Nezir Aydin^1,2,*, Melike Cari³, Betul Kara³, Ertugrul Ayyildiz^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2625-2650, 2025, DOI:10.32604/cmc.2025.067290 - 23 September 2025

Abstract Transportation systems are rapidly transforming in response to urbanization, sustainability challenges, and advances in digital technologies. This review synthesizes the intersection of artificial intelligence (AI), fuzzy logic, and multi-criteria decision-making (MCDM) in transportation research. A comprehensive literature search was conducted in the Scopus database, utilizing carefully selected AI, fuzzy, and MCDM keywords. Studies were rigorously screened according to explicit inclusion and exclusion criteria, resulting in 73 eligible publications spanning 2006–2025. The review protocol included transparent data extraction on methodological approaches, application domains, and geographic distribution. Key findings highlight the prevalence of hybrid fuzzy AHP and… More >

Yunus Dogan¹, Goksu Tuysuzoglu¹, Elife Ozturk Kiyak², Bita Ghasemkhani³, Kokten Ulas Birant^1,4, Semih Utku¹, Derya Birant^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1677-1715, 2025, DOI:10.32604/cmes.2025.069255 - 31 August 2025

Abstract Accurate traffic flow prediction (TFP) is vital for efficient and sustainable transportation management and the development of intelligent traffic systems. However, missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision. This study introduces REPTF-TMDI, a novel method that combines a Reduced Error Pruning Tree Forest (REPTree Forest) with a newly proposed Time-based Missing Data Imputation (TMDI) approach. The REPTree Forest, an ensemble learning approach, is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urban mobility solutions. Meanwhile, the TMDI approach exploits temporal patterns… More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Dehai Zhang^*, Yu Ma, Qing Liu, Haoxing Wang, Anquan Ren, Jiashu Liang

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2901-2920, 2023, DOI:10.32604/cmc.2023.038264 - 08 October 2023

Abstract Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn… More >