Efficient Transport System Research Articles

Environmental Performance and SDGs: How Do Income Disparity, Urbanization, Resource Consumption, and Female Employers Affect the Ecological Footprint in Emerging Economies?

ABSTRACTThe significance of environmental deterioration as a pressing issue has led to its prominence in global discussions on strategies for prevention. It can be asserted that there exists a universal agreement that the enhancement of environmental quality is imperative for the promotion of sustainable development and societal well‐being, especially in emerging economies. In this perspective, the present research uses panel data of the E7 economies from 1992 to 2021 to assess the effects of income disparity, urbanization, resources consumption (nonrenewable and renewable energy consumption), and women employers on ecological footprint. This study employs second‐generation empirical techniques such as CIPS and CADF unit root tests, Westerlund bootstrap cointegration, and DFE/MG/PMG‐ARDL models to check the relationships between the studied variables. The empirical findings of this study reveal that in the E7 countries, the ecological footprint is increased with an increased income disparity and nonrenewable energy consumption. On the other hand, urbanization, renewable energy consumption, and female employers help reduce the ecological footprint. Additionally, the results of the causality measure under the Dumitrescu and Hurlin test unveil bidirectional causality between all the independent variables except nonrenewable energy and ecological footprint. From the policy perspective, the selected countries must implement policies that promote equitable income distribution, planned urbanization to promote compact cities, efficient transportation systems, and green infrastructure, and provide opportunities for women to attain higher positions in organizations, particularly at the managerial and decision‐making levels in order to address the adverse environmental consequences in emerging economies.

Relationship Between Historic Evolution, Urbanization and Mobility in Ibadan, Nigeria

Ibadan is said to be the biggest indigenous city in Nigeria with a population of about 4.04 million, making it the third largest city in the country after Lagos and Kano. Since its founding in 1829, Ibadan has experienced urban evolution that is mostly unguided. With its high fertility rate, population growth and rural-to-urban migration, Ibadan has experienced significant urban sprawl that has resulted in challenges in urban mobility especially to the lower-income earners. This study looks at the relationship between urban planning, urbanization and mobility in the city of Ibadan. The study found that lack of integrated land use planning that includes transport networks including a masterplan is one of the major issues facing mobility in the city. Moreover, unregulated sprawl mostly made of slums and informal settlements has ecological, economic and social effects. Upgrading of slums coupled with integrated urban planning and enforcement is proposed as part of the solution for an effective urban mobility in the city. Policies that encourage compact urban development, mixed land use, and the provision of efficient public transportation systems to reduce the dependence on private transportation and create more walkable, bikeable, and transit- friendly communities are recommended.

Energy-based approach to the assessment of traffic flow

This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality significantly affects acceleration noise. With frequent stop-and-go traffic, urban roads produce moderate but irregular noise patterns. Highways show stable acceleration noise at moderate speeds, but noise increases sharply as vehicles approach higher speeds due to aerodynamic forces. Rural roads, especially those in poor condition, exhibit the highest variability in acceleration noise, even at low speeds. The proposed model has been validated using real-world data. It demonstrates a strong correlation between the predictions and actual vehicle behavior on various road types. One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. This adaptive approach enhances the model’s accuracy and applicability, especially in intelligent transport systems. The model can inform traffic management strategies, allowing for real-time adjustments to speed limits, traffic signals, and routing decisions based on road conditions. This contributes to safer, more efficient, and sustainable transport systems, particularly in regions with inconsistent road infrastructure. The research concludes that integrating acceleration noise modeling into intelligent transport systems can significantly improve traffic flow and vehicle safety. Future research will expand the dataset to include a broader range of vehicle types and road conditions, further refining the model's predictive capabilities.

Detecting Urban Community Based on Urban Mobility Patterns From the Public Transportation Trip-Chain Data: A Case Study of Seoul, South Korea

Urban mobility has been studied in various areas of geography and related disciplines. Commuting patterns are highly linked to the spatial structure of functional districts in a city, which is substantially influenced by public transportation systems. Understanding urban mobility patterns are significant for revealing actual living districts and planning efficient transportation systems and infrastructure. This study aims to detect urban communities with spatially strong interaction based on similar mobility patterns. To extract homogeneous urban communities, we utilize a network community detection method, focusing on the multimodal transfer travels of the public transport system. As a case study, we examined urban communities in the Seoul metropolitan area of South Korea. The results showed newly detected living communities based on the actual mobility patterns of urban populations including multiple-choice behavior of transport mode. Such empirical findings depict more realistic dynamics of populations in large metropolitan areas and the significant influence of public transportation systems on urban mobility patterns.

INTEGRATED OPTIMIZATION MODELS FOR CARRIER SELECTION AND ROUTE PLANNING IN MULTIMODAL TRANSPORT SYSTEMS

This paper presents a comprehensive approach to the optimization of carrier routes and warehouses within multimodal transport systems, with a focus on a localized approach that takes into account specific regional features and constraints. The study develops an economic and mathematical model that considers both operational efficiency and cost minimization by integrating different modes of transport such as road, rail, and sea. Through a comprehensive analysis of existing literature and application of advanced optimization algorithms, the study proposes a new framework that improves the decision-making process in route planning and carrier selection. The proposed model is validated with real-world examples, demonstrating its practical applicability and potential to significantly improve the efficiency of multimodal transportation systems under different scenarios.

Comprehensive Approaches to Traffic Flow Prediction

Intelligent transportation systems require traffic flow prediction, and anomaly detection is the key to ensuring accuracy. Using traditional statistical models to handle complex traffic scenarios is becoming increasingly challenging as urbanization accelerates. Consequently, to enhance the precision of forecasting traffic flow and detecting anomalies, various deep learning techniques, and emerging methods have been introduced. The purpose of this paper is to examine how traditional and deep learning methods, as well as emerging technologies, can be used to predict traffic flow and detect anomalies, including techniques such as the autoregressive integrated moving average model (ARIMA), K-nearest neighbor (KNN) algorithm and convolutional neural network (CNN). The result shows that emerging technologies can improve system performance in multiple traffic environments by accurately extracting features from complex data. The research of this paper provides a theoretical basis for the traffic management department and helps to realize a safer and more efficient advanced transportation system.

Assessment Of Spatiotemporal Variation In Inter-State Passenger Flow From Katsina, Katsina State, Nigeria

This paper examines the nature, pattern, and spatiotemporal variation of inter-state passenger movement from Katsina to other states across the country. Two major terminals were purposefully selected for the study, which are the Nigerian Union of Road Transport Workers (NURTW) and Katsina State Transport Authority (KTSTA). Questionnaires were systemically administered to departing passengers. Information on passenger flow was collected from the passenger register at the motor parks. A flow line map was used to show the direction of the flow, while a bar graph was used to show the volume or magnitude and temporal variation of passenger movement from Katsina to other states across the country. The findings reveal that the majority of the passengers were males who were married, and most of them were traders and civil servants; thus, work- or business-related trips account for most of the reasons for travelling. The findings reveal that the NURTW terminal has a wider spatial coverage of 11 states, while the KTSTA terminal has a spatial coverage of 9 states. Kano State is the most popular destination, followed by Kaduna and Abuja. Generally, the findings show a seasonal pattern of passenger flow with higher numbers in the later months of the year (October to December) and lower numbers in the middle of the year (June and July). Recommendations include the enhancement of transport infrastructures to cater to work- or business-related trips, seasonal adjustment, passenger demographic consideration, and promotion of off-peak travel. This is to ensure a more efficient and responsive transport system that meets the needs of travellers and maximises customer satisfaction.

Automatic Passenger Counting System on Public Buses Using CNN YOLOv8 Model for Passenger Capacity Optimization

This study presents the development of an automatic passenger counting system for public buses using YOLOv8 based on Convolutional Neural Networks (CNN). The system detects and counts passengers in real-time to optimize bus capacity and enhance operational efficiency. Results indicate that the system achieves high accuracy in the front camera view (confidence score of 0.82). However, in the rear camera view the accuracy slightly decreases (confidence score of 0.76) due to object overlap, emphasizing the importance of proper camera placement. The system offers potential improvements in bus capacity management, reduced operational costs, and enhanced passenger comfort. These findings contribute to advancing smarter and more efficient public transportation systems.

Tourist mobility packages in an island mass tourism destination: Exploring preferences heterogeneity and willingness-to-pay

Ensuring a sustainable and efficient transportation system meeting visitor mobility needs is paramount for mass tourist destinations. Recognising this challenge, some destinations are embracing Mobility as a Service (MaaS) solutions as a strategy to create a more sustainable transportation system for tourists. This study uses a discrete choice experiment to analyse tourists’ preferences and willingness to pay for MaaS packages in two well-known municipalities of the Canary Islands, one of Europe’s premier mass tourism destinations. The empirical evidence of taste heterogeneity revealed by the analysis indicates that a one-size-fits-all solution is inadequate. Therefore, the development of mobility packages must be tailored to the specific needs of distinct tourist segments. This approach caters to individual preferences and maximises the perceived value for each tourist segment, promoting the adoption of sustainable transportation options.

EXplainable DEA approach for evaluating performance of public transport origin-destination pairs

Understanding public transportation efficiency is crucial to enhancing urban mobility and economic growth. This study aims to evaluate the efficiency of the public transportation system using an explainable data envelopment analysis approach. Specifically, data envelopment analysis and explainable artificial intelligence techniques were incorporated to uncover the black-box relationship between the inputs and outputs of the efficiency score. The efficiency of the public transportation system was defined as travel times for subway, bus, and multimodal modes relative to the number of trips and travel distances. The efficiency score was evaluated by the origin-destination pair unit. As a result, the efficiency score was estimated to be 0.69 on average, indicating that a reduction of 31% in travel times is required to achieve a perfect score of 1.00. Among the 33,313 origin-destination pairs, 39 had a perfect score of 1.00. The results of the interpretation model showed the order of importance for features—buses, subways, and multimodal modes—with SHapley Additive Explanations values of 0.047, 0.029, and 0.022, respectively. These results suggest that focusing primarily on reducing bus travel times is effective for improving overall efficiency. In this manner, the explainable data envelopment analysis helps measure performance, understand results, and suggest improvement directions.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

Non-parametric efficiency analysis of urban transport systems

ABSTRACT To promote territorial mobility and reduce environmental impacts at the same time, transport policy often targets (energy-)efficiency of urban transport systems. Thus far though, related studies are focused on particular transport modes or operators. Given, the multi-modal character of urban mobility, the present study suggests broadening the perspective and accounting for public and private transport. Based on a two-stage non-parametric technique, the efficiency of 49 German municipalities alongside transport systems’ inefficiency determinants is assessed. In line with previous studies, we find highly populated municipalities to operate more efficiently than smaller ones. Second, efficiency is driven by modal split and individual mobility patterns, with a positive correlation between territorial mobility and trips made by bike or public transport. Finally, the results suggest that negative impacts of trip density (e.g. congestion) might counter the positive influence of higher compactness on efficiency, once a certain threshold of urbanization is reached.

Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace

Urban transportation data are crucial for smart city development, enhancing traffic management’s intelligence, accuracy, and efficiency. This paper conducts a comprehensive investigation encompassing policy analysis, a literature review, concept definition, and quantitative analysis using CiteSpace from both domestic and international perspectives. Urban transportation data comprise multiple dimensions, such as infrastructure status, real-time monitoring, policy planning, and environmental assessment, which originate from various sources and stakeholders. Highly influential authors and active institutions, particularly in the USA, China, Canada, and England, contribute significantly to extensive and collaborative research. Key areas include intelligent transportation, traffic flow prediction, data fusion, and deep learning. Domestic research focuses on practical applications, while international studies delve into interdisciplinary research areas. With advancements in intelligent systems and big data technology, research has evolved from basic data collection to sophisticated methodologies, such as deep learning and spatiotemporal analysis, driving substantial progress. This paper concludes by recommending enhanced data integration, improved privacy and security, fostering big data and AI applications, facilitating policy formulation, and exploring innovative transportation modes, thereby underscoring the importance of urban transportation data in shaping the future of smart cities. The findings provide theoretical and practical guidance for the future intelligence, efficiency, and sustainability of urban transportation systems.

Pengaruh Perceived Price Terhadap Willing To Buy Jasa Transportasi Indriver dengan Perceived Quality Sebagai Mediator

Transportation is a fundamental necessity for individuals across all social strata. An efficient transportation system ensures that people can easily traverse geographical distances, connect various communities, and facilitate interactions. The proliferation of online transportation companies has led to increased competition in the industry. As a result, these companies are increasingly focused on creating attractive promotions to influence users' decisions when choosing which application to use. To stand out in this crowded market, companies often introduce various promotions and discounts to attract potential customers. InDrive, a ride-hailing platform, distinguishes itself in the competitive market by offering unique and innovative features: price negotiation. The benefits of this feature include serving price-conscious consumers and offering flexibility to find a ride with a tariff that matches their budget. This innovative approach can generate cost savings for users while providing additional income opportunities for drivers. The research population consists of Telkom University students from the 2021-2023 cohorts, with 100 respondents selected through online questionnaires distributed via social media. The sampling method used was simple random sampling, and the data was analyzed using SPSS. The research findings indicate that the perception of price and the perception of quality on the InDrive application have a significant relationship, quality perception affects the willingness to buy InDrive transportation, price perception affects the willingness to buy InDrive transportation, and quality perception serves as a mediator between price perception and the willingness to buy..

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

ABSTRACTVehicular ad hoc networks (VANETs) in portable broadband networks are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs are open networks that require regular information sharing, it might be difficult to ensure the security of data delivered through VANETs as well as driver privacy. This paper proposes a blockchain technology that supports trusted routing and deep learning for traffic prevention and security enhancement in VANETs. Initially, the proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts the driver's behaviors such as normal, drowsy, distracted, fatigued, aggressive, and impaired. Next, the Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used to route traffic after trust values have been assessed. Furthermore, Binary Fire Hawks Optimization (BFHO) determines the best routing path based on criteria such as link stability and node stability degree. Finally, blockchain storage is supported by the Interplanetary File System (IPFS) technology to improve the security of VANET data. Additionally, the validation process is established by using Delegated Practical Byzantine Fault Tolerance (DPBFT). As a result, the proposed study employs the blockchain system to securely send data to neighboring vehicles via trust‐based routing, thereby accurately predicting the driver's behavior. The proposed method achieves a better outcome in terms of latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, and computational cost. According to simulation results and efficiency evaluation, the proposed approach outperforms existing approaches and enhances vehicle communication security in an effective manner.

Electric Bicycle Battery Charging System Design Using Solar Panel

This solar-powered electric bicycle charging system design and development uses solar panels as a renewable energy source to charge the battery, which drives the electric motor. The system consists of four main components: solar panel, solar charge controller, battery, and electric motor. The solar panels are connected in parallel to generate 36V, which is then stored in the battery. The system is designed to optimize energy harvesting and reduce dependence on fossil fuels. Testing was conducted with research stages, including system design, hardware implementation, and testing. The results show that the system can charge the battery efficiently, with a charging time of 3 hours. The system was also tested under different conditions, including morning, afternoon, and night, to evaluate its performance. It can be concluded that the solar-powered electric bicycle charging system is a viable alternative to traditional fossil fuel-based systems. The system is environmentally friendly, reduces energy dependence, and provides a sustainable solution for transportation. The results of this research can be used to develop a more efficient and sustainable transportation system.

Evaluation of traffic management at Futa Northgate, Akure Ondo state, Nigeria

Due to increasing usage of automobiles, traffic congestion in most cities is becoming an issue and slowing down the day-to-day activities of human and also deteriorating the quality of the road due to the constant congestion. The movement of products and services between locations has become essential and integral to both global and metropolitan economic existence, and modern enterprises, industries, trades, and general activities are dependent on transportation and transportation infrastructure, hence this project is to evaluate the traffic management on the FUTA North Gate Road and propose recommendations to improve traffic flow and reduce congestion. The research identified the causes of traffic congestion, proffered a solution to traffic congestion for the single carriageway under study and analyzed the implications of the proffered traffic management strategy. Data collection on the road was done during peak days of the week (Tuesday – Friday) and also during peak hours of the day i.e. early hours of the morning and in the evening using the traffic count method. The study revealed that the existing single carriageway has a dimension of 3.75-meter width per each side, with a 2.7-meter shoulder, showed 2,585 as the maximum number of vehicles that ply the road causing traffic congestion as a result of poor road conditions, lack of proper traffic management systems, increased population and vehicle ownership, inadequate road infrastructure, and poor driving habits exceeding the capacity. The study concludes that implementing traffic management strategies which includes promoting the use of public transportation, improving the design of intersections and roundabouts, investing in road infrastructure, and educating drivers on safe and responsible driving habits can help improve the traffic flow and reduce congestion on busy roads. While some recommendations may require significant investment, the benefits of a more efficient and sustainable transportation system will far outweigh the costs in the long run. It is important that all stakeholders work together to implement these recommendations and create a transportation system that benefits everyone. Overall, this project highlights the importance of having an efficient and effective traffic management system, and the need to consider all possible impacts before implementing road expansion projects. By adopting a holistic approach to traffic management and implementing the recommended measures, we can create a more efficient and sustainable transportation system that benefits both motorists and the environment giving a long-term solution going ahead.

ORGANIZATION OF COUNTERMEASURES AGAINST THE NEGATIVE IMPACT OF RISKS IN LOGISTICS TRANSPORT SYSTEMS

The article is devoted to the issue of effective management decision-making in the organization of counteracting the negative consequences of the impact of risks in logistics transport systems. The purpose of the article is to identify risks in the logistics transport system and provide adaptive information on risk management strategies and recommendations. Modernization of the management mechanism based on risk management tools is relevant to ensure the efficiency and reliability of the logistics transport system at all its levels. The study established the fundamental characteristics of the risk management process and developed a set of management measures to reduce transport risks for logistics transport systems. This set of measures significantly affects the reduction of the negative impact of risks on each of the components of the transport and logistics system at different levels of management and is the basis for the integration of management methods, models and tools into a single mechanism, which is a systematic approach to risk management of the logistics transport system. The results of this study create a comprehensive basis for risk management of the logistics transport system. The results of this study indicate that the proposed systematic approach to risk management of the logistics transport system can be defined as an integrated management of business processes related to the promotion of products and the corresponding flows from their source to the final consumer. The effectiveness of this approach has been demonstrated in achieving optimal efficiency of transport enterprises. In the light of the above considerations, the implementation of this systemic approach serves to create an appropriate organizational basis for the implementation of countermeasures against the negative consequences of the impact of risks in logistics transport systems. In essence, it is a risk management tool.