Sustainable Transport Development Research Articles

Unveiling The Nexus: Exploring the Impact of Behavioral Finance on Green Finance Initiatives

This research paper delves into the dynamic landscape of global climate finance during the fiscal years of 2021/2022, examining the collaborative efforts of public and private actors in addressing the multifaceted challenges posed by climate change. The equitable distribution of financial resources reflects a shared commitment to transformative action, with a focus on critical sectors such as energy, transport, and buildings and infrastructure. Both sectors emphasize the transition to renewable energy, sustainable transportation, and climate-resilient urban development. Public finance plays a pivotal role by directing support to relatively underserved sectors, including Agriculture, Forestry, and Other Land Use (AFOLU), water and wastewater management, and industry. This targeted intervention ensures inclusivity in climate action and contributes to broader sustainable development goals. The financial commitments not only reduce carbon footprints but also foster innovation, job creation, and economic resilience. The collaborative model of climate finance observed during this period sets a precedent for future endeavors, emphasizing a holistic and inclusive approach. The research suggests potential avenues for future studies, such as assessing the long-term impact of climate finance, evaluating evolving patterns in public-private collaboration, and exploring the integration of climate finance within global policy frameworks. Ultimately, this research contributes to the ongoing discourse on climate resilience, offering insights into the interconnectedness of financial commitments, sustainable development, and the global fight against climate change.

Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This model seeks to enhance the precision of traffic flow prediction by integrating both historical and prospective data. Specifically, the model achieves prediction through two steps: encoding and decoding. In the encoding phase, convolutional neural networks are used to extract spatial correlations between weather and traffic flow in the input sequence, while the BiGRU model captures temporal correlations in the time series. In the decoding phase, an additive attention mechanism is introduced to weigh and fuse the encoded features. The experimental results demonstrate that the CNN-BiGRU model, coupled with the additive attention mechanism, is capable of dynamically capturing the temporal patterns of traffic flow, and the introduction of isolation forests can effectively handle data anomalies and missing values, improving prediction accuracy. Compared to benchmark models such as GRU, the CNN-BiGRU-AAM model shows significant improvement on the test set, with a 47.49 reduction in the Root Mean Square Error (RMSE), a 30.72 decrease in the Mean Absolute Error (MAE), and a 5.27% reduction in the Mean Absolute Percentage Error (MAPE). The coefficient of determination (R2) reaches 0.97, indicating the high accuracy of the CNN-BiGRU-AAM model in traffic flow prediction. It provides a good solution for short-term traffic flow with spatio-temporal features, thereby enhancing the efficiency of traffic management and planning and promoting the sustainable development of transportation.

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.

Current social impact assessment practices for transport projects and plans in Chinese cities

ABSTRACT The importance of sustainable transport development is being increasingly realised in China. However, the understanding of social sustainability in the transport sector remains inadequate. Transport social impact assessment (SIA) at the local level, especially for small- to medium-sized cities, lacks systematic guidance. As a starting point for improving social sustainability in the transport sector, this research explores transport SIA practices in different-sized Chinese cities. Following a critical review of transport planning and appraisal documents collected from 83 sample cities in mainland China, 5 categories were identified based on the characteristics of their transport SIA practices. It was found that except for a few megacities, transport SIA in Chinese cities is generally weak, compared to developed countries. SIA has received little attention and had limited influence on decision-making in transport planning in China. Current guidance on transport SIA in China is relatively general and ambiguous compared to many developed countries, an established transport SIA framework and indicator set for local governments to refer to are lacking. The disparities in transport SIA among different-sized Chinese cities and different project types are significant. Interviews with Chinese transport practitioners were conducted to further explore the gaps in transport SIA practices and the potential for improvement. Based on the interview analysis, key aspects for improving transport SIA practices in China have been provided.

Alternative prioritization for mitigating urban transportation challenges using a Fermatean fuzzy-based intelligent decision support model

Practitioners and decision-makers often face difficulties in selecting and prioritizing effective strategies to address challenges to sustainable urban transportation development. Although there has been considerable research conducted on the subject, the Tanzanian context, which is greatly affected by social and environmental problems, has received inadequate attention. Therefore, this study intends to bridge this gap by pinpointing the obstacles to sustainable urban transportation and proposing the most appropriate strategies to tackle them. The study proposes seven strategies and determines five criteria to prioritize them. To accomplish this, the study proposes a novel Fermatean fuzzy-based intelligent decision support model to assess the criteria weights and prioritizes strategies based on the weighted criteria. The study validates the proposed methodology by conducting a sensitivity analysis, which indicates that restricting car use (A5), improving sector coordination (A1), and conducting extensive research on transportation issues (A7) are the top three strategies for promoting sustainable urban transportation. The study’s findings hold significant value in providing urban transportation planners with helpful guidance to develop optimization techniques that can improve transportation systems.

Transportation Infrastructure and Common Prosperity from the Perspective of Chinese-Style Modernization: Enabling Effects and Advancement Paths

“Common prosperity depends on development, and development comes first in transportation.” Achieving common prosperity is the essential requirement for Chinese-style modernization under the construction of a sustainable transportation power country. Based on China’s 30 provincial panel data from 2001 to 2021, this paper calculates the common prosperity index under modernization construction and uses the fixed-effect model and bootstrap mediation effect analysis method to examine the impact and promotion path of transportation infrastructure empowering common prosperity (mediating effect). The study found that transportation infrastructure has a significant enabling effect on common prosperity, and the higher the level of common prosperity, the stronger this effect. After a variety of robustness tests, the conclusion is still reliable. The specific path is that transportation infrastructure further strengthens its role in empowering common prosperity through the four intermediaries of economic growth, factor flow, industrial upgrading, and market expansion. Except for the complete intermediary effect of factor flow, the other intensity ratios are all at 65% or above. In addition, there is heterogeneity in the impact of transportation infrastructure on common prosperity, which is reflected in the significant effect in areas with large urban populations, eastern regions, and first-tier or former new first-tier cities. To this end, a quadrilateral orientation focusing on “economy, factors, industries, and markets” is proposed, with dense transportation networks and regional differentiation as the focus of sustainable transportation development, providing empirical support for promoting common prosperity.

A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users’ willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation.

The role of the state in ensuring sustainable development of air transport in the post-war

The article examines the important role of the state in ensuring the sustainable development of air transport after the war. The influence of the state on various aspects of the aviation industry, including aviation safety, financial support for airlines, aircraft leasing fleet and infrastructure, is studied. The article discusses the need to modernize the state policy to influence the support and development of the aviation sector and create a favourable investment climate in Ukraine. It is important not only to restore the aviation industry after the war, but also to ensure its modernization and sustainable development, taking into account international experience and innovative approaches.

Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021

The leaping forward of the economy has promoted the rapid growth of road traffic demand, resulting in the carbon emissions of road traffic increasing significantly. It is well known that a one-size-fits-all emission reduction policy is not feasible. Therefore, conducting an investigation on the carbon emissions of all provincial-level regions within a country can assist the government in formulating carbon emission policies at a macro level tailored to different regions. In this study, the whole provincial-level administrative regions in the mainland of China were selected to quantify the carbon emissions of road traffic, and the carbon emissions from 2006 to 2021 were obtained by employing the top-down model. What's more, spatiotemporal characteristics of road transportation carbon emissions in those regions were explored based on Moran's I spatial autocorrelation method. In addition, the LMDI model was constructed based on five driving factors, namely energy intensity, energy consumption intensity, industrial scale, economic development, and population size, and the decomposition analysis of driving factors is carried out. The results show that carbon emissions from road traffic in all provincial regions showed an overall rising trend in the research period, with an average annual growth rate of 11.83 %. The distribution of road transportation carbon emissions exhibited an east-high, west-low distribution, with significantly higher emissions in the eastern and coastal regions compared to inland areas, additionally, China's seven geographical regions showed an initial rapid increase in carbon emissions followed by a stable growth trend. Secondly, five types of spatial clustering were identified of carbon emissions within provincial regions. Thirdly, the impacts of energy intensity and industrial scale were detrimental to road transportation carbon emissions, whereas economic development, energy consumption intensity, and population size had contrasting effects. Implications according to the above conclusions were put forward, aiming to provide guidance for the sustainable development of road transportation and expediting the achievement of the “carbon peaking and carbon neutrality” objective.

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Mining smart card data to estimate transfer passenger flow in a metro network

AbstractMetro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.

Sustainable Development of Urban Mobility through Active Travel and Public Transport

A sustainable development of urban transportation relies on our ability to offer citizens reliant and attractive alternatives to car travel, such as efficient and appealing public transport (PT) services, alongside an urban environment that is stimulating for active travel alternatives, such as walking and cycling [...]

Study on the Sustainable Development Strategy of Container Rail-Water Intermodal Transport in Chinese Ports

The sustainable development of container rail-water intermodal transport in China faces a series of deep-seated constraints. In this paper, we quantitatively summarize the development status of container rail-water intermodal transportation in China’s ports. Next, the typical problems faced by the sustainable development of rail-water intermodal transport are analyzed from the bottom constraints and direct constraints. Finally, this paper proposes strategic ideas to promote sustainable development of rail-water intermodal transport, taking into account network layout, technical equipment, data exchange rules, and policy environment.

A Comprehensive Review of Electric Vehicle Efficiency Enhancement Through Drive Cycle Optimization: Evaluating its Influence on Electric Range and Longevity

The best driving range for electric cars (EVs) is a major difficulty because this parameter is mostly determined by the amount of energy in the battery pack. However, it is crucial to understand that user behavior and particular driving habits have a significant impact on this range. The necessity for a reevaluation of typical drive cycles, which have grown increasingly out of step with changing customer needs, has been highlighted by recent controversy around the stated range statistics by EV manufacturers. Drive cycles must be modified in accordance with the major variances in driving behaviors across various vehicle segments. In addition, the driving cycle and patterns change across different demographic areas in varied countries like India, creating a special difficulty in the development of a new standard driving cycle that can accommodate a wide range of users. Real-world driving patterns must be taken into account in order to effectively anticipate an EV’s range and evaluate the durability and warranty of its battery pack. The careful selection of a very-specific driving-based realistic profile is therefore crucial for the environment surrounding electric vehicles. We explore the complex process of creating driving profiles particularly designed for electric vehicles in this in-depth review article. We examine the subtleties and traits of these profiles to offer light on their importance in the context of EVs. This assessment provides insightful information on how electric car technology is developing as well as the crucial part that driving styles will play in the development of sustainable transportation in the future.

Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector

This study applies three different artificial intelligence algorithms (Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) to estimate CO2 emissions in Türkiye’s transportation sector. The input parameters considered are Energy consumption (ENERGY), Vehicle Kilometers (VK), POPulation (POP), Year (Y), and Gross Domestic Product Per Capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO2 emissions using statistical indicators (R2, RMSE, MSE, and MAE). Across all scenarios and algorithms, R2 values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful in estimating CO2 emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer the potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO2 emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector’s environmental impact. It also contributes to the understanding of AI-assisted CO2 emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.

Simulation modeling as a tool for predicting pedestrian flows when planning a Moscow Metro surface station

The article substantiates the use of simulation modeling methods to organize efficient and safe passenger movement at the metro station. The list of key objects and their corresponding processes at ZIL station, a part of the Moscow Central Ring (surface line of the Moscow Metro), is determined using structural analysis methods. Based on the results obtained, the input parameters of the simulation model and the rules for realizing the processes corresponding to them were determined. Using specialized software tools, a 3D computer model has been developed that complies with the established rules and demonstrates all aspects of the subject area. Fragments of the model realizing certain scenarios of passenger movement through the station facilities are presented. The obtained model can be used to develop the concept of sustainable development of urban areas and transportation.

AI-Driven Decision Support System for Green and Sustainable Urban Planning in Smart Cities

Abstract This study focuses on innovative practices in sustainable urban planning, demonstrating significant advancements in key areas such as solar panel efficiency, rainwater harvesting capacity, community garden space, and bike-sharing station accessibility through in-depth experimentation and analysis. The research results show that the energy conversion rate of solar panels reached 25%, surpassing the market standard of 24%, which is crucial for enhancing self-sufficiency in energy in urban areas. The rainwater harvesting system performed well, achieving a capacity of 600 liters per square meter, slightly below the market rate of 650 liters, but still demonstrating significant potential in dense urban environments. Additionally, our project provided 3 square meters of community garden space per resident, exceeding the market average, effectively promoting urban greening and improving residents’ quality of life. In terms of transportation, our experimental model featured 1.5 bike-sharing stations per 1000 residents, better than the market data of 1.2 stations, contributing to the development of sustainable urban transportation. These outcomes not only showcase the potential of sustainable urban planning but also provide practical references and guidance for future urban development.

Cargo Consolidation in a Supply Chain in the Aspect of Sustainable Development of Transport

Cargo Consolidation in a Supply Chain in the Aspect of Sustainable Development of Transport

Development Trends in Digital Highway Systems and Processes under Intelligent Technology

With the acceleration of China's urbanization process, the phenomenon of urban road traffic congestion is becoming increasingly serious, and the problem of road bottlenecks is becoming increasingly prominent. The operational efficiency of urban roads has encountered challenges. The rapid development of intelligent technologies such as artificial intelligence, big data, and the Internet of Things has provided more possibilities and opportunities for the research of digital highways. These technologies can be applied to traffic flow monitoring, road condition prediction, intelligent traffic management, and other aspects to improve highway traffic efficiency and service quality. This article designs a digital highway system based on intelligent technology to alleviate traffic congestion and other issues. Firstly, traffic information is collected and tested through channels such as circular coil detectors and cameras. This article would integrate and store the collected data into the Redis database; Then, the data is analyzed, processed, and predicted using the Kalman filtering algorithm. It then sends the predicted information to the client, providing convenience for users to choose the best route. This article tests the performance of the system through experiments. The experiment shows that the system can make correct analysis and prediction even in high pedestrian traffic, find the fastest driving route, and effectively resist the impact of fluctuations in pedestrian traffic. In addition, the system has achieved good results in accuracy testing of analysis and prediction. In summary, the digital highway system based on intelligent technology is expected to improve traffic efficiency, enhance traffic safety, achieve intelligent travel services, promote sustainable transportation development, and also bring broad prospects for commercial innovation and market opportunities. With the continuous progress and application of intelligent technology, digital highway systems would become an important tool for future road traffic to respond to challenges and improve service quality.

High-resolution mapping of transport CO2 emission in Beijing–Tianjin–Hebei region: Spatial-temporal characteristics and decoupling effects

Numerous studies estimating transport CO2 emissions in developing countries were on the large scales and few provided high-resolution mapping of transport CO2 emissions from different transport modes according to their respective distribution and diffusion patterns. This study proposed a hybrid method for high-resolution mapping of transport CO2 emissions in metropolitan regions, and applied it to the Beijing–Tianjin–Hebei (BTH) region of China during 2000-2019. The results indicate that: (1) The transport CO2 emissions in the BTH region increased rapidly, of which road transport carbon emissions accounting for approximately 80%, air transport carbon emissions accounting for nearly 20%, railway and water transport accounted for a relatively small proportion. (2) For the transport CO2 emission structure from 2000 to 2019, road transport was still the main source, but its proportion has decreased since recent years, which air transport became the second largest source of emissions and had greater impact on Beijing and Tianjin. (3) The transport carbon emission hot spots were mainly located in Beijing and kept shrinking, while the cold spots were mainly located in the southern part of Hebei Province and kept expanding. (4) The decoupling relationship between county-level transport carbon emissions and economic growth changed from weak decoupling to expansive negative decoupling, showing a non-ideal development in the BTH region. Overall, this study revealed the spatiotemporal characteristics of transport CO2 emissions in the BTH region, based on which specific carbon reduction strategies could be promoted to facilitate sustainable low-carbon transport development in metropolitan regions in developing countries.