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Urban Design, Urban Planning and Traffic Safety

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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: closed (20 March 2023) | Viewed by 11585

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Special Issue Editors

Dr. Mehdi Moeinaddini

E-Mail Website
Guest Editor

1. Institute of Computer Science, University of Tartu, 50090 Tartu, Estonia
2. Centre for Public Health, Queen's University Belfast, Belfast BT7 1NN, UK
Interests: traffic safety; walking; cycling; urban mobility

Dr. Mozhgan Pourmoradnasseri

E-Mail Website
Guest Editor

Institute of Computer Science, University of Tartu, 50090 Tartu, Estonia
Interests: human mobility; spatiotemporal data analysis; active mobility

Special Issue Information

Dear Colleagues,

Sustainable urban areas are encouraged in this day and age; however, promoting traffic safety is crucial for sustainable urban areas. Therefore, modern cities are seeking to implement policies ensuring sustainable urban mobility by reducing traffic-related crashes and fatalities. Traffic safety is related to several factors, such as the number of vehicles, road design, facilities, environment, vehicle characteristics, driver behavior, road users, etc. However, few studies have examined the relationship among urban design, planning-related factors, and traffic-related crashes and fatalities at the city level. Therefore, this Special Issue takes a closer look at this relationship, exploring effective design and planning strategies aiming to decrease fatalities at the city level, especially those of vulnerable road users. There is scant research available on vulnerable road users, such as cyclists and pedestrians, and more specifically children, the elderly, and the disabled. Thus, this issue will also focus on studies that identify effective indicators for non-motorized trips, such as walking and cycling, in addition to motorized trips. Crash prediction models operating at both the macrolevel and the microlevel can improve traffic safety analyses. In addition to microlevel models, these macrolevel models provide practical tools for engineers and planners that allow for proactive road safety planning. The relationship among urban design, planning-related factors, and traffic safety can also inform traffic safety enhancement strategies in different cities. This relationship can identify existing problems and inform potential solutions for reducing traffic-related crashes and fatalities. In addition, considering the correlation between urban planning indicators and traffic safety, macroscale planning decisions can be improved for new cities. Therefore, this issue discusses challenges facing traffic safety in urban areas and introduces new possible avenues for further studies in this field. This issue intends to fill the literature gap on cities and their effects on traffic safety. Accordingly, papers concerned with various planning and design elements related to mobility, public transport, parking design, and the movement of people and goods affecting traffic safety are welcome.

Dr. Mehdi Moeinaddini
Dr. Mozhgan Pourmoradnasseri
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

linking urban design and planning to traffic safety
traffic safety analysis
traffic safety monitoring and management
systematic methods in traffic safety assessment and planning
new sources of data and their use in traffic safety
advances in technologies and methods used in collecting data for traffic safety
new data and methods for improving the safety of pedestrians, bicyclists, e-scooters, etc.
traffic safety and automated and connected vehicle data

Published Papers (5 papers)

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Research

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17 pages, 3493 KiB

Open AccessEditor’s ChoiceArticle

Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership

by Zhiqiang Xu, Mahdi Aghaabbasi, Mujahid Ali and Elżbieta Macioszek

Sustainability 2022, 14(17), 11094; https://doi.org/10.3390/su141711094 - 05 Sep 2022

Cited by 8 | Viewed by 1799

Abstract

Predicting household vehicle ownership (HVO) is a crucial component of travel demand forecasting. Furthermore, reliable HVO prediction is critical for achieving sustainable transportation development objectives in an era of rapid urbanization. This research predicted the HVO using a support vector machine (SVM) model optimized using the Bayesian Optimization (BO) algorithm. BO is used to determine the optimal SVM parameter values. This hybrid model was applied to two datasets derived from the US National Household Travel Survey dataset. Thus, two optimized SVM models were developed, namely SVMBO#1 and SVMBO#2. Using the confusion matrix, accuracy, receiver operating characteristic (ROC), and area under the ROC, the outcomes of these two hybrid models were examined. Additionally, the results of hybrid SVM models were compared with those of other machine learning models. The results demonstrated that the BO algorithm enhanced the performance of the standard SVM model for predicting the HVO. The BO method determined the Gaussian kernel to be the optimal kernel function for both datasets. The performance of the SVM#1 model was improved by 4.27% and 5.16% for the training and testing phases, respectively. For SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, respectively. Moreover, the BO method enhanced the AUC of the SVM models used to predict the HVO. The hybrid SVM models also outperformed other machine learning models developed in this study. The findings of this study showed that SVM models hybridized with the BO algorithm can effectively predict the HVO and can be employed in the process of travel demand forecasting. Full article

(This article belongs to the Special Issue Urban Design, Urban Planning and Traffic Safety)

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21 pages, 1621 KiB

Open AccessArticle

Can Complete-Novice E-Bike Riders Be Trained to Detect Unmaterialized Traffic Hazards in the Urban Environment? An Exploratory Study

by Anat Meir

Sustainability 2022, 14(17), 10869; https://doi.org/10.3390/su141710869 - 31 Aug 2022

Cited by 2 | Viewed by 1337

Abstract

Although hazard perception is an important skill found to be crucial for negotiating traffic among various types of road users, few studies have systematically investigated e-bike riders’ ability to perceive potential unmaterialized hazardous situations or aimed to enhance these abilities through training. The present study explored the formation of two hazard perception training interventions based upon exposing young complete-novice e-bike riders to a vast array of materialized traffic hazards, with the aim of evaluating their effectiveness in enriching the ability to anticipate unmaterialized hazards. Young complete-novice e-bike riders were allocated into one of two intervention modes (‘Act and Anticipate Training’ or ‘Predictive and Commentary Training’) or a control group (ten in each group). AAT members underwent a theoretical tutorial, then observed clips depicting real-time hazardous situations footage taken from an e-bike rider’s perspective and were asked to perform a hazard detection task. PCT members underwent a theoretical tutorial, then a ‘what might happen next?’ task, followed by observation of video footage with expert commentary. A week later, participants were requested to complete a hazard perception test, during which they viewed ten videos and pressed a response button whenever they identified a hazardous situation. Overall, participants in both interventions were more aware of potential unmaterialized hazards compared to the control in both their response sensitivity and verbal descriptions. Trainees were responsive to the developed training interventions. Thus, actively detecting materialized hazards may produce effective training that enriches these road users’ hazard perception skills and allows them to safely negotiate traffic. Advantages of each of the training methodologies along with implications for intervention strategies, licensing, and policy development are discussed. Full article

(This article belongs to the Special Issue Urban Design, Urban Planning and Traffic Safety)

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18 pages, 4841 KiB

Open AccessArticle

Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestrians

by Lei Yang, Mahdi Aghaabbasi, Mujahid Ali, Amin Jan, Belgacem Bouallegue, Muhammad Faisal Javed and Nermin M. Salem

Sustainability 2022, 14(17), 10467; https://doi.org/10.3390/su141710467 - 23 Aug 2022

Cited by 9 | Viewed by 2038

Abstract

Over the past three decades, more than 8000 pedestrians have been killed in Australia due to vehicular crashes. There is a general assumption that pedestrians are often the most vulnerable to crashes. Sustainable transportation goals are at odds with the high risk of pedestrian fatalities and injuries in car crashes. It is imperative that the reasons for pedestrian injuries be identified if we are to improve the safety of this group of road users who are particularly susceptible. These results were obtained mostly through the use of well-established statistical approaches. A lack of flexibility in managing outliers, incomplete, or inconsistent data, as well as rigid pre-assumptions, have been criticized in these models. This study employed three well-known machine learning models to predict road-crash-related pedestrian fatalities (RCPF). These models included support vector machines (SVM), ensemble decision trees (EDT), and k-nearest neighbors (KNN). These models were hybridized with a Bayesian optimization (BO) algorithm to find the optimum values of their hyperparameters, which are extremely important to accurately predict the RCPF. The findings of this study show that all the three models’ performance was improved using the BO. The KNN model had the highest improvement in accuracy (+11%) after the BO was applied to it. However, the ultimate accuracy of the SVM and EDT models was higher than that of the KNN model. This study establishes the framework for employing optimized machine learning techniques to reduce pedestrian fatalities in traffic accidents. Full article

(This article belongs to the Special Issue Urban Design, Urban Planning and Traffic Safety)

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Review

Jump to: Research

19 pages, 631 KiB

Open AccessReview

A Systematic Review of Factors Influencing the Vitality of Public Open Spaces: A Novel Perspective Using Social–Ecological Model (SEM)

by Danning Zhang, Gabriel Hoh Teck Ling, Siti Hajar binti Misnan and Minglu Fang

Sustainability 2023, 15(6), 5235; https://doi.org/10.3390/su15065235 - 15 Mar 2023

Viewed by 2203

Abstract

A number of studies address the spatial planning, architectural design, and management of public open spaces (POSs) to curb the overuse of spaces to create high-quality spaces. Little attention has been paid to the problem of underutilization of POSs. Therefore, this paper undertakes a comprehensive analysis of the literature on the factors that influence the vitality of POSs, proposing Bronfenbrenner’s social–ecological model (SEM) as a conceptual framework. In this work, we conducted a systematic literature search using the PRISMA method to screen and select articles from three major databases (Science Web, Elsevier, and Scopus). Thirty-four journal articles from 2000 to 2022 were selected for the final review. This study systematically identifies and classifies a set of variables related to the vitality of POSs and develops an SEM-based framework of factors that influence the vitality of POSs. The framework examines the influence of individual user characteristics, the social environment, the physical environment, and the political environment on the vitality of POSs. Finally, strategies to improve the vitality of POSs are proposed: (1) POSs’ optimization and promotion strategies should be developed gradually, starting from the most basic needs, stage by stage; (2) To improve the vitality of POSs, we should consider both the general public and special groups; (3) Through the synergistic effect between social, material, and policy environments, the comprehensive improvement of POSs’ vitality is achieved. This study provides the latest insights into the vitality of POSs and makes a theoretical contribution by conceptualizing the SEM framework and summarizing the influencing factors at different levels. The study of these factors should also have practical implications, as the results will ultimately provide improvement strategies to help policy-makers and local communities to effectively and sustainably improve the vitality of POSs. Full article

(This article belongs to the Special Issue Urban Design, Urban Planning and Traffic Safety)

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17 pages, 2460 KiB

Open AccessEditor’s ChoiceReview

Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership

by Lidong Zhu, Mujahid Ali, Elżbieta Macioszek, Mahdi Aghaabbasi and Amin Jan

Sustainability 2022, 14(10), 5795; https://doi.org/10.3390/su14105795 - 11 May 2022

Cited by 5 | Viewed by 2891

Abstract

Bike-sharing is known as a sustainable form of transportation. This travel mode is able to tackle the “last mile” transit issue and deliver financial, well-being, and low-carbon lifestyle advantages to users. To date, many studies have analysed the influence of various factors, including built environments, on bike-sharing ridership. However, no study has exclusively synthesised these findings regarding the association between built-environment attributes and bike-sharing ridership. Thus, in this study, a systematic literature review was conducted on 39 eligible studies. These studies were assessed with respect to (1) bike-sharing usage, (2) studies’ geographical distribution, (3) data collection and analysis method, and (4) built environment factor type. Most studies were carried out in the US and Chinese cities. Variables associated with diversity, density, and distance to public transport stations and public transport infrastructure were frequently employed by the studies reviewed. It was found that BS stations with an average capacity of 24.63 docks and street network systems with an average length of 12.57 km of cycling lanes had a significant impact on the bike-sharing ridership. The findings of these studies were combined, and a series of recommendations were proposed based on them for bike-sharing service providers and researchers in academia. The findings of this evaluation can help practitioners and scholars understand the important built environment elements that influence bike-sharing ridership. Knowledge in this field will enable bike-sharing service providers to direct their resources sufficiently to enhance the more essential aspects of bike-sharing users’ satisfaction. Full article

(This article belongs to the Special Issue Urban Design, Urban Planning and Traffic Safety)

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