Quality is one of the most essential and comprehensive attributes of a transportation system′s performance. High-quality, comfortable, safe, and disability-responsive transportation for Healthcare Facilities (HFs), pharmacies especially in this article, are vital in urban areas. This study aims to measure Pharmacy Transport Index (PTI) containing many complex parameters, in a practical, effective, easy-to-understand and fast way within the framework of the logic perspective that reflects human reasoning provided by the fuzzy approach. The fuzzy approach comprises 4 sub- and one-main Mamdani-type Fuzzy Inference System (FIS). Subsystems are: Comfort (1st), Safety (2nd), Security (3rd), and Disabled condition (4th). While the 1st FIS includes 4 inputs: Road type, Parking spaces, Entrance, and Rest areas, the 2nd FIS contains 3 inputs, which are: Bus stop, Pedestrian crossing, and Signalized intersection. Simultaneously, the 3rd FIS encompasses 2 inputs, which are: Entry door width and Street lamps; the 4th FIS has 6 inputs, which are including: Disabled parking spaces, Entrance ramps, Entrance steps, Entry door type, Sidewalk ramps, and Tactile paving. The PTI is obtained by combining the outputs of these subsystems in a final FIS. The system is implemented according to the data of 492 pharmacies in the city centre of Antalya (Turkey). These used data were revealed by visiting the locations of all pharmacies one by one. Results demonstrate that some pharmacies have good transportation quality, while others require progress on safety and security dimensions. Therefore, it can be observed that the proposed practical system contributes to providing an important insight into decision-making for transportation policy and environment. This system can also be easily integrated into other critical places such as schools, hospitals, police stations, etc., in the cities.
Urban freight transport is a significant challenge nowadays because of its high impact on the sustainability of cities, and the pandemic has further increased the importance of these challenges in the last one and a half year, which has generated new challenges for urban logistics by changing the customer demands. Based on previous research results, it became clear that a ranking methodology would be very useful for city logistics projects, which makes it possible to evaluate the current city logistics state and future city logistics development potentials of urban zones to select the best zones for developments, which would be significant for the city logistics aspects of urban planning and logistics system planning as it could help to avoid selecting development and pilot areas based on limited information only. Previously, we developed a complex ranking system to handle this need. This article presents an improved version of this ranking model, which handles the criteria under consideration in a two-level system by using the Analytic Hierarchy Process (AHP). In our article, we are presenting the results of the literature review and the novelty of our research, then we will describe our previous results regarding the model development, our actual objectives, the two-level multi-criteria ranking model itself, its structure, the ranking criteria, the application of the ranking model in the city logistics system of the concentrated sets of delivery locations in Budapest (Hungary), and the results of a complex sensitivity analysis. The main result of our article is a complex city logistics ranking methodology, which can help future city logistics projects and developments to define those urban areas where the city logistics system should be developed and is possible to implement developments.
To mitigate resource inefficiencies arising from the homogeneous competition among ports and advance a low-carbon transportation network of hubports, multimodal freight transportation systems with low-pollution and low-consumption have gained significant scholarly attention. In the port hinterland transportation network, a reasonable hinterland for the main hubport can effectively eliminate homogeneous competition, realize the optimal allocation of transport resources, and promote the integrated and coordinated development of the region. From the perspective of hinterland division and coordination of transportation organization methods, a multi-objective mixed integer programming model is established to minimize the total transportation cost, total transportation time, and carbon emission cost. A novel CF-based selection Hyper-Heuristic (HH) is developed that employs an initial population generation mechanism based on tabu-list and an elite solution pool to explore the solution space. A case study of the Yangtze River Delta in China is conducted to verify the validity of the model and algorithm. The results show that the hinterland of Shanghai Port and Ningbo-Zhoushan Port are basically equal in scope, the Shanghai Port is in the upper part of the Yangtze River Delta region, mainly in Jiangsu and parts of Anhui and the Ningbo-Zhoushan Port is in the lower part of the Yangtze River Delta region and Jiangxi. Additionally, the carbon emission reduction effect reaches 34.2%. Finally, this research contributes dual policy implications: network design guidelines for port authorities, and carbon trading mechanism refinements through regional spatialization.
The number of Light Utility Vehicles (LUVs) in the national vehicle registers keeps increasing, especially in the context of the COVID-19 pandemic. This trend is mainly due to the accelerated growth of e-commerce and home delivery services, as well as the shift in delivery methods from traditional courier and postal services to the use of parcel lockers. Although LUV accounts for a relatively small share of total freight tonnage compared to other freight modes, it accounts for a disproportionately large share of Vehicle-Kilometres (vkm) travelled, particularly in densely populated urban areas. This contributes to increased transport-related emissions and poses significant challenges for urban transport systems, including increased infrastructure pressure, traffic congestion and complexity in traffic management, particularly in relation to the planning of low or zero-emission zones in cities. In the EU, official freight transport statistics may not include vehicles with a maximum permissible load of up to 3500 kg, meaning that such vehicles are not systematically collected. Therefore, the development of appropriate methodologies and the analysis of available data on LUV are both necessary and valuable, especially given their increasing prevalence. These efforts can provide essential insights to support evidence-based transport planning and policymaking. The importance of detailed freight transport statistics is underlined by major EU policy initiatives such as the White Paper (EC 2011), the European Green Deal (EC 2019) and the Sustainable and Smart Mobility Strategy (EC 2020) aimed at reducing GreenHouse Gas (GHG) emissions from the transport sector. The goal of this research is to propose a methodology for LUV data collection, share data insights from its initial approval by presenting key findings, and suggest further uses of the data. First published online 16 June 2026
Properly developed public transport infrastructure, a well-organised public transport route network and mixed territorial development contribute to regional development, strengthen the regional labour market and help reduce social exclusion. Mobility issues in sparsely populated areas receive less attention from policy makers and territorial planners than in cities. The accessibility of public transport in sparsely populated and difficult to reach rural areas is less studied. The need for public transport for residents depends not only on the distribution of places of work, education, and leisure of residents, on their transport mobility, but also on public transport infrastructure and the supply of transport. It is accepted that in European cities the average distances to public transport stops are less than 500 m. In remote, sparsely populated areas, the distances are much greater. The analysed foreign examples showed that in rural areas the distance varies from 500 m to 4.5 km. Collectively, these studies demonstrate that no consensus has yet been reached on the optimal walking distance to public transport stops that would ensure adequate service accessibility for residents of sparsely populated areas. The purpose aim of this study, based on data from one Lithuanian region (Klaipėda district municipality), is to identify statistically significant distance thresholds that influence public transport use by integrating Geographic Information System (GIS) based spatial population data with passenger demand analysis, thereby creating a data-driven basis for optimizing public transport stop locations in sparsely populated regions. The study was conducted in 4 steps. In the Step 1, a database of stops was prepared, where groups of stops serving the same population were combined. In the Step 2, data on the need for stops were collected and the main service distances of stops that will be studied were determined. In the Step 3, the population of residents living at selected distances from specific stops was calculated, thus forming the main database that will be studied in the Step 4. In the Step 4, the level of dependence between stop demand and population was found at different distances. The study showed that the maximum distance at which the dependence remains strong is 1.5 km from the stops. Longer distances do not seem attractive to residents and they no longer consider public transport as an option for making a trip and private vehicles are most often chosen. It has also been found that with smaller distances between stops, the speed of public transport decreases significantly and thus increases travel time for residents who already travel long distances, thus taking up a significant part of their daily journey, and at the same time the correlation between 500 m and 300 m does not have a significant difference. Based on this, it is not recommended to arrange stops more often than every 500 m in rural regions.
