to possible ways to optimize transports in terms of costs, travel times, full loads etc., with the aim of reducing inefficiencies and impacts on profit, planet and people, i.e., the well-known triple bottom line approach to sustainability, also thanks to new technologies able to instantly provide probe data, which can detail information as far as the vehicle behavior. In line with this, an adapted version of the metaheuristic water wave optimization algorithm is here presented and applied to the context of the capacitated vehicle routing problem with time windows. This latter one is a particular case of the vehicle routing problem, whose aim is to define the best route in terms of travel time for visiting a set of customers, given the vehicles capacity and time constraints in which some customers need to be visited. The algorithm is then tested on a real case study of an express courier operating in the South of Italy. A nearest neighbor heuristic is applied, as well, to the same set of data, to test the effectiveness and accuracy of the algorithm. Results show a better performance of the proposed metaheuristic, which could improve the journeys by reducing the travel time by up to 23.64%. Full article
(This article belongs to the Special Issue Probe Vehicle Data and Sustainability)
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