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.
The valuation of aircraft engines in the secondary market is critically influenced by the condition and remaining life of their Life-Limited Parts (LLPs). Traditional approaches often rely on fixed depreciation coefficients that fail to reflect the true operational history or probabilistic risk of failure. This study proposes a lifecycle-aware valuation framework based on Weibull-distributed failure modelling, enabling the computation of adaptive cost reduction coefficients for individual LLPs. Using simulated and operationally realistic data, the model estimates reliability-driven depreciation curves and quantifies residual value degradation over time. Comparative analysis confirms that the Weibull distribution provides superior flexibility and interpretability compared to alternative statistical laws. The methodology captures nonlinear wear-out dynamics and supports the generation of part-specific valuation profiles. The resulting insights enhance the accuracy and transparency of engine appraisal, teardown pricing, and leasing negotiations. This approach offers a practical foundation for integrating condition-based economic modelling with digital asset management systems in aviation). First published online 9 June 2026
This study proposes a weather-aware short-term urban traffic prediction framework that combines microscopic traffic simulation with Long Short-Term Memory (LSTM) based Machine Learning (ML) for forecasting vehicle flow at selected urban junctions. The research is motivated by the need to improve traffic-state prediction under varying environmental conditions, since weather disturbances such as rain, fog, and snow significantly affect driver-behaviour, vehicle speed, spacing, and intersection throughput. The simulation environment was developed in the Simulation of Urban Mobility (SUMO) platform using an urban road segment derived from geographic map data and traffic-flow information from the Vilnius city traffic database. 2 monitored junctions were selected as observation points, and traffic behaviour was simulated under 5 weather scenarios: normal conditions, light rain, heavy rain, fog, and snow. Weather-dependent driver parameters, including speed factor, reaction time, acceleration, deceleration, driver imperfection, and minimum gap, were incorporated into the simulation in order to reproduce realistic traffic dynamics. The generated simulation data, including vehicle count, speed, waiting time, temporal features, and encoded weather conditions, were used to train and evaluate multiple LSTM models through an AutoML-based tuning procedure. Among different configurations, the best-performing model consisted of 3 recurrent layers and achieved a validation RMSE of 0.1056 with a validation loss of 0.0652. The results show that the proposed framework is capable of reproducing the general temporal structure of urban traffic flow and preserving the relative ordering of traffic intensity across weather scenarios. Prediction quality was highest under normal conditions, with RMSE of approximately 0.21 veh/min, while the poorest accuracy was observed under snow conditions (about 2.5 veh/min). The model captured dominant traffic trends effectively, although it tended to smooth short-term local oscillations, especially under more unfavourable weather conditions. Overall, the study demonstrates that integrating SUMO-based simulation with LSTM forecasting provides an effective and flexible approach for short-term urban traffic prediction under varying meteorological conditions and may support future intelligent traffic management and weather-adaptive mobility applications.
With the fast development of civil aviation, large number of flights operate in major airports in rush hour. Extreme natural disasters, terrorism, and incidents may interrupt its normal operation, even lead temporary closure. Abrupt airport outage causes significant flights diverting to alternate airports. In this article, a centralized optimization method is proposed for the management and optimization of widespread flight diversion. Based on the idea of Collaborative Decision-Making (CDM), a linear programming model formulation is developed to assign flights, who are inbound to a temporary closed destination airport in an emergency, to divert to appropriate alternate airports. The objectives are minimizing total diverting time of flights as well as maximizing the expectation to alternate airports for airlines. Incorporating relevant real-world features, flights remaining flying time available and expectation of alternate airports are taken into account from airlines operation perspective. Airport alternate capacity, the category of aircraft could be accepted and the arriving flight slots assigned are considered for alternate airports. In addition, aircraft cruise speed, en-route wind, air traffic congestion and so on is considered. In the case study, it is found that under the given condition of 50 flights and 8 alternate airports, all flights can be accommodated within the remaining flying time, 33 (66%) flights are less 60 min and 48 (98%) flights are less than 120 min. The model solving time expenditure is less than one 2nd. It can meet the emergency condition and prevent a longer decision-making process. Comparing with the objective of the shortest diverting time as literature, the total diverting time is suboptimal but the formulation can get a better expectation of alternate airports for flights, which provides more flexibility of operations in airlines.
The aim of this article is to identify the factors that influence the evolution of CO2 emissions in gasoline passenger cars. Specifically, the factors that can be obtained during emission measurements at technical inspection stations are analysed. This identification is used to determine the factors obtained by measurements at the technical inspection stations that should be used as a design element for the taxation of petrol road vehicles in order to reduce CO2 emissions. The underlying source was data recorded by the vehicle technical inspection stations in 2019 and worked with passenger petrol cars of category M1. In total, almost 460000 passenger cars were analysed. 12 factors were used for each car, from which 7 variables were identified through 2 methods (BMA and WALS) as likely to have a statistically significant effect on the magnitude of CO2 emissions. Subsequently, the effect of each factor was determined by OLS with robust Std. Err. Our results show that the maximum engine power and the latest emission standards can be used as design element for the taxation of gasoline road vehicles, as a positive relationship between the growth of this factor and the magnitude of CO2 emissions of gasoline vehicles has been demonstrated.
Quick Commerce (Q-Commerce) allows customers to receive their orders from near Micro Fulfilment Centers (MFCs) in a short time. However, the fulfilment centers are difficult to maintain responsive and reliable delivery services with traditional Single Order Picking (SOP) systems. Motivated by the above challenge, this study develops a hybrid picking method for an order fulfilment center of a Korean delivery company. The hybrid method considers certain rules for order batching to complement a SOP method. The order batching rules enable pickers to deal with 2 customer orders within a specific range of order lines. The order picking development are based on the results of a Warehouse Activity Profiling (WAP) analysis. This analysis shows possible approaches for the development of the hybrid method by considering arrival rates of customer orders and their variability in terms of item type and quantity. Afterwards, the hybrid method is validated with an agent-based simulation model using actual order information of the case study company. This study finds that the hybrid method shows consistent performance in terms of order picking and fulfilment times under wide ranges of order arrival rate and variability.
The popularity of electric scooters as an individual means of transport results from their availability in the urban sharing system, ease of movement in the city and reduction of driving time compared to other means of passenger transport. The user can choose from a whole range of vehicles with different driving range and equipment with elements increasing the functionality of using the scooter. The article presents a proposal for changes to the design of a typical electric scooter. The main objective of the work is the engineering design of suspension and braking systems, in particular the swing arm suspension of the front and rear wheels and an additional disc brake. Increasing the diameter of the wheels and equipping it with a front and rear suspension system allowed for the reduction of vibrations and shocks transferred to the vehicle when driving on uneven surfaces. The results of analytical calculations confirming the positive effects of the introduced modifications were included. Adding a disc brake allowed for shortening the braking distance from 13.7 to 8.9 m, which has a positive effect on driving safety. A Finite Element Method (FEM) strength analysis was also performed, the results of which confirm the correctness of the new design. The modernized design improved the ride comfort and safety of using the electric scooter. First published online 9 February 2026
Creating a composite indicator is a popular concept in evaluating and comparing road safety of territories. While many other approaches are used, most current interest is on Data Envelopment Analysis (DEA) for measuring the relative performance in efficiency terms. Usefulness of DEA, which measure performance in efficiency terms, has already been proven. However, the indicators commonly used to construct a road safety composite index are not always precise and accurate (particularly safety performance indicators), and the results obtained from this type of data does not seem to be solid. So, the main aim of this paper is to represent the novel methodology to effectively evaluate the state of road safety and create a reliable composite index of the selected entities when imprecise data are involved and, to produce reliable composite index under uncertain environment. DEA, the weighting method Fan–Ma, and the Grey Relational Analysis (GRA) were integrated into hybrid methodology to obtain a more realistic and relevant picture of road safety. Applying this hybrid methodology, peculiarity of DEA is retained, scores are further differentiated, and entities are ranked and classified according to the road safety level. A case study was conducted to evaluate and rank municipalities, determine road safety classes and benchmark the territories under study. Results were verified indicating the robustness and effectiveness of the proposed methodology and its superiority to basic DEA as regards the territory ranking. First published online 3 February 2026
Urban logistics distribution accounts for a large proportion of CO2 emissions generated by urban transportation. Reducing CO2 emissions in the process of logistics distribution is one of the urgent urban problems to be solved. This article investigates the location of urban logistics distribution centers considering cargo transportation services of a public transportation system. Considering one or several bus lines for representing a public transportation system, several collaborative distribution scenarios are studied, and 2 mixed integer linear programming models are established to explore the impact of the public transportation system on the location of distribution centers in urban logistics. Numerical experiments show the influence of different bus lines on the location of distribution centers, collaborative distribution and truck carbon emissions in Dalian (China). In any case, the possibility of establishing distribution centers in blocks 5 and 42 of Dalian is very high. When bus lines are used, the highest bus line utilization can reach 47.83%, and the CO2 emission can be reduced by up to 36.3%. In terms of different bus lines, line 2002 is more suitable to participate in the collaborative distribution in Dalian compared with other bus lines. First published online 2 February 2026
