Green Transportation System Research Articles

Research on the aging-suitability of community outdoor spaces in Zhengzhou based on the analytic hierarchy process and semantic differential method.

As global populations age, the suitability of community outdoor spaces for older adult residents has become a critical urban planning concern. However, comprehensive evaluation methods for assessing these spaces are lacking. Objective: This study aims to evaluate the aging-suitability of community outdoor spaces in Zhengzhou, China, and provide optimization suggestions for urban planners and policymakers. An evaluation index system was established using the analytic hierarchy process (AHP), with aging-suitability as the goal layer. The criteria layer included outdoor behavioral activities, green space configuration, transportation and road systems, and basic service facilities. The semantic differential (SD) method was employed to conduct a comprehensive evaluation among older adult residents in three communities (A, B, and C) in Zhengzhou city. The evaluation revealed that older adult residents generally had a favorable assessment of their community spaces' aging-suitability. Community A received the highest overall rating. Green space configuration and transportation systems emerged as critical factors influencing aging-suitability. The combined AHP-SD approach proved effective in evaluating the aging-suitability of community outdoor spaces. The study identified key areas for improvement in each community, with variations in strengths and weaknesses across the three sites. Implications: Based on the findings, optimization suggestions are proposed in four aspects: enhancing outdoor behavioral activities, improving green space landscapes, refining road transportation systems, and upgrading basic service facilities. These recommendations can guide urban planners and policymakers in creating more age-friendly community environments.

The Hydrogen Vehicle in the Context of the Sustainable Development of the Green Road Transport System

Abstract The scientific work presents concrete research carried out by the authors to implement some concepts of technical and managerial nature, so that those interested in this field can get acquainted with a certain way of exposing the problems concerning the hydrogen-powered vehicle, in the context of the sustainable development of the ecological road transport system. At the moment, the car whose energy for propulsion is generated by means of hydrogen or hydrogen fuel cells does not enjoy a high popularity among users, due to the very high purchase price (where we have high technology, we also have a price to match - a fuel cell is more expensive than an electric battery), the reluctance of the population to use this type of fuel, defined by the danger of explosions and fires generated by hydrogen, and the lack of hydrogen refueling stations in Romania and in some European countries. The use of alternative fuels in internal combustion piston engines has the advantage of significantly reducing pollution due to combustion products. With regard to the direct use of hydrogen as a fuel in internal combustion piston engines, we can say that it has proved its inefficiency, because the hydrogen injected or sucked directly into the engine cylinders is unstable, produces uncontrolled burns and detonations, and in the liquid state it is very difficult to store, it requires special transport methods, hydrogen tanks must be armored with Kevlar because hydrogen in the event of a car accident can explode. For example, if we use it as a fuel in a diesel engine, hydrogen may not ignite itself because the self-ignition temperature in the cylinders of compression-ignition engines is high (600°C). However, at a normal stoichiometric ratio, hydrogen has a high flame rate, much higher than gasoline. This creates a thermodynamic cycle of the thermal engine almost perfectly, but ideally, let us not forget that in poor mixtures of air-hydrogen the flame propagation speed is considerably reduced. The compression ratio of the engine is of utmost importance because it is finally defining the thermal efficiency of the engine. In the following, we present a technical and managerial study with engineering nuances, through which readers can look and understand the stage of development, place and role of the vehicle propelled by hydrogen fuel cells. Finally, the conclusions and further directions of research and development in the field addressed are presented.

An integrated probabilistic bipolar fermatean hesitant fuzzy transportation configuration for sustainable management of floral waste with risk assessment

This paper proposes the probabilistic bipolar fermatean hesitant fuzzy set (PBFHFS) and its triangular version as generalizations of bipolar fuzzy set (BFS) for delineating randomness and imprecision in a single framework. To enhance pragmatic applicability, the devised set has been examined based on its fundamental operations, basic properties, and defuzzification mechanism. Offerings made to temples are considered sacred, so they cannot be disposed of in landfills. Waste from temples is usually disposed of in nearby lakes, ponds, and rivers. Thus, management of the temple floral waste (TFW) is essential to enhance the sustainability of the environment. In this regard, a green 4-dimensional transportation system under PBFHF settings is developed for the effective and sustainable managements of floral waste that is dumped from places of worship. For the most cost-effective and environmentally friendly results, this transportation system optimises a number of metrics, transportation expenditure (TE), carbon emission (CE), transportation time (TT), and labour cost (LC). Three sub-models are further developed from the proposed model under PBFHF conditions by incorporating the theory of carbon policy and risk assessments. The suggested transportation system is addressed by employing a fuzzy methodology, fuzzy programming (FP) as well as two non-fuzzy approaches, weighted sum technique (WST) and goal programming technique (GP). At last, there is a numerical computation along with comparison analysis, management insights, a conclusion with limitations, and a scope for further study.

Evaluating the Development Levels of Green Urban Transportation Systems

To promote the green and resilient development of urban transportation and the sustainable development of cities, we developed a technique that can be used to evaluate the level of urban green transportation development. First, the toughness evaluation indexes were integrated according to the connotation requirements of green transportation development, and the evaluation index system was constructed using the hierarchical analysis method. Second, subjective and objective weighting methods, such as the G1, entropy weighting, and “addition” integration methods, which provide a simple and easy-to-use evaluation method that can improve evaluation accuracy, were adopted. Finally, Harbin, a typical city in the north, was chosen as a representative city to verify the method effectiveness. The study findings can be used to evaluate the current level of green development of a city as well as for future planning, providing a basis for formulating sustainable urban transportation development strategies. We will continue to conduct comparative studies on different evaluation methods and cities to improve the generalizability of the method.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the “dual carbon” strategy, intelligent networked new energy vehicle technology plays a crucial role. This type of vehicle combines the advantages of new energy technology and intelligent network technology, effectively reduces carbon emissions in the transportation sector, improves energy utilization efficiency, and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles, making significant contributions. This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective

The multi-modal combination of "dockless bikesharing (DBs) + metro" can effectively addresses the "last mile" challenge, and its importance to improve the quality of metro integration and residents' commuting efficiency is becoming increasingly prominent. As one of the important determining factors of transportation, the urban built environment directly affects the integration travel behavior of residents. To better understand this issue, it is necessary to conduct in-depth research on the influence mechanism of urban built environment on DBs-metro integration cycling (DBsMIC), and reveal its complex nonlinear characteristics and spatial heterogeneity. Therefore, taking Shenzhen as a case study, this study first introduced the OLS regression model to detect the significant built environmental impact factors of DBsMIC. Secondly, the random forest regression model was used to analyze its nonlinear influence. Finally, aiming at the problem of spatial non-stationarity of influencing effects, the spatial heterogeneity is analyzed from both global and local perspectives based on the MGWR model. The results show that the significant impact indices of the built environment of the integration cycling volume vary in different time periods. Overall, seven factors such as road and residential density have a significant impact on DBsMIC. Among them, the job-housing balance and the bus line density have a negative impact, while the rest have a positive impact. Meanwhile, the nonlinear effect of the built environment on DBsMIC is obvious, and there is an obvious marginal effect. In addition, the nonlinear impact of the built environment on DBsMIC in different time periods has spatial heterogeneity and spatial marginal effects. For example, the influence of road density shows a global positive correlation effect in multiple time periods, and the promotion effect increases from southwest to northeast. This study can provide auxiliary decision-making support for the construction of an efficient, green and sustainable urban transportation system according to local conditions.

Prof. Gerard-André Capolino Delivers a Distinguished Lecture at the First Summer School on “Electrical Machines and Drives for Green Transportation Systems” [Society News

Prof. Gerard-André Capolino Delivers a Distinguished Lecture at the First Summer School on “Electrical Machines and Drives for Green Transportation Systems” [Society News

Towards a green transportation system in Mexico: The role of renewable energy and transport public-private partnership to curb emissions

Given the substantial financial requirements, greening the capital-intensive based infrastructure like transport requires pondering upon new innovative strategies. As the public sector has demonstrated a lack of sufficient funds in numerous instances, promoting the Public-Private Partnership (PPP) has become a promising pathway toward green transportation. However, the role that PPP investments in the transportation sector in reducing transport CO2 emissions (TCO2e) on the road to meeting the sustainable development goals (SDGs) has received minimal attention. In this context, this study investigates the impact of PPP in the transportation sector and renewable energy on TCO2e for Mexico from 1990 to 2022, while controlling economic and urban settlement growth. For a novel attempt, this study leverages several modern econometric methodologies, such as the Augmented and Dynamic ARDL which allows estimating long-run and short-run elasticities vis-à-vis future scenario analysis. Results divulge that the PPP in transport curbs TCO2e over the short term, but the long-term effect is insignificant. Moreover, findings uncover that while the usage of renewable energy strengthens environmental circumstances, a rise in the urban population has a short-term negative impact on TCO2e. Moreover, the economic progress boosts Mexico's TCO2e and has a more devastating long-term effect, which is contradictory to the Environmental Kuznets Curve (EKC) hypothesis. The current study suggests that in order to advance the nation's green transportation system, authorities in Mexico should implement several measures to increase the pie of renewable energy and effective public-private partnership investment in the transportation sector.

Can the construction of a high-speed rail alleviate haze pollution: an empirical analysis based on social networks and dynamic spatial econometric models.

With the increase in haze pollution in Chinese cities, answering the question of whether using a high-speed rail (HSR) as a green and efficient transportation system can alleviate haze pollution in China has become a research hotspot. This study empirically tests the impact of HSR network construction on haze pollution and its spatial spillover effects. (1) The construction of a HSR would have a mitigation effect on haze pollution in node cities and surrounding cities, and the more developed the HSR is, the more significant that this effect would be. (2) Haze pollution persists for a long time, the haze pollution from the previous year may have a positive promoting effect on the haze pollution in the following year. (3) The use of a HSR reduces haze pollution by replacing traditional road transportation and promoting industrial structure upgrading and technological innovation. (4) The inhibitory effect of HSR use on haze pollution varies due to regional differences and variation in city size.

Analysis of spatial spillover effects and influencing factors of transportation carbon emission efficiency from a provincial perspective in China.

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation.

How transit-oriented development concepts and strategies influenced green transport systems: A meta-analysis approach

Transit-oriented development (TOD) is a major planning tool to promote green transport mode share. There are many previous studies in this field but with very diverse research focuses and outcomes. This study has classified the research purposes of previous studies into four categories: exploratory; potential and challenges; performance evaluation; and in-depth investigation. To identify critical factors that might influence green transport systems, a systematic literature review is delivered via a meta-analysis. Specifically, green transport mode share was set as the dependent variable and a proxy for TOD outcomes. From the model results, several key factors are identified. For example, built environment planning (e.g., pedestrian sidewalks) plays a critical role in green transport systems. The main findings of this paper can provide clear direction to current TOD, such as how to improve TOD outcomes and providing planning guidance for potential TOD.

Assessment and Analysis on the Structure Status and CO2 Emissions from Freight Transport in China

With the booming freight industry in China in recent years, the freight volume has presented a growing trend, but the accompanied motor vehicle pollution emission becomes increasingly prominent. For the purpose of green development in freight industry, China is taking active measures to reduce emissions. Among them, the structural emission reduction enjoys the greatest potential in energy conservation and emission reduction in the transportation industry in the future. Its key is to adjust the current unreasonable transportation structure in China, and promote the transfer of freight transport from road to rail and water through the development of multimodal transport. Therefore, systematically finding out the structural characteristics of China’s freight industry and sorting out the problems existing in the freight structure can provide technical support for promoting the structural emission reduction of the freight industry and building an intensive, efficient and green cargo transportation system. This study systematically analyzed the overall development and structural characteristics of China’s freight transportation, calculated the current situation and characteristics of CO2 emissions in China’s freight transportation sector, summarized and analyzed the main problems facing energy conservation and emission reduction in China’s freight transportation sector, and put forward countermeasures and suggestions.

Evaluating Roadblocks to Implementing a Green Freight Transportation System: An Interval Valued Intuitionistic Fuzzy Digraph Matrix Approach

Nowadays, freight logistics industries are seeking to adopt green freight practices in their transport systems to reduce environmental concerns; efficient green freight practices lead to reductions in greenhouse gas emissions while using less energy and material. The freight logistics industry, despite its significant contribution to a country's monetary development and economic welfare, is not well regarded because of its role in contaminating the climate. However, the freight logistics industry is trying to implement a green freight transportation system to improve their image with buyers. However, they are facing a lot of obstacles. Therefore, this article seeks to identify the barriers to the implementation of green freight in first world and third world nations and formulate them into a single numeric index. The interval-valued intuitionistic fuzzy set based graph theory and matrix approach technique has been used in this research to derive the green freight barrier impact index value. The PERMAN algorithm is used to compute the permanent function of matrices. Findings suggest that the freight logistics industries in third world nations should pay more attention to societal and managerial barriers for successful implementation of green freight. This research will assist policymakers and managers of freight logistics industries to create strategies to overcome the hurdles in properly implementing green freight practices.

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward—CO2 Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster’s method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.

A green 4-dimensional multi objective transportation system for disaster relief operations under time-sequential complex fermatean framework with safety measure

In a post-disaster response operation of a severe cyclone, like Biparjoy, the transportation of humanitarian aid (HA) is the foremost activity in the aftermath of the disaster. Here, due to the ubiquity of uncertainty in such operations, we have first devised a time-sequential complex Fermatean hesitant fuzzy set (TS-CFHFS) with all-dimensional aspects such as its structure, fundamental operations, score and accuracy function. For the transportation of HA, we have developed a multi objective multi-item green 4-dimensional transportation system under the TS-CFHF configuration. In our proposed framework, the HA from both private and public sectors is transported to distribution centers (DCs). The aid was then distributed to the disaster relief shelters (DRS) established in the affected areas. We have also incorporated several significant factors that enhance its realistic portrayal, including vehicle speed, disturbance rate, and safety while transporting HAs. The first and foremost objective is to minimize transportation time (TT), for which we have incorporated the objective vehicle speed, while other objectives are transportation cost (TC), carbon emission (CE) and job opportunities (JO) in our proposed model. To obtain the optimal solution, three approaches namely fuzzy programming (FP), global criterion method (GCM) and weighted sum technique (WST) have been utilized. The proposed model has been complemented with two specific case studies based on the nature of the safety constraints by using a numerical computation along with a comparative analysis of the results obtained by three distinct approaches that may be useful to the decision-maker to make an empirical decision that would affect emergency response actions.

A lightweight, efficient, and secure identity-based authentication scheme for underwater green transport systems

A lightweight, efficient, and secure identity-based authentication scheme for underwater green transport systems

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

Analysis of urban heating island effect in Tokyo and sustainable solution

Urban heating island effect (UHI) becomes a global serious problem these days, which should be caught attention to try to reduce it. Tokyo is one of the countries which faced this curious circumstance. The urban area of Tokyo had higher average temperature than rural area. The main reasons include too many high-rises and the materials in buildings which could increase the temperature. This article discuss the practical solutions for UHI in Tokyo and other countries. Firstly, the wall should be painted with light colors. Secondly, Establishing the green transportation system which has low energy consumption and light pollution. Thirdly, wood should be put more use in building and rebuilding constructions. By solving or reducing urban heat island effect, not only human will be more comfortable but also the environment problems such as global warming could be relieved which gives it more global meaning.