Sustainable Urban Transport Research Articles

Integrated Sustainability Assessment of Electric Bus Adoption: A Comprehensive Model for Ecological, Economic, and Operational Impact

The spread of electric stations in cities around the world is an important step towards sustainable urban transportation. In Task 1,We developed the FE-VC model to determine carbon emission indicators based on the BWM approach, and demonstrated the ecological consequences over the life cycle through quantitative models. In the quantitative calculation of urban data, compared with diesel buses, the carbon emission reduction rate of pure electric buses is 28.68%, mainly from the fuel cycle, in which the carbon emission reduction rate of the fuel cycle stage is as high as 40.11%, and it takes about 3.5 years to achieve the relative carbon emission reduction. In Task 2, we construct an economic impact consequence model based on the time value of capital with the theory of time value of money as the core, consider the full life cycle of acquisition, operation and vehicle maintenance, quantify and visualize the impact mechanism of electric buses on the economy, and provide a method to understand the economic impact for metropolitan areas. For task 3, based on the optimal enterprise operating cost and the optimized economic impact model, we construct the initial replacement model of the electric bus. The initial replacement model was optimized by simplifying the social cost of carbon emission, and an electric bus replacement model based on the optimal enterprise operating cost was obtained. In city-specific applications, we find that vehicle choice has a significant impact on fleet replacement plans and operating costs, and that using multiple vehicles can significantly reduce operating costs as electrification rates increase.

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.

Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes

A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions.

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.

Urban Environment’s Contributory Factors for the Adoption of Cargo Bike Usage: A Systematic Literature Review

The supply chain sector plays a crucial role in driving economic development and globalization. However, the environmental repercussions of logistics and freight transport have become more pronounced. Nowadays, there is an ever-increasing acceptance regarding the opinion that the use of more sustainable urban freight transport has the potential to offer great social, economic, and environmental benefits. This study examines and highlights, via a systematic literature review, the urban environment’s factors that can essentially influence the promotion and usage of cargo bikes for last-mile deliveries in the urban environment. The aforementioned literature review revealed the importance of the quality of the urban environment’s components for the perceived and objective safety of people who make use of cargo bikes. In particular, the most essential factors for the increased use of cargo bikes were found to be traffic load, speed limits, and heavy vehicle traffic. Bicycle infrastructure is also an important factor in bicycling acceptance, as it provides the backbone for a comfortable and safe bicycle ride. Two other factors that can seriously affect cyclists’ sense of safety are street intersections and the width and number of road lanes, as the interaction between cargo bikes and motorized vehicles increases the possibility of traffic accidents. All the above factors need to be considered via various public policies that are not isolated countermeasures but form part of Sustainable Urban Mobility Plans that are currently being implemented in many European cities to ensure continuity and create a sustainable future.

Integrating Cargo Bikes and Drones into Last-Mile Deliveries: Insights from Pilot Deliveries in Five Greek Cities

Currently, there is a growing consensus that the use of more sustainable urban freight transportation has the potential to offer major energy and efficiency benefits which can be achieved through the appropriate combination of cargo bikes and the integration of drones into the urban logistics system. This study presents the results of a stepwise regression analysis that examines the role, benefits, and impact of electric vehicles such as e-bikes, cargo bikes, and drones for intermodal freight transportation in five Greek cities to support the green logistics paradigm. The results show that between routes of almost equal length, the most bicycle-friendly routes, such as routes along pedestrian areas or residential streets, should be avoided, as they reduce delivery speed and increase delivery costs in terms of energy and time expenditure. In addition, priority roads, which usually have higher traffic speeds and more trucks, are preferred by cyclists due to the quality of the road surface, even though the feeling of safety is lower on such roads. Finally, regarding drones, energy consumption is relatively low in the 0–5 mph and 6–10 mph wind speed ranges, indicating efficient energy use. In the 11–15 mph and 16–20 mph wind speed ranges, energy efficiency is significantly lower than the battery capacity, indicating that the cargo drone exhibits excessive energy consumption under these conditions.

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.

Selecting Indicators to Assess the Sustainability of Urban Freight Transport Using a Multi-Criteria Analysis

Background: Urban freight transport has recently garnered significant attention from both professionals and academics due to its pivotal role in fostering economic and social development. Despite notable progress, this sector faces challenges that hinder its long-term sustainability. Addressing these issues and ensuring the lasting sustainability of urban freight transport require a thorough assessment and monitoring process utilizing specific indicators. Methods: This paper introduces a set of indicators developed using a three-step methodology aimed at assessing the sustainability progress of urban freight transport. Initially, we present a long list of indicators drawn from the existing literature. Subsequently, we consider five essential properties: achievability, data availability, predictability, relevance, and comprehensibility. Lastly, we apply a multi-criteria analysis methodology that utilizes these properties to assess the long lists encountered during the selection process. The chosen indicators are those that do not register a value of “0” for any of the specified properties. To enhance reliability, the indicators are collaboratively identified by a minimum of two experts. Results: By carefully selecting 18 indicators based on five sustainability dimensions (economic, environmental, social/societal, political, and spatial), our approach ensures a robust evaluation framework. Conclusions: These indicators serve as valuable tools for stakeholders in comprehensively evaluating the sustainability aspects of urban freight transport.

Forward-Thinking for Sustainable Shared Mobility Solutions in Amman

This study presents a novel examination of shared mobility’s viability and impact in Amman, Jordan, framed within the context of sustainable urban transportation. A rigorous methodological approach that integrates advanced statistical models including Probit and Decision Tree analyses was utilized to evaluate the propensity of Amman’s residents to adopt shared mobility solutions. Notably, the Ordered Probit Model provided superior model prediction compared to the multinomial logit model, evidenced by a better goodness of fit measure. The results showed that public transportation users would highly use shared mobility services based on cost and reliability, with service convenience emerging as a pivotal factor. The classification tree identified the convenience of the service as the most important factor in adopting shared mobility. The survey data revealed an initial adoption rate of 25.4%, indicating a significant inclination towards shared mobility among respondents. This is pivotal in understanding the current readiness and potential growth of shared mobility in the city. This study is one of the first to quantify the readiness and potential growth of shared mobility in a Middle Eastern urban setting. Furthermore, the impact of this adoption rate on CO2 emissions was conducted. Emission analysis is crucial for assessing the environmental benefits of transitioning towards shared mobility options and aligning with global sustainability goals. Finally, the study extrapolates strategic guidelines for advancing sustainable transportation in Amman, identifying shared mobility options with the highest potential for successful adoption and proposing strategies to foster their implementation. This research contributes a unique perspective to the discourse on urban mobility, particularly in developing urban contexts like Amman, offering valuable insights for policymakers and urban planners.

Challenges Ahead for Sustainable Cities: An Urban Form and Transport System Review

This article reviews the critical issues surrounding the development of sustainable urban environments, focusing on the impact of transport and urban form on energy consumption and greenhouse gas emissions. The aim is to provide an overview of the state-of-the-art on the subject and to unravel what directions the literature suggests for sustainable urban planning. Current research and practices are synthesized, highlighting the interdependence of urban design and transportation systems in achieving sustainability goals. Important dimensions and practices of city planning and transport policies are explored, including urban form, urban sprawl, mixed land use, densification and infill, and urban public spaces, and how these directly influence transport dynamics, including modal choices and energy consumption. Innovative approaches in urban planning, such as transit-oriented development, and technological advancements, such as electric mobility, are also examined and their potential roles in sustainable urban transport. The conclusion underscores the urgency of adopting holistic and adaptable strategies to foster sustainable urban environments, calling for concerted efforts from policymakers, urban planners, and communities. Awareness of the conclusions can help municipal decision-makers in planning their cities for a sustainable future. Finally, the authors analyze important directions for future research and practical applications towards developing cities that are environmentally sound, socially equitable, and economically viable.

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

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

Congestion pricing for sustainable urban transportation systems considering carbon emissions and travel habits

Effective congestion pricing is essential for the sustainable development of urban transportation systems. To explore the impact of congestion pricing on travel behaviors and the urban environment, this paper investigates vehicular carbon emissions and travelers' car dependency under three different schemes of congestion pricing (CP), namely, charging by travel distance (CP1), charging by travel time (CP2), and charging by distance+time (CP3). The interaction between travel behavior adaptation and the corresponding CP scheme is considered in the bi-level model we constructed to determine the optimal rate for the best of regional traffic and carbon emissions. The results of the case study indicate that all schemes perform well in meaningful ways, while CP3 works the best in terms of carbon emission (-23.06 %) and car-mode share (-4.9 %). This suggests that CP3 is a more promising charging mode, as it tends to increase the complexity and psychological pressure for travelers looking for alternatives and inhibiting their car reliance and habitual paths. The findings are conducive to forming the habit of green mode use so as to facilitate carbon emission reduction and traffic jam alleviation, which provide references for policy-making and promote urban transportation systems to operate in an environmentally sustainable and socially responsible manner.

Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration

To enhance cycling comfort, a critical investigation of vibration effects in non-motorized bicycle riding is essential, focusing on road characteristics and traffic features. The analysis of how these elements influence cycling vibrations identified 13 key factors. This study utilized non-motorized bicycle lanes in Wuhan City for empirical research. Three-axis accelerometers were attached to riders’ torsos to measure vibration comfort levels. The observed road segments ranged from slightly to relatively uncomfortable. This study employed the random forest algorithm and logistic regression to analyze the influencing factors further. Six factors emerged as significant in affecting cycling comfort: the existence of dedicated non-motorized bicycle lanes, the lack of a physical barrier between non-motorized and motorized traffic, cycling speed, road surface irregularities, parking areas within non-motorized lanes, and bicycle type. This research offers valuable insights into non-motorized bicycle lane usage and contributes to the development of urban non-motorized bicycle infrastructure, supporting sustainable urban transportation.

The scope for more sustainable urban freight transport through shared resources, An exploratory study

Urban freight transport is vital for cities, yet it presents challenges like air quality, noise, safety, and visual impacts. Public and private entities aim to enhance citizen well-being and economic viability, necessitating a sustainable urban freight transport system. This study explores opportunities and barriers to resource sharing in urban freight through an exploratory investigation. The research included a literature review on sustainable shared freight resources. McKinsey reports were also reviewed for a balanced perspective. Furthermore, five semi-structured interviews were conducted to deepen insights. The results highlight seven distinct shared resources, both tangible and intangible.

Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.

Efficient Transport Sharing Systems

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, Django, Bootsrap, MySQL, Python.

Contribution to the assessment of the sustainability of urban freight transport in Morocco: A PLS-SEM Approach

With globalization and the expansion of cities, the movement of goods and people has significantly increased, both economically and socially. This sector serves as a vital component of any economy, fostering economic and social development. However, its detrimental effects pose significant challenges for countries seeking to pursue sustainable development policies. Being one of the most energy-intensive sectors, it emits greenhouse gases and pollutants, contributing to environmental degradation and noise pollution. While increased traffic and mobility offer benefits, they also strain resources and lead to higher energy consumption. The primary objective of this article is to assess the sustainability of freight transport in the Moroccan city of Fez and propose supportive solutions for various stakeholders in urban logistics. This involves examining the complex relationship between different factors and the sustainability of Urban Freight Transport (UFT), including accessibility, congestion, road occupancy, environmental impacts, health impacts, and road safety. The research data were collected from 100 managers and employees of logistics and transportation companies in Morocco. Structural equation modeling was utilized to test and confirm the hypotheses and the research model. The results of these analyses demonstrated a positive impact relationship between the various factors and the sustainability variable. Subsequently, we suggest the establishment of delivery areas and an urban distribution center as two sustainable logistics solutions. The analysis and its findings can be applied to any other city.

Indicators for assessing progress towards urban freight transport sustainability

Today, assessing the sustainability of urban freight transport is crucial to deal with its negative impacts. In this context, many indicators were developed to track the progress of sustainable transport systems. These indicators guarantee communication and facilitate the interaction between private and public actors. However, selecting the appropriate indicator presents a particular challenge because of the large number of the available indicators. This study provides a literature review to discuss the selection of sustainability indicators in the field of transport and focuses on the main methodological choices. Its aims are to identify and to compare the advantages and limitations of selecting approaches. A total 14 studies conducted in the period 2002-2022 are examined. Important methods to select indicators are summarized. The results obtained show that most researchers focused on public transport; as well as a lack of consideration of others sustainability dimensions was highlighted. Then, the most frequently-used methods to select indicators are based on previous literature and properties. A set of recommendations is presented in order to develop future research.

Simulation and Optimization of Bus Interior Noise Based on Hybrid Method for Engine Vibration Load Identification

Buses are crucial for urban public transportation systems as they help reduce traffic congestion and promote environmental sustainability. The noise inside buses significantly affects the comfort and physical health of both drivers and passengers. Although numerous studies have focused on the interior noise of buses, few have explicitly addressed the methods of reducing bus noise through comprehensive bus simulation and testing. Challenges of the full bus simulation include obtaining accurate engine vibration loads and building the finite element model of the full bus. This study presents a comprehensive approach for analyzing and optimization bus interior noise caused by engine vibration excitation. A novel hybrid method is proposed to identify bus engine vibration load, combining experimental testing of engine cylinder pressure with multibody dynamics simulation. Structure–acoustic coupled simulations were conducted to predict bus interior noise based on a detailed finite element model of the full bus. The study demonstrates a strong correlation between simulation and experimental results, validating the accuracy and effectiveness of the proposed method. The primary causes of the interior booming noise were identified by simulation analysis and experimental diagnosis. Optimization analysis led to a notable noise reduction by adjusting the support stiffness of the drive shaft, body structure, and integrating rear leaf spring dampers. These processes contribute to the analysis and improvement of bus interior noise, ultimately enhancing bus ride comfort and promoting sustainable urban public transportation.