This investigation employs statistical learning techniques to analyze service quality within Nigeria's aviation industry, a sector integral to the nation's economic vitality and connectivity. The industry has faced challenges exacerbated by economic downturns, notably the rise in fuel prices and the devaluation of the Nigerian Naira since early 2022. Previously reported customer dissatisfaction prompted a thorough examination of passenger and stakeholder experiences. A cross-sectional survey methodology was adopted, yielding data subsequently analyzed through advanced machine learning algorithms. A principal component analysis (PCA) model was refined via leave-one-out cross-validation (LOOCV), an unsupervised learning approach. Findings reveal that crew member performance is the most influential factor on service quality, exhibiting an inverse relationship with other variables in the first principal component. In the second principal component, flight rescheduling emerges as a significant negative determinant. Recommendations from this analysis are directed at aviation industry practitioners, policymakers, and stakeholders, emphasizing the enhancement of crew member recruitment and training processes. Additionally, strategies to adhere to scheduled travel times are advocated. These insights are pivotal for advancing service standards in Nigeria's airline industry.

Traffic congestion stands as a primary urban development hurdle encountered by major cities. Managing the extensive network comprising these transportation systems is an immensely complex task. Unfortunately, this activity poses significant challenges in numerous cities worldwide. In this article, a hybrid method ant colony and discrete symbiotic organism optimization are proposed to enhance the traffic flow of intersections. The first one is a metaheuristic inspired of the foraging behavior of ant colonies; it is used successfully to address a variety of intricate optimization problems. The second one is DSOS adaptation which is an ecosystem-based metaheuristic optimization inspired of interrelated symbiotic strategies observed on ecosystems. This approach involves determining the ideal durations for each phase of traffic lights. In the first level, an ACO method is utilized to extract critical path of given urban zone (congested path). In the second, the DSOS algorithm is employed to enhance the optimization of querying time of delayed vehicles. The obtained results show the superiority of DSOS compared with the fixed time control method (static approach). In contrast to the conventional timing method, the mean number of queued vehicles is decreased by 20%. This confirms the effectiveness of the suggested approach in alleviating traffic congestion.

Public transport plays an important role in facilitating productivity and allows transporting skills, labor, and knowledge within and between countries. Many studies were conducted to enhance the public transit system performance, especially the travel time. Travel time in this study represents the total journey time including time on bus, delay time, and waiting time at stops. In this study, two predicting models were developed to estimate the bus travel time by employing two different techniques statistical analysis which involve the use of mathematical models, methods, and tools to analyze and interpret data using SPSS program and Gene Expression Programming (GEP) techniques which is a type of evolutionary algorithm inspired by biological evolution to find computer programs that perform a user-defined task, using GeneXproTools. Four routes have been selected that are served by minibus with a capacity between 22-28 sets, the length of these routes was (11.9, 7.2, 9.0 and 15.2 km), respectively. In this study sixteen trips have been observed for each route (eight trips for each direction) through five weekdays and two weekend days at peak and off-peak period for each day using En-route survey the form of datasheet has been using to obtain the required data. Forty-three data points have been observed from all routes. The first model has developed a relationship between operating bus speed (Vo) and the other independent variables affecting bus speed while the second model has predicted the relation between bus operating speed, private vehicle speed, and the number of stops. The results of model 1 showed that the number of bus stops, signalized intersections, route length, and the average traffic volume is the most effective factors that affect Bus operating speed. Also, the predicted model has a high coefficient of determination (R-square) with 0.888 and 0.93 for SPSS and GeneXpro5.0, respectively. On the other hand, the second model showed that the number of bus stops and the speed of the private vehicle also have a strong relationship with the bus operating speed with the coefficient of determination (R-square) with 0.96 and 0.97 for SPSS and GeneXpro5.0, respectively. The main recommendations that there are several strategies that can contribute to enhancing the travel time of a public transit system: Increase service frequency during peak hours, Enhance the reliability of transit services, improve quality control over the bus operators, and use the bus with multi-door to reduce the dwelling time.

Decision-making while commuting in big cities is still challenging for many citizens in developing countries. The implementation of diverse transportation modes operating in silos combined with the inaccessibility of real-time travel information prevents commuters from these countries from making informed travel decisions. Commuters often have to choose the specific means of transport that will yield the highest value in terms of cost, safety, convenience, and timeliness among alternatives. This paper uses a case study of Cape Town in South Africa to explore stakeholders' perspectives on implementing an integrated real-time information system (IRIS) and the requirements that must be satisfied. We employed a qualitative methodology, utilising semi-structured interviews and co-design sessions as the means of data collection. Four categories of stakeholders associated with transportation, including taxis, trains, Bus Rapid Transit (BRT), and municipal buses, within the context of South Africa, participated in the study. The findings reveal that the commuters and the public transport operators agreed that challenges around socio-traffic incidents, infrastructure development, lack of technology resources and lack of real-time travel information are major concerns that must be addressed for successful IRIS implementation. Functional features, change management, data privacy, system integration and information sharing were the main priorities on the list of requirements. The study represents a first attempt at understanding the requirements of an IRIS from the stakeholders' perspective in the context of South Africa. It extends the discussion on using IRIS to support transportation in developing countries, which has received limited attention thus far in the literature. The study is relevant for developing futuristic policies, advanced infrastructure, and optimised service delivery in developing countries because it provides a good foundation for understanding the critical requirements for the design and development of IRIS.

Batu City, a premier tourist destination in Indonesia, has experienced a significant influx of tourists, leading to an upsurge in vehicular traffic. This increase in vehicular load has precipitated premature deterioration of the city's road pavements. A systematic approach to addressing this degradation is imperative for the refinement of road planning strategies, tailored to the pavement's lifespan, and for the development of a holistic road construction policy that aligns with the actual traffic load. This study employs a systematic literature review (SLR) to investigate the effects of vehicle overloading on the structural longevity of road pavements in Batu City. A keyword-driven search was conducted, resulting in the selection of 50 pertinent articles which were scrutinized to determine the extent of the impact that overloaded vehicles have on road infrastructure within tourist-heavy urban centers and to identify effective management solutions. The findings from the SLR indicate that excessive vehicle axle loads, or the presence of cities with high vehicular traffic, considerably expedite pavement damage and diminish the structural lifespan, as supported by evidence from 48% of the analyzed journals. These insights have practical implications for the assessment of road geometric designs, the examination of construction techniques and materials, and the formulation of models or policies that are congruent with the functional requirements of the city.

This study represents the complex terrain of smart transport applications, focusing on the synergistic potential that emerges from the strategic confluence of Machine Learning (ML) and Internet of Things (IoT) methodologies. This review provides insight into how the dynamic nature and large volume of data created by IoT systems make them an excellent environment for the integration of ML approaches by exploring the interplay between these areas. Notably, a wide range of ML algorithms have been reviewed and suggested in the context of smart transportation, with a focus on critical areas such as route optimization, parking management, and accident detection/prevention. A crucial finding from this investigation is the noticeable gap in ML coverage throughout the range of smart lighting systems and parking applications. This highlights the need to refocus on these topics from an ML standpoint, opening the path for future investigation and innovation. This research tackles important topics including sustainability, cost-effectiveness, safety, and time efficiency, highlighting the fascinating possibilities of fusing IoT, ML, and smart mobility. Proactively preventing accidents, expedited parking reservations, cutting-edge street lighting, and accurate route suggestions are just a few benefits of the integration of these technologies. The study does, however, highlight the need for more research, particularly in unexplored areas like parking applications and smart lighting. By bridging these gaps and improving ML and IoT cooperation, smart transportation will be greatly improved and creative solutions for improved urban mobility will be offered.

In the archipelago of Makassar, Indonesia, maritime resources within the sectors of trade, fisheries, and tourism present a significant opportunity to elevate community welfare across small rural islands. This study aims to evaluate the performance of the existing inter-island transportation system and formulate strategies for the development of sea transportation between these islands. Employing a comprehensive integrated planning approach (IPAp), the research utilizes correlation analysis, importance performance analysis (IPA), the problem orientation policy model, and analysis of strengths, weaknesses, opportunities, and threats (SWOT). Through both qualitative and quantitative descriptive methods, the investigation delineates the critical role and performance of inter-island sea transportation, laying the groundwork for the establishment of policy priorities aimed at enhancing these services. Findings indicate that the facilities and infrastructure of inter-island transportation are pivotal, with correlation R ranging from 0.565 to 0.7602. However, the current performance of inter-island sea transportation services across all island clusters remains below par, with user satisfaction levels ranging from 38% to 50%. This underscores the necessity for a targeted action program focused on the development of sustainable transportation facilities and infrastructure. Such initiatives are essential for the formulation and revision of local transportation development plans, providing a foundation for infrastructure investment decisions that support the growth of marine and maritime economy in archipelagic areas.

Road accidents are the leading cause of death; this increase is usually due to speeding. For road safety, intelligent systems have been designed to keep a constant speed and a safe distance between vehicles in a convoy. This article focuses on the synthesis of an inter-distance control system using intelligent methods and algorithms. The main idea presented in this article is to implement a model-free control for physical model “spring-damper” known as intelligent control based on an algebraic filter. Our comparative analysis extends beyond comparing the use of a simple derivative and an algebraic filter for intelligent control. We also take into account the effect of noise directly affecting the model’s behaviour to demonstrate the robustness of our approach. Through MATLAB simulations, we highlight that our approach exhibits better robustness and stable tracking in the inter-distance control system.

A training program aimed at augmenting energy efficiency in freight transportation through the cultivation of eco-driving skills was systematically developed and evaluated. The emphasis of this program was on harnessing the potential of drivers to reduce fuel consumption via eco-driving techniques. The study employed a methodical 5-step approach, with energy consumption assessments conducted both prior to and subsequent to the training to determine its efficacy. The process entailed: (1) an initial evaluation of driver proficiency in energy-conserving practices using eco-trucks, (2) the establishment of precise training objectives, (3) the enrichment of drivers’ knowledge with advanced eco-driving information and techniques, (4) the selection of suitable pedagogical methods for imparting eco-truck driving skills, and (5) a comprehensive analysis of the outcomes derived from the eco-driving training. Through a comparative analysis of fuel usage before and after the intervention, the study revealed a marked reduction in energy consumption. Post-training data demonstrated a significant decrease in fuel usage by 17.57%, affirming the training’s effectiveness. These findings suggest that the implementation of eco-driving training programs can substantially elevate energy efficiency among truck drivers. The resultant decrease in fuel expenditure, alongside the reduction in carbon emissions, contributes to both economic and environmental sustainability, with profound implications for the freight logistics sector.

Airports serve as critical nodes for tourist ingress within nations and cities; yet, the efficacy of public transportation systems connecting these gateways to final destinations remains suboptimal. This systematic literature review interrogates public transport integration systems (PTIS) to elucidate determinants of their efficacy and to explore their capacity as a service that enhances passenger mobility. An analysis of the extant literature indicates that the success of PTIS is contingent upon an array of factors that collectively influence the physical, operational, and institutional quality of transport integration. It has been identified that governmental entities play a pivotal role in provisioning reliable transport amenities, with an emphasis on infrastructure and operations predicated on integration to augment passenger mobility, diminish expenses, and curtail transfer durations. Nonetheless, the enactment of collaborative measures between regulatory bodies and service providers in the PTIS domain emerges as a formidable challenge, given its intrinsic linkage to business operations, revenue allocation, promotional strategies, and fiscal policies regarding subsidies.

Developing insight into the determinants that impact communities' willingness to accept autonomous buses has become a crucial aspect of smart city advancement. This study investigated the inclination of residents to utilize autonomous buses by employing the expanded Unified Theory of Acceptance and Use of Technology model, encompassing satisfaction, trust, and perceived risk. The UTAUT model is an influential theoretical framework used to forecast and elucidate the acceptance of new technology by people or organizations. The results show that (1) Effort expectancy, performance expectancy, social influence, and facilitating conditions have a considerable beneficial effect on both behavioral intention and satisfaction. (2) A significant positive correlation exists between behavioral intention and satisfaction and trust. (3) Perceived risk also has a detrimental moderating impact. The results offer governments and public transportation operators a valuable blueprint for the development and promotion of autonomous buses in metropolitan regions. Current findings can play as a helpful point of reference for enhancing development of autonomous public transportation in China.

Enhancing performance standards by judiciously fusing established methods with innovative strategies. This paper aims to combine the existing YOLOv5 algorithm, which is well-known for its object identification abilities, with new models, such as the Autoencoder-CNN (Convolutional Neural Network), Autoencoder-LSTM (Long Short-Term Memory), and Recurrent Neural Network (RNN) frameworks, in order to improve its performance. Through combining these disparate methods, the study seeks to use each of their unique advantages, ultimately resulting in a thorough comparison study that reveals their separate effects on precision and productivity. This methodical assessment, characterized by rigorous optimization and careful testing, not only improves traffic sign recognition systems’ accuracy but also reveals useful connections between the suggested and known methods. The main goal of this endeavor is to unravel how these seemingly unrelated components, when brought together, can potentially usher in a new age of higher performance standards. This study aims to pave the way for the development of more sophisticated, flexible, and well-tuned traffic sign detection and identification systems by bridging the gap between the established and the cutting edge. The ramifications of this work encompass a wide range of real-world applications. Robust optimization and experimentation not only improve traffic sign recognition systems' accuracy but also reveal useful connections between the suggested and proven methods.

The number of cars on the road has increased significantly as a result of the development of society, and this is one of the problems that traffic officials have focused on when diagnosing seat belts. With the increase in traffic accidents in recent years due to drivers not adhering to safety rules, it has become necessary to focus on this area. Seat belt diagnosis is an important rule that must be followed in the field of deep learning. In this paper, transfer learning is applied in seat belt diagnosis to reduce the number of risks and to protect passengers and drivers from traffic accidents when the seat belt is not used. The Xception model is proposed because this model has very deep hidden layers which leads to good metrics, the model is trained on the ImagNet dataset using fine-tuning learning. We find that previous training on ImageNet leads to a significant increase in the efficiency of the proposed architecture and extracts the important feature in a variety of situations to determine whether the driver has fastened his seat belt or not. The results show that the model can inspect seat belts with a high accuracy of 99.42% and a loss function of 8.15%.

In light of the recurring tsunami threats faced by coastal cities, the significance of transportation infrastructure resilience is underscored, particularly in regions such as Padang City, Indonesia, which has previously experienced the devastating impacts of tsunamis, notably the Mentawai event. This study is aimed at developing a robust evacuation planning strategy to mitigate potential loss of life during tsunami occurrences. Through a quantitative analysis utilizing the PTV Visum software, optimal evacuation routes were identified, emphasizing the importance of infrastructure performance in emergency scenarios. The analysis revealed that certain road segments, including Jl. Raya Balai Baru 2, Jl. Mustika Raya, Jl. Rimbo Tarok - Belimbing, Jl. Koto Baru Banuaran, Jl. Thui Raya 2, Jl. Raya Gadut, and Jl. Durian Taruang, achieved a level of service A, indicating very good performance. These routes are essential for an effective evacuation plan, demonstrating superior efficiency and playing a pivotal role in disaster response strategies. The findings advocate for the integration of these optimal routes into urban planning and disaster preparedness initiatives, aiming to enhance the city’s resilience to tsunami threats. Recommendations are extended to the relevant authorities, highlighting the criticality of incorporating advanced transportation planning tools like PTV Visum in the development of evacuation strategies. Such measures are deemed instrumental in minimizing casualties during tsunami events, thereby contributing significantly to the safety and well-being of the populace.

This paper is the result of the electric vehicle (EV) powertrain and fuel powered vehicle analysis conducted by using the Ipoh driving cycle (IDC) in Simulink. This thorough analysis is on studying the effectiveness of fuel-powered vehicle and EV on the IDC which involves a several main components of an EV which includes motor and controller subsystem, battery system, driver system and the parameter calculations. The model also involves a dashboard in which all parameters are viewed in it. Several parameters were chosen for this analysis, which is time, distance travelled, average speed, average running speed, average acceleration, average deceleration, acceleration percentage, deceleration percentage, idling percentage, cruising percentage, kWh and fuel costing, battery voltage, current, state-of-charge (SOC) and power. This paper involves two major methods which are EV modelling and EV analysis. Several parameters are considered during the modelling process; aerodynamic drag force, rolling resistance force, gravitational force and cumulative tractive force which impacts the efficiency of the EV. EV is proven to be more efficient in which the cost of travelling with an EV on IDC is 60% lower compared to a fuel powered vehicle.

The study is carried out with the aim to investigate the barriers impacting the sustainable smart city planning and implementation of Intelligent transportation systems (ITS). This research study will contribute to the current knowledge by highlighting the main challenges that prevent the implementation of ITS for smart urban mobility. The findings demonstrate that various challenges, including management, resource-related, technical, economic, personal, interoperability, and individual factors hinders the successful implementation of ITS. The outcome of the findings holds few managerial implications. Policymakers and urban planners need to develop strategies that help resolve the identified challenges to guarantee the successful execution of ITS. Because of the proliferation of big data and the interconnectedness of vehicular, infrastructural, and pedestrian settings, acquiring, storing, and analyzing multi-source data has become easier and less expensive. The linked model brings new techniques to adaptable coordination and monitoring in real-time to better monitor and regulates transportation systems. IoT is developed, and it is now possible to integrate complicated solutions into existing frameworks and procedures for city administration. As the number of people who possess a vehicle rises, the difficulty of finding a parking spot and the consequent impact on air quality. New smart parking solutions must be developed to save time and minimize greenhouse gas emissions. Smart parking solutions are the primary focus of this article, which emphasizes the available systems and sensors, as reported in the literature. This analysis aims to provide an in-depth detail at the development of smart transportation solutions. The inclusion of large vehicle detection technologies in a complete examination of the present state of smart parking systems should be a top priority. As a result, the communication modules are provided clearly and concisely.

One of the advanced driver assistance systems (ADAS) technologies that can address the issue of high-traffic accidents is adaptive cruise control (ACC). However, a challenge arises due to the lack of control algorithm development in ACC technology that accommodates curved road conditions. This paper proposes a comprehensive solution by introducing ACC for curved roads through the utilization of a multidimensional control system model. This paper aims to implement the crow search algorithm (CSA) into the ACC technology: (1) Our objective is to apply the original crow search algorithm (OCSA) to find the most optimal values for the parameters v_err, x_err, v_x of ACC, and k_p and k_i of lateral displacement control; (2) We also implement the archived crow search algorithm (ACSA) into the control system, which is considered to have faster computation time than OCSA. Based on the obtained results, ACSA demonstrates faster computation time. The optimal values for achieving enhanced performance are found to be k_p at 0.7492, k_i at 0.6506, v_err at 0.9716, x_err at 0.9778, and v_x at 0.7012. This model was developed using MATLAB and compared to the non-optimized version. The research aims to contribute to ADAS development by addressing the optimization challenges of control algorithms for ACC parameters on curved roads. Ultimately, this solution enhances driver safety by providing more effective control in challenging road conditions.

Underwater vehicles are now mainly researched using the 6-DOF equations of motion. The research on 4-DOF Autonomous Underwater Vehicles (AUV) for small Underwater Vehicles regularly focuses on fully actuated control algorithms. Research on underactuated systems has been conducted frequently for surface ships, while 4-DOF underactuated AUV using a nonlinear control system has received little attention. Little research focuses on devices with quadrotor UAV configuration, also known as QUV, but evaluations have yet to be conducted to advise on which controller to use for different cases. Therefore, in this article, the authors focus on building a control algorithm for an AUV object that lacks a typical recursive executive structure, which is the Backstepping controller when dividing the 4-order strict backpropagation nonlinear system into subsystems to design feedback controllers and Lyapunov control functions for each subsystem. Using this same approach, the authors built a controller that combines Backstepping controller and Hierarchical Sliding Mode Controller (HSMC). This is the guiding premise for research on improving the quality of 4-DOF AUV control before comparing and evaluating the two controllers for specific cases. Newly proposed algorithms and stability analyses are based on Lyapunov's theory, and an evaluation survey is carried out through simulation by Matlab software.

In the realm of transport development, the fusion of modern technology and vehicle surveillance in roadside areas becomes indispensable. Traditional surveillance demands continuous monitoring through closed-circuit television cameras. It results in a huge amount of data, which requires high computation. This study delves into the challenges of real-time processing of vehicle surveillance within smart cities with quality data. In addition to a specific focus on monitoring the roadside traffic region despite technological advancements, including target variability, lighting conditions, and occlusion, the manuscript introduces a lightweight contour-based convolutional neural network to address these challenges. The proposed work aims to gain the maximum features from the vehicle via detecting the optimal position and incorporating a Region-Proposal-Network, Region-of-Interest-Align and pooling, Non-Maximum-Suppression, Structural-Similarity-Index, and Peak-Signal-to-Noise-Ratio. The proposed work extracts hierarchical information from a custom video dataset and demonstrates superior performance with an accuracy rate of 97.36% and a minimum loss of 0.0816 in an elapsed time of 1s 159ms. Furthermore, it achieves a validation loss of 0.1506, and a validation accuracy of 96.46%. Additionally, manuscripts illustrate different datasets and models through a systematic literature review. Moreover, the manuscript also illustrates the Smart-City framework and Integrated Traffic Management System architecture.