Special Issue: “Fuzzy Logic Applications in Traffic and Transportation Engineering”

1. Introduction

Traffic and transportation engineering involves the application of scientific principles to the planning, design, and operation of facilities for any mode of transportation and to human resource management in industry, which are required to enable the safe, efficient, economical, and environmentally compatible movement of people and goods. Bearing in mind the complexity of the processes at work in transportation engineering, fuzzy logic is a convenient tool for the modeling of these processes. A detailed description of the mathematics of fuzzy set theory was presented by Zadeh [1] in 1975. Fuzzy logic is based on human-like reasoning that is approximate rather than precise, which provides various possibilities for applications in the transportation field, a domain characterized by constant transformations that often lead to uncertainty and imprecision. A particular advantage of fuzzy systems is the ability to include multiple goals in calculations and, using adequate optimization algorithms, to achieve a high level of similarity to real-world phenomena.

This Special Issue of the journal Mathematics, titled “Fuzzy Logic Applications in Traffic and Transportation Engineering”, is devoted to examples of implementing fuzzy logic to solve various traffic and transportation engineering problems, for all modes of transportation, including road, rail, air, and waterborne transport, the postal and logistics industries, and telecommunications. The Special Issue’s Guest Editors were inspired by the current gap in the literature regarding this topic and the realization of the enormous potential of fuzzy logic for applications within the field of transportation. The Guest Editors’ previous research within this field of study involved modeling driver behavior using fuzzy inference systems and optimizing them using metaheuristic algorithms. Additionally, previous research has incorporated fuzzy sets into decision-making theory.

2. Contributions

This Special Issue contains 11 papers. The 46 authors represent 15 countries, with 4 authors listing multiple affiliations. The research results were obtained on three continents: Africa, Asia, and Europe. The authors’ geographical distribution is shown in

Table 1. Geographic distribution of authors by country.

Country	Number of Authors
Serbia	21
China	4
Hungary	3
Iran	3
Israel	3
Spain	3
Croatia	2
Czech Republic	2
Bosnia and Herzegovina	1
India	1
Italy	1
Republic of Korea	1
Slovenia	1
South Africa	1
Turkey	1

. A notable contribution of this Special Issue is its facilitation of collaboration between authors from multiple countries.

Contribution 1 introduces a hybridized decision-making model, which combines fuzzy logic, Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and Fuller methods to enhance the selection process for professional drivers. It identifies 14 criteria for driver evaluation through a literature review and expert interviews, then narrows them down to the seven most important using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and Fuller’s pairwise comparisons. The model integrates these criteria into a fuzzy environment for the first time, demonstrating its applicability with a case study involving bus drivers. The proposed approach aims to improve traffic safety and reduce financial costs for transportation companies by selecting the most suitable candidates. Additionally, it offers a general framework that can be applied to various multi-criteria decision-making problems beyond driver selection.

Contribution 2 is related to the postal services. It introduces an interval type-2 fuzzy AROMAN decision-making method to optimize the sustainability of postal networks in rural areas. It identifies and prioritizes key criteria for postal network optimization through expert interviews and the Full Consistency Method (FUCOM). The proposed model is applied to a case study in Serbia, demonstrating its effectiveness in ranking postal units for reorganization. This approach provides a robust tool for policymakers to enhance service provision in rural areas, ensuring equitable access to essential services.

Contribution 3 presents a fuzzy logic model used to assess accident proneness based on passenger-vehicle speed perception in real and virtual traffic conditions. It highlights the significant impact of speed on road crashes and explores how different environments affect speed estimation accuracy. The study involves an experimental setup with 87 participants, analyzing their speed perception at various speeds and from various perspectives. The results show statistically significant differences in speed estimation based on gender, driving experience, and frequency of driving. The proposed model can be used to predict the likelihood of road crashes, offering valuable insights for improving road safety measures.

Contribution 4 is from the field of logistics. It introduces a novel hybrid multi-criteria decision-making model combining improved fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods to select optimal warehouse locations for a logistics service provider in Serbia. It identifies seven key criteria for evaluation and assesses five potential locations, emphasizing the importance of factors such as land cost, infrastructure access, and workforce availability. The proposed model offers a systematic approach to decision-making, enhancing the efficiency and cost-effectiveness of supply chain operations. The study demonstrates the model’s robustness through sensitivity analysis and validation against other Multi-Criteria Decision-Making (MCDM) methods. This approach provides valuable insights for logistics professionals and can be adapted to various industries and geographical contexts.

Contribution 5 examines the hidden relationship between long-distance transport (LDT) and information and communication technology (ICT) use among European Union (EU) citizens, utilizing a fuzzy clustering eco-extended apostle model. It reveals that the majority of EU citizens neither travel long distances nor frequently use ICT for transport, while a minority engages in both activities extensively. The study highlights significant socio-demographic influences on these patterns, such as gender, age, education, and employment status. The novel methodology provides deeper insights into the complex interactions between LDT and ICT use, offering a robust framework for future research. This approach enhances the understanding of how ICT adoption impacts travel behavior and vice versa.

Contribution 6 is related to road transportation engineering. It introduces a novel multi-criteria decision-making model, which integrates data envelopment analysis (DEA) with interval-valued fuzzy-rough-numbers (IFRN) and Weighted Aggregated Sum Product Assessment (WASPAS) methods. The model is named DEA-IFRN WASPAS. This model is applied to evaluate the efficiency of 14 road sections based on headway analysis, incorporating criteria such as annual average daily traffic (AADT) and road gradient. The study’s main contribution lies in the development of the IFRN WASPAS method, which enhances decision-making under uncertainty. The results provide valuable insights for infrastructure managers and traffic experts to optimize road section efficiency and improve traffic flow.

Contribution 7 investigates the development of neuro-fuzzy motion controllers for autonomous vehicles navigating rugged terrains, focusing on cautious, intermediate, and courageous behaviors. It introduces a sequential transfer optimization approach, utilizing multiple bi-objective source problems to enhance the evolution of controllers. The study demonstrates that edge solutions from Pareto fronts provide better transferability than center solutions, substantiating this hypothesis through extensive experimental analysis. This research contributes to the efficient design of versatile neuro-fuzzy controllers capable of adapting to various environmental challenges.

Contribution 8 proposes a hybrid multi-criteria decision-making (MCDM) model combining fuzzy factor relationship (FARE) and axial distance-based aggregated measurement (ADAM) methods to assess the risks associated with using different types of drones in city logistics. This model identifies the least risky drone type by evaluating nine alternatives based on seven criteria, ultimately selecting the single-rotor microdrone as the optimal solution. The study introduces a comprehensive framework for assessing drones optimized for urban logistics applications, emphasizing risk mitigation. Additionally, the paper validates the robustness of the proposed model through sensitivity analysis and comparison with other MCDM methods. This research contributes to safer and more efficient logistics operations in urban environments.

Contribution 9 introduces a novel heuristic for generating candidate sets for large-scale instances of the traveling salesman problem (TSP) using fuzzy clustering. This method significantly improves the efficiency and quality of candidate sets, outperforming traditional techniques in terms of time complexity and solution quality. Extensive testing on benchmark instances demonstrates that the heuristic covers nearly all edges of optimal solutions and produces smaller candidate sets, enhancing the performance of subsequent improvement methods like the Lin–Kernighan heuristic. Overall, this approach offers a robust solution for effectively solving large-scale instances of the TSP.

Contribution 10 proposes a novel framework for the location and size planning of charging parking lots (CPLs) based on electric vehicle (EV) charging demand prediction and fuzzy bi-objective optimization. It introduces a method to predict the spatial–temporal distribution of EV charging demand using real vehicle usage data and a fuzzy logic inference system. The study then develops a bi-objective optimization model to minimize EV users’ charging waiting time and maximize CPL operators’ profits, solved using a fuzzy genetic algorithm. The proposed approach is validated using a case study, demonstrating its effectiveness in reducing travel and waiting times while controlling construction costs. This research provides a practical solution for the efficient planning and management of EV charging infrastructure.

Contribution 11 introduces a novel approach to solving fully intuitionistic fuzzy transportation problems without using ranking functions, thereby addressing the shortcomings of existing methods. It transforms the problem into a crisp multi-objective form and proposes a new hybrid multi-objective solution procedure. The effectiveness of this approach is demonstrated through computer experiments with benchmark problems, showing superior results compared to existing methods. Additionally, the study highlights practical applications in real-world scenarios where demand, supply, and transportation costs are vague and inadequate.

3. Conclusions

The Guest Editors would like to thank all the authors for presenting their research results in the Special Issue “Fuzzy Logic Applications in Traffic and Transportation Engineering”. Furthermore, we are extremely grateful to all the reviewers for their timely and insightful reports, and to the staff of the Editorial Office for their support in preparing this Special Issue. We hope that this Special Issue will inspire other researchers to carry out new research using fuzzy logic in solving practical problems in the field of transportation engineering.