Sustainable Transport System Research Articles

Enhancing CO2 emissions prediction for electric vehicles using Greylag Goose Optimization and machine learning

Electric vehicle (EV) emissions should be predicted and mitigated, which requires lowering EV emissions in line with global sustainability goals. Such accurate forecasting supports policymakers and other industry stakeholders make marketing decisions to reduce environmental impacts and optimize resource utilization. In this research, a novel Greylag Goose Optimization (GGO) algorithm is integrated with a Multi-Layer Perceptron (MLP) model to improve emissions prediction. Finally, the study does a comparative analysis with some established optimization algorithms in hyperparameter tuning regarding an improved accuracy model. In addition, statistical analyses such as ANOVA, sensitivity analysis, and T-test were used to substantiate performance differentiation between models. For the optimal model, the GGO-optimized MLP significantly outperformed baseline models and other optimization techniques, having minimum error metrics such as correlation coefficient and RMSE and an MSE of . As a result, the emissions forecast is very reliable. The proposed approach provides actionable insights for environmental policies, EV adoption strategies, and infrastructure planning. The model enables stakeholders to achieve climate objectives, optimize EV charging systems and foster the creation of sustainable transportation systems, as said accurate emissions estimates are enabled.

Investigating Subcontracting Partnership in Sustainable Urban Transportation System Design

This study investigates the role of subcontracting partnerships in enhancing collaboration and sustainability in urban rail transit system design, addressing the challenges posed by fragmented practices and environmental goals under China’s “Dual Carbon” policy. Using a mixed-methods approach, we integrate structural equation modeling (SEM) and factor analysis to identify critical success factors (CSFs) and their impacts on design performance. SEM, a statistical technique capable of analyzing complex relationships between unobservable “latent variables” (e.g., trust, innovation) and measurable outcomes, was employed to validate the hypothesized relationships among five key factors: Excellence in Quality, Interactive Collaboration, Collaborative Vision, Risk Strategy, and Strategic Innovation. Factor analysis consolidated 19 CSFs from the literature into these five constructs, explaining 69.09% of the variance. The SEM results revealed that Excellence in Quality, Interactive Collaboration, Risk Strategy, and Strategic Innovation directly improve design performance, while Collaborative Vision indirectly influences outcomes through mediating effects on risk management and innovation. These findings provide actionable strategies for leveraging BIM/blockchain tools and institutional frameworks to enhance sustainability in urban transportation projects. By contextualizing partnership dynamics within China’s state-led infrastructure ecosystem, this research enriches the theoretical understanding of partnership mechanisms.

Selection of Light Weight Materials for Railway Vehicles Using WASPAS Method

This study attempts to determine and assess appropriate lightweight materials for different railway vehicle components, taking into account elements like durability, strength, cost-effectiveness, and production viability. To compile pertinent data on lightweight materials, their characteristics, and their uses in the railway industry, a thorough assessment of the body of current literature and business practises was carried out. Introduction: Modern transport networks depend heavily on railway carriages to transfer people and commodities across large distances. The emphasis on enhancing the performance and lowering the environmental effect of railway vehicles is expanding as the need for effective and environmentally conscious transportation rises. The use of lightweight materials in their design is a viable strategy to meet these goals. For railway cars, using lightweight materials has a number of benefits. First of all, it can help people lose a lot of weight, which increases energy efficiency and lowers fuel usage. Less energy is needed by lighter cars to accelerate and maintain speed, which lowers operating costs and lowers greenhouse gas emissions. Reduced weight also makes it possible to carry more payloads and maybe travel at faster speeds. Research significance: The importance of choosing lightweight components for railway vehicles lies in its potential to increase payload capacity, decrease environmental impact, improve efficiency and safety, achieve cost-effectiveness, spur technological advancements, and guarantee regulatory compliance. For the railway sector to advance towards effective and sustainable transport systems that serve society as a whole, this study issue is essential. Method: A decision-making process called the Weighted Aggregated Sum Product Assessment (WASPAS) method is used to assess and rank a group of options based on several criteria. Without plagiarising, it uses a multi-criteria decision analysis (MCDA) technique to take both quantitative and qualitative aspects into account when making a choice. Alternate parameters: Dual Phase, DP600, Transformation Induced Plasticity, TRIP700, Twinning Induced Plasticity, TWIP, Aluminium, Al6005 T6, Aluminium, Al6082 T6, Porous Structure (Al—Closed cell). Evaluation parameters: Yield Strength, Tensile Strength, Young’s Modulus, Density, Price. TRIP700 got 1st rank, TWIP got 2nd rank, DP600 got 3rd rank, Al6082 T6 got 4th rank, Al6005 T6 got 5th rank and Porous Structure (Al—Closed cell) got 6th rank. TRIP700 is got the first rank with less compensation.

NEV battery recycling innovation strategy considering pro-social behavior from the game theory perspective

Technological innovation serves as the core driving force for achieving sustainable and circular transportation systems. However, during the recycling process, consumers’ pro-social behavior such as willingness to return used batteries and support green initiatives, directly influences recycling rates and incentivizes recyclers to adopt advanced technologies. To explore this dynamic, this paper constructs a closed-loop supply chain (CLSC) decision-making game model involving new energy vehicle (NEV) manufacturers, recyclers, and consumers, with a focus on power battery recycling. The study aims to investigate the impact of technological innovation subsidy and the level of consumers’ pro-social behavior on the innovation decisions of recycling agents. The findings reveal that: (1) Pro-social behavior significantly enhances the power battery recycling rate by increasing consumers’ willingness to participate in waste battery recycling and support green initiatives, thereby incentivizing recyclers to actively adopt technological innovations. (2) Both NEV manufacturers and power battery recyclers have a free-rider scenario in their technological innovations, and higher free-rider revenues will discourage both parties from technological innovations. (3) Greater subsidies can effectively promote the technological innovation of NEV manufacturers and recyclers, and manufacturers are more sensitive to government subsidies for technological innovation.

PENGAMANAN ASET PRASARANA JALAN DAN BANGUNAN PT X

Asset security encompasses control and regulation activities in asset management to ensure proper utilization.. The security of road infrastructure and building assets at PT X is crucial for maintaining sustainable public transportation operations in DKI Jakarta Province. This research aims to understand asset security based on theory by (Agus Puji & Riyanto, 2012) using three main indicators: administrative security, physical security, and legal security. The research employs a descriptive qualitative method with data collected through observation, interviews, and document analysis. The results show that PT X has implemented administrative security through comprehensive asset recording and ownership documentation. However, challenges exist in asset utilization that is still under discussion for utilization schemes to ensure benefits for both parties. Physical security has been implemented with advanced technology security systems and regular guarding. Legal security shows that local government regulations and internal regulations are already running side by side. PT X needs to highlight improvements in administrative systems and physical security standards, as well as strengthening legal policies in PT X asset management. With an integrated security strategy, PT X infrastructure assets are expected to continue benefiting the public and supporting Jakarta's development as a global city with modern and sustainable transportation systems.

Electric Vehicle Shared Services: A Decade of Innovation, Challenges, and Transformative Impact on Sustainable Urban Mobility — A Systematic Literature Review

Introduction Research on Electric Vehicle Shared Services (EVSS) has significantly grown over the past decade, emerging as a transformative solution to urban mobility challenges while advancing sustainable transportation. Through innovation and scalable mobility solutions, EVSS has garnered attention for their potential to address pressing environmental issues, including climate change and urban air quality. Material and Methods This Systematic Literature Review (SLR) examines the evolution, challenges, and impacts of EVSS from 2014 to 2023. A total of 52 studies were analyzed using the PRISMA methodology, ensuring a comprehensive and rigorous evaluation of the literature. Key themes were identified to synthesize trends, challenges, and benefits associated with these services. Results Findings reveal a significant growth in EVSS research driven by technological advancements, supportive policy frameworks, and heightened global awareness of environmental issues. Studies highlight that EVSS can achieve a reduction in greenhouse gas emissions by 14–65% compared to traditional vehicles, alongside notable improvement in local air quality. These benefits are pivotal in global efforts to mitigate climate change and enhance urban environmental health. Moreover, EVSS provides affordable and flexible transportation options, particularly for underserved populations, contributing to social equity. Integration with public transportation systems further reduces traffic congestion and enhances urban mobility efficiency. Discussion Despite their promise, EVSS faces several challenges. Limited charging infrastructure necessitates significant investment in public charging networks. High upfront costs for purchasing and maintaining electric vehicle (EV) fleets remain a financial obstacle for operators. Furthermore, user perception issues, such as range anxiety, require targeted public education campaigns to enhance acceptance. Collaborative efforts among policymakers, community organizations, and private operators are crucial for addressing these barriers and maximizing the potential of shared EV services. Conclusion EVSS represents a transformative approach to achieving sustainable urban mobility. Their environmental, social, and mobility benefits underscore their role in addressing critical urban challenges. However, overcoming adoption barriers will require a robust and coordinated policy framework alongside investments in infrastructure and public engagement strategies. Continued research and stakeholder collaboration are essential for unlocking the full potential of EVSS in fostering sustainable and equitable urban transportation systems.

A Web-Based Platform for Urban Bike Rentals Enhancing Green Mobility Through Digital Innovation

Abstract Cities around the world are facing increasing difficulties in maintaining inclusive effective and sustainable transportation systems as urbanization picks up speed. Rapid population growth in cities has put a great deal of strain on the infrastructure supporting public transportation in addition to causing a sharp rise in vehicle traffic. In many urban areas traffic congestion has become a daily annoyance leading to higher fuel consumption air pollution travel delays and a lower standard of living in cities. These problems highlight how urgently creative eco-friendly transportation options are needed. Integrating bike rental services into the urban mobility ecosystem is one such promising solution. In order to meet the urgent mobility needs of contemporary urban dwellers these services provide a useful low-emission form of transportation. Bike rentals offer a sustainable and accessible shared economy model in contrast to personal vehicle ownership which comes with high expenses parking restrictions and maintenance needs. The focus of this study is a carefully planned online bike rental service created to satisfy the changing needs of urban residents. By combining user-centric technology with environmental stewardship the platform offers easy access to a shared fleet of bicycles with the goal of promoting environmentally friendly commuting. It does this by addressing a number of inefficiencies in urban transportation and advancing sustainability affordability and health. The core of this platform is an easy-to-use interface designed to make renting a rental easier. Potential users can quickly browse the bikes that are available based on availability and location register or log in with little effort and finish a booking in a matter of seconds. Because of its emphasis on simplicity even inexperienced users can easily navigate the system. This is especially helpful for travellers infrequent riders and people who might not be familiar with conventional rental systems. The platform has a mobile-optimized application that provides full functionality on smartphones and tablets to facilitate access while on the go. Because the mobile app integrates real-time GPS users can locate the closest docking stations and check the availability of bikes in real time. This makes it easier for users to plan their trips and eliminates the uncertainty that comes with traditional rentals. The platform guarantees a smooth ride whether for an impromptu trip or a scheduled commute. Another fundamental component of the systems architecture is security. Digital wallets credit/debit cards and UPI are among the safe digital payment methods that the platform incorporates. To protect users data and prevent fraud all payment transactions are encrypted using industry-standard cybersecurity protocols. Automatically generated ride summaries booking confirmations and payment receipts are saved in user accounts for convenient access. The platforms backend architecture is a major strength.

E-bikes and travel behaviour change: systematic review of experimental studies with meta-analyses

ABSTRACT Transitioning from private cars to active modes of transport is key to reducing transport related greenhouse gases emissions and promoting physical activity. Electrically assisted bicycles, often referred to as e-bikes, play a pivotal role in facilitating this shift. However, the environmental and health benefits of e-bikes depend on the modes they substitute, with the highest benefits obtained when private cars are replaced. This systematic review and meta-analysis targets quasi-experimental (i.e. pre–post measures of travel behaviours without control group) and experimental (i.e. pre–post measures of travel behaviours with control groups) studies assessing the impact of acquiring an e-bike on overall travel behaviour changes expressed in both distances and mode share (in % of kilometres travelled). Ten studies, all conducted in Northern Europe, were included. Results from the narrative synthesis and meta-analysis show that: (i) when participants have access to an e-bike, either through a free loan programme or a purchase, they engage in e-cycling; the meta-analysis reveals a significant difference of 5 km travelled daily after the interventions between participants that got access to an e-bike compared to those from control groups with no e-bikes, reflecting a substantial increase of 26% in e-bike mode share; (ii) e-bikes can substitute for all other modes of transport, but car use appears to be the most affected in both the systematic review and meta-analyses difference (2.4 km fewer travelled per day by car between the intervention and control groups at follow-up, reflecting to a 10% decrease in car mode share); (iii) baseline travel behaviours may influence modal shift, with e-bikes substituting for the most prevalent means of transport in baseline. E-bikes have the potential to be a reliable and competitive alternative to cars in a healthier and more sustainable transport system and our study brings new empirical evidence to support this claim.

Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system.

Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system.

Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications

Inductive power transfer (IPT) systems are transforming railway infrastructure by enabling efficient, wireless energy transmission for electric locomotives equipped with Li-ion batteries. This technology eliminates the need for overhead power lines and third rails, offering financial and operational advantages over conventional electric propulsion systems. Despite its potential, IPT deployment in rail applications faces significant challenges, including the fragility of materials (i.e., ferrite and Litz wires), thermal management during high-power transfers, and electromagnetic interference (EMI) on the transmitter side. This study discusses several factors affecting IPT efficiency and introduces magnetic concrete as a durable and cost-effective material solution for IPT systems. Magnetic concrete combines soft ferrite powder with water and coarse aggregates to enhance magnetic functionality while maintaining structural strength comparable to conventional concrete. Its durability and optimized magnetic properties promote consistent power transfer efficiency, making it a viable alternative to traditional ferrite cores. A comparative study has been performed on non-magnetic and magnetic concrete (with 33% ferrite powder) using both permeability tests and finite element analysis (FEA). The FEA includes both thermal and electromagnetic simulations using Ansys Maxwell (v.16), revealing that magnetic concrete can improve temperature management and EMI mitigation, and the findings underscore its potential to revolutionize IPT technology by overcoming the limitations of traditional materials and enhancing durability, cost-efficiency, and power transfer efficiency. By addressing the challenges of fragility, thermal management, and shielding of the unique coil topology design presented, this study lays the groundwork for improving IPT infrastructure in sustainable and efficient rail transport systems.

Planerische Adressierung von Logistikflächen in der Raumordnung. Eine beispielhafte Auswertung (über)regionaler Raumordnungspläne

Currently, there are reasons for taking logistics up in spatial planning, including the large land consumption of logistics facilities and the urgent implementation of a sustainable transport system. One planning option is to secure suitable land for logistics facilities in formal spatial development plans. To date, only a limited number of studies have been carried out on this specific implementation. This paper uses 17 regional plans as examples to examine the planning instruments and planning focus with which this task is currently being implemented in German regional plans. The analysis of the regional plans at hand shows that half of the regional plans use planning instruments to address logistics facilities. In some regional plans, only defined logistics segments are addressed in planning, e.g. the establishment of port-related industries in the proximity of ports. To term land for logistics infrastructure like ports and inland terminals on the other hand, is much more established and is included in almost all plans. Taking up planning for locations of logistics facilities in regional plans takes place when logistics is defined as an economic focus, logistics is considered relevant for the functioning of the regional economy or planning locations for logistics facilities are required by state planning regulations.

A Robust Adaptive Strategy for Diesel Particulate Filter Health Monitoring Using Soot Sensor Data

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data.

DHDRDS: A Deep Reinforcement Learning-Based Ride-Hailing Dispatch System for Integrated Passenger–Parcel Transport

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals.

Assessing public transport accessibility for people with physical disabilities in burgos, spain: A user-centered approach to inclusive urban mobility.

Public transport accessibility is vital for fostering inclusive and sustainable urban development, ensuring equitable mobility for individuals with physical disabilities or reduced mobility. Globally, over 1.3 billion people, including 4.12 million in Spain, live with disabilities. Despite legislative progress, many cities fail to meet accessibility standards that guarantee safe and independent public transport use. This study evaluates the accessibility of 431 bus stops in Burgos, Spain, using a methodological design that combines compliance with accessibility standards and the lived experiences of individuals with disabilities. By identifying critical barriers and opportunities for improvement, this research provides actionable insights for urban planners and policymakers, offering a replicable framework for cities facing similar challenges. A validated and reproducible methodology was employed to evaluate accessibility conditions through in situ observations, including the geolocation of bus stops and photographic documentation. This approach guarantees a user-centred perspective through collaboration with local disability organisations. The analysis identified significant barriers, including inadequate vehicle encroachment prevention, poorly designed shelters, unsuitable stop locations, and limited accessible formats such as Braille and audio. These challenges hinder the independent use of public transport for individuals with disabilities or reduced mobility. Addressing these barriers can substantially enhance urban mobility, reduce environmental impacts, and support Sustainable Development Goal 11, particularly Target 11.2. The proposed methodological design provides a practical framework for urban planners to create more inclusive, resilient, and sustainable transport systems globally.

Strategies to Improve Public Transportation Services for Students of The University of Bengkulu

The choice of transportation mode is a crucial aspect of student mobility, especially in higher education environments with high levels of activity. Students tend to select transportation modes based on cost, travel time, convenience, accessibility, and safety. However, the high use of private vehicles among students has led to increased traffic congestion and road accidents, as seen in Bengkulu City. On the other hand, the suboptimal quality of public transportation services has contributed to the low interest of students in using public transport. This study aims to analyze the factors influencing the transportation mode choice of Universitas Bengkulu students and to evaluate the effectiveness of public transportation services. The method used is binary logistic regression analysis combined with stated preference to understand mode preferences based on cost and travel time comparisons. The resulting mode choice model indicates that students prefer private vehicles unless public transportation offers more competitive costs and travel times. The study results reveal that travel cost and time are the most influential factors in mode selection. If public transportation becomes more affordable or offers shorter travel times, students are more likely to switch to it. The study concludes that improving the quality of public transportation services, particularly in terms of cost efficiency and punctuality, can increase student interest in using public transport, thereby supporting a more sustainable transportation system.

Use of Machine Learning in Predicting Electric School Bus Battery Range for Optimized Routing

The transition to electric school buses (ESBs) promises significant environmental and economic benefits. However, optimizing their operations remains a challenge due to the limited and variable range of their batteries. This paper contributes to addressing this challenge by introducing a machine learning (ML)-based framework for accurately predicting ESB battery range under diverse operational conditions. By leveraging historical and real-time data on energy consumption, traffic patterns, weather conditions, and charging infrastructure, this study develops predictive models that enhance routing efficiency, reduce operational costs, and improve fleet reliability. Our approach integrates advanced ML techniques such as regression models, ensemble learning, and neural networks to create robust range predictions. The study's key contributions include (1) the development of a comprehensive ML-driven predictive model tailored for ESB fleets, (2) the integration of real-time environmental and operational data for dynamic decision-making, and (3) the demonstration of the model's effectiveness through numerical experiments using both simulated and real-world datasets. The findings illustrate the potential of ML in optimizing ESB routing and reducing energy wastage, paving the way for more sustainable student transportation systems.

Sustainable and Profitable Urban Transport: Implementing a ‘Tire-as-a-Service’ Model with Regrooving and Retreading

Rapid urbanization has intensified pressure on transport infrastructures, with urban bus networks playing a crucial role in promoting sustainable mobility. However, managing operational costs while minimizing environmental impacts remains a major challenge. This study investigates the innovative “Tire-as-a-Service” (TaaS) model applied to bus fleets, incorporating regrooving and retreading techniques to improve tire durability and efficiency. The TaaS model shifts the focus from purchasing tires to a service-based approach, where users pay according to usage (i.e., kilometers driven), promoting proactive maintenance and waste reduction. Solving this problem is based on a discrete-event simulation algorithm to optimize tire inspection schedules and, consequently, minimize total costs while guaranteeing a minimum level of service and reducing environmental impact. A robustness analysis will validate the model developed, thus contributing to a more sustainable urban transport system.

Vehicle Trajectory Recovery Based on Road Network Constraints and Graph Contrastive Learning

Location-based services and applications can provide large-scale vehicle trajectory data. However, these data are often sparse due to human factors and faulty positioning devices, making it challenging to use them in research tasks that require precision. This affects the efficiency and optimization of sustainable transportation systems. Therefore, this paper proposed a trajectory recovery model based on road network constraints and graph contrastive learning (RNCGCL). Vehicles must drive on the road and their driving processes are affected by the surrounding road network structure. Based on the motivations, bidirectional long short-term memory neural networks and an attention mechanism were used to obtain the spatiotemporal features of trajectory. Graph contrastive learning was applied to extract the local feature representation of road networks. A multi-task module was introduced to guarantee the recovered points strictly projected onto the road. Experiments showed that RNCGCL outperformed other benchmarks. It improved the F1-score by 2.81% and decreased the error by 8.62%, indicating higher accuracy and lower regression errors. Furthermore, this paper validated the effectiveness of the proposed method by case studies and downstream task performance. This study provides a robust solution for trajectory data recovery, contributing to the overall efficiency and sustainability of transportation.

Control Strategy for Current Stress Mitigation in Dual Active Bridge for Electrical Vehicle Application

ABSTRACTThis paper presents a novel control strategy for the isolated dual active bridge bidirectional DC‐DC converter (IBDC), aimed at addressing the challenges of current stress in conventional phase‐shift control methods. The proposed modified dual phase‐shift (DPS) technique incorporates an innovative switching approach, effectively reducing current stress, minimizing power losses, and enhancing overall efficiency. Theoretical modeling, simulations, and practical experiments validate the proposed technique, demonstrating its ability to achieve soft switching and expand the zero voltage switching (ZVS) region. Experimental results from a 2.4‐kW DAB prototype further confirm the effectiveness of the proposed control strategy. This research contributes to advancing the efficiency and performance of IBDCs, particularly in electric vehicles, by enabling enhanced energy efficiency, extended range, and reduced operational costs, thereby supporting the development of sustainable transportation and robust power electronics systems.

Ai-Based Traffic Congestion Prediction for Smart Cities Using Artificial Neural Network

As urbanization accelerates, efficient traffic management has become a critical challenge for smart cities. Traditional traffic prediction methods often struggle with the complexity and dynamic nature of city-wide congestion patterns. This study explores deep learning-based approaches for accurate and real-time traffic congestion forecasting. Using historical and real-time traffic data, we develop and evaluate neural network models (ANN) to capture spatiotemporal traffic dynamics. The proposed AI-driven framework integrates diverse urban data sources, such as road sensors, GPS trajectories, and weather conditions, to enhance predictive accuracy. Experimental results demonstrate that deep learning models outperform conventional statistical approaches in congestion prediction, offering valuable insights for traffic control, route optimization, and urban mobility planning. The findings highlight the potential of AI-powered traffic intelligence in developing smarter, more efficient, and sustainable urban transportation systems. When we evaluate using Kaggle datasets, we see that ANN model does better than other methods for precision, recall, F1-score as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Moreover the ANN model outperforms other methods in various time ranges. This comparison provides more evidence to support the effectiveness of this method for improving prediction accuracy in traffic congestion. It shows promise for a future where urban transport systems are smarter and more efficient