Multi-modal transportation systems (MMTS) play a critical role in enhancing urban mobility by integrating multiple transport modes to improve efficiency and accessibility. This paper presents a comprehensive review of modelling approaches in MMTS, focusing on optimization techniques, travel behaviour analysis, and network resilience. The study synthesizes a range of methods, including agent-based models, equilibrium approaches, and data-driven simulations, aimed at improving system efficiency, adaptability, and user satisfaction. While significant strengths include real-world data integration and dynamic performance modelling, a thematic analysis reveals recurring limitations across studies, such as model assumptions, data limitations, limited behavioural realism, narrow scope, and high computational complexity. These weaknesses constrain the scalability and applicability of current MMTS models. The review emphasizes the need for frameworks that integrate real-time analytics, support diverse travel behaviours, and incorporate emerging trends like Mobility-as-a-Service (MaaS) and micromobility. It concludes by recommending that future research prioritize cross-regional validation, computational scalability, and dynamic system responsiveness to ensure MMTS can meet evolving urban transport demands. This synthesis serves as a critical reference for researchers, planners, and policymakers aiming to develop resilient and efficient multimodal transit networks.

The growing air transport industry is under pressure to identify strategies for greenhouse gas (GHGs) emission reduction, specifically CO₂, by incorporating sustainable and carbon-neutral operations. This study follows an Integrated Environmental Assessment (IEA) methodology through the Driver-Pressure-State-Impact-Response (DPSIR) framework and policy analysis to evaluate the relationship between aviation-related activities and carbon emissions. It also suggests future policy pathways to achieve a sustainable scenario. The study area is the Kingdom of Bahrain, a Small Island Developing State (SIDS) in the Arabian Gulf region. The findings reveal that aviation activities and related ground operations have increased in recent years, resulting in a 7% annual increase in emissions since 2013 and a 4.88% projected increase for the coming years by 2030. In addition, Bahrain’s location and its economic developments have been the main factors influencing aviation emissions. The average growing population rate of 2.7% has put an additional demand on the air transport system to expand its infrastructure, increase aircraft fleets, and upgrade facilities. The study uniquely identified a lack of distinct institutional mechanisms and a requirement for legislative standardization at both national and industrial levels. Through policy analysis, Bahrain’s national policies and industry-level policies are mostly regulatory instruments, with varying degrees of effectiveness. It also recommends that research gaps in local aviation impacts be technically filled to assist Bahrain in achieving its 2060 goal of Net Zero emissions.

This study assessed the effectiveness and sustainability of using barge versus feeder vessels to transport containerized cargo to Bangkok Port, Thailand. A survey of 387 stakeholders in marine logistics was conducted from October to December 2024. Multiple regression analysis (MRA) showed that cost-effectiveness, environmental impact, and operational flexibility primarily influenced transport mode choice, explaining 56.2% of the variance. Cost-effectiveness emerged as the key factor, while environmental impact was the strongest predictor of perceived sustainability. While operators favored feeders due to cost and time efficiency, barges scored higher due to environmental friendliness and operational flexibility. Notably, 68% of respondents preferred barges for short routes under 100 km due to their role in reducing road congestion and pollution. Furthermore, 73% expected greater barge use over the next five years, driven by technology and environmental policies. Improved waterway infrastructure would lead 82% to use barges more frequently, and 76% believed better intermodal integration would enhance logistics efficiency. This study is limited to the context of Thailand’s domestic maritime logistics and stakeholder perceptions, which may not be fully generalizable to other ASEAN or global port systems. Future research should explore multi-country comparative studies and assess longitudinal trends as green port policies evolve across Southeast Asia.

First-mile and last-mile connectivity remains a significant challenge in developing cities as inadequate feeder systems often hinder public transport efficiency. While prior studies have examined access and egress mode choices, few have explored how income levels and travel distance shape commuters’ travel mode behavior in Indonesia. This study addresses this gap by analyzing the influence of income level and travel distance on mode selection for first-mile and last-mile trips in Jakarta’s commuter rail system. This study used a multinomial logit model (MNL) to examine the hypotheses across 24 Jakarta Kota–Bogor stations. The findings show that lower-income commuters prefer to walk and use microtransit and Bus Rapid Transit (BRT), while higher-income groups prefer private vehicles and ride-hailing services. In addition, travel distance strongly influences mode choice, with walking decreasing significantly as the distance increases. The results also highlight a high private vehicle dependency for first-mile access and a tendency for ride-hailing in last-mile travel, reflecting a wide gap in Jakarta’s feeder system. This study recommends expanding and integrating feeder transport, improving pedestrian infrastructure, unifying fares across modes, and regulating ride-hailing services to enhance connectivity. These measures can promote sustainable urban mobility and reduce dependency on private vehicle.

Public transportation (PT) plays a vital role in promoting sustainable mobility, particularly in congested urban areas. Effective PT planning requires attention to country-specific objectives and challenges. Evaluating bus services is essential for ensuring that transit systems meet mobility demands while reducing congestion and pollution. However, no globally recognized evaluation indicators currently exist for assessing PT services and stations in developing countries. To address this gap, a systematic literature review was conducted to establish a weighted assessment checklist tailored to the context of developing nations. The study employed expert judgment from 15 professionals and applied the Analytic Hierarchy Process (AHP) to identify best practices. Key indicators for evaluating bus stations included infrastructure, operations, and facilities, while the level of service (LOS) and sustainability emerged as the most critical indicators for bus service evaluation. Among the highest-weighted factors were reliability, safety and security, connectivity and integration, and operational efficiency, underscoring their importance in delivering effective and sustainable PT solutions. This research contributes to the body of knowledge by proposing context-specific indicators that account for the unique challenges faced by low-income countries, such as limited resources, infrastructure constraints, and socio-economic conditions.

Transitioning to electric vehicles (EVs) is crucial for sustainable transportation. This study investigates the factors influencing consumers’ attitudes and purchase intentions toward EVs in Indonesia. Using a quantitative approach, data were collected from 400 respondents through an online survey, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for analysis. The findings indicate that performance expectancy, environmental concern, charging infrastructure, and financial incentives positively impact attitudes toward EVs, whereas price and operating costs are significant barriers. Attitude toward EVs is confirmed as a key mediator, linking these factors to purchase intention. The results suggest that improving EV infrastructure, reducing perceived costs, and increasing public awareness through education and incentives can enhance adoption rates. Policymakers and industry stakeholders should focus on strategic initiatives that address affordability, charging accessibility, and technological advancements to accelerate EV market growth. While this study offers valuable insights, future research should explore regional disparities and additional determinants such as brand perception and social influence. Indonesia can move toward a more sustainable transportation ecosystem by fostering a more favorable perception of EVs.

In this study, the environmental impact of car noise in the two largest cities of Uzbekistan - Samarkand and Tashkent-was compared in depth. The main objective is to determine how factors such as the level of urbanization of different cities, traffic density, road infrastructure and industrial location affect the level of traffic noise. The study used a modern Assistant SIU 30 v3rt type noise meter at a total of 12 points (8 in Samarkand, 4 in Tashkent) with measurements of car number and noise level at 2-minute intervals of 10-15 minutes per location. During the measurements, the number of cars, maximum and average equivalent noise levels (Leq) were determined. The results showed that noise levels in Tashkent were higher, as well as a very strong correlation (R=0.97) between the number of vehicles and noise. In contrast, in Samarkand, this association is moderately strong (R=0.635), and other environmental and infrastructural factors have also been found to affect noise. The study was also carried out on the basis of international standards, while the results serve as an important basis for ensuring environmental safety, urban planning and the development of anti-noise strategies. The results showed significant differences in noise levels and their relationship to traffic between cities. The analysis confirmed an increase in the permissible noise level in residential areas, public buildings and recreation areas, especially in large cities, taking into account their specific characteristics and factors affecting the noise level. The cited correlation indicators will serve as a statistical basis for the development of noise forecasting and monitoring systems in the future by year. The facts of the article are necessary for the scientific justification of the policy of combating noise in the cities of Uzbekistan.

Most road accidents are caused by drivers engaging in driving practices and being distracted while driving, which contributes to the concern of road safety awareness in society today. Activities like using a phone while driving carelessly and displaying driving habits increase the likelihood of these actions leading to an accident. In this research paper, we introduce a driver behavior detection system based on CNN technology that employs a 22-layer convolutional neural network (CNN) to identify intricate behaviors in real time situations. The proposed method systematically incorporates layers with 3×3 kernels and ReLU activations, along with max pooling layers to classify five main categories: turning movements, using a phone for texting or talking, safe driving practices, and other activities. The system underwent training and testing on a dataset of 10776 RGB images, in 480×640 pixels resolution, depicting driving situations and surroundings. The first test results showed a notable drop in misclassification errors and a notable rise in accuracy rates for classification tasks using a CNN approach could have advantages for enhancing vehicle safety systems by accurately and swiftly detecting driving behaviors to reduce accident risks and enhance road safety overall. The experiment findings were obtained through GPU processing in Matlab. Resulted in a training accuracy of 100% along with a testing accuracy of 100%, achieved within 23.46 seconds. The method suggested for assessing driving habits has been effectively executed.

Driver distraction, particularly due to mobile phone usage, significantly contributes to road traffic accidents. This study proposes a real-time detection system using the YOLOv8 object detection algorithm to identify drivers using mobile phones. The system combines two datasets: one for phone usage behavior and another for phone object detection, aiming to improve recognition performance in various conditions. Data augmentation techniques such as zoom, blur, and noise were applied to simulate real-world scenarios including lighting variations and occlusion. The YOLOv8 model was trained and evaluated using this dataset combination, achieving a detection accuracy of 92.5% and a mean average precision (mAP@0.5) of 89.5%. These results demonstrate the model’s ability to accurately detect mobile phone usage, even under challenging conditions. This approach presents a promising solution for early warning systems to monitor driver focus and reduce the risk of accidents caused by distraction, contributing to improved road safety through intelligent driver behavior detection.

Traffic signal control systems play a critical role in managing urban mobility by regulating the flow at intersections. The Florida Department of Transportation (FDOT) installed a new signal timing system at several signalized intersections along multiple corridors in Central Florida. In December 2013, Orange County began implementing this system, which was completed in June 2015. This action was taken to reduce the frequency of red-light running incidents. The primary objective of this study was to assess how signalized intersections and corridors are affected by extended change and clearance intervals. Specifically, it aimed to evaluate FDOT’s signal timing effort and its potential impact on the safety and operational performance of selected intersections. To address this, twenty signalized intersections along three corridors in Central Florida were investigated. Additionally, three signal timing patterns were examined to evaluate the effectiveness and safety of the baseline (Pattern 1), the current FDOT implementation (Pattern 2), and the proposed alternative (Pattern 3). Microsimulation analysis was conducted using SimTraffic, a component of the Synchro 8 software. The study found that extended signal timing in Pattern 2 and the proposed Pattern 3, which incorporate longer change and clearance intervals, significantly increased intersection delay and volume-to-capacity (V/C) ratios. Furthermore, these patterns also led to noticeable increases in overall delay and travel time along the studied corridors.

Short-term traffic forecasting is one of the significant subjects in order to create more sophisticated transportation systems that regulate traffic volume and prevent congestion. The number of vehicles in Karbala City is growing quickly, which raises wait times and decreases Level of Service (LOS). It is essential to predict the traffic performance to ensure a correct traffic operation. The aim of this work is to create a short-term traffic forecasting model for intersections within a study area based on an Artificial Neural Network (ANN). The data has been used to create and train a number of ANN models. Two models were selected based on the most effective parameters that cause traffic congestion, longer travel times, and accidents for each type of vehicle, serving as the main input for the models. The results of models to predict the traffic volume and travel time found that the neural network performed in a good way, achieving R2 values of 0.9101,0.9748 and 0.8877, which is a good score. Sensitivity analysis was adopted to evaluate the model's performance when the input values are changed. It was found that the passenger car (PC) was the most effective parameter for both models.

Urban transportation systems in developing cities like Yogyakarta face challenges such as congestion, limited infrastructure, and fragmented policies. This study aims to develop a context-specific framework for Intelligent Transportation System (ITS) adoption by integrating the Technology Acceptance Model (TAM) with external readiness factors, including infrastructure quality, technology access, socioeconomic status, and policy support. A survey of 300 transportation users was conducted, and data were analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS). Instrument validity was confirmed through expert review and Content Validity Index (CVI). The study introduced two new constructs Smart Readiness and Social Affordability to capture individual and systemic influences on technology adoption in ITS. Results show that perceived usefulness and ease of use mediate the relationship between external readiness and behavioral intention. Government policy and infrastructure were the strongest predictors of ITS adoption. The model explained 70% of the variance in behavioral intention, indicating strong explanatory power and model fit. In conclusion, contextual factors such as infrastructure, governance, and digital access play a pivotal role in enabling ITS adoption in mid-sized developing cities. The proposed framework extends TAM by incorporating systemic urban readiness, offering both theoretical advancement and practical guidance for policy makers and urban planners.

This study addresses the high costs and emissions associated with diesel freight operations on the busy Dammam–Riyadh corridor by developing a hybrid, data-driven optimization framework that combines regression modeling, the Taguchi method, and a Genetic Algorithm (GA). First, a multiple linear regression model was trained on 30 real freight trips validated via 5-fold cross-validation and reporting R² = 0.87 and RMSE = 3,200 SAR to predict total trip cost from six operational variables. Next, a Taguchi L9 orthogonal array was used to perform a sensitivity analysis under the “smaller-is-better” Signal-to-Noise (S/N) ratio, identifying wagon count and trip duration as the most influential factors, with a minimum predicted cost of 42,388.64 SAR. Finally, we applied a DEAP-based GA (population = 50; generations = 100; blend crossover; Gaussian mutation) to globally optimize all six variables within empirically derived bounds, achieving a predicted cost of 34,054.33 SAR (≈ 44% reduction versus the dataset mean). Key assumptions include linear cost relationships in the regression and fixed stop/truck counts during Taguchi screening; limitations stem from the single-corridor dataset. This combined approach balances rapid factor screening with precise global optimization, offering both strategic insights and actionable recommendations for reducing freight transportation costs while maintaining operational reliability.

Walkability and environmental attitudes significantly influence the adoption of public transport in Banda Aceh, Indonesia, as revealed by our extended Theory of Planned Behavior (TPB) analysis of 500 respondents. This study uniquely integrates walkability and environmental attitudes into the TPB framework—an approach rarely applied in mid-sized Southeast Asian cities with limited infrastructure. Key findings show: (1) Better walkability improves people's attitude (β=0.26, p<0.01) and their sense of control (β=0.32, p<0.01) about using public transport; (2) Positive environmental attitudes directly influence the intention to use public transport (β=0.19, p<0.05); while (3) Gaps in infrastructure reduce the effectiveness of perceived control. These results suggest that integrated interventions—improving pedestrian connectivity while promoting sustainability awareness—could effectively increase transit ridership in this religiously conservative, infrastructure-limited city. Policymakers should prioritize walkability upgrades near transit stops and community-based environmental campaigns tailored to local cultural norms.

Transportation logistics for fuel delivery face persistent challenges in routing under uncertain demand and complex operational constraints. This study addresses the gap between theoretical models and practical fuel distribution by introducing a hybrid framework that integrates Deep Reinforcement Learning (DRL), graph-based spatial reasoning, and deterministic constraint validation. The method combines Proximal Policy Optimization (PPO) with a graph neural architecture to capture spatial dependencies in vehicle routing while ensuring operational feasibility via constraint-checking mechanisms. The approach was evaluated on 300 synthetic problem instances across three network scales (10, 50, and 100 stations) and a real-world case study involving 38 gas stations and 6 vehicles in a regional fuel distribution system. Compared to a standard deep learning baseline and a Clarke-Wright heuristic, our method reduced operational costs by 7.2% and 9.9%, respectively. Constraint violations dropped from 6% with classical reinforcement learning to 1%, demonstrating improved feasibility. While we report averaged results over large instance sets, formal statistical significance testing remains a direction for future work. The proposed approach maintained robust performance under varying levels of demand uncertainty and produced feasible daily routing plans within 45 seconds, confirming their practical applicability. By integrating learning, spatial reasoning, and operational compliance, this research advances scalable and adaptive optimization for fuel delivery in uncertain and dynamic environments.

Road damage surveys in Indonesia are still conducted manually through visual inspections based on the Surface Distress Index (SDI) method. Consequently, the process often requires extended completion times and yields results that lack objectivity due to heavy reliance on the surveyor's experience. As a result, road repairs frequently do not correspond accurately to the actual damage conditions. Road deterioration intensifies during the rainy season, when water accumulates in potholes, accelerating their erosion and expansion. To facilitate more objective damage assessment, particularly for potholes, a tool employing an image sensor capable of distinguishing between water-filled and dry potholes is necessary. This study utilized an image processing model based on a convolutional neural network employing MobileNet SSD V2. In detecting water-filled potholes, the system achieved a precision of 0.95, a recall of 0.514, and an F1 score of 0.667. Furthermore, performance testing across various vehicle speeds indicated that the optimal speed for the edge device system was an average of 15 km/h, at which the system maintained a precision of 0.95, a recall of 0.514, and an F1 score of 0.667.

This research paper presents an in-depth exploration of container vessel accidents and preventive measures through semi-structured interviews with industry professionals and subject matter experts. Building on a previous study, utilizing the NASAFACS methodology to analyze container vessel accidents, this paper aims to deepen understanding of the underlying challenges and emerging trends in container vessel safety. The interviews focused on key aspects, such as industry insights, causal factors, environmental risks, crew competency, the regulatory landscape, collaboration with authorities, industry partnership, and crisis management. Participants shared valuable perspectives on major challenges affecting container vessels and the wider industry. Interview data were analyzed using MAXQDA Software, allowing a comprehensive thematic analysis. The findings inform recommendations to improve safety, including the development of comprehensive standards for emerging risks. Specific suggestions include the upgrade of firefighting systems for ultra-large container ships, stricter enforcement of cargo declaration and lashing practices, mandatory IMDG training for shippers and freight forwarders, higher manning levels, and structured inspection regimes akin to those in the tanker industry. While the NASAFACS analysis of accident reports identified preconditions as primary contributory factors, the interview findings highlight systemic organizational issues and external influences. This research contributes to the ongoing maritime safety discourse by integrating expert insights with NASAFACS analysis, offering a holistic perspective on container vessel accidents and proactive measures for their prevention.

Vehicle tires are subjected to sudden and significant loads when driving at high speeds due to unexpected bumps in the road. To reduce the occurrence of these cracks, this research will address the occurrence of these cracks using various techniques. The Solid Works program will be used to design various wheel models and reinforce the areas where cracks occur. The models will then be loaded into the ANSYS program to determine the various deformations and stresses they experience, as well as the degree of improvement of the wheel models whose designs have been developed. The results demonstrated that the deformation models' values were substantially lower than those of the first model, with the third model showing the biggest percentage decrease (59.56%). The results showed that the Von Mises models' values and the maximum shear stress were considerably lower than those of the first model, with the third model showing the biggest percentage decline at (68.12 and 61.2%), respectively. The fact that these improved percentages are reached in the three models (64.74, 93.12, and 88.72%) indicates that the fatigue damage values of the three improved models in the design are significantly lower than the fatigue damage values of the first model. It is clear that the third model, with a safety factor increase of 93.12%, has the highest increase. This suggests that the third model, which has three collars reinforcing it in the region where cracks are developing, is the best.

This study evaluates the operational cost efficiency and environmental implications of transitioning from diesel to electric buses, using the Trans Jogja public transport system in Indonesia as a case study. Employing a total cost of ownership (TCO) framework and emissions analysis, the study compares the financial performance and greenhouse gas (GHG) emissions between diesel and battery electric buses. Results show that electric buses incur approximately 50% higher operating costs, primarily due to elevated capital expenditures and depreciation. Moreover, under Indonesia's coal-dominated electricity grid, electric buses generate higher indirect CO₂ emissions than their diesel ones, highlighting a critical energy-emission paradox. However, electric buses eliminate tailpipe pollutants such as NO_x and PM2.5, offering considerable public health benefits. A systemic scenario analysis reveals that full fleet electrification without concurrent reform in the energy sector could raise annual emissions by over 2,200 tons. The study identifies key barriers—including high upfront costs, limited charging infrastructure, and regulatory misalignment—and proposes phased policy interventions. Recommendations include targeted subsidies, contract revisions, integration with renewable energy, and technical capacity-building. The findings offer valuable insights for Indonesian cities seeking to scale sustainable urban mobility through electric transportation.