Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Intelligent Transportation Systems Application in Smart Cities
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Transportation Systems Application in Smart Cities

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 10434

Special Issue Editors

Prof. Zhiyuan Liu

E-Mail Website
Guest Editor
School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 211189, China
Interests: transport network modeling; public transport; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Xinyuan Chen

E-Mail
Guest Editor
Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Hong Kong
Interests: urban transport network modeling; parallel computing in transport system analysis; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Di Huang

E-Mail
Guest Editor
Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Hong Kong
Interests: public transit system; transportation network modeling; urban mobility modeling and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are calling for papers for a Special Issue of the journal Sustainability on research into the intelligent transportation system applications in smart cities. Recently, new technologies on transportation are developing rapidly, such as connected and automated vehicles, and shared mobility services. The rapid evolution of techniques brings great opportunities and challenges to refine the urban transport systems. Successful implementation of intelligent transportation applications depends on multiple factors, including technical, operational, and political aspects. Designing, testing, and implementation of effective intelligent transportation applications require multi-disciplinary and emerging techniques. Meanwhile, new sensing and data resources bring opportunities for data-driven applications to better reflect the features and dynamics of urban transportation systems. The increasingly available data and the complexity of mathematical models also bring the challenge of large-scale computation. Therefore, new algorithms and computation methods have become a significant area of research. The overall objective of this special issue is to collect innovative contributions to the application of advanced transport techniques in smart cities. Proposed papers for this special issue may cover a broad range of modelling, control, design, monitoring, management, and optimization of intelligent transportation system applications, as long as the focus on emerging techniques.

Prof. Zhiyuan Liu
Dr. Xinyuan Chen
Dr. Di Huang
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

  • artificial intelligence
  • big data and machine learning
  • ITS deployment experiences
  • ITS innovations
  • smart cities
  • connected vehicle systems
  • mobility as a service systems
  • shared mobility services
  • traffic management and control
  • sustainable transportation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 929 KiB  
Article
Prediction of Shipping Cost on Freight Brokerage Platform Using Machine Learning
by Hee-Seon Jang, Tai-Woo Chang and Seung-Han Kim
Sustainability 2023, 15(2), 1122; https://doi.org/10.3390/su15021122 - 6 Jan 2023
Cited by 5 | Viewed by 3646
Abstract
Not having an exact cost standard can present a problem for setting the shipping costs on a freight brokerage platform. Transport brokers who use their high market position to charge excessive commissions can also make it difficult to set rates. In addition, due [...] Read more.
Not having an exact cost standard can present a problem for setting the shipping costs on a freight brokerage platform. Transport brokers who use their high market position to charge excessive commissions can also make it difficult to set rates. In addition, due to the absence of a quantified fare policy, fares are undervalued relative to the labor input. Therefore, vehicle owners are working for less pay than their efforts. This study derives the main variables that influence the setting of the shipping costs and presents the recommended shipping cost given by a price prediction model using machine learning methods. The cost prediction model was built using four algorithms: multiple linear regression, deep neural network, XGBoost regression, and LightGBM regression. R-squared was used as the performance evaluation index. In view of the results of this study, LightGBM was chosen as the model with the greatest explanatory power and the fastest processing. Furthermore, the range of the predicted shipping costs was determined considering realistic usage patterns. The confidence interval was used as the method of calculation for the range of the predicted shipping costs, and, for this purpose, the dataset was classified using the K-fold cross-validation method. This paper could be used to set the shipping costs on freight brokerage platforms and to improve utilization rates. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems Application in Smart Cities)
►▼ Show Figures

Figure 1

19 pages, 4488 KiB  
Article
Experimental Method to Estimate the Density of Passengers on Urban Railway Platforms
by Paulo Aguayo, Sebastian Seriani, Jose Delpiano, Gonzalo Farias, Taku Fujiyama and Sergio A. Velastin
Sustainability 2023, 15(2), 1000; https://doi.org/10.3390/su15021000 - 5 Jan 2023
Cited by 3 | Viewed by 2562
Abstract
The platform–train interface (PTI) is considered a complex space where most interactions occur between passengers boarding and alighting. These interactions are critical under crowded conditions, affecting the experience of traveling and therefore the quality of life. The problem is that urban railway operators [...] Read more.
The platform–train interface (PTI) is considered a complex space where most interactions occur between passengers boarding and alighting. These interactions are critical under crowded conditions, affecting the experience of traveling and therefore the quality of life. The problem is that urban railway operators do not know what the density at the PTI is in real time, and therefore it is not possible to obtain a measure of the personal space of passengers boarding and alighting the train. To address this problem, a new method is developed to estimate the density of passengers on urban railway platforms using laboratory experiments. In those experiments, the use of computer vision is attractive, through the training of neural networks and image processing. The experiments considered a mock-up of a train carriage and its adjacent platform. In the boarding process, the results showed that the density using Voronoi polygons reached up to a 300% difference compared to the average values of density using Fruin’s Level of Service. However, in the case of alighting, that difference reached about 142% due to the space available for wheelchair users who needed assistance. These results would help practitioners to know where passengers are located at the PTI and, therefore, which part of the platform is more congested, requiring the implementation of crowd management measures in real time. Further studies need to include other types of passengers and different situations in existing stations. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems Application in Smart Cities)
►▼ Show Figures

Figure 1

17 pages, 5106 KiB  
Article
Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon
by Fan Ding, Jiwan Jiang, Yang Zhou, Ran Yi and Huachun Tan
Sustainability 2020, 12(23), 9955; https://doi.org/10.3390/su12239955 - 28 Nov 2020
Cited by 9 | Viewed by 2520
Abstract
With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not [...] Read more.
With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems Application in Smart Cities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop