Sustainable Urban Transport Research Articles

A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions

Sustainable urban transport is the key factor for surviving the cities and developing the supply quality of the urban transport system has been esteemed in sustainable improvement for the cities. This work attempts to provide a sustainable and efficient solutions for ameliorating public bus transport system in Dublin city, Ireland. The developed system will attract private car users, which interns will achieve detract CO2 emissions, minimize traffic congestions and maximize commuter satisfaction. To evaluate this complex problem, the novel Parsimonious Analytic Hierarchy Process (P-AHP) is structured in a spherical fuzzy environment. The parsimonious spherical fuzzy analytic hierarchy process (P– SF-AHP) model considers as an efficient solution not only for evaluating a large number of alternatives or criteria when using AHP, however, it esteems the hesitant scoring of the decision maker. The results are demonstrated and analyzed in detail and the step-by-step description of the procedure might foment other applications of the model. The unique process for evaluating the supply quality of urban transport system consumes less time and effort during estimating the survey, moreover, it provides more consistent and reliable outcomes through avoiding the uncertainty and ambiguity of decision makers during evaluation process.

Unveiling the roles of public bike systems: From leisure to multimodal transportation

As bike-sharing systems gain popularity, understanding their roles in urban transportation, such as the first/last mile problem and the replacement of public transit, is crucial to comprehensively promote urban transport systems. Due to the Internet of Things (IoT) technique, copious data from public bike trips worldwide are available to urban researchers. In this study, we examine how public bikes function not just as a means of transportation, but also for leisure. The leisure function was ignored in previous empirical studies using data mining techniques, an oversight that could skew results in places like Seoul where leisure trips constitute a significant portion of the trips. We investigate when, where, and to what degree bike-sharing serves as a leisure tool and substitutes for, integrates with, or complements the public transit system. We propose an integrated data-mining approach to identify the above four categories of public bike trips. Our method combines multiple sources of real-world data, the distributed density-based spatial clustering of applications with noise (DBSCAN), and spatial feature engineering. The results reveal that the distribution of each category shows substantial spatial–temporal heterogeneity. Longitudinal analysis reveals that the use of public bicycles as a transportation mode primarily contributes to the growth observed from 2018 to 2021, compared to use for leisure. The determining factors and non-linear functions of each type of bike trip were interpreted by explainable machine learning methods, Shapley additive explanations (SHAP), and partial dependence plot (PDP). We discovered that lower slopes, higher residential densities, and more commercial neighborhoods consistently attract users across all categories. Additionally, rivers and green areas attract more leisure trips, while areas with denser transit routes see more substitution trips and fewer complementary trips. By understanding the characteristics, spatial–temporal distribution, and determining factors of public bike trips across these four categories, city planners and operators can tailor services to meet the diverse needs of citizens, fostering a more convenient, sustainable, and accessible urban transport system.

Microelectronics innovation and implementation in intelligent transportation systems

Under the background of urbanization and rapid development of transportation, the innovation of intelligent transportation system has become the key to improving traffic efficiency, relieving traffic pressure, and solving traffic problems. With the continuous progress of microelectronics technology, its application in the field of intelligent transportation is becoming more and more eye-catching. This paper focuses on the innovation and realization of micro-electronic technology in intelligent transportation system, and discusses the application of micro-electronic technology in intelligent navigation, intelligent parking, traffic flow optimization, etc. Through a literature review approach, this study demonstrates how microelectronics technology can drive the development of intelligent transportation systems to improve the efficiency and sustainability of urban transportation. The research results show that microelectronics technology not only brings revolutionary changes to the field of intelligent transportation, but also provides accurate positioning and navigation functions in intelligent navigation systems, realizes more efficient parking process management in intelligent parking systems, and plays a key role in traffic flow optimization. Microelectronic technology has wide application prospects and a positive social influence in the field of intelligent transportation.

Building A Penta Helix Collaboration Model On Mass Rapid Transit Jakarta

The population in Jakarta as a metropolis continues to increase along with the growing phenomenon of urbanization, which impacts increasing population mobility. The Jakarta Mass Rapid Transit (MRT) public transportation policy is significant in addressing urban mobility challenges and improving efficient transportation systems. This study aims to analyze the collaboration of the five pillars of penta helix on the Jakarta Mass Rapid Transit (MRT), both in government, companies, academics, media, and the environment in the development of the Jakarta MRT. The research used a qualitative method with a descriptive and exploratory design approach from January to August 2023. This study uses data collection techniques through in-depth interviews, observations, literature reviews, and policy analysis related to the Jakarta MRT. The results showed that the Penta Helix model is a framework for developing the Jakarta MRT. Close collaboration between government, companies, academics, the environment, and the media is needed to achieve effective and sustainable MRT services. Practical cooperation between the helixes is critical to overcoming obstacles in the Jakarta MRT system. The Jakarta MRT can contribute more to an efficient and sustainable urban transportation system. The study recommends that the company facilitate cross-sector cooperation, increase community participation, and adopt the latest technological solutions in the development and sustainable operation of the Jakarta MRT.

Fermatean fuzzy Archimedean Heronian Mean-Based Model for estimating sustainable urban transport solutions

Public transportation frameworks assume a critical role in the metropolitan region, especially in huge urban communities, where they offer a feasible answer for easing gridlock, moderating commotion contamination, and diminishing CO2 discharges. This paper presents some novel Fermatean fuzzy Heronian mean operators based on Archimedean t-norms, specifically the generalized Fermatean fuzzy Archimedean Heronian mean (GFFAHM) and the Fermatean fuzzy Archimedean geometric Heronian mean (FFAGHM). The study investigates different unique instances of these operators while exploring their essential properties. Besides, a powerful multiattribute decision-making (MADM) method is developed using the proposed operators. This approach offers a key asset for handling complex decision-making problems. Overcoming the capabilities of conventional BM operators, the GFFAHM and FFAGHM operators effectively minimize the potential redundancy in interrelationships during the decision-making process. The inclusion of the flexible parameters ρ and ϕ, which have a big impact on the decision-making process’ outcomes, increases the adaptability and resilience of the Archimedean t-based operators. To depict the feasibility of the proposed MADM method, a thorough quantitative model is introduced, outlining its useful execution. Through an exhaustive case study, the predominance of the proposed approach over existing strategies is experimentally established. Remarkably, the study uncovers that reducing fares is the most compelling factor in expanding the public transport framework, subsequently advancing sustainable urban transport. This study’s findings provide useful insights into decision-making processes and practical implications for policymakers, allowing them to make informed and impactful changes to the public transportation system. The proposed MADM method can play a vital role in upgrading the public transport framework, making way for remarkable strategy interventions in urban transport sustainability.

Dynamic Timeframe and Anticipation-Based Migration: A Real-Time Framework for Ride-Sharing

Efficient and reliable ride-sharing services have gained significant attention in recent years as a means to address traffic congestion and reduce environmental impact. This paper presents a novel real-time ride-sharing framework that incorporates dynamic timeframes and anticipation-based migration to enhance the overall experience for users. The proposed framework leverages advanced algorithms and intelligent systems to optimize the matching of riders and drivers, considering various factors such as proximity, destination compatibility, and anticipated travel patterns. By dynamically adjusting the timeframe for ride requests and introducing anticipation-based migration, the framework aims to minimize waiting times and maximize resource utilization. The effectiveness of the framework is evaluated through extensive simulations, demonstrating its ability to improve the efficiency, scalability, and reliability of ride-sharing systems. The results highlight the potential of the proposed approach to revolutionize the ride-sharing industry and contribute towards more sustainable urban transportation solutions.

Artificial Intelligence-Based Real-Time Traffic Management

The paper proposes applying the ant colony optimization algorithm within a distributed multi-agent architecture, leveraging IoT technology, to address path routing challenges in urban traffic. The study emphasizes the potential of advanced AI techniques and multi-agent systems in revolutionizing traffic management for efficient and sustainable urban transportation systems. Our study addresses the challenging issue of traffic congestion in modern urban areas and the limitations of traditional solutions like road expansion and network indicators. To effectively tackle traffic congestion, the study explores various strategies that analyze traffic elements, falling into the Macroscopic and Microscopic Models. However, conventional traffic modeling faces significant challenges in dealing with complex traffic systems. The rise of the Internet of Things (IoT) offers opportunities for effective traffic analysis by collecting vast and uncertain data. Nevertheless, most existing systems focus on local events, leaving a gap in their effectiveness. To address this, multi-agent systems become a novel strategy, deploying distributed agents across road intersections for comprehensive traffic management. Artificial Intelligence (AI) techniques, including fuzzy logic, evolutionary algorithms, neural networks, and reinforcement learning, emerge as promising solutions for traffic control. For instance, artificial neural networks accurately predict urban traffic flow, and meta-heuristic AI approaches like the artificial bee colony algorithm optimize signal timings.

A two-stage hybrid ant colony algorithm for multi-depot half-open time-dependent electric vehicle routing problem

This article presents a detailed investigation into the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) within the domain of urban distribution, prompted by the growing urgency to mitigate the environmental repercussions of logistics transportation. The study first surmounts the uncertainty in Electric Vehicle (EV) range arising from the dynamic nature of urban traffic networks by establishing a flexible energy consumption estimation strategy. Subsequently, a Mixed-Integer Programming (MIP) model is formulated, aiming to minimize the total distribution costs associated with EV dispatch, vehicle travel, customer service, and charging operations. Given the unique attributes intrinsic to the model, a Two-Stage Hybrid Ant Colony Algorithm (TSHACA) is developed as an effective solution approach. The algorithm leverages enhanced K-means clustering to assign customers to EVs in the first stage and employs an Improved Ant Colony Algorithm (IACA) for optimizing the distribution within each cluster in the second stage. Extensive simulations conducted on various test scenarios corroborate the economic and environmental benefits derived from the MDHOTDEVRP solution and demonstrate the superior performance of the proposed algorithm. The outcomes highlight TSHACA’s capability to efficiently allocate EVs from different depots, optimize vehicle routes, reduce carbon emissions, and minimize urban logistic expenditures. Consequently, this study contributes significantly to the advancement of sustainable urban logistics transportation, offering valuable insights for practitioners and policy-makers.

Development of micromodels of traffic flows, taking into account environmental factors

The article considers an actual problem of organizing a safe and sustainable urban transport system. We have examined the existing positive global experience in both infrastructural and managerial decisions. Then to assess possible solutions at the stage of infrastructure design, we have developed the simulation micromodels of transport network sections of the medium-sized city (Naberezhnye Chelny) with a rectangular building type. The models make it possible to determine the optimal parameters of the traffic flow, under which pollutant emissions from cars would not lead to high concentrations of pollutants. Also, the model allows to obtain the calculated values of the volume of emissions of pollutants and the parameters of the traffic flow (speed, time of passage of the section, etc.). On specific examples, the proposed method’s effectiveness is shown. Case studies of cities of different sizes and layouts are implementation examples and possible uses proposed by the models. This study has shown the rationality of the suggested solution at the stage of assessing infrastructure projects and choosing the best option for sustainable transport development. The proposed research method is universal and can be applied in any city.

Fuzzy ZE-numbers framework in group decision-making using the BCM and CoCoSo to address sustainable urban transportation

Urban transportation plays a crucial role in cities that host sports events. Mexico City, as one of the World Cup 2026 hosts, is one of the world's largest and most populous cities, with a population of over 22 million people. This city faces several challenges related to urban transportation. The purpose of this study is to develop a novel group decision-making framework for evaluating six sustainable alternatives for the management of urban transportation crises. Our proposed group decision framework develops the Base Criterion Method (BCM) and Combined Compromise Solution (CoCoSo) under the fuzzy ZE-numbers for the first time in the literature to obtain reliable decisions. Based on the opinions of decision-makers and expert votes on those opinions, the proposed approach offers a unique feature in decision sciences in that the reliability of decisions can be increased in two stages. Also, sensitive analyses were executed for each of the urban transportation alternatives based on the different states of the criteria groups. According to the findings, the optimum plan should be an investment in the Metro and electric Minibusses development to manage the urban transportation crisis in Mexico City. Also, findings show that applying the fuzzy ZE-numbers leads to more accurate and reliable results.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

The Role of Public Transport in Transport Safety and Public Safety

Public transport has a significant role in sustainable urban transport including the reduction of congestion, noise and air pollution. The main purpose of this paper is to present other important impacts of public transport i.e., the improvement of transport safety and public safety, too. Public transport is one of the safest transport modes according to the indication numbers in traffic accident and injuries. This phenomenon is illustrated through literature research as well as statistical data. Further opportunities are presented with a case study based on the transport system of the city of Győr, especially the positive impacts of the possible expansion of night public transport services. The severity of traffic accidents at nights is significant, and the enhancement of public safety is outstanding. Marketing is one of the most important tools for attitude formation, the paper contains some suggestions in this regard as well as in order to promote the goals above.

Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA

The motivation for sustainable urban mobility is driven by the need to create environmentally friendly, socially inclusive, economically viable, and healthier cities for current and future generations. It requires a comprehensive approach that combines infrastructure development, policy changes, behavioral shifts, and technological advancements to transform urban transportation systems. The importance of creating increasingly sustainable urban transportation is now greater than it has ever been. One of the attempts in this field is the sustainable urban transport index (SUTI) developed by the United Nations (UN). Although the SUTI has a comprehensive pool of criteria for the evaluation of mobility sustainability of cities, considering the criteria weights as equal and disregarding the uncertainties present in expert opinions is viewed as a significant drawback. To satisfy the aforementioned deficiency, a novel integrated approach including the best-worst method (BWM), multi-objective optimization by ratio analysis (MOORA) and MOORA plus full multiplicative form (MULTIMOORA) method is develloped and applied for the SUTI evaluation. In order to manage ambiguous linguistic approximations, the utilization of BWM is applied within the framework of interval type-2 fuzzy (IT2F) sets. While IT2F-BWM is used to prioritize the main and sub-indicators of SUTI, MULTIMOORA approach is applied to rank the Asian cities. The findings imply that the proposed approach not only provides a more scientific perspective but also provides the opportunity to perform sensitivity analysis on criteria weights. Therefore, the results obtained in this study are expected to help cities to create a road map for planning sustainable urban mobility (e.g. Sustainable Urban Mobility Plan).

Techno‐economic study for the implementation of electric buses for sustainable urban and interurban transportation

AbstractA cleaner transportation sector requires the implementation of a variety of measures, such as the use of electric vehicles. Electric mobility has seen increasing use in public transportation, even though it still faces significant challenges. Electric bus fleets require high‐performance batteries to minimize the cost and weight of the buses. This paper investigates the energy consumption of an electric bus in urban and interurban transit using two methods: forward‐facing and backward‐facing approaches. The obtained findings have shown that the energy consumption of a bus depends on the road and its characteristics, ranging between 0.98 and 1.39 kWh/km. Furthermore, a regenerative braking analysis has been performed, and it has been found that this factor plays a significant role in reducing energy consumption by 21%. Finally, a life cycle assessment and an environmental analysis have been conducted. The obtained results have shown that the total cost of ownership of the buses in this project will be around 1.76 $/km. The use of electric buses could eliminate the greenhouse gas emissions produced by public transportation in the investigated province.

Strategies and Policy Frameworks for Low Carbon Mobility in Indian Cities

This paper endeavours to illuminate the challenges and issues that need to be addressed to achieve sustainable urban transportation system in Indian cities. This paper will try to trace pattern of urbanization, motorization and their impact on mobility, environment, health and road safety. Further, the paper will explain various efforts taken by government authorities to address the transportation issues and their outcomes. The paper also explains various strategies which if be adopted will ensure low carbon growth of urban areas. Low carbon cities will help in enhancing India's energy security and lower its carbon footprint.

Optimizing Traffic Control with Real-time Data: A HERE API-Based Approach

Traffic congestion is a pervasive issue in urban areas, leading to increased commute times, environmental pollution, and reduced overall quality of life. To address this challenge, this paper introduces a dynamic traffic control system that leverages the HERE API for real-time traffic data. The system provides a web- based interface with user authentication and allows users to input the address of a signal they wish to control. Utilizing the HERE API, it fetches live traffic information, including jam factor and free flow speed, for all four roads at the specified signal. This data serves as the foundation for an intelligent traffic management system that dynamically allocates green signal times based on the intensity of traffic, with roads experiencing higher congestion receiving longer green time. The system's backend is built using Python and the Flask framework, while the frontend features HTML and CSS for a user-friendly interface. Authentication ensures that only authorized users can access and utilize the system. Geocoding is employed to convert user-provided addresses into geographic coordinates, enabling precise data retrieval from the HERE API. Once the traffic data is collected, an analysis is performed to identify the road with the highest traffic intensity. The dynamic allocation of green signal times is a fundamental feature, allowing for real-time traffic optimization and a reduction in congestion. The paper explores the development, implementation, and testing of this dynamic traffic control system, emphasizing its potential to significantly improve urban mobility. Furthermore, considerations for scalability, security, and maintenance are addressed to ensure the system's practicality in real-world scenarios. This project contributes to the growing field of intelligent transportation systems by showcasing the benefits of data-driven decision-making in traffic signal control. By utilizing the HERE API, this system not only provides a solution to mitigate traffic congestion but also represents a step towards more sustainable and efficient urban transportation systems. Key Words: Traffic control, HERE API, Dynamic signal allocation, Real-time traffic data, Urban mobility, Traffic congestion, Traffic optimization, Intelligent transportation systems, Geocoding, User authentication, Python backend, Flask framework, User interface, Green signal time, Scalability, Security, Maintenance, Sustainable transportation.

Categorization of urban logistics stakeholders

The main challenge for urban logistics is to shift towards a system, working under the guidance of a competent authority working in collaboration with all the stakeholders involved, whether near or far. The ultimate goal of this coordination is to optimize resources and durations while maximizing benefits in a sustainable urban context. The choice of the route to be preferred is to be justified at the level of this article. This is the purpose of this document, which aims to prioritize the most important players in the field of goods transport at the urban level to highlight the areas of action. In this article, we recall several notions by providing several definitions related to the actors of urban logistics, including last-mile delivery and standardized categorizations. We then propose our own classification based on a questionnaire, which provides the necessary data for the development of three decision-making graphs based on the results of our analysis. By highlighting the most important stakeholders in urban logistics, we hope to provide a framework for more efficient and sustainable urban goods transport in the future.

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China

Despite the rapid growth in the popularity of technology-enabled dockless bikesharing services, comprehensive research on their causal effects on other modes of transportation is still scarce. To fill this gap, this study exploits the staggered entry of Ofo and Mobike into 36 Chinese central cities as a quasi-natural experiment using a difference-in-differences approach to examine the overall impact of dockless bikesharing services on bus, rail, and taxi use. The results show that the entry of dockless bikesharing services significantly reduced city bus ridership by 5.1% and taxi passenger miles by 6.4%, but had no significant impact on rail ridership overall. Further heterogeneity analysis shows that a higher proportion of BRT vehicles can attenuate the negative impact of bikesharing services on bus ridership, while bikesharing seems to promote rail transit use in cities with low population densities and limited rail transit networks. Moreover, the reduction effect of bikesharing services on taxi passenger miles was driven by a decrease in average trip distance, with the unintended consequence of total taxi deadheading increased by 9.7%. This study provides important and valuable references for city governments to better utilize bikesharing services to develop efficient and sustainable urban transportation systems.

Exploration on the application of a new type of superconducting energy storage for regenerative braking in urban rail transit

Regenerative braking technology has become increasingly attractive due to its ability to recover and reuse the energy that would otherwise be lost. In recent years, a new superconducting energy storage technology is proposed and it has been proved experimentally and analytically that the technology has promising application potential in urban rail transit for regenerative braking. However, a comprehensive assessment of the new technology has not been conducted up to date. In this paper, the currently available energy storage technologies for regenerative braking, such as batteries, supercapacitors, flywheels, and SMES are introduced along with the new superconducting energy storage technology. Comparative studies between the existing technologies and the new one are conducted in terms of energy density, energy conversion efficiency, energy storage duration, capital cost and environmental impact. It is concluded that a regenerative braking system with the new superconducting energy storage has very high cycle efficiency and is superior to the existing energy storage systems. It has the potential to revolutionize the regenerative braking technology and to develop more efficient and sustainable urban rail transportation systems.