Abstract Drivers’ behavior is one of the most important factors affecting road transportation safety. In particular, studying this issue in relation to young people aged 25 and below becomes more sensitive because they are not experienced and there are some age-related elements. Moreover, a significant percentage of beginner drivers fall into this age category, which can lead to risky behavior. Overconfidence, indiscipline, careless driving, or speeding tendencies may contribute greatly to their vulnerability to hazards on the roads. Hence, there is a need for further research to establish the constraints and possible risks involved with young drivers to improve road safety. Hence, this study aims to analyze the potential hazards associated with youth driving behavior in order to facilitate the development of relevant remedies through a thorough understanding of their behavior for safe transportation on the roads. To achieve this goal, a multi-criteria decision-making approach has been used. The proposed approach uses measurement of options and ranking based on the compromise solution method in an intuitive fuzzy environment to evaluate and rank risks. In addition, through consultation with experts and experienced technicians, a selection of 17 potential hazards were identified from existing risk factors. These risks are classified into three groups: working on the phone, distractions, and non-compliance. The present stud shows that risky driving and driving in reverse represent the highest level of risk, while speeding represents the lowest level of risk among young drivers.

Smart roads are a key component of intelligent transportation. Strict implementation of road smartification measures can help solve many of the traffic problems societies face today. These measures address new challenges of urbanization, reduce traffic and pollution, improve road safety, and bolster the economy through the enhancement of road networks. The objective of this study is to develop a robust hybrid framework to evaluate and rank potential smartification measures, keeping sustainable mobility goals in mind. For this purpose, the roads network in Sicily, Southern Italy, was considered as the case study, and 27 potential smartification measures were selected. The study proposed a robust hybrid framework that integrates the Stepwise Weight Assessment Ratio Analysis (SWARA) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, based on a Spherical Fuzzy (SF) set. The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking smartening measures, and the correlation coefficient of each approach was calculated. The findings demonstrated the superiority of the proposed approach in ranking and assessing potential smartification measures. According to the results, the traffic conditions reporting emerged as the most effective smartification method for the case study roads, while the green islands for charging electric vehicles was found to be the least effective.

Urban transportation plays a crucial role in cities that host sports events. Mexico City, as one of the World Cup 2026 hosts, is one of the world's largest and most populous cities, with a population of over 22 million people. This city faces several challenges related to urban transportation. The purpose of this study is to develop a novel group decision-making framework for evaluating six sustainable alternatives for the management of urban transportation crises. Our proposed group decision framework develops the Base Criterion Method (BCM) and Combined Compromise Solution (CoCoSo) under the fuzzy ZE-numbers for the first time in the literature to obtain reliable decisions. Based on the opinions of decision-makers and expert votes on those opinions, the proposed approach offers a unique feature in decision sciences in that the reliability of decisions can be increased in two stages. Also, sensitive analyses were executed for each of the urban transportation alternatives based on the different states of the criteria groups. According to the findings, the optimum plan should be an investment in the Metro and electric Minibusses development to manage the urban transportation crisis in Mexico City. Also, findings show that applying the fuzzy ZE-numbers leads to more accurate and reliable results.

The academic and technical sectors of road transportation both find analysis of road network performance to be an essential area of research. There has been an increase in recent years in the number of studies investigating the function and potential applications of machine learning (ML) in the analysis of performance on road networks. Although machine learning has been shown to be useful in assessing performance on road networks, there is still a dearth of in-depth quantitative and qualitative research on the topic. The key goal of this research is to assess machine learning's quantitative and qualitative applications in transportation engineering's study of driver behavior analysis. In this paper, we looked at the research that has been done on how to use machine learning (ML) to study driving behavior analysis (DBA) from 1995 to 2022. All of the reviewed studies in this research were taken from the Web of Science (WOS) platform and analyzed. The analysis and discussions in this study try to provide a broad view of the changes that have occurred in the development process of these studies to other researchers and can be useful for showing the opportunities and challenges confronted by researchers in the use of ML in the analysis of driver behavior.

Crashes and the resulting damage are major issues in most countries' road transportation. Several factors play a role in causing road crashes and have been identified by researchers. One of the most significant factors in causing road accidents is the role of humans as drivers. Human errors play a key role in road crashes. So, the main aim of this research is to find, study, and rank some of the most significant human factors so that, with a good understanding of how important they are, solutions can be found to reduce road crashes. For this purpose, after examining and identifying the most common human factors, 15 factors that can have the greatest impact on road accidents were selected. An evaluation model was also used to evaluate and rank these factors using a combination of the Fuzzy Delphi Method (FDM) and the Analytic Hierarchy Process (AHP), which is one of the best ways to make decisions. The study was conducted on urban roads in Cosenza, located in the southern region of Italy. The obtained results indicated that the Drug and alcohol use (A9) and the Gender (A2) had the greatest and least impact on causing road crashes, respectively. Finally, some solutions were proposed to improve road safety in current situation.

Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios.

There are a lot of elements that make road safety assessment situations unpredictable and hard to understand. This could put people's lives in danger, hurt the mental health of a society, and cause permanent financial and human losses. Due to the ambiguity and uncertainty of the risk assessment process, a multi-criteria decision-making technique for dealing with complex systems that involves choosing one of many options is an important strategy of assessing road safety. In this study, an integrated stepwise weight assessment ratio analysis (SWARA) with measurement of alternatives and ranking according to compromise solution (MARCOS) approach under a spherical fuzzy (SF) set was considered. Then, the proposed methodology was applied to develop the approach of failure mode and effect analysis (FMEA) for rural roads in Cosenza, southern Italy. Also, the results of modified FMEA by SF-SWARA-MARCOS were compared with the results of conventional FMEA. The risk score results demonstrated that the source of risk (human) plays a significant role in crashes compared to other sources of risk. The two risks, including landslides and floods, had the lowest values among the factors affecting rural road safety in Calabria, respectively. The correlation between scenario outcomes and main ranking orders in weight values was also investigated. This study was done in line with the goals of sustainable development and the goal of sustainable mobility, which was to find risks and lower the number of accidents on the road. As a result, it is thus essential to reconsider laws and measures necessary to reduce human risks on the regional road network of Calabria to improve road safety.

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog‐based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution‐Map‐Transfer‐Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision‐Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.