Search Results (109)

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article

Electrifying urban last-mile logistics is an important step towards reducing carbon emissions which requires replacing conventional vehicles with low-carbon alternatives that offer comparable operational and cost characteristics. This study presents a methodology for evaluating the feasibility of electrifying an urban delivery fleet, using data from a major beverage company in Seville as a case study. Applying a fleet and route optimization algorithm for various vehicle combinations, we demonstrate that emerging electric vehicle options, combined with a redesigned fleet mix and an optimized routing, can already enable cost-efficient electrification of distribution activities in the city centre. Furthermore, our analysis suggests that full electrification of the company’s local distribution network may be possible by 2030, depending on the availability of larger electric trucks. Our results show that currently available electric vehicles can fully substitute conventional options in the case study context, with higher capital costs offset by lower energy costs in most cases. The electrification of urban logistics can yield significant environmental benefits, particularly if powered by a clean energy mix. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article

Urban freight transportation is facing significant challenges due to increasing demand, driven by globalization, e-commerce growth, and the adoption of just-in-time logistics. These trends have led to rising vehicle flows in urban areas, negatively impacting sustainability, economic efficiency, and road safety. In response, cities are exploring innovative last-mile delivery strategies that emphasize sustainability, flexibility, and cost efficiency. Among these strategies, cargo bikes—particularly electric cargo bikes (e-cargo bikes)—are emerging as promising low-emission solutions for urban freight distribution. However, despite their potential, a generalized methodology for estimating their demand share in urban contexts remains underdeveloped. This study proposes a comprehensive modelling framework to evaluate the freight demand share that can be addressed by e-cargo bikes, integrating quantity, restocking service, modal, and delivery sub-models, calibrated using data from a case study in Italy. The results demonstrate that e-cargo bikes could fulfil up to 20% of urban freight demand, depending on the category of goods transported, and underscore the feasibility of integrating e-cargo bikes into urban logistics systems. However, critical challenges related to scalability and cost-effectiveness persist, highlighting the need for further research and reliable cost data to support broader implementation. Full article

Cities are looking for an approach to affordable, integrated and sustainable transport systems across all transport modes and services. Automated vehicle (AV) technologies use emerging technologies to integrate multimodal transport systems and ensure sustainable mobility in a city. Vehicle automation has entered the public conscious with several auto companies leading recent developments in legislation and affordable cars. Governments support AVs through policies and legal frameworks, and it is the responsibility of AV dealers to comply with legal and policy provisions so that the benefits of this new and promising industry can be felt. Despite the growing interest in AVs as a potential solution for sustainable transportation, several research gaps remain in relation to technology and infrastructure readiness, policy and regulation, equity and accessibility concerns, public acceptance and behaviour, and integration with public transport. This paper discusses the challenges and dilemmas of adopting AVs within the existing urban transportation system and within existing design standards in the United Kingdom and explores the progress and opportunities related to policies of transportation that may stem from the emergence of AV technologies in the UK. The potential of AVs is still limited by cyber insecurity, incompetent infrastructure, social acceptance, and public awareness. However, AVs are crucial to a city’s efficiency and prosperity and will become essential components for the provision of more flexible, convenient, integrated and sustainable travel options. Full article

Decision making in city logistics (CL) is complex due to the numerous concepts and alternatives, as well as the intricate relationships between measures and effects. This study introduces a novel approach to evaluating urban freight transport (UFT) by combining multi-criteria decision making (MCDM) and social cost–benefit analysis (SCBA). This combination aims to improve decision making for sustainable CL concepts, particularly in reducing externalities in last-mile delivery. The model assesses various CL initiatives and urban consolidation center (UCC) concepts for their impact on UFT externalities. It uses the MCDM for ex ante scenarios assessment and prioritization. Input data were collected through a survey of experts from various sectors, and the Analytic Hierarchy Process (AHP) was applied in the case study of Novi Sad, Serbia. The prioritization highlighted the significance of implementing restrictive regulatory measures, alternative transport modes, and operational optimization within UCC concepts. By estimating capital, operational, and external costs, SCBA was applied to the prioritized UCC concepts, which were then further evaluated using the SCBA outputs. Sensitivity analysis was employed to assess the robustness of the proposed model. This paper offers valuable insights into the potential use of existing tools within a hybrid model to enhance decision making in CL. Full article

As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions. Full article

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap. Full article

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. Full article

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions. Full article

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: “Closest Allocation” and “Negotiation” consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the “Closest Allocation” in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability. Full article

Green transportation is the core embodiment of ecological civilization and the concept of green development within the field of transportation, and it is an important strategic choice for sustainable urban development. National central cities represent the highest level in China’s urban system planning. This paper aims to evaluate the level of green transportation development in national central cities. It established a set of 29 specific evaluation indicators from five dimensions: basic indicators, green transportation infrastructure, traffic environmental protection, traffic travel, and traffic safety. It constructed an evaluation index system for the development level of green transportation. The entropy weight TOPSIS method was utilized to evaluate the development levels of green transportation in nine national central cities from 2020 to 2022. An obstacle degree model was constructed to identify key obstacle factors at both the criterion and indicator layers of the green transportation development level evaluation index system for national central cities. Suggestions were proposed from five aspects: establishing a comprehensive policy framework, promoting regional collaborative development, accelerating infrastructure construction, improving transportation service quality, and fostering the green upgrading of industries. The results showed that the comprehensive ranking of green transportation development levels among the national central cities from high to low for the years 2020–2022 was as follows: Shanghai, Chongqing, Chengdu, Beijing, Guangzhou, Tianjin, Wuhan, Xi’an, Zhengzhou. In terms of the regional spatial layout, the green transportation development levels of the nine national central cities generally exhibited a “high on the periphery, low in the center” distribution characteristic. The comprehensive ranking of the obstacle degree in the criterion layer was as follows: basic indicators, traffic travel, green transportation infrastructure, traffic environmental protection, traffic safety. After screening the criteria level where the obstacle degree calculation results are above 15%, traffic safety is eliminated. The nine cities, which were located in different regions, generally maintained consistent internal obstacle factors and their order. The top five indicators with the highest frequency of obstacle degrees at the indicator layer were as follows: total passenger transport volume, number of taxis, new energy vehicle production, expenditure for transportation, and total freight transport volume. The specific key obstacle factors at the indicator level were different in the nine cities. Full article

Road transport plays a crucial role in sustaining all the personal and freight movement needs of residential, commercial, and industrial activities, and in Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads contribute to as much as 50% of particulate matter pollution in a year. Therefore, effective management of vehicle exhaust emissions is vital not only for improving current air quality but also for ensuring the long-term benefits from efforts to reduce air pollution. In the approved clean air action plans for 131 cities under the national clean air program (NCAP), more than 50% of the implementable actions are transport-centric. Having a reliable and replicable vehicle exhaust emissions inventory is essential for effective planning, which can help establish a baseline, support scenario analysis, and allow for tracking progress in the sector. This process begins with accessing accurate vehicle stock numbers, typically obtained from vehicle registration databases, traffic surveys, and other governmental records. Often, in low- and middle-income countries like India, these numbers require extensive data cleaning before they can be used for emissions and pollution analysis. This paper presents a cleaned, open-access vehicle stock database for India and outlines a methodology to build and maintain an in-use vehicle age-mix database for future years. The database covers the years 1993 to 2018 for the entire country and individual states, along with estimates of the age distribution of vehicles using survival functions. By offering a comprehensive and reliable data source, this paper aims to support sustainable national and urban air quality management efforts, helping policymakers and stakeholders make informed decisions to improve air quality and public health. Full article

This study explores the role of local authorities in achieving net zero emissions in the context of the climate crisis, with a particular focus on the freight transport sector. The study identifies the challenges of decarbonising urban freight transport and highlights the need for planning frameworks to facilitate this transition. The authors examine freight innovation ecosystems and the various actors involved in designing public policies that incentivise the creation or enhancement of these ecosystems. Through a systematic literature review, bibliometric analysis, and content analysis, this study identifies a lack of connection between the literature on these topics. However, it also highlights the potential for lessons to be learned from successful incentive frameworks in the four clusters identified. The authors propose a comprehensive incentives framework that includes both direct and indirect incentives aimed at the ecosystem and the public sector, respectively, as well as city conditions that can facilitate systemic change. Overall, this study provides valuable insights for policymakers and stakeholders seeking to promote sustainable transport and achieve net zero emissions. Full article