Sustainable Transport Research Articles

A novel method for evaluating ship concept performance in transport systems

Abstract Transport carries significant external costs such as climate change, accidents, pollution, and road congestion, driving national and international strategies for the development of new transport concepts. This includes shifting larger cargo volumes away from roads to more sustainable transport modes such as waterborne. The SEAMLESS project was launched in 2023 to answer to these needs by developing technology for autonomous waterborne zero-emission feeder-loop services. The realisation of such services depends on their modal competitiveness. Autonomous ships are expected to reduce transport costs and emissions, and ultimately improve logistical performance. There are, however, few published studies that quantify these impacts of autonomy. Furthermore, commercial waterborne autonomous transport services do not exist yet, limiting the possibilities for empirical analysis. Hence, research is needed to address exactly how and to what extent, autonomy improves competitiveness in different applications. This paper addresses the need for more empirical analyses of innovative waterborne transport performance, by presenting a novel method for evaluating ship concept performance in transport systems. The impacts of design choices are captured through hydrodynamic and logistical simulations. The method can be applied to transport systems consisting of both conventional and novel ship designs, operating on one or more routes including transhipments. It is implemented in the software SIMPACT and applied to a case study which establishes a shortsea feeder-loop service in the Bergen area in Norway. The Bergen municipality has decided that the container terminal is to be moved out of the city centre to reduce local traffic and emissions. However, in the absence of a competitive waterborne transport service in this region, the relocation will have the unfortunate consequence of an estimated annual net increase in regional truck traffic of 40,000 additional truck trips over 25km. By means of the proposed methodology, this paper investigates the feeder-loop concept and compare its quantified performance to truck transport and finds that competition is feasible.

Thermal Reactivity of Bio-Oil Produced from Catalytic Fast Pyrolysis of Biomass.

Catalytic fast pyrolysis (CFP) of biomass is a versatile thermochemical process for producing a biogenic oil that can be further upgraded to sustainable transportation fuels, chemicals, and materials. CFP oil exhibits reduced oxygen content and improved thermal stability compared to noncatalytic fast pyrolysis oil. However, some level of reactive oxygenates remain in CFP oils, and reactions between these species can result in molecular weight growth and increased viscosity, leading to the potential for challenges during transportation, storage, and downstream processing. Previous research has provided considerable insight into the reactivity of noncatalytic fast pyrolysis oils, but CFP oils have yet to be studied in a similar fashion. Consequently, the degree of catalytic upgrading that is necessary to effectively stabilize CFP oils has yet to be established, and little is known about the mechanistic details underlying the process. The current study addresses this knowledge gap by controlling the CFP reaction conditions to systematically vary the oxygen content of the resulting oil. Accelerated thermal reactivity studies were then performed, and the CFP oils were analyzed using gas chromatography-mass spectrometry (GC-MS), Fourier transform ion cyclotron mass spectrometry (FT-ICR MS), gel permeation chromatography (GPC), and viscometry to evaluate the impact of heating on their physical and chemical properties. The results revealed that short chain carbonyls, anhydrosugars, and lignin derivatives with conjugated vinyl groups likely play a role in the thermal reactivity of CFP oils. Additionally, experiments performed across a wide variety of feedstocks revealed relatively low thermal reactivity for CFP oils with oxygen contents of <20 wt %. However, above this threshold value, the thermal reactivity grew exponentially as a function of oxygen content, resulting in large increases in viscosity and molecular weight. These results serve to deepen the mechanistic understanding of CFP oil thermal reactivity and help inform the development of quality specifications for catalytic upgrading to effectively stabilize CFP oils.

Vulnerability simulation and evaluation of urban metro operation system: A hybrid structural equation model with system dynamics approach

To effectively identify the influence factors of urban metro operation system (UMOS) vulnerability and research the dynamic evolution process of system vulnerability, ensuring the long-term safety of UMOS, a hybrid method integrating structural equation model (SEM) and system dynamic (SD) is proposed to evaluate the UMOS vulnerability. The vulnerability evaluation indexes system of UMOS is established by questionnaire design, which includes four dimensions of personnel, equipment, management and environment. The effectiveness of the method is verified by taking Wuhan metro system in China as an example. The results indicate that (1) The influence of personnel, equipment, management and environment factors on UMOS is 30.2 %, 27.6 %, 21.6 % and 20.6 %, respectively. (2) The passenger flow level and emergency rescue system level have the greatest influence on the vulnerability of UMOS via sensitivity analysis. (3) Extreme weather conditions have the greatest impact on the environment sub-system vulnerability, which in turn affects the UMOS. Hence, the proposed method herein can simulate the long-term vulnerability of the entire UMOS and formulate corresponding improvement measures, thereby achieving sustainable transportation development.

Exploring the effect of government incentives on electric vehicle purchase intention in smart cities

The use of electric vehicles is gaining momentum throughout the world. Low-carbon mobility is a core area of focus for sustainable transportation in smart cities. Policy makers and governments in many countries are incentivizing the manufacture, sale, and purchase of low carbon-emitting vehicles such as electric vehicles. Not many studies have focused on the usage of electric vehicles in smart cities from the perspectives of customer behavior and government incentive. Therefore, the aim of this study is to understand how the moderator government incentives influence potential customers' intention to purchase electric vehicles for smart mobility in smart cities. With the help of the theory of planned behavior and various literature, a theoretical model has been developed which is later validated using CB-SEM technique considering responses from 415 potential customers of electric vehicles. This study has demonstrated that subjective norms and personal norms supported by users’ attitude and behavioral control to use EVs could impact on the purchase intention of electric vehicles with active support of the government incentives. This study has proposed a pragmatic framework helpful for the policymakers to motivate the potential purchasers of the smart cities to purchase EVs.

Safety and bicycle route choice – To what extent does accident risk and perceived safety influence bicycle route choice?

Safety is a major issue in bicycle traffic and understanding safety-related factors that influence bicycle route choice is crucial for improving safety and promoting sustainable transportation. However, there is a notable lack of research on the impact of accident risk and perceived safety on cyclists' route choice. This study addresses this gap by investigating whether accident-prone areas or perceived insecure locations affect actual route choice decisions. The contribution explores this relationship by leveraging an extensive dataset comprising approximately 4,000 trips from around 170 participants, alongside additional data on infrastructure, operations, exposure, accidents, and mobility diaries on critical incidents. The findings broadly confirm the results from other studies regarding the influence of route characteristics (e.g., existence of cycling infrastructure, traffic volumes of motorized traffic, or presence of signal-controlled intersections). Moreover, the study reveals that a high accident risk along a route does have a slight negative influence on route choice. Surprisingly, perceived safety does not significantly influence route choice of cyclists.

Income moderates the nonlinear influence of built environment attributes on travel-related carbon emissions

Policymakers have adopted built environment policies to modify people's travel behavior and the related emissions. However, few studies have examined the interactive impact between income level and built environment attributes on travel-related carbon emissions (TCE), and only several studies consider their nonlinear relationships. With data from the Twin Cities, US, this study estimated the nonlinear effects of built environment attributes and demographics on TCE. It further examined the interactive impacts between household income and built environment attributes. The findings highlight that demographics exert a greater influence on TCE than the built environment. Employment status, job accessibility, and gender are the most important predictors. Besides individual nonlinear relationships, household income and built environment attributes have salient interactive impacts on TCE. The results suggest that providing environment friendly and affordable transportation choices to low-income population, switching to clean energy vehicles, and offering more matched job opportunities to low-income population near their residence are promising to create a sustainable transportation system.

Impacts of the sea-rail intermodal transport policy on carbon emission reduction: The China case study

China introduced the Intermodal Transport Demonstration Project Policy in 2015, spurring the rapid expansion of sea-rail intermodal transport (SRT) at coastal ports. This policy is of crucial importance as SRT is pivotal in supporting the transport sector's achievement of the “double carbon target”. Despite strong policy backing and notable growth of SRT, the impact of these SRT demonstration projects on curbing transport carbon emissions has not been comprehensively evaluated. This study addresses this gap by using panel data from hub seaports across nine provinces and applying a multi-period-continuous difference-in-differences model. The research quantifies SRT's contribution to reducing transport carbon emission reductions as a result of the policy. Key findings indicate: (1) Each 10,000 TEU increase in containerized SRT volume results in a 0.162% reduction in transport carbon emissions, with projected growth by 2025 leading to a 9.02% decrease in emissions. (2) SRT lowers transport energy consumption and carbon emission intensity, fostering a green transformation in energy use. (3) Geographical analysis indicates that policy implementation in the southern region requires further strengthening. This study enhances empirical understanding of low-carbon SRT development in hub ports, emphasizing its critical role in China's transition to decarbonized transport. It also highlights the importance of regional policy effectiveness, offering valuable insights for policymakers striving to promote sustainable transport modes.

Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks

This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.

Workforce development in the transport sector amidst environmental change: A conceptual review

This study embarks on an exploratory journey to elucidate the complex interplay between workforce development and environmental sustainability within the transport sector. It underscores the critical importance of nurturing a skilled workforce capable of propelling the sector towards sustainable practices, thereby addressing pressing environmental challenges. To achieve a comprehensive understanding, this review synthesizes a wide array of data sources, adopting a strategic framework for literature search and analysis. The methodology hinges on stringent criteria for literature selection, ensuring the relevance and quality of the information reviewed. Central to the study is the development of a theoretical framework that situates workforce development within the paradigm of sustainable transport. This includes an examination of the architectural underpinnings of sustainable transport systems, the potential for modal shifts to lessen environmental impacts, and the pivotal role of technological and methodological advancements. Moreover, the review highlights innovative practices in workforce training and development that are crucial for meeting future demands. The in-depth analysis sheds light on the multifaceted impacts of workforce development initiatives on achieving sustainable transport objectives, emphasizing the economic, social, and environmental dimensions. It identifies existing gaps in workforce competencies and outlines strategies for skill enhancement. Additionally, the study emphasizes the indispensable role of policy, standards, and stakeholder collaboration in fostering sustainable transport. Conclusively, the study presents strategic recommendations for policymakers, educators, and industry leaders, charting a course for a resilient and sustainable transport workforce. It posits that an integrative approach, marrying workforce development with environmental sustainability, is essential for navigating the future challenges and opportunities within the transport sector.

Supply Chain Management Construction: The Role of IoT in Human Resource Management and Green Logistics

Environmental practices are incorporated into the supply chain management and logistic process of human resource management. It is referred to as a green logistics supply chain. To reduce the negative effects of logistical activities on the environment, sustainable transportation, eco-friendly packaging, and effective waste management techniques are used. Adopting environmentally conscious supply chain practices can improve a company's environmental reputation while lowering carbon emissions, increasing operational effectiveness, and saving money on human resource management. In order to adopt sustainable practices across every aspect of the value chain, from acquiring raw materials to delivering completed items to clients, this strategy necessitates coordination across stakeholders in the supply chain management and logistic process. In general, using a green logistic supply chain is a crucial approach for companies looking to grow sustainability while reducing their environmental impact. Using IoT in human resource management can boost operational effectiveness, cut down on transportation expenses, and boost customer satisfaction. For instance, current information on inventory levels, the quality of goods, and shipment status can be provided by smart sensors and devices, allowing businesses to optimize the way they manage their supply chains and reduce waste in human resource management.

Application of Catalysts Used in Biodiesel Production - A Review

Biodiesel stands as a promising alternative to traditional fossil fuels for transportation, boasting renewability, biodegradability, and environmental friendliness. However, the efficiency of biodiesel production hinges on the catalytic processes that expedite the crucial transesterification reaction between triglycerides and alcohols. The choice of catalyst becomes a multifaceted decision, influenced by factors such as feedstock quality, specific reaction conditions, and the environmental footprint associated with the catalyst itself. This comprehensive paper deals the current state of catalysts employed in biodiesel production, offering readers a nuanced understanding of the subject. The catalyst landscape is explored through an intricate analysis of various types, encompassing homogeneous, heterogeneous, enzymatic, nano, and bio-derived catalysts. A critical component of the paper is the exploration of recent advancements in catalyst synthesis techniques. This includes an assessment of the performance of these novel catalysts, elucidating their potential contributions to enhancing the biodiesel production process. By offering insights into the future prospects of these catalysts within the context of biodiesel production, the paper contributes to the evolving discourse on sustainable energy sources. In pursuit of its overarching goal, the paper aspires to furnish readers with more than just an up-to-date overview; it aims to provide a thorough examination of the prevailing trends and challenges in the realm of catalyst utilization for biodiesel production. As the world seeks greener alternatives, this paper offers valuable insights that extend beyond the present, fostering a deeper understanding of the dynamics shaping the future of biodiesel production and sustainable transportation.

CHINA’S LEADERSHIP IN NEW ENERGY VEHICLES: STRATEGIES AND GLOBAL IMPACT

This article examines the transformative effect of China’s New Energy Vehicle (NEV) initiatives, highlighting its importance in global automotive innovation and environmental sustainability. While the term NEV is exclusively associated with China, it also encompasses electric vehicles, plug-in hybrids, and hydrogen fuel cells, as a result of the country’s strategic push to reduce pollution and greenhouse gas emissions. China’s dominance in the NEV sector is attributed to its policies, which include substantial subsidies and tax benefits, as well as a rapidly expanding domestic market and robust infrastructure investments. The article examines the way these policies have facilitated domestic production, pushed technological advancements, and improved production efficiency. It emphasizes China’s 14th Five-Year Plan, which aims to increase the market share of NEVs and develop infrastructure while aiming for carbon neutrality by 2060. The analysis suggests that other nations could be benefited by adopting similar strategies, which include government support, R&amp;D investment, and infrastructure expansion, to create their own NEV industries. The issue of integrating foreign technology and developing local expertise is also addressed, along with potential challenges and solutions. Keywords: new energy vehicles, charging infrastructure, battery technology, sustainable transportation, market expansion, international partnerships.

Energy Harvesting Technologies in Electric Vehicles and Applications in Sustainable Agricultural Transportation: A Review

The increasing challenges of energy depletion, environmental pollution, climate change, and agricultural transportation costs demand innovative solutions. Electric vehicles (EVs) offer a promising solution towards mitigating these issues through renewable energy sources. However, limited driving range remains a crucial barrier to their widespread adoption, particularly in environmentally friendly agricultural transportation. This review paper comprehensively analyzes energy harvesting technologies in electric vehicles and their application in agricultural transportation. The focus is on their potential to improve driving range and address the specific needs of farmers in terms of maximizing their income. Very few review articles have been published on maximum number of possibilities of harvesting energies in electric vehicles, application in agricultural transportation and future prospects for improvement. Existing and current literature was systematically reviewed on various energy harvesting techniques applicable to EVs, including solar energy harvesting, regenerative braking, aerodynamic (wind) energy recovery, and vibration energy harvesting through suspension systems which are all renewable energy sources. Their advantages, working principle, limitations, and recent advancements were critically discussed. All explored energy harvesting methods show the ability to extend EV driving range. However, solar energy and wind or aerodynamic energy harvesting in EVs are theoretically possible but still under research for practical applications due to lots of limitations. Regenerative braking and vibration energy harvesting show the most promising current applications. The Electrical or electromagnetic vibration energy harvesters produce better results over mechanical (hydraulic and pneumatic harvesters). When optimization is employed, better results were achieved. Research on the application of artificial intelligence based computer algorithms to optimize and produce the best amount of harvested energy in EVs will play a significant contribution in the adoption of EVs for sustainable agricultural transportation.

Motorcycle Recognition System Using Convolutional Neural Network

Vehicle detection is becoming increasingly important for highway management, particularly in Aklan, where motorcycles and tricycles are the predominant modes of transportation. Due to their diverse designs, accurate detection of these vehicles remains challenging. This study addresses this issue by developing a vision-based motorcycle recognition system using a Convolutional Neural Network (CNN) implemented in MATLAB. A new high-definition dataset, comprising 34,002 annotated instances from 17,785 motorcycle images and 16,217 tricycle images, was created. The dataset was collected from various locations around Ibajay, Aklan, using regular cameras and smartphones. The images were classified into motorcycles and tricycles and then used to train the CNN model, which was compared with the You Only Look Once (YOLO) version 2 network. Experimental results indicate that the proposed CNN-based motorcycle recognition system achieves high detection accuracy, comparable to the YOLOv2 model. This system contributes to Sustainable Development Goal (SDG) 11 by improving sustainable urban transportation systems and traffic management. Additionally, it holds significant commercial value for motorcycle brands through enhanced vehicle tracking and market analysis.

AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLE. SIMULATION OF ENERGY CONSUMPTION

The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

Evaluation of Lithium-ion Batteries in Electric Vehicles

Growing awareness of climate change concerns and the environmental impacts of fossil fuel vehicles has heightened interest in electric vehicles (EVs). Therefore, EVs represent a significant component of sustainable transportation solutions. Additionally, with advancements in battery technology, EVs now have longer ranges and are offered at more competitive prices. With their notable features such as high energy density, lightness, low maintenance requirement, and long life, lithium-ion batteries (LiBs) appear to be the most suitable battery option for EVs. Nevertheless, current LiB technology faces battery costs, energy storage capacity, charging times, and safety issues. In this context, it is clear that future research and development will focus on improving the efficiency of LiB technology and making these batteries more sustainable, reliable, and economical. This study aims to provide an evaluation of the LiBs used in the automotive sector by examining the historical development, basics of operational principles, various geometric types, cost evaluation, and their advantages and disadvantages. By covering these aspects, the study seeks to offer a comprehensive assessment of the LiBs employed in the automotive industry, spanning from their historical evolution to their presentday utilization. The study also intends to serve as a reference source for researchers planning to conduct studies on LiBs in EVs by providing fundamental concepts and evaluations related to these batteries.

Assessment of Sustainable Transportation Model Using Energy-Efficient Algorithm

The proliferation and extensive utilization of vehicles have escalated energy use and increased environmental damage. Utilizing large amounts of information on metropolitan traffic patterns can enhance vehicle transportation's environmental and financial aspects. This can be achieved through an efficient decrease in fuel consumption and contaminants. This study aims to present a novel framework for evaluating the Sustainable Transportation Model with Energy-Efficient Algorithm (STM-EE) by utilizing Route Planning Algorithms (RPAs) in a simulated environment. The CARLA simulator was used to compare the widely used conventional Dijkstra technique and an Integrated Genetic Algorithm (IGA) that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) in different driving scenarios. This study aims to quantify the effects of RPA decisions on the energy consumption of vehicles. The efficiency of comparing the two RPAs has been enhanced by employing an offline energy estimate methodology. The proposed architecture is assessed through EE simulations to demonstrate its efficacy and adaptability. The results support the development of energy-efficient RPA solutions, which contribute to advancing the STM field.

MONITORING OF THE IMPLEMENTATION OF THE EUROPEAN UNION'S SUSTAINABLE TRANSPORT DEVELOPMENT POLICY REGARDING THE USE OF LOW- AND ZERO-EMISSION FLEET ON THE EXAMPLE OF THE GÓRNOŚLĄSKA-ZAGŁĘBIOWSKA METROPOLIS (POLAND)

Nowadays, the majority of society decides to use individual transport for everyday travel, which negatively affects the congestion of cities. Nevertheless, in the transport policy of the European Union, public transport is becoming more and more important, which is reflected in strategic documents in the field of sustainable transport development. The effect of this action is to increase the attractiveness of public collective transport, which will also translate into a reduction in pollutant emissions. This article aims to verify the implementation of the European Union's sustainable transport development policy concerning the use of low- and zero-emission rolling stock in the Upper Silesian and Zagłębie Metropolis. As part of the work, the strategic documents of the European Union in the field of sustainable development of transport were analysed. In addition, the idea of establishing the Upper Silesian-Zagłębie Metropolis in the context of the policy of sustainable transport development was introduced and the public transport system functioning in the area of the Metropolis GZM was characterized.

Partial replacement of sand by fine-grained crushed waste glass along with fly ash stabilization for geotechnical applications in pavement

Natural sand (NS) is one of the most used engineering materials in almost all types of construction worldwide. Considering environmental sustainability, the replacement of natural sand with crushed glass waste (CWG) can provide a solution for both geo-environmental problems of natural sand depletion and waste glass disposal at a time, since sand and glass share almost similar chemical components. This research aimed to investigate the mechanical behaviors of natural sand replaced with fine CWG particles by 5 %, 10 %, 15 %, 20 %, 25 %, 30 %, and 35 % of dry weight. NS-CWG optimum composition was then stabilized by adding Class C fly ash (FA) from 5 to 35 % of NS-CWG to find out the optimum FA content to stabilize NS-CWG. The effects were analyzed through compaction, direct shear, and California Bearing Ratio (CBR) tests. Test results showed that maximum dry density increased, and optimum moisture content decreased due to adding CWG, and a contrasting effect was found after adding FA. 25 % FA significantly stabilized the NS-CWG mixture's strength parameters, such as increased cohesion, angle of internal friction, improved shear strength by 55.67 %, and increased CBR by 44 %. The decreased roundness index and scanning electronic microscopy (SEM) test results further evaluated and supported the improved geotechnical properties of NS + CWG + FA. The findings of the study can be useful in producing recycled materials for constructing road pavements of sustainable transport network development.