Sustainable Transport Network Research Articles

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.

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.

Unveiling Pakistan's transport problems: a call to safeguard public health.

Public transportation is an important mode of transportation in developing countries like Pakistan since it is accessible and convenient. But there are also serious health hazards associated with it, especially when it comes to the transmission of infectious diseases including COVID-19, TB, and Haemophilus influenzae. Worldwide transportation systems are vulnerable, as the COVID-19 pandemic has shown, underscoring the necessity for study and mitigating measures. The danger of disease transmission is increased in Pakistan by crowded metropolitan areas, inadequate sanitation, and low health awareness. In addition, congested public transportation and inadequate ventilation lead to reduced air quality and elevated stress levels among commuters. Comprehensive actions are needed to address these health hazards, such as promoting physical distance, improving cleanliness, enforcing traffic safety laws, and implementing policy changes that support sustainable transportation. Community involvement and advocacy are critical in campaigning for safer and more sustainable transportation networks. Pakistan can enhance public health outcomes and reduce the health hazards linked to public transportation by giving priority to these measures.

Solar Operated Car Enabled with AI

The convergence of renewable energy and artificial intelligence (AI) has spurred a transportation revolution, exemplified by the rise of solar-powered cars equipped with advanced AI capabilities. This abstract digs into the integration of solar energy and AI technology in autos, examining their synergistic potential to transform the automotive industry and pave the way for a more sustainable future.The paper presents the concept of solar-powered cars and discusses their importance in reducing environmental concerns connected with regular fossil-fuelled vehicles. Solar energy, captured by solar panels built into the vehicle's construction, provides a clean and abundant power source, minimizing reliance on non-renewable resources and lowering greenhouse gas emissions. Furthermore, advances in solar panel efficiency and energy storage technology have increased the practicality of solar-powered vehicles for widespread use.. Furthermore, AI-powered autonomous driving capabilities improve safety by reducing human error and allowing for proactive collision avoidance methods. The paper highlights how AI-enabled solar-powered vehicles have the potential to completely change the automotive sector and hasten the shift to a sustainable transportation network

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Intelligent transportation systems enhance the potential for sustainable, user-friendly, and efficient transport. By eliminating driver costs, autonomous buses facilitate the redesign of networks, timetables, and fleet structure in a cost-effective manner. The electrification of bus fleets offers the opportunity to further improve the environmental sustainability of transportation networks, but requires adjustments to vehicle schedules due to the limited range and charging requirements. This paper examines the intricate relationship between electrification and autonomous buses. To this end, timetables for autonomous electric buses of different sizes were developed for a real bus route in Aachen, Germany. The resulting electric vehicle scheduling problem was then solved using an adaptive large neighborhood search to determine the number of vehicles needed and the total cost of ownership. By eliminating driver costs, vehicles with lower passenger capacity become much more attractive, albeit at a slightly higher cost. In comparison, the incremental costs of electrification are low if the right approach is taken. Fluctuations in typical passenger numbers can be used to modify timetables and vehicle schedules to accommodate the charging needs of autonomous electric buses. In particular, electric bus concepts with fewer charging stations and lower charging power benefit from adapting the timetable to passenger numbers. The results demonstrate that the specific requirements of electric buses should be considered when adapting networks and timetables in order to design a sustainable transport network.

Car Pooling System

Traffic jams, pollution, and wasteful use of resources are just a few of the transportation-related issues that have arisen as a result of urbanization. Adapting sustainable transportation networks is crucial to address these issues, and carpooling is one of the most important tactics in this effort. To support the development of a sustainable transportation ecology in metropolitan areas, this abstract provides a thorough foundation for a carpooling system. Utilizing contemporary technology such as GPS monitoring, data analytics, and mobile applications, the suggested Carpooling System maximizes vehicle occupancy and reduces travel times by pairing drivers and passengers in the most efficient way possible. Ride-sharing experiences are made easy and convenient by the system's integration of user preferences, including routes, departure hours, and passenger compatibility. To further ease traffic congestion, minimize carbon emissions, and encourage the economical use of transportation resources, the Carpooling System was created with sustainability as its primary goal. The approach promotes widespread adoption and involvement by offering incentives like cost-sharing and awards for eco- friendly behavior. The Carpooling System not only improves the environment but also the social and economic spheres by increasing social connections, encouraging community involvement, and saving consumers money on commuting. The concept helps create more inclusive and resilient metropolitan communities by encouraging a shared mobility culture. The importance of including carpooling in the larger context of sustainable urban mobility is generally highlighted by this abstract. A realistic way forward for livable, equitable, and ecologically conscious cities in the face of rising urbanization and transportation issues is provided by the suggested Carpooling System, which embraces innovation, technology, and cooperative efforts.

Interoperability for intelligent traffic management systems in smart cities

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.

Unveiling transportation disparities: investigating accessibility gaps in metropolitan cities using GIS—a case study of Alexandria, Egypt

IntroductionPublic transportation is essential to reduce pollution and traffic congestion in urban areas. However, in densely populated regions like Alexandria, characterized by high population density and limited road infrastructure, accessibility to diverse transportation options is crucial.MethodsThis study investigates the accessibility of public transportation modes, including railways and buses, and their interaction with informal transit. Questioning if public transportation alone will be sufficient for pedestrians or if the presence of informal transit is crucial, and whether all residents of Alexandria have their rightful accessibility for different transport modes, the goal was to establish an efficient and interconnected transportation network. A survey was conducted in cooperation with Transport for Cairo to investigate all transport routes throughout Alexandria, further using ArcGIS Pro to identify gaps in public transportation coverage.ResultsFindings revealed that public transportation alone is insufficient due to limited bus and railway coverage because of their fixed linear routes. Informal transit, like microbuses, plays a vital role in filling these gaps.DiscussionThe study emphasizes the need for a comprehensive and sustainable transportation network that considers both formal and informal transit. These insights can guide policymakers and urban planners in improving public transportation in Alexandria and similar cities worldwide.

Comparative Analysis of Sustainable Electrification in Mediterranean Public Transportation

The Mediterranean region is a hot spot for climate change, with transportation accounting for a quarter of global CO2 emissions. To meet the 2030 Sustainable Development Goals (SDGs), a sustainable urban transport network is needed to cut carbon emissions and improve air quality. This study aims to investigate the electrification of public transport in both developed and underdeveloped countries by examining the existing public transport network of two modes of transportation (buses and trams) across the Mediterranean region. This study suggests that the electrification of public transportation could result in a significant additional demand for more than 200 GWh of electricity, depending on the size and congestion of the city. It also studies the potential reduction of greenhouse gas (GHG) emissions through the electrification of buses. Results show that electrification significantly impacts decreasing GHG emissions, helping achieve SDG 13. Furthermore, a financial analysis was conducted to determine the feasibility of using different bus fuel technologies. Regarding economic benefits, electric buses are not consistently optimal solutions, and diesel buses can be advantageous. Our finding shows that, at a 5% discount rate, the diesel bus is most favorable for Marseille, and, as discount rates increase, the advantage of electric buses diminishes. However, the high purchase price of electric buses compared to diesel buses is currently a major obstacle in achieving SDG 11, particularly for developing countries.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

Assessing the effects of copolymer modifier addition on asphalt attributes: Towards achieving performance optimization

The use of polymers to modify asphalt binders has become a widely accepted practice in modern pavement construction, offering a promising solution to address key factors contributing to asphalt pavement deficiencies. These factors include issues such as rutting, moisture-induced deterioration, and fatigue-related wear. The copolymer referred to as PGXpand was utilized as the modifying additive for the virgin binder in this study. It demonstrates exceptional performance in terms of high-temperature resilience and resistance to rutting. The impact of incorporating the PGXpand on asphalt properties was investigated. A comprehensive series of tests were conducted, covering storage stability, penetration, softening point, ductility, viscosity, and elastic recovery. Further analyses involved calculations of stiffness modulus, complex modulus, and phase angle. Mechanical and performance evaluations were also carried out, including Marshall properties, moisture susceptibility, creep compliance, and Kim tests. The results demonstrate the effectiveness of the PGXpand in enhancing asphalt performance, with modified mixtures exhibiting significant improvements compared to their unmodified counterparts. Consequently, integrating the PGXpand into asphalt technology emerges as a highly promising approach for optimizing road infrastructure, especially in regions with high-temperature conditions. Ultimately, such integration contributes substantially to the development of sustainable and resilient transportation networks.

Understanding the Determinants of Lane Inefficiency at Fully Actuated Intersections: An Empirical Analysis

As urban traffic challenges intensify, the growing interest for fully actuated control systems in intersection management is on the rise due to their capacity to adapt to dynamic traffic demands. These systems play a crucial role in sustainable traffic solutions, significantly reducing delays and emissions and enhancing overall system efficiency. The optimal performance of these systems relies on effectively facilitating vehicle discharge at the saturation flow rate throughout the green period. This study introduces a new parameter, lane inefficiency, evaluating vehicle discharge effectiveness by comparing saturation flow rate with instantaneous discharge for each green period. It provides a comprehensive assessment of green utilization for specific lanes. This study also explores the impact of signal control system parameters and traffic flow characteristics on lane inefficiency using principal component analysis (PCA) and multiple linear regression models. This approach holistically evaluates how both signal control system and traffic flow parameters collectively influence efficient green period utilization. The findings emphasize the impact of critical factors on lane inefficiency, including green time, the proportion of total unused green time to green time, total unused green time, the percentage of heavy vehicles in departing traffic, the ratio of effective green time to cycle time, the total time headways of the first four vehicles, and queue length. Decision makers need to pay due attention to these parameters to enhance intersection performance and foster a more sustainable urban transportation network.

Opportunities for Multimodal Transport Development to Promote a Sustainable Environment

The rapidly growing global production and trade increase the demand for transport and logistics, leading to the establishment of increasingly more new companies providing these services. This also results in a much larger number of vehicles, which is not always a positive thing. Excessive vehicle traffic leads to an increase in environmental pollution, noise and traffic accidents, which have a highly negative impact on society and, more specifically, on human health. The negative impact on the environment has been increasing, and therefore representatives of national politics, the transport and logistics sectors should be alarmed and take action to reduce the environmental pollution. The development of the transport system should be carefully planned and geared towards achieving green and sustainable transport. In order to create a sustainable freight transport network, certain alternatives that could help to stop or at least reduce the process of environmental pollution should be pursued. One of such alternatives is the use of multimodal transport, which involves several different modes of transport for transporting one freight. This both distributes and reduces the burden on the currently dominant road transport mode, which contributes significantly to environmental pollution and is not sufficiently sustainable.

Sustainable road network design considering hydrogen fuel cell vehicles

Environmental pollution and energy shortages have brought about an increased focus on new energy vehicles. Hydrogen fuel cell vehicles (HFCVs) have experienced rapid development due to the potential to alleviate energy pressures and reduce pollution emissions. Near zero-carbon emissions offer a promising avenue for promoting sustainable transportation development. To evaluate the impact of HFCVs on the transportation environment, this paper investigates the problem of sustainable transportation network design including HFCVs. Specifically, the problem is formulated as a bi-level multi-objective programming problem, with the upper level aimed at determining the optimal network design scheme considering multiple objectives, while the lower level addresses the mixed traffic flow that comprises both HFCVs and fuel vehicles. To solve the multi-objective sustainable network design problem, an integrated solution framework that combines the particle swarm optimization (PSO) algorithm with the Frank-Wolfe algorithm (FW) is developed. Specifically, the PSO algorithm is utilized to solve the upper-level model and identify the optimal network design schemes, while FW algorithm is adopted to handle mixed traffic flow assignments. Finally, the proposed model and algorithm are implemented in two numerical experiment to demonstrate their effectiveness and efficiency.

Smart Traffic Management System

With the rapid urbanization and increasing vehicular density in modern cities, the demand for efficient and intelligent traffic management systems has become paramount. This paper presents a comprehensive overview of a Smart Traffic Management System (STMS) designed to optimize traffic flow, enhance safety, and minimize congestion in urban areas. The STMS leverages advanced technologies such as artificial intelligence, sensor networks, and data analytics to create a dynamic and adaptive traffic control infrastructure. The key components of the proposed system include real-time traffic monitoring, predictive analytics, and adaptive signal control. Utilizing a network of sensors, cameras, and other data sources, the STMS continuously gathers information on traffic conditions, identifying patterns and potential issues. This data is then processed through machine learning algorithms to predict traffic trends, enabling proactive management strategies. One of the innovative features of the STMS is its adaptive signal control mechanism. Traditional traffic signal timings are often static and fail to respond promptly to changing conditions. The STMS, however, dynamically adjusts signal timings based on real-time traffic data, optimizing traffic flow and minimizing delays. This adaptability is further enhanced by the integration of vehicle-to-infrastructure (V2I) communication, allowing direct communication between vehicles and the traffic management system. Additionally, the STMS incorporates intelligent decision-making algorithms to prioritize emergency vehicles, public transportation, and other critical services. This ensures a swift and efficient response to emergencies while maintaining overall traffic efficiency. Furthermore, the system includes a user-friendly interface accessible through mobile applications and web platforms. This interface provides real-time traffic updates, alternative route suggestions, and other relevant information to empower commuters with the knowledge needed to make informed travel decisions. The implementation of the Smart Traffic Management System holds the potential to revolutionize urban mobility by significantly reducing travel times, fuel consumption, and environmental impact. This paper discusses the underlying technologies, design considerations, and potential benefits of the STMS, highlighting its role in creating smarter and more sustainable urban transportation networks

Numerical Modeling and Simulation of a Small-Scale Locomotive Powered by Solid Oxide Fuel Cells

The adverse environmental effects of fossil fuels resulted in a sharp rise in demand for renewable energy sources such as solar, wind, and hydrogen. Environmental pollution might be drastically reduced by using renewable energy sources. Various examples of solid oxide fuel cells being employed in locomotives exist in the literature. This work used numerical techniques to simulate and model a vehicle with eight wheels, considered as a small-scale locomotive powered by solid oxide fuel cells. The feasibility of solid oxide fuel cell-based locomotive was investigated through numerical modeling and simulation. The Matlab-Simulink platform, which includes an electrical system, an energy management system, and vehicle dynamics, was used to run the simulations. Four different locomotive configurations were created, (1) The mass of the locomotive (m) = 65000 kg, operating temperature (T) = 750°C, (2) m = 65000 kg, T = 1000°C, (3) m = 80000 kg, T = 750°C, and (4) m = 80000 kg and T = 1000°C. The performance parameters of the vehicle were recorded after the simulations. These factors are crucial for optimizing the locomotives’ design and operation since they provide light on their performance. Future locomotive design and operation may be influenced by the findings of this study, resulting in more environmentally friendly and sustainable transportation networks.

জিআইএস এবং রিমোট সেন্সিং কৌশল ব্যবহার করে SDG লক্ষ্য এবং সূচক নিরূপন:

Among the Sustainable Development Goals, SDG-11 calls for a variety of initiatives to build sustainable cities for cities and their communities. The main agenda of SDG-11 is to make cities and human settlements more inclusive, safe, resilient and sustainable. The main objective of this study is to map the road network and road density of the Bil Baghil Basin using GIS and remote sensing (mainly satellite images) in urban areas and assess the accessibility of public transport to the public for sustainable development goal 11 (SDG-11) to assess and map the current status in order to achieving SDG indicator 11.2.1. SDG Indicator 11.2.1 strives to provide safe, affordable, accessible, and sustainable transportation networks for all, increase road safety, notably through the growth of public transportation, and respond to the needs of women, children, people with disabilities, and the elderly in crisis situations. In order to ensure a sustainable transportation system it is important to determine route designs and density. Here a very high resolution (30 cm) satellite image called WorldView-03 along with Bangladesh Bureau of Statistics is used to determine the road network pattern of the study area. As the Bil Baghil basin is a fully urbanized area which has already rapidly changed into an urban area resulting maximum amount of agricultural land, water bodies, forests and open areas in the region have all decreased. According to the research findings, the study area has three types of road networks- katca, pucca and semi-pucca, of which semi-pucca roads are the most and pucca roads are the least. Some areas have high population density as well as dense road network but no bus stops for transport accessibility. The percentage of overall transport accessibility for the study area is 35.68% and the percentage of inaccessible area is 64.22%. The survey area has very few areas with high transport accessibility and the percentage of population with high transport accessibility is also very low. The study area has only 10 bus stops which did not cover the entire study area. For higher transport accessibility it is necessary to build more bus stops in the study area. Which will make this place more convenient for the movement of its residents.

Transport Planning beyond Infrastructural Change: An Empirical Analysis of Transport Planning Practices in the Rhine-Main Region in Germany

The transport system in Germany is characterised by a dominance of individual automobility. Acknowledging the necessity of a transition in the transport sector towards a more equal and sustainable transport system, the paper takes a closer look at how local and regional transport planning actually responds to the demands of the transition. Following a practice theoretical approach, the empirical analysis of local and regional transportation planning in the Rhine-Main region aims at an understanding of planning actions that go beyond infrastructural and technological change. Based on document analysis and interviews, the analysis highlights spatial and material changes promoted by transport planning, normative aims, and assumptions followed by transport planning and skills and competencies supporting transport planning in the Rhine-Main region. The paper identifies two overarching transport planning practices that shape the way the transport transition is addressed in the Rhine-Main region: the expansion of sustainable transport networks and the redistribution of road space. The empirical results are discussed with regard to the potential for change in local and regional transport planning.

Stochastic regional-based profit-maximizing hub location problem: A sustainable overview

Climate change is one of the most important concerns globally, and countries need to improve their transportation infrastructure to handle flows with the least air pollution. In specific cities and regions, carbon emissions may cause in future many issues for people and societies. Although transportation hubs are very helpful in the reduction of transportation costs, they may cause an increase in carbon emissions in some regions, leading to long-term environmental problems. In this study, a model is developed for the profit-maximizing hub location problem that incorporates three pillars of sustainability. The proposed model is not only designed to maximize profit but also to control carbon emissions using a carbon cap policy and to reduce differences in emissions between regions with consideration of population density. This scheme will lead to achieving a sustainable transportation network. A two-stage stochastic programming approach is employed to cope with demand uncertainty. An enhanced sampling based on the self-organizing map method was utilized to cluster scenarios that lead to dealing with small-sized problems. Furthermore, classical Benders decomposition, Pareto-optimal cut Benders decomposition, and L-shaped algorithms are employed to solve the proposed models more efficiently. The proposed models are analyzed using the well-known Turkish Network (TR) data set. Computational results demonstrate that the proposed models help to achieve sustainability. Factors of sustainability such as environmental and social can be achieved by slightly reducing the amount of profit in the economic factor. Furthermore, results show that the L-shaped algorithm with a multi-cut scheme outperforms the commercial solver, classical Benders decomposition, and Pareto-optimal cut Benders decomposition algorithms in large-size instances.

Sustainable Transportation Networks

Sustainable Transportation Networks