Sustainable Urban Transport System Research Articles

Research Progress in the Design Concept of Green Commuting Cities

Abstract: The connection between green commuting and urban design was intimately intertwined in this study. As urbanization progressed, issues such as population growth, housing, transportation, and the environment had become increasingly salient. Green commuting had emerged as a vital strategy to mitigate these challenges and enhance the quality of life for urban residents. This paper elucidated the principles underlying green commuting, highlighting a human-centric approach, the interconnections between public transit, walking, and cycling as modes of travel, and the significance of the digital city paradigm. By examining the transportation systems and urban design methodologies, techniques, and characteristics of Vienna and Copenhagen, the paper delineated the strengths and weaknesses in the development of these cities. It delved into the design approaches and philosophies in green commuting from these two cities that were exemplary and worthy of emulation. The paper proposed potential strategies for green and low-carbon design in future urban transportation, aiming to construct a more ecologically sustainable urban transportation system and infuse new dynamism into urban development.

Electrifying the bus network with trolleybus: Analyzing the in motion charging technology

Currently, electric buses are becoming more and more popular, and their number in operation is increasing. The range of electric buses is also increasing and solutions that seem to be working almost without fixed infrastructure are being promised. However, this requires the use of high-capacity batteries, which increases the weight and price of the vehicle and causes high costs of battery replacement during operation. Moreover, if we take into account the growing demand for batteries, limited raw material resources, and the environmental impact of the battery production process, the optimization of battery capacity in vehicles may turn out to be a key issue. In this light, trolleybus becomes a sustainable and economically efficient bus electrification technology, if considered in an international scope and a medium- to long-term approach. The article provides a comprehensive study of challenges and potential solutions related to electric buses, which covers the theoretical analysis, technical aspects and practical applications, thus making a valuable resource for readers interested in sustainable urban transport systems. It presents the trolleybus technology, especially with modern solutions, as a sustainable and economically efficient tool for bus electrification. The article shows that the In Motion Charging (IMC) system reduces the need for high-capacity batteries under 100 kWh, which allows to extend their service life up to 15 years and, consequently, to reduce the number of buses needed for operation. The research was based on real measurement data from the transport system in Gdynia (Poland).

Motorcycle Recognition System Using Convolutional Neural Network

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

Analyzing Energy Efficiency and Battery Supervision in Electric Bus Integration for Improved Urban Transport Sustainability

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.

Enhancing Traffic Control with AI Blockchain and Dynamic Computation Techniques

The rapid urbanization and increasing vehicular density in modern cities have led to significant challenges in traffic management and control. As urban areas continue to expand, the demand for more efficient and intelligent traffic control systems has become increasingly critical. This paper presents a novel approach to enhancing traffic management by integrating Artificial Intelligence (AI), Blockchain technology, and Dynamic Computation Techniques. AI is utilized to analyze and predict traffic patterns, enabling real-time adjustments to traffic signals and flow management. Blockchain provides a secure, transparent, and decentralized platform for data sharing and coordination among various stakeholders, ensuring data integrity and trust. The incorporation of Dynamic Computation Techniques allows for flexible and scalable processing of complex traffic data, facilitating rapid decision-making and adaptation to changing conditions. This multidisciplinary approach not only improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems. The findings highlight the transformative potential of combining AI, Blockchain, and advanced computation methods in the field of traffic control.

Smart Mobility in German-Speaking Cities and Sarajevo: Differences, Challenges, Opportunities, and Lessons for Implementation Success

As urbanization increases, cities face challenges related to sustainability and mobility. This study, conducted through interviews in March and April 2023, investigates the implementation of smart mobility solutions in German-speaking cities (Austria, Germany, and Switzerland) and Sarajevo, Bosnia and Herzegovina, through a comparative analysis of stakeholder perspectives. Using semi-structured interviews with 25 experts, we explored the opportunities and challenges associated with smart mobility in these distinct socio-economic contexts. The findings reveal significant differences in technological advancement, infrastructural support, and financial resources, providing valuable insights for policymakers and urban planners. This study contributes to the existing literature by bridging the gap between developed and developing regions, offering practical recommendations for achieving sustainable urban transportation systems.

Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system

Electric vehicles (EVs) are positioned as an alternate green energy technology, which potentially can enable the efficient transition to sustainable low-carbon emission transportation systems. However, different barriers hamper the public adoption of EVs. The degree to which these barriers prevent the adoption of EVs and the potential alternatives to these barriers are mostly unknown. To fill this research gap, the current study contributes by systematically identifying those barriers based on their importance. Initially, several barriers were identified using existing literature, and then by employing the modified Delphi method, a total of twenty-five barriers were classified into five significant dimensions. The Analytical Hierarchical Process (AHP) determined the rankings of these barriers based on weight allocation. As a next step, the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) ranked alternative solutions to these barriers. The findings indicate that 'technological barrier' is the top-ranked barrier among all major dimensions. The overall ranking indicated that 'battery technology' is most influential among sub-dimensions. It is recommended that "investment in research and development" is the best alternative to overcome these barriers. In order to improve the renewable and sustainable urban transportation system, all stakeholders should cooperate in a consistent and coherent manner.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.

Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles

As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

THE ENVIROMENTAL AND SOCIAL IMPACT OF CARPOOLING

Abstract: Car pooling System is an automated system which reduces the misery of travelers and makes them find cars in short period of time. Car pooling is an application of finding car in which drivers who are traveling to work alone can ask for fellow passengers through our application. For those who use public-transport system to go to work daily can use this application to find drivers who are traveling to the same destination in a short path. It provides with a simple riding platform between the car owner and car user. This project enables users to access mobility assets own by others exactly when they need. I t shows a medium for available cars to pick up them on the interest of car owner with time and capacity. This project aims at creation of a Car-pooling System. This paper delves into the design and implementation of a novel carpooling project aimed at addressing the challenges of urban transportation sustainability. With burgeoning urban populations, congestion, and environmental concerns, the need for innovative solutions is paramount. Carpooling presents a viable avenue to alleviate these issues by fostering shared mobility, reducing traffic congestion, and cutting down carbon emissions. The project employs a multi-faceted approach, leveraging advancements in mobile technology, data analytics, and user incentives to optimize the carpooling experience. Key components include a user-friendly mobile application for seamless ride matching, dynamic routing algorithms to optimize trip efficiency, and incentive structures to encourage participation and retention. Through a combination of quantitative analysis and qualitative feedback, the efficacy of the carpooling project is evaluated. Metrics such as reduction in vehicle miles traveled, greenhouse gas emissions, and user satisfaction levels are assessed to gauge its impact on urban transportation sustainability. The findings of this research contribute to the growing body of knowledge on sustainable transportation solutions and provide insights for policymakers, urban planners, and transportation stakeholders seeking to promote carpooling initiatives. Ultimately, the project aims to foster a culture of shared mobility and pave the way for more efficient and sustainable urban transportation systems. IndexTerms – HTML, CSS, Javascript. Bootsrap, MySQL, Python.

Reasonable Planning of King County's E-bus Replacement Plan

With the increasingly intensified global warming severe air pollutions, governments all over the world have begun or is right now looking for ways to fix the problem. Among all the solutions, sustainable urban transportation system is what many governments pay attention to because of their apparent contribution to the reduction in greenhouse gases emissions and pollutants. In this passage, we focus on the exact ecology impacts the promotion of e-buses will cause. On the other hand, the potential financial burdens the transitioning processes will bring are perceived by us in order to make a decent plan for the government to implement.For the first part of constructing the model to measure the ecology impact of transition in one area, we start with the identification of King County as a metropolitan area suitable for prediction. Then we collect information and data of its local bus fleet and e-bus transitioning plan, find out the exact number of each type of buses (diesel, hybrid and electric) around these years and the emissions of corresponding buses. We use both ARIMA and Least Squares Regression to predict the number these buses in the future until the year the local government aim to complete the plan but choose the result displayed by the better one. In this case, we obtain the data for emission of carbon dioxide, oxynitride and PM10 in each year and evaluate the ecological impact. At the same time, we predict the data for the emissions later if the bus fleet keep the same and observed over 90% of decline when we compare the value after transition to the control group.Afterwards, to estimate the financial cost, we identify the main parts involved in the processes of transitioning, classify them with one group of cost for long-term and the other for short-term---that is, changed as the plan is gradually implemented. We build models for each factor we identified and use Riemann Sum to unify the long-term and short-term costs. Based on the data we predict in problem one and from the local government’s website, we easily gain the financial implications of for about 50 million dollars in King County. Through the analysis of the government’s grant in other area, we roughly verify that 50 million dollars would be an acceptable cost.Finally, in our 10-year roadmap development, we explored further into the population distribution of King County and the existing traits for public transportation. Our starting point in this article is to classify the urban pattern into four types of bus operation routes, and then define the passenger flow from high to low. At the same time, we define the total number of vehicles that need to be replaced for different types to develop transportation replacement plans. Based on our assumptions, calculate the carrying capacity of each type of bus in line with the passenger flow. Eventually, it can be proven that the total carrying capacity of the bus meets the transportation needs of all King counties. Then apply to other regions.

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Assessment of passenger safety risk level on the Transjakarta public transportation system

Passenger safety is a crucial factor in public transportation, especially now when people prefer using public transport due to its cost-effectiveness compared to private vehicles. This study aims to identify and assess the hazards that can occur in the passenger cabin of a bus and the procedures to be followed in the event of an accident. The assessment of passenger safety levels is conducted using the Hazard and Operability (HAZOP) and Hazard Identification (HIRADC) methods, which are designed to identify and evaluate these potential hazards. This research, after thorough examination and study, aims to establish new protocols that can further enhance passenger safety. With the actions suggested by the author to ward off all these potential hazards, it is hoped that the hazard level will be reduced from extreme to medium and so on. The research study, “Assessment of Passenger Safety Risk on the Transjakarta Public Transportation System,” has the potential to make a substantial contribution to sustainable transportation. In line with sustainable development goals, it immediately tackles safety concerns in the Transjakarta system, improving all-around safety and promoting public transportation over private vehicles. By lowering accident-related expenses, promoting inclusiveness, and enhancing community well-being, the study also indirectly increases resource efficiency. In essence, it contributes to a more secure, egalitarian, and sustainable urban transportation system while advancing Jakarta’s sustainable mobility strategy by placing safety first.

A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China

Urban public transport systems, characterised by their complexity, generate vast data sets that pose challenges to traditional analytical methods. To address this issue, our research introduces an innovative natural feature profile framework, leveraging a comprehensive, data-driven approach that incorporates big data, data mining, machine learning, and correlation analysis. This approach provides detailed insights essential for transport planning and policy development. The framework's core is its three-layered structure: the data layer, the feature layer, and the application layer, complemented by a unique four-level feature tagging system. This system investigates correlations, significance, and sensitivities amongst feature tags. It facilitates the extraction of natural feature profiles from voluminous data sets, rendering the framework highly applicable in practical scenarios. The implementation of this framework in Suzhou and Lianyungang demonstrated its adaptability and effectiveness. The findings underscored distinct city-specific transport patterns, highlighting the necessity for customised transport strategies. Furthermore, our framework excels at capturing spatial–temporal dynamics, offering essential insights grounded in evidence. Overall, this paper introduces a methodical, adaptable, and data-oriented framework, signalling a promising future for the development of intelligent and sustainable urban public transport systems.

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.

Evaluation indicators for road traffic energy consumption: a review and prospect.

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

Will gated community affect public transit service coverage?

The proliferation of gated communities (GC) has prompted researchers worldwide to investigate its causes and consequences. The phenomenon has been extensively discussed as a significant residential form in the world, but most of the studies are primarily concerned with non-technical issues such as inequality and segregation, while technical issues such as urban transit (PT) systems have not been fully discussed. The significance of accessibility-focused PT planning to establish a sustainable urban transportation system makes this issue a crucial topic to explore. PT is a public good expected to be accessible to everyone, and this means the distance between people and the system needs to be minimized to maximize the service coverage, but the GC design is observed to be increasing the distance of its residents willing to use this service. Therefore, this study analyzed the impact of GC on the PT system using Bogor Municipality as a case study through GIS (Geographical Information System) methodologies and spatial data. The quantitative evidence showed that GC has a relatively strong negative correlation with PT service coverage (r (66) = from -0.611 to -0.631, p <.001). This means PT service coverage tends to be low in areas with a high GC ratio and vice versa. It was also discovered that an increase in GC ratio is likely to cause a reduction in PT service coverage in the study area (Beta = -0.48, t(67) = -5.17, p =.000). This article concludes with a discussion of a few potential ideas based on the observations to alleviate the negative impact of GC.

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.