IntroductionBiking-both shared and non-shared-has become a vital component of sustainable urban mobility across African cities like Kigali. Despite this progress, empirical research and demand modeling of biking behavior remain limited. This study predicts and explains biking behavior in Kigali by integrating advanced ensemble machine learning (ML) techniques with structural equation modeling (SEM). MethodsA dataset of 6,386 observations was compiled by merging survey responses on biking with secondary data on weather and air quality. Both traditional statistical and advanced ensemble models were developed for comparison. The dataset was partitioned into training (70%) and testing (30%) subsets, with correlation-based and model-based feature selection applied. SEM examined latent constructs representing spatial, social-demographics, temporal, environmental, and attitudinal factors. ResultsEnsemble ML models substantially outperformed traditional approaches, with random forest and XGB classifiers achieving the highest predictive performance. The SEM demonstrated good model fit and explained the variance in biking frequency. Perceived station accessibility emerged as the strongest determinant of biking behavior, while temporal and environmental factors indirectly influenced demand patterns. DiscussionThe combination of ML and SEM has revealed a coexistence of accurate prediction and behavioral insight. Accessibility emerged as central to biking uptake, highlighting the potential of station placement and spatial equity. Indirect effects of temporal and environmental conditions highlight the impact of user perceptions in shaping biking demand. ConclusionIntegrating ensemble ML and SEM provides predictive robustness and behavioral insight. The findings highlight that improving spatial accessibility and adopting adaptive urban planning strategies enhance sustainable biking uptake.
Introduction/Objective The need for sustainable logistics in the bottled water distribution sector is growing rapidly. This study addresses this issue by developing a multi-objective optimization model that creates a balance between travel distance and carbon dioxide emissions. The aim is to create and design efficient delivery routes while utilizing intelligent vehicle allocation to reduce environmental impact. Methods A mixed-integer linear programming model is proposed, integrated with K-means clustering and real-time traffic data imported from Google Maps API. Two different case studies were examined: one employed a heterogeneous fleet consisting of large, medium, and small-sized vehicles, and the other one employed a homogeneous fleet consisting of small vehicles with various emission rates. The two cases were analyzed according to three weighing scenarios: equal priority (50%–50%), distance-prioritized (80%–20%), and emissions-prioritized (20%–80%). Results In the first case, the model selected the same fleet composition in all scenarios. The total objective function values were 467.30, 546.15, and 388.42. In the second case, the model assigned the heaviest loads and longest routes to lower-emission vehicles, resulting in objective values of 65.34, 99.49, and 31.20, with stable route assignments and optimized load utilization. Discussion The model showed strong adaptability across fleet structures and operational priorities. It was able to reduce emissions without compromising operational efficiency, which shows its practical value for logistics planning. The utilization of real-time traffic and emission-based routing enhances its environmental applicability further. Conclusion The proposed approach supports sustainable route planning and fleet utilization. According to the various scenarios and vehicle configurations used to test the robustness of the model, it can be used on a broader scale in logistic systems with environmental constraints.
Introduction The study of car adoption examines the processes and determinants that shape individuals’ decisions to purchase vehicles. These decisions result from a complex interaction between personal characteristics and contextual factors, including technological, social, and environmental considerations. Although academic interest in this topic has increased substantially, the literature remains fragmented, revealing theoretical and empirical gaps that limit a comprehensive understanding of car purchase behavior. Methods A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. The search strategy was applied to the Scopus and Web of Science databases, focusing on peer-reviewed empirical studies addressing vehicle purchase and adoption decisions. Eligible studies were screened, assessed, and analyzed using descriptive and thematic synthesis techniques. Results The findings indicate that questionnaires and surveys are the predominant data collection instruments. The highest research output originates from the United States, China, and India. The most frequently analyzed populations include car buyers, electric vehicle consumers, drivers, and university students. The most commonly employed theoretical frameworks are the Theory of Planned Behavior, the Technology Acceptance Model, and the Protection Motivation Theory. Key variables repeatedly examined include car purchase intention, age, perceived behavioral control, attitudes, and social norms. Discussion The results reveal a strong concentration on a limited set of theories and variables, suggesting a need to expand analytical perspectives. This theoretical convergence may limit the explanatory power of existing models and overlook emerging factors such as sustainability concerns, technological transitions, and market-level heterogeneity. Conclusion This review highlights the need to adopt more integrative and diverse theoretical approaches to advance understanding of car adoption. Future research should incorporate broader conceptual frameworks and contextual variables to better capture the complexity of vehicle purchase decisions.
Introduction Advanced Traveler Information Systems (ATIS) provide real-time route and traffic data to drivers, allowing them to adjust travel based on shared insights. This study examines the factors influencing ATIS adoption and employs a Dynamic Traffic Assignment (DTA) model to evaluate varying levels of information access within the Bucaramanga Metropolitan Area. Methods To achieve this, surveys were designed to identify the variables affecting drivers' route choice behavior. Based on these insights, key variables for ATIS acceptance were defined, and seven DTA scenarios were simulated under specific assumptions of guidance acceptance or rejection. Results The results indicate that drivers primarily consult ATIS to avoid congestion, save time, and navigate unfamiliar routes. Consequently, utilizing this real-time information increases travel speeds and reduces incident-related congestion. However, a critical efficiency threshold emerged: performance improvements are strictly limited to a 0-30% acceptance rate locally and 0-50% network-wide. When over 50% of drivers act on ATIS guidance, overall network performance declines. Discussion This finding highlights the necessity of careful monitoring and strategic traffic management to prevent secondary congestion on alternative routes. Conclusion Ultimately, while providing real-time information effectively mitigates traffic incidents, the benefits of ATIS are non-linear. Maximizing urban network efficiency requires strategically managing information distribution to keep user acceptance within these optimal local and global thresholds.
Introduction Many large cities, particularly in Latin America, have adopted Bus Rapid Transit (BRT) systems to reduce emissions from individual road transport, yet their impacts on air quality are rarely evaluated. This study aims to estimate fuel consumption and emissions from the BRT fleet in Guayaquil and to explore several decarbonization scenarios. Methods The study applies macroscale Tier 1, Tier 2, and Tier 3 methodologies from the European Environment Agency to estimate fuel consumption and emissions of the Guayaquil BRT fleet. Several “what if” decarbonization scenarios are assessed, including upgrading emission-control technologies, switching to low-sulfur fuels and higher biodiesel blends, and incorporating air-conditioning systems. The analysis also accounts for refrigerant leakage and key upstream CO 2 emissions from fuel desulfurization and biodiesel production. Results The results reveal substantial uncertainty in pollutant inventories. For example, NO X emissions range from 433 to 724 tons per year, depending on the applied methodology. Current palm-oil biodiesel output (16,259 tons per year) is sufficient to supply the Guayaquil BRT fleet without requiring additional land. Discussion Although the local biodiesel supply can support the Guayaquil BRT system, extending this bioenergy BRT strategy nationwide would likely generate significant upstream environmental pressures. The findings also highlight variability across emission estimation methods. Conclusion The study shows that while the Guayaquil BRT system can be supplied with existing biodiesel production and evaluated through established emission methodologies, scaling such strategies requires careful consideration of upstream environmental impacts and methodological uncertainty.
Introduction While a substantial body of literature explores the relationship between transport infrastructure and urban development, empirical evidence combining macro- and micro-scale impacts in Southern European metropolitan contexts remains limited. This study addresses this gap by investigating the effects associated with the opening of new metro stations (Line 2) on local economic development and land-use change in the southern sector of the Athens Metropolitan Area. Methods Employing a multi-scale approach combining macro-level statistical analysis and micro-scale geospatial analysis using Geographic Information System (GIS) tools, the study evaluates changes in employment, car ownership, objective property values, and economic activities (reflected as land uses) before and after the construction of the new stations (2011-2021). Results The results indicate a statistically significant increase in total employment and employment in the accommodation and catering sector, alongside an increase in the share of carless households. Objective property values and economic activities exhibited notable growth near metro stations, with spatial variation, pointing to local economic revitalisation but also emerging risks of gentrification. Discussion The findings underscore the contradictory role of rail-based transport infrastructure as both a catalyst for local economic growth and a driver for societal challenges in Southern European metropolises. This outcome underlines the need for accompanying policy measures to ensure socially equitable and sustainable urban development. More specifically, economic and spatial strategies should include measures to support local businesses, promote sustainable mobility, and regulate property values. Conclusion This research contributes to the broader discourse on the interrelation between transport infrastructure and urban development in evolving metropolitan contexts. The findings may be beneficial to policymakers, transport providers, and local communities. Finally, the research methodology could be applied to other similar metropolitan contexts.
Introduction This study estimates traffic demand response to road capacity changes in Rawalpindi Division, Pakistan, using panel data on Vehicle-Kilometers Traveled (VKT) and lane-kilometers. It provides subnational evidence from a South Asian context where link-level panel data are rarely available. Methods A dynamic panel dataset comprising 69 road segments observed over nine years (2014–2023) was analyzed. Arellano–Bond and Blundell–Bond System GMM estimators were applied to address potential endogeneity, unobserved link heterogeneity, and the dynamic adjustment in traffic demand. All continuous variables (VKT, lane-kilometers, fuel price, vehicle ownership, and population growth rate) were log-transformed to estimate elasticities and stabilize variance. Results The preferred System GMM yields an elasticity of 0.967 for lane-kilometers, implying that a 1% increase in lane-kilometers is associated with an approximately 1% increase in VKT. Fuel price, vehicle ownership, and population growth rate are statistically insignificant in the preferred model. Diagnostic tests support instrument validity and indicate no evidence of second-order serial correlation in differenced residuals. Discussion The near-unit elasticity is consistent with induced-demand mechanisms, suggesting that capacity expansions are associated with higher traffic volumes and erode congestion relief. The weak effects of fuel prices and vehicle ownership reflect Pakistan’s institutional and behavioral context, including regulated fuel prices and a vehicle fleet dominated by motorcycles, along with limited modal alternatives. Conclusion Road capacity expansion in Rawalpindi Division is strongly associated with higher traffic volumes. Supply-only strategies are therefore likely to deliver limited long-run congestion relief unless accompanied by multimodal mobility measures and demand-management policies.
Introduction/Objectives Road accidents on inter-urban expressways in Malaysia exhibit spatial concentration and varying severity levels, requiring empirical analysis to support targeted safety interventions. This study examines spatial–temporal accident patterns and identifies key determinants of accident severity on the Shah Alam Expressway. Methods A dataset of 2,823 accidents (2013–2017) was analysed using descriptive statistics and 400 m spatial segmentation. Clustering was assessed using scatter plot outlier analysis and Global Moran’s I. An ordinal logistic regression model was applied to examine factors influencing accident severity. Results Accident frequency peaked at 728 cases in 2013, declined by about 30% in 2014–2015, then increased in later years. Persistent hotspots were identified at KM40.5–40.9 (both directions), with additional high-risk segments at KM27.0–27.4 and KM47.5–47.9 (eastbound) and KM49.0–49.4 (westbound). Global Moran’s I confirmed significant clustering at smaller scales (z = 3.086, p = 0.002). Most accidents involved property damage (61.9%), while 9.2% were serious or fatal. The regression model was significant ( p < 0.001), with vehicle type as the strongest predictor. Discussion Accident reductions were not sustained, while persistent hotspots indicate location-specific risks requiring targeted interventions. Significant clustering supports localized measures. Although most cases were minor, severe accidents remain concerning. Vehicle type strongly influences severity, suggesting the need for differentiated safety strategies. Conclusion Accidents are spatially concentrated, and severity is strongly linked to vehicle characteristics. Site-specific and vehicle-focused interventions are essential to reduce accident severity effectively.
Problems of sustainable development in rural areas are highlighted in the context of Sustainable Development Goals. Both Goal 9 and Goal 11 refer to the accessibility of infrastructure and public transport systems. Passenger and freight transport often lack adequate infrastructure and services in rural areas, typically characterized by lower population density and greater territorial dispersion. Integrating passengers and goods flow through scheduled or on-demand services is crucial to reducing territorial gaps. Services like Demand Responsive Transport (DRT), with its logistics aspects and other passenger-goods integration services, are well-suited to contexts with weak transport demand situations that can lead to potential social exclusion and demand niches not covered by traditional public transport. This research presents a general overview to assess some of the main applications of passenger-goods integration services in rural contexts. An evaluation of the current state of the literature on integration between passenger and goods transportation, available in scientific databases (Scopus, ScienceDirect, Web of Science, and IEEE Xplore), is performed. 18 contributions were in-depth analyzed through a geographical and temporal classification, focusing on several aspects, including the methodologies adopted, types of services, and classification of study areas. The study reveals a growing interest in the topic, with 10 contributions published in 2023 alone. Most of these contributions originate from China and Japan, respectively, 7 and 3 of them. Additional contributions come from several European countries, especially Sweden and Italy. The research findings highlight the growing interest in this topic in the scientific literature, underscoring the planning and implementation of combined transport services in line with the goals of the Green Deal and Agenda 2030, and considering methodologies from Transportation Systems Models.
Introduction Considering the rapid advancement of the airline sector, research must offer a comprehensive perspective on service quality and customer viewpoints. The study examines the specific relationship between service quality and consumer satisfaction in the airline industry, with service convenience and perceived risk as mediators. Methods The gathered data were analyzed by employing a technique known as Partial Least Squares Structural Equation Modeling (PLS-SEM), which is appropriate for complex models that contain latent variables. The research sample consisted of 375 customers who had experience travelling by air and were willing to participate in the study. Results The findings underscore the mediating role of service convenience in the attainment of customer satisfaction derived from service quality. Another key finding is that service quality directly affects customer satisfaction, whereas perceived risk does not substantially affect customer satisfaction. Discussion This study presents a discussion of the research findings, encompassing four primary components: (i) service quality has a direct impact on customer satisfaction, (ii) service quality directly influences service convenience and indirectly affects customer satisfaction through service convenience, (iii) service quality has an inverse relationship with perceived risk, and (iv) the perceived risk does not significantly impact customer satisfaction. Conclusion The study offers valuable insights for both researchers and managers in airlines and tourism organizations, enabling them to enhance consumer satisfaction by improving service quality and convenience in airline services. This is crucial in the present context owing to intense rivalry in the hospitality sector.
Introduction The COVID-19 pandemic has significantly disrupted urban life, including long-lasting changes in public transportation usage patterns. This study investigates how behavioral shifts in public transport ridership after the pandemic have influenced urban air quality. Materials and Methods The research analyzed data from eight major European cities: Paris, Berlin, Athens, Rome, Lisbon, Madrid, Istanbul, and London. A Public Transport Usage Index (PTUI) was developed based on five key behavioral factors affecting public transport use post-COVID. The relationship between this index and air quality was statistically examined by calculating the correlation between PTUI and the post-pandemic Air Quality Index (AQI). Results A strong negative correlation (r = -0.9304) was identified between the PTUI and AQI, indicating that increased public transport use is associated with improved air quality. All cities showed this trend, except Berlin, where the pattern deviated. Notably, cities with higher PTUI scores experienced more significant reductions in pollution indicators such as PM2.5 and NO2. Discussion These results underscore the role of sustainable public transport usage in mitigating urban air pollution. The findings align with existing literature emphasizing modal shift as a key strategy in urban environmental improvement. However, the study is limited by potential variations in data reporting standards across cities and by short-term observational scope. Conclusion This study demonstrates that post-pandemic behavioral changes in public transport usage have had a measurable impact on urban air quality. The findings offer valuable insight for urban planners and policymakers seeking to design resilient, health-conscious transportation systems that support long-term environmental sustainability.
Parcel lockers (PLs) are gaining recognition for their role in improving last-mile logistics efficiency and sustainability. They provide a reliable, eco-friendly solution by reducing failed deliveries and optimising logistical operations. However, their relationship with accessibility—both in terms of the user's ability to access the parcel (e.g., convenience) and its spatial distribution, a fundamental criterion for companies —remains an area requiring further investigation, particularly across urban, suburban, and rural contexts.This review examines studies from 2000 to 2024 to evaluate how PLs contribute to accessibility and identify different adoption factors across context, urban and non-urban (e.g. peripheral, rural). Total 48 research papers were analysed to assess trends, challenges, and opportunities related to PLs accessibility.Findings highlight contrasting perspectives between end-users and logistics providers on parcel locker accessibility. While PLs enhance convenience through 24/7 availability, widespread distribution, and on-demand service, challenges remain in peripheral areas due to unequal distribution, digital literacy barriers, and integration issues within logistics networks. Logistics efficiency and user equity must be balanced to ensure sustainable and inclusive last-mile delivery solutions.It highlights that accessibility and proximity are notably influencing the implementation and acceptance of parcel lockers. Rural accessibility remains a challenge. Digital divides, cultural barriers, and weak logistics integration create significant obstacles. To maximise the benefits of PLs networks, future research should focus on equitable last-mile strategies, enhanced stakeholder collaboration, and integrated urban planning approaches, ensuring that parcel lockers contribute to a more inclusive and sustainable logistics system.
Introduction Tunnel construction is a high-risk, complex task requiring precision, safety, and efficiency. With growing infrastructure demands, this study proposes a hybrid framework integrating Building Information Modeling (BIM), machine learning models such as Artificial Neural Network (ANN), K-Nearest neighbors (KNN), Support Vector Machines (SVM), and advanced optimization techniques to improve decision-making, predict geological challenges, and automate key operations in large-diameter tunnel projects, enhancing overall project performance and risk management. Methods Various methods are employed in the study, including BIM, machine learning, and robust optimization, which can be perceived as enhancing tunnel construction. Prediction using AI-based algorithms, namely ANN, KNN, and SVM, was made possible with real-time sensor data on geological issues. FANUC ROBOGUIDE software was also used to simulate the actions of robots, ensuring that material handling was performed with precision. Among these three, the optimal performance of SVM outshines ANN and KNN. Results The results have shown that BIM integrated with machine learning and optimization significantly increased tunnel construction performance. In predicting critical operational parameters, AI-based models, especially SVM, were found to provide an accuracy of 98.56%, outperforming KNN and ANN. Hence, this kind of predictability may allow for real-time modifications in the Tunnel Boring Machines (TBM) settings, thereby decreasing the risks associated with geological uncertainties. Additionally, the FANUC ROBOGUIDE software will ensure more precise and collision-free material handling, further enhancing safety and efficiency in tunnel construction projects. Discussion The study demonstrates that integrating BIM with machine learning and robotic simulation significantly enhances tunnel construction efficiency and safety. Among the models evaluated, SVM achieved the highest accuracy (98.56%) in predicting geological challenges. Real-time data processing enabled timely adjustments to TBM operations, while FANUC ROBOGUIDE ensured precise material handling, reducing risks and delays in complex construction environments. Conclusion The research currently underway has established the efficacy of integrating BIM, machine learning, and optimization in improving tunnel construction. The applications of AI models, such as SVM, KNN, and ANN, have improved targeted operational parameters and reduced geological risks, with SVM yielding the highest accuracy at 98.56%. Efficiency and safety were further enhanced by real-time data-driven decisions and robotic simulations. The developed framework offers a practical solution for enhancing decision-making and operational efficiency in complex engineering projects.
Introduction This paper introduces a novel collaborative filtering recommender system designed to optimize work schedule assignments for Straddle Carrier (SC) drivers at container terminals. The proposed Straddle Carrier Assignment Model (SAM) addresses critical operational challenges by integrating multi-dimensional rating matrices with seniority-based similarity metrics to create an intelligent scheduling system that balances operational efficiency with workforce satisfaction. Methods The system was implemented at the RADES container terminal using a three-tier architecture that incorporates real-time feedback mechanisms and an intelligent scoring algorithm that dynamically adapts to changing operational conditions. The mathematical framework combines collaborative filtering with domain-specific constraints through hybrid similarity computation, dynamic neighbor selection, and constrained optimization algorithms. Results The implementation demonstrated significant operational improvements, including a 93% reduction in schedule response time, a 64% decrease in assignment disputes, and a 31% increase in container handling efficiency, over a 24-month evaluation period. The system achieved 99.9% uptime, with a 28% improvement in resource utilization and an 85% positive driver satisfaction rating. Discussion SAM's innovative approach represents a significant advancement over traditional rule-based scheduling methods by introducing machine learning techniques to the maritime logistics domain. The mathematical framework combines collaborative filtering with domain-specific constraints to produce schedules that optimize both terminal productivity and driver satisfaction. Conclusion By addressing the fundamental challenges of schedule optimization in container terminals, this research provides both theoretical contributions to recommender systems and practical value to maritime logistics operations.
Introduction/Background Multi-criteria decision-making (MCDM) approaches have been utilized recently in several types of research, including transport systems. MCDM can aid researchers in the decision-making process in complex situations that entail more than one criterion in the selection process. Among MCDM approaches, Decision-Making Trial and Evaluation Laboratory (DEMATEL), or the decision-making trial and evaluation laboratory, is a useful tool for examining the underlying connections between various components of complex systems. Materials and Methods This research conducts a systematic literature review and a thematic analysis that synthesizes the current body of literature that employed the DEMATEL technique within the context of transportation systems. We searched five electronic databases (ScienceDirect, Springer, Taylor & Francis, Scopus, and Web of Science) for studies from 2018 to 2024 using preset keywords. A total of 37 papers were retrieved, and after removing duplicates in Mendeley, 28 remained. Abstracts were manually reviewed based on inclusion criteria, followed by a quality assessment, resulting in a final dataset of 26 studies. Results The study presents potential research and future directions focusing on the sustainability and environmental impact within transportation systems. Discussion Findings underscore the diversity of DEMATEL applications, ranging from transport type, sustainable initiatives, and safety and risk management. Conclusion This review concludes with recommendations for future research directions aimed at addressing emerging challenges and advancing the field of decision-making in the transportation sector.
Background Accurate knowledge of passenger volumes is critical for enhancing public transportation, particularly in Open (without barriers) Mass Transit (OMT) systems where traditional counting methods may be inadequate. Automatic Passenger Counting (APC) systems offer a reliable solution, yet their performance in OMT remains underexplored. Additionally, a lack of a comprehensive framework for evaluating APC accuracy has limited a full understanding of their effectiveness. Objective This study proposes an integrated framework to evaluate the accuracy of APC systems in OMT. The practical effectiveness of this framework is demonstrated through a real-world case study conducted on the Brescia Metro (Italy), showcasing how user-friendly outputs can highlight potential areas for improving the efficient management of APC systems. Methods The framework is divided into two blocks. Block 1 selects representative stations and collects passenger data through manual counts and APC systems. Pre-processing ensures the synchronisation of both data sources. Block 2 analyses APC performance at aggregated and disaggregated levels. The aggregated analysis uses several error metrics to assess overall accuracy, with confidence intervals identifying potential systematic errors. The disaggregated analysis examines station-specific performance. Results The Brescia Metro’s APC system demonstrated high reliability, with slight overestimations for both entering (+0.52%) and exiting (+1.41%). Errors were classified as random, indicating no need for corrective coefficients. Station-specific analyses revealed that simpler layouts and lower passenger volumes yielded higher accuracy. The performance metrics showed consistency with literature findings but highlighted unique error patterns influenced by open environments. Conclusions This study offers practical insights for transportation authorities (TAs), public transport companies (PTCs), and APC producers. It encourages TAs and PTCs to optimise service using real-time passenger flow data from APC. It also advises producers to customise APC systems for stations with complex layouts or higher passenger density. Future research should enhance APC accuracy using advanced analytical methods, integrate emerging technologies such as Wi-Fi and cellular tracking, include more complex case studies, and address data security concerns.
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. 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. 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. 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. 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.
With the rapid development of electric vehicles in China, there is an imbalance between supply and demand in the layout of decentralized charging facilities in some cities. At present, there is more research on the layout of public fast charging stations, while there is less research on the layout of decentralized charging facilities. This study constructs a quantitative model to optimize the urban layout of decentralized charging facilities, addressing the imbalance between supply and demand. Based on the classification research method, a predictive model for the distribution of decentralized charging facilities was designed. This model was used to conduct a case study on the planning area of Nantong City, and the carbon emissions and energy saving effects generated by the operation of charging facilities in the area were discussed using comparative analysis. This study predicted the charging demand for the target year, determined the upper limit, lower limit, and reasonable value of the number of decentralized charging facilities, and determined the number of fast and slow charging devices based on the reasonable value. These charging facilities can effectively meet the charging demand while reducing carbon emissions and urban transportation costs. This study provides a method for predicting and laying out decentralized charging facilities for urban electric vehicles, which can provide a reference for relevant departments to build decentralized charging facilities in advance to meet the growing demand for charging.
Integrating sustainability is a crucial process that every organization must incorporate into its supply chain. Transportation is one of the primary contributors to carbon emissions, accounting for approximately 24% of global CO 2 emissions. Notably, road transport is responsible for nearly 75% of these total emissions worldwide, according to recent studies conducted by the International Energy Agency (IEA) in 2021. This paper explores the Green Vehicle Routing Problem (GVRP), which represents an extension of the classical Vehicle Routing Problem (VRP). GVRP integrates environmental considerations by focusing on reducing the carbon footprint. Within this framework, we focus on the Green Vehicle Routing Problem Pickup and Delivery with Time Windows (GVRPPDTW) variant, which introduces additional complexity by requiring vehicles to serve customers with specific pickup and delivery requests within predefined time windows. This variant reflects realistic constraints in the logistics field, such as schedule synchronization and the balance between efficiency and customer satisfaction. This study presents a short comprehensive review conducted over the period 2017-2024, identifying and analyzing research conducted in the context of this variant and analyzing the efficiency of using metaheuristic algorithms in solving this optimization problem. We will discuss existing research gaps and propose future directions for further advancements in the field. This analysis aims to provide a comprehensive understanding of GVRPPDTW, offering valuable insights for researchers and practitioners seeking to address its challenges.
Introduction The study investigates the problem of customer assignments to existing warehouses within the scope of supply chain management, aiming to achieve cost savings and operational efficiency in warehouse operations. Specifically, the customer assignment problem is modeled under the assumptions of horizontal collaboration, utilization of a shared digital infrastructure, fair distribution, and cold chain logistics. These assumptions are applied within a framework consisting of clusters of service receivers and service providers. Objective This research aims to assess the impact of horizontal collaboration, common digital infrastructure, cold chain logistics, and fair income distribution assumptions on assignment problems under the title of warehouse management. Methods A decision support model is proposed for the assignment problem under the title of warehouse management. This model incorporates the key assumptions and operational parameters to optimize customer assignments under varying scenarios. Results The findings obtained from the study reveal that in the presence of horizontal collaboration among service providers, the costs of service providers are minimized, the demands of service receivers are met more, the profit obtained from service receivers increases, and the number of warehouses used increases. Under the assumption of fair distribution, it has been observed that cold warehouses contribute more to the total profit, in other words, the income obtained per volume of warehouses, since they generate more income compared to standard warehouses. Conclusion This study highlights the benefits of fostering horizontal collaboration and fair income distribution among supply chain actors through a shared online platform. The results underscore the potential for improving profit levels, meeting customer demands more effectively and optimizing warehouse utilization. These insights provide valuable guidance for decision-makers aiming to enhance supply chain efficiency and equity.
