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Methods and Measures to Improve Road Safety and Travel Efficiency in Sustainable Urban Transport Management

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A topical collection in Sustainability (ISSN 2071-1050). This collection belongs to the section "Sustainable Transportation".

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Dr. Jacek Oskarbski

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Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
Interests: traffic modelling and forecasting; transport planning; intelligent transportation systems; traffic engineering and mobility management
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Prof. Dr. Kyandoghere Kyamakya

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Institute for Smart Systems Technologies, University Klagenfurt, A9020 Klagenfurt, Austria
Interests: intelligent transport systems; telecommunications; neuro-computing; machine learning and pattern recognition; nonlinear dynamics
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Dr. Miroslava Mikušová

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Department of Road and Urban Transport, Faculty of Operation and Economics of Transport and Communications, University of Žilina, 010 26 Žilina, Slovakia
Interests: operation and economics of road and urban transport; urban road safety; smart cities; ITS
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Dr. Stanisław Iwan

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Collection Editor

Faculty of Economics and Transport Engineering, Maritime University of Szczecin, 70-507 Szczecin, Poland
Interests: telematics in transport and logistics; intelligent transportation systems; urban freight transport; sustainable transport systems
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Dr. Krzysztof Małecki

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Faculty of Computer Science, West Pomeranian University of Technology, 52, 71-210 Szczecin, Poland
Interests: computer simulation; cellular automata; multi-agent systems; complex systems; modeling and simulation of transport systems and processes; transportation systems modeling
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Topical Collection Information

Dear Colleagues,

Many cities have modernized and developed their road transport networks to focus on improving the efficiency of motor vehicle traffic and minimizing congestion. Such activities have usually been aimed at increasing the capacity of road network elements. However, congestion can also be reduced more rationally by introducing methods and measures to change the modal split and increase the share of other modes of transport on daily trips. Currently, cities are transforming selected areas to create urban spaces that are friendly to unprotected traffic users—pedestrians and cyclists (including the elderly and disabled)—and introducing measures to improve the accessibility and effectiveness of public transport. At the same time, according to the United Nations, road safety is also the key to achieving Sustainable Development Goals, yet the complexity of how road accidents occur makes this a difficult challenge, leaving many cities struggling with the problem. Technological development also creates opportunities for the implementation of intelligent transport systems services that support modal shift, accessibility management, congestion reduction, and improvement of traffic safety. An increasing number of travelers use multiple modes of mobility (bike-and-ride, park-and-ride, sharing services, Mobility as a Service).

The emerging challenges in shaping changes in urban mobility patterns and mobility management require the development of methods and tools to assess the effectiveness of improvements in terms of travel conditions, road traffic safety, and the environmental impact of transport. It is also necessary to develop principles and guidelines for the safe and efficient use of transport modes that are increasingly used in urban spaces, such as scooters, cargo bikes, and electric bikes.

This Special Issue aims to answer the questions of how to plan urban transport under the circumstances described above and how to reduce the need to travel by motor vehicles while ensuring a higher level of road traffic safety through strategic planning and operational activities.

This Special Issue will help researchers and practitioners understand the impact of current trends in changing mobility patterns, new technologies, and the challenges of transforming public spaces in cities on travel efficiency and traffic safety improvement, as well as identify the barriers, analytical tools, and techniques required in this area.

Given the established objectives of this Special Issue, we invite scholars and practitioners to submit their original research or review articles. Potential topics of interest include, but are not limited to, the following:

Transport demand management
Reduction of congestion in urban space
Road traffic safety management in urban areas
Traffic management systems that improve mobility and/or safety
Mobility as a Service
New modes of transport in urban space
Intelligent transportation systems services and road infrastructure that improve road safety and mobility
New technologies for road safety and mobility management
Vulnerable road users’ safety and mobility
Methodologies, practices, and policies for achieving behavioral change
Urban mobility versus road safety
New methods in road safety and/or mobility data collection and analysis
Effects of large-scale events (e.g., pandemic) on road traffic safety and/or mobility

Assoc. Prof. Dr. Jacek Oskarbski
Dr. Krzysztof Małecki
Dr. Stanisław Iwan
Prof. Dr. Kyandoghere Kyamakya
Dr. Miroslava Mikušová
Collection 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 collection 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

road traffic safety analysis
sustainable safety
smart mobility
intelligent transport systems services
Mobility as a Service
data analytics
cooperative intelligent transport systems
mobility/traffic data collection and analysis
sustainable transportation
transportation safety
safety management
transportation management
congestion

Published Papers (11 papers)

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2022

Jump to: 2021

13 pages, 1281 KiB

Open AccessArticle

The Use of Mobile Phones and Other Unsafe Behavior While Cycling in the Metropolitan Area of Mexico City

by Jaime Santos-Reyes, Yareli Pastenes-Medina and Diego Padilla-Pérez

Sustainability 2023, 15(1), 61; https://doi.org/10.3390/su15010061 - 21 Dec 2022

Viewed by 1491

Abstract

Unsafe behavior while driving contributes to road accidents. The paper addresses cyclists’ risky behavior by employing a questionnaire-based survey to a sample of n = 1136 in the metropolitan area of Mexico City. The main results are as follows: (a) 31.4% and 24.2% of the participants use a mobile phone for talking and text messaging while cycling, respectively, with males engaging in these unsafe acts more often than females; (b) a high percentage of participants are most likely to communicate with their parents, through either talking (48.9%) or text messaging (39.6%); (c) regarding the use of mobile phones for talking/texting (along with social network and gender) as predictors of a crash/fall while cycling, it was found that a one SD change in the frequency of talking while cycling increased the odds of a crash/fall by a factor of 1.198, as did a one SD increase in the frequency of texting by 1.232, while gender contributed to the outcome but not the individuals to whom cyclists talk or text; (d) cycling “without holding the handlebars” contributed significantly to the outcome. An education campaign or legislation enforcement (or both) may be needed to prevent single-bicycle crashes related to this unsafe act. Full article

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36 pages, 3158 KiB

Open AccessArticle

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

by Darcin Akin, Virginia P. Sisiopiku, Ali H. Alateah, Ali O. Almonbhi, Mohammed M. H. Al-Tholaia and Khaled A. Alawi Al-Sodani

Sustainability 2022, 14(24), 16654; https://doi.org/10.3390/su142416654 - 12 Dec 2022

Cited by 3 | Viewed by 2275

Abstract

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed. Full article

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31 pages, 6736 KiB

Open AccessArticle

A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

by Umair Hasan, Andrew Whyte and Hamad AlJassmi

Sustainability 2022, 14(23), 15657; https://doi.org/10.3390/su142315657 - 24 Nov 2022

Cited by 5 | Viewed by 1799

Abstract

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies. Full article

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16 pages, 3652 KiB

Open AccessArticle

Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data

by Abdul Rashid Mussah and Yaw Adu-Gyamfi

Sustainability 2022, 14(22), 15348; https://doi.org/10.3390/su142215348 - 18 Nov 2022

Cited by 3 | Viewed by 1783

Abstract

Assessment of roadway safety in real-time is a necessary component for providing proactive safety countermeasures to ensure the continued safety and efficiency of roadways. A framework for utilizing data from connected vehicles and other probe sources is proposed in this study. Connected vehicles present an opportunity to provide live fingerprinting and activity monitoring on roadways. Taking advantage of high-resolution trajectory data streaming directly from connected vehicles, variables are extracted and the relationship with crashes are explored utilizing statistical and machine learning models. Hard acceleration events, in conjunction with segment miles are shown to have strong positive correlations with historical crash outcomes as proven by OLS, Poisson and Gradient Booster regression models. An XGBoost classification model is then trained to predict the real-time instances of crash outcomes at 5 min temporal bins with high levels of accuracy when trained with data including the real-time segment speed, reference speed, segment miles, a segment crash risk factor and other variables related to the difference in speeds between consecutive segments as well as the hour of the day. A weighted ensemble model achieved the best performance with an accuracy of 0.95. The results present evidence that the framework can capitalize on the richness of data available via connected vehicles and is implementable as a component in Advanced Traffic Management Systems for the analysis of safety critical situations in real-time. Full article

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20 pages, 4027 KiB

Open AccessArticle

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features

by Balaji Ganesh Rajagopal, Manish Kumar, Pijush Samui, Mosbeh R. Kaloop and Usama Elrawy Shahdah

Sustainability 2022, 14(21), 14049; https://doi.org/10.3390/su142114049 - 28 Oct 2022

Cited by 2 | Viewed by 1328

Abstract

Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset. Full article

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20 pages, 895 KiB

Open AccessArticle

Perceptions of Parents of the Quality of the Public Transport Services Used by Children to Commute to School

by Sajjakaj Jomnonkwao, Chinnakrit Banyong, Supanida Nanthawong, Thananya Janhuaton, Vatanavongs Ratanavaraha, Thanapong Champahom and Pornsiri Jongkol

Sustainability 2022, 14(20), 13005; https://doi.org/10.3390/su142013005 - 11 Oct 2022

Cited by 1 | Viewed by 2010

Abstract

The risk of accidents is a danger in public transport that could lead to threats to property, the environment, and the lives and health of people. In particular, parents are keenly aware of and concerned about the safety of school trips. Thus, this study aims to examine the factors that influence the perceptions of parents about the safety of the school trips of children. The study recruited 750 respondents from Northeast Thailand. Data were obtained from responses to a self-report questionnaire. The model consisted of six factors, namely, transportation satisfaction, infrastructure, information, the safe behavior of drivers, the safety of the transportation systems, and the safety policy. The results of the confirmatory factor analysis indicated that all six factors were in accordance with the empirical data (model fit statistic: χ² = 758.098, df = 276, χ²/df = 2.747, CFI = 0.962, TLI = 0.955, SRMR = 0.038, RMSEA = 0.048). The results can serve as a reference for developing guidelines and recommending policies for the management and the implementation of safe school trips for students. Full article

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17 pages, 2811 KiB

Open AccessArticle

Fuzzy Analytic Hierarchy Process Used to Determine the Significance of the Contributing Factors for Generalized Travel Satisfaction

by Lin Zhao, Hongzhen Zhu, Dongmei Liu, Liping Yang and Xiaohua Zhao

Sustainability 2022, 14(18), 11509; https://doi.org/10.3390/su141811509 - 14 Sep 2022

Viewed by 1109

Abstract

This study develops the Generalized Satisfaction with Travel Scale covering travelers’ all-round emotional experience and cognitive evaluation. After checking the validity by factor analysis, the key influencing factors are extracted by optimal scale regression, and then the influence degree of key influencing factors is determined based on the Analytic Hierarchy Process. The reliability and validity of the developed Generalized Satisfaction with Travel Scale meet the requirements. The dynamic travel parameters (travel pattern, travel duration, etc.) (βs₁ = 0.448) have the most important impact on generalized travel satisfaction, followed by the demographic information (age, occupation, etc.) (βs₂ = 0.220) and static travel parameters (travel period, travel purpose, etc.) (βs₃ = 0.178), whereas the main travel areas (residential area, work/study area) (βs₄ = 0.154) have the weakest influence. This study aims at developing a Generalized Satisfaction with Travel Scale for Chinese travelers and exploring the influencing factors so as to provide an efficient travel experience survey mechanism for relevant departments. Full article

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2021

Jump to: 2022

21 pages, 2816 KiB

Open AccessArticle

Contextual Route Recommendation System in Heterogeneous Traffic Flow

by Surya Michrandi Nasution, Emir Husni, Kuspriyanto Kuspriyanto, Rahadian Yusuf and Bernardo Nugroho Yahya

Sustainability 2021, 13(23), 13191; https://doi.org/10.3390/su132313191 - 29 Nov 2021

Cited by 4 | Viewed by 2354

Abstract

The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value). Full article

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18 pages, 2443 KiB

Open AccessArticle

An Interactive Model Based on a Mobile Application and Augmented Reality as a Tool to Support Safe and Efficient Mobility of People with Visual Limitations in Sustainable Urban Environments

by Edgar Herberto Medina-Sanchez, Miroslava Mikusova and Mauro Callejas-Cuervo

Sustainability 2021, 13(17), 9973; https://doi.org/10.3390/su13179973 - 06 Sep 2021

Cited by 6 | Viewed by 2340

Abstract

An increasing availability and reliability of open-source geographical resources, options in design of mobile applications together with smartphones of a high quality, featuring top cameras and number of sensors, bring us an extraordinary opportunity to provide the visually impaired people with relevant and comprehensible information on their vicinity, and thus to improve their mobility in a sustainable environment. The paper presents an interactive tool based on a mobile application created for mobile devices with Android operation system, and on using the augmented reality. It is a tool to support safe and efficient mobility of blind people and people with severe visual limitations in a sustainable urban environment. The essential benefit from using this tool lies in preventing risky, possibly dangerous and hardly accessible places. The first part briefly presents the problem of the visually impaired including the forms of the visual impairment, personal and economic costs for the entire society and the importance of improving the mobility of this group of people. The second part of the paper introduces the current state of the problem being solved as well as some basic tools which were developed to bring the surrounding environment closer to the visually impaired. Further, the process of the mobile application development is described. The application is meant to indicate information on places where the visually impaired users of the application are present while walking in an external environment (including the distance to their destination); the pilot testing of the application by a selected groups of the visually impaired is introduced, too. Full article

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19 pages, 1400 KiB

Open AccessArticle

Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

by Ainhoa Serna, Aitor Soroa and Rodrigo Agerri

Sustainability 2021, 13(4), 2397; https://doi.org/10.3390/su13042397 - 23 Feb 2021

Cited by 5 | Viewed by 3165

Abstract

Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset. Full article

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17 pages, 2873 KiB

Open AccessArticle

Behavior Evolution of Multi-Group in the Process of Pedestrian Crossing Based on Evolutionary Game Theory

by Ran Zhang, Zhonghua Wei, Heng Gu and Shi Qiu

Sustainability 2021, 13(4), 2009; https://doi.org/10.3390/su13042009 - 13 Feb 2021

Cited by 7 | Viewed by 2171

Abstract

The mixed traffic flow has an increasingly impact on the operation of urban traffic. To study the evolution law of multi-group behaviors in pedestrian crossing, we used the evolutionary game theory to establish a multi-group behavior evolution model for pedestrian crossing. The process of concern started from the risk perception and multi-group behavior choice. The evolutionary stability strategies, evolution trends, and factors affecting the evolutionary path of multi-group behaviors are discussed in this paper. This study found that evolutionary strategy equilibrium of pedestrians, drivers, and traffic managers not only relied on their own earning, but also on those of the other two groups. The factors affecting its behavior included the revenue factor and the limiting factor. Evolutionary game theory was used to analyze the multi-group interaction behavior of pedestrians, vehicle drivers, and traffic managers in the process of pedestrian crossing, as well as to analyze the behavior of traffic subjects in the process of pedestrian crossing. This paper provides a basis for decision-making for the traffic management department to manage road traffic, offering a new idea from the perspective of evolution for solving the conflict of interest at the crosswalk of the road section. Full article

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Methods and Measures to Improve Road Safety and Travel Efficiency in Sustainable Urban Transport Management

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Published Papers (11 papers)

2022

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2021

Jump to: 2022

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