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Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5055

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Special Issue Editors

Dr. Sunghoon Kim

E-Mail Website1 Website2
Guest Editor

Korea Transport Institute, Sejong 30147, Republic of Korea
Interests: smart city; smart mobility; intelligent transportation system; traffic control; traffic simulation

Dr. Sehyun Tak

E-Mail Website1 Website2
Guest Editor

Korea Transport Institute, Sejong 30147, Republic of Korea
Interests: automated vehicles; intelligent transportation system; traffic control
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Special Issue Information

Dear Colleagues,

Smart cities are being promoted in many countries to solve existing urban issues, enhance the sustainability of cities, and improve the quality of citizens’ lifestyles by using innovative technologies. Smart cities deal with numerous urban issues, such as energy, health care, education and transportation. The common major issue in the concept of smart cities provided by many countries is “mobility”, which focuses on providing convenient transportation services to individuals and saving the environment. These days, mobility services are evolving from the existing transportation system, which includes shared vehicles, shared micromobility, demand responsive transit, and automated vehicles. In order to introduce and operate these new mobility services, innovative strategies and detailed technologies are required, particularly with the aid of data science and artificial intelligence.

This Special Issue aims to provide selected contributions on advances in active traveling and transportation planning in the field of smart cities, by showcasing innovative ideas for introducing and operating various mobility services for improving the convenience of individuals and saving the environment.

Potential topics include (but are not limited to):

Planning or operating shared micromobility services;
Planning or operating demand responsive transit (DRT) services;
Automated vehicle operations in mixed traffic;
Mobility as a Service (MaaS) operation;
Planning or operating urban air mobility (UAM) services;
Smart intersection operation;
Smart crosswalk operation;
Pedestrian safety.

We look forward to receiving your contributions.

Dr. Sunghoon Kim
Dr. Sehyun Tak
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

smart mobility
shred mobility services
intelligent transportation systems
data science
artificial intelligence

Published Papers (5 papers)

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Research

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15 pages, 255 KiB

Open AccessArticle

Measuring the Effect of Built Environment on Students’ School Trip Method Using Neighborhood Environment Walkability Scale

by Saeed Esmaeli, Kayvan Aghabayk and Nirajan Shiwakoti

Sustainability 2024, 16(5), 1937; https://doi.org/10.3390/su16051937 - 27 Feb 2024

Viewed by 477

Abstract

School trips affect different aspects, such as air pollution and urban traffic, and of personal wellbeing, such as students’ physical and mental health. The increasing concern about environmental sustainability has prompted a reevaluation of daily activities, including school transportation. While different factors that affect students’ school trips have been investigated in the literature, the effect of the built environment has been evaluated only sporadically in previous studies. To fulfil this knowledge gap, this study aims to investigate the effect of the built environment on students’ school trips by adapting and extending the well-known Neighborhood Environment Walkability Scale (NEWS) questionnaire. The questionnaire survey was conducted with parents from 36 schools in Yazd, Iran, providing a sample of 1688 students aged 7–18 years. The items from the NEWS questionnaire were placed in nine factors by performing factor analysis. The Multinomial Logit Regression model was applied to check the predictive power of these nine factors. It was found that the variables of land use mix-diversity, land use mix-access, crime, age, gender, household income and car ownership had a significant effect on students’ school trips. The more easily students have access to different places, the less they use public services and cars compared with the active travel mode. The use of public services and cars increases with the increase in crime rate along the route to school. The findings indicate that built environment features may impact students’ shift from traditional transportation modes to active alternatives, such as walking and cycling, contributing to the attainment of broader sustainability objectives. Full article

(This article belongs to the Special Issue Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future)

15 pages, 1429 KiB

Open AccessArticle

Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran

by Mohammadhanif Dasoomi, Ali Naderan and Tofigh Allahviranloo

Sustainability 2023, 15(20), 14764; https://doi.org/10.3390/su152014764 - 11 Oct 2023

Cited by 1 | Viewed by 879

Abstract

This study examines the determinants of online and offline shopping trip choices and their implications for urban transportation, the environment, and the economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1000 active e-commerce users who successfully placed orders through both online and offline services in districts 2 and 5 of Tehran during the last 20 days of 2021. A deep neural network model was applied to predict the type of shopping trips based on 10 variables including age, gender, car ownership, delivery cost, and product price. The model’s performance was evaluated against four other algorithms: MLP, decision tree, LSTM, and KNN. The results demonstrated that the deep neural network model achieved the highest accuracy, with a rate of 95.73%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. It aims to help them design effective strategies to reduce transportation costs, lower pollutant emissions, alleviate urban traffic congestion, and enhance user satisfaction all while promoting sustainable development. Full article

(This article belongs to the Special Issue Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future)

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19 pages, 5305 KiB

Open AccessArticle

Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities

by Malliga Subramanian, Jaehyuk Cho, Sathishkumar Veerappampalayam Easwaramoorthy, Akash Murugesan and Ramya Chinnasamy

Sustainability 2023, 15(18), 13840; https://doi.org/10.3390/su151813840 - 18 Sep 2023

Cited by 1 | Viewed by 1429

Abstract

Due to global ecological restrictions, cities, particularly urban transportation, must choose ecological solutions. Sustainable bike-sharing systems (BSS) have become an important element in the worldwide transportation infrastructure as an alternative to fossil-fuel-powered cars in metropolitan areas. Nevertheless, the placement of docks, which are the parking areas for bikes, depends on accessibility to bike paths, population density, difficulty in bike mobility, commuting cost, the spread of docks, and route imbalance. The purpose of this study is to compare the performance of various time series and machine learning algorithms for predicting bike demand using a two-year historical log from the Capital Bikeshare system in Washington, DC, USA. Specifically, the algorithms tested are LSTM, GRU, RF, ARIMA, and SARIMA, and their performance is then measured using the MSE, MAE, and RMSE metrics. The study found GRU performed the best, with RF also producing reasonably accurate predictions. ARIMA and SARIMA models produced less accurate predictions, likely due to their assumptions of linearity and stationarity in the data. In summary, this research offers significant insights into the efficacy of diverse algorithms in forecasting bike demand, thereby contributing to future research in the field. Full article

(This article belongs to the Special Issue Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future)

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21 pages, 3809 KiB

Open AccessArticle

Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea

by Junhee Kang, Sehyun Tak and Sungjin Park

Sustainability 2023, 15(16), 12655; https://doi.org/10.3390/su151612655 - 21 Aug 2023

Viewed by 1066

Abstract

This paper analyzes the impact of C-ITS service on driving behavior, focusing on a pilot project in Daejeon–Sejong, South Korea. C-ITS, an advanced technology, enables bidirectional wireless communication between vehicles or infrastructure, allowing for real-time traffic data collection and dissemination. The study uses a unique analytical method, employing parallel processing techniques for variable extraction and a paired t-test to examine the short-term effects of C-ITS on driving behavior. Findings indicate a significant change in drivers’ behavior, particularly in average speed, hard braking rate, severe deceleration rate, speeding rate, and excessive speeding rate, towards safer trends after receiving C-ITS warning services. Reductions in hard braking and severe deceleration were immediate after C-ITS service initiation, while a decrease in excessive speeding was observed after four months. Further research is needed to identify other potential influencing variables and provide an unbiased evaluation of C-ITS effectiveness. The study’s implications highlight its role in promoting public acceptance of C-ITS-service-based cooperative autonomous driving strategies. Full article

(This article belongs to the Special Issue Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future)

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25 pages, 4546 KiB

Open AccessReview

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

by Zhengbo Hao, Yizhe Wang and Xiaoguang Yang

Sustainability 2024, 16(7), 2917; https://doi.org/10.3390/su16072917 - 31 Mar 2024

Viewed by 594

Abstract

Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment. Full article

(This article belongs to the Special Issue Advances in Active Travel and Transportation Planning in Smart Cities: Past, Present and Future)

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