Large-scale electric vehicle (EV) adoption has the potential to offer flexible and distributed energy resources to support the power distribution system (DS) during emergent incidents. We developed a network-based multiagent stochastic optimization model with equilibrium constraints (N-MSOPEC) to investigate the potential value of EVs on the emergency load pickup in DSs, considering the uncertainties of line outage and EV participation. Decentralized stakeholders from both transportation and DSs, including the distribution system operator, distributed generator owners, charging station aggregator, and EV drivers, were modeled to reflect realistic decentralized decision making. EV participation was incentivized in a market equilibrium framework for DS support. An exact convex reformulation technique was additionally developed for the proposed N-MSOPEC model, which significantly improved the computational efficiency required to solve high-dimensional complementarity problems. Simulation results on coupled transportation and distribution test systems showed how EV participation could reduce load loss under different line outages and EV adoption scenarios, and demonstrated the effectiveness of our model in capturing the interdependencies of the coupled systems. The DS characteristics and load pickup needs influenced the incentives provided to EV drivers, resulting in different charging station selections and load pickup patterns. We also investigated the value of stochastic modeling in the multiagent framework by calculating metrics such as the value of stochastic solution and the expected value of perfect information for the stakeholders involved.
This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed. Research has shown that using the RF+IML method to study the impact of weather variables on shared bicycle transfer volume is feasible. There is a significant nonlinear relationship between various weather factors and shared bicycle transfers. Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. When humidity is below 60%, the variation in transfer volume remains relatively stable; however, once it exceeds 60%, the transfer volume drops sharply. When the temperature exceeds 17 °C, its impact tends to reach saturation. Similarly, when rainfall reaches around 20 mm, its adverse effect also approaches the threshold. Temperature is the most important factor affecting the prediction of shared bicycle transfer volume, with temperature, cold weather, and cold forecasts contributing over 35% to the total effect. The interaction effect between temperature and other weather factors accounts for 22% of the total effect.
Abdulrahmon Ghazal, Santhanakrishnan Narayanan, Ibraheem Oluwatosin Adeniran
et al.
Abstract The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.
Transportation engineering, Transportation and communications
Paulina Wiączek, Iryna Sitak, Alena Novak Sedlackova
et al.
The purpose of this research was, among other things, to show that the carpooling model can be regarded as a manifestation of collective intelligence and a response to the crisis of sustainability in today's ICT-based economy, which makes carpooling services a cheaper alternative to transportation services provided by other modes of transport: train and bus. The main research problem is contained in the question: How do collective intelligence and the tenets of sustainability influence the popularity of carpooling and the lower cost of carpooling services, compared to those provided by other forms of transportation? The critical literature analysis method, the comparison method and the statistical method, among others: the Mann Whitney U test and the Kruskal Wallis ANOVA test, were used in implementation of the study.
The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies.
Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
To quantitatively describe the damage degree and failure process of the cemented backfill (CB) under dynamic loading, this paper performed numerical split Hopkinson pressure bar (SHPB) impact experiments on CB samples using the ANSYS/LS-DYNA. The damage pattern and failure process of CB samples with four mix ratios (cement-to-sand (<i>c</i>/<i>s</i>) ratios of 1:4, 1:6, 1:8, and 1:10) at different impact velocities (<i>v</i>) (1.5, 1.7, 1.8, and 2.0 m/s) were numerically investigated using the micro-crack density method to define the damage variable (<i>d</i>). The results revealed that the use of a waveform shaper in the numerical simulation yielded a more ideal rectangular wave to ensue uniform stress distribution across the sample’s plane without stress concentration. Numerical simulations effectively depicted the dynamic failure process of the CB, with the overall failure trend exhibiting edge spalling followed by the propagation and interconnection of internal cracks. When the <i>v</i> increased from 1.7 m/s to 1.8 m/s, the <i>d</i> increased by more than 10%. As the <i>v</i> increased from 1.5 m/s to 2.0 m/s, the <i>d</i> for <i>c</i>/<i>s</i> ratios of 1:4, 1:6, 1:8, and 1:10 ranged from 0.238 to 0.336, 0.274 to 0.413, 0.391 to 0.547, and 0.473 to 0.617, respectively. A significant “leap” phenomenon in damage was observed when the <i>c</i>/<i>s</i> ratio changed from 1:6 to 1:8.
The poor friction coefficient (COF) stability and overlarge abrasion have become the bottleneck problem of Cu-based friction materials (CBFMs). At present, the structure optimization of CBFMs were neglected. This paper proposed a new method of constructing and regulating 3D mesh structure inside CBFMs. The samples were prepared by step mixing, wet granulation, particle coating, pressing and sintering. The structure activity relationship between 3D mesh structure and comprehensive properties were investigated; the force-thermal dynamic multifield characteristics were investigated by Finite Element Method. The results showed that 3D mesh structure inside CBFMs could quickly conduct the friction heat outside, which could alleviate COF heat fade and abnormal abrasion. Sample holding 3D mesh structure had a greatly enhancement in heat conductivity and a great improvement in mechanical properties of 61.5 %, which could enhance the comprehensive tribological properties. Sample holding 3D mesh structure got a reduced wear rate of 50.17 %. This study can provide a new idea for the design and fabrication of structured friction materials, and lay a theoretical foundation for the further study of CBFMs.
Improving the spatial-temporal balance between the supply and demand of urban transportation and alleviating traffic congestion are important ways to build sustainable cities. The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. This study aims to determine the optimal reservation volume (ORV) for urban roads and verify the effectiveness of the TRS. First, we employed the sustained flow index to estimate the ORV from the degree of trade-off between the road breakdown probability and capacity. Then, a bilevel programming model based on ORV constraints was established to analyse the effectiveness of the TRS. The results indicated that the ORV range is 0.79–0.89 times the road capacity. The TRS can achieve the best steady benefit when the demand for reservation travel reaches at least 40%. Selecting the most congested critical roads in the network to implement TRS is more effective than on a large area. The driver default behavior will increase the V/C ratios and travel costs of all roads in the network. It has been proven that the reservation transportation mode will promote the spatial-temporal balance between supply and demand to alleviate traffic congestion.
Transportation engineering, Transportation and communications
This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.
Abdullah Alourani, Farzeen Ashfaq, N. Z. Jhanjhi
et al.
The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.
Transportation engineering, Transportation and communications
The drone industry is one of the most important areas of the Fourth Industrial Revolution. In the drone industry, delivery systems using drones are now facing commercialization as they have undergone many experiments and discussions. The purpose of this study is to find the best route in a delivery system using a drone. In this study, we have developed the existing Vehicle Routing Problem (VRP) into a more realistic mathematical model considering the two differences between drones and vehicles; one is that power consumption varies with the weight of the loaded cargo and the other is that velocity is influenced by wind. This study also presents an Ant Colony System (ACS) algorithm to effectively solve VRP, a well-known NP-hard problem. The methodology of this study is quite successful and is expected to enable more realistic and effective routing decisions.
Unmanned aerial vehicles (UAVs) are gaining considerable interest in transportation engineering in order to monitor and analyze traffic. This systematic review surveys the scientific contributions in the application of UAVs for civil engineering, especially those related to traffic monitoring. Following the PRISMA framework, 34 papers were identified in five scientific databases. First, this paper introduces previous works in this field. In addition, the selected papers were analyzed, and some conclusions were drawn to complement the findings. It can be stated that this is still a field in its infancy and that progress in advanced image processing techniques and technologies used in the construction of UAVs will lead to an explosion in the number of applications, which will result in increased benefits for society, reducing unpleasant situations, such as congestion and collisions in major urban centers of the world.
Non-orthogonal multiple access (NOMA) is envisioned as a promising technology that enhances the spectrum efficiency for future cellular networks. On the other hand, there is a growing interest in wireless powered sensor networks toward the sustainable realization of Internet of Things (IoT), as they may not require any battery replacement in principle. Moreover, the use of unmanned aerial vehicle (UAV) as a moving access point or base station has emerged as a potential solution to the high traffic demands of wireless networks. This article focuses on the UAV-aided data collection from multiple wireless powered sensor nodes, where a single-antenna UAV supplies all the sensor nodes located in the coverage area with energy for data transmission, with which the sensor nodes send back their information to the UAV. We study several transmission schemes including NOMA as well as cooperative relaying, together with two representative sensor node pairing strategies. Based on the theoretical analysis in terms of the achievable outage probabilities as well as the corresponding numerical results, we elucidate suitable node pairing strategies that depend on selected transmission schemes.
Telecommunication, Transportation and communications
Skrúcaný Tomáš, Kendra Martin, Čechovič Tomáš
et al.
The consumption of internal combustion vehicles and the resulting emissions associated with their operation have been at the forefront in the development of the automotive industry in recent years. This development is supported and required by a number of public administration organizations. This research deals with the monitoring of selected parameters for different types of means of transport, while the unifying element is consumption and emissions per passenger. The aim of the research is to determine the optimal vehicle for passenger transport for selected transport between two specific cities in Slovakia, Europe. The research used methods of analysis of energy intensity and emissions of individual transport units, while fuel consumption in a particular transport relationship is a decisive evaluation factor, which includes the calculation of experimental measurements in a real traffic situation. The various data and diagrams provide relevant data on the fuel consumption values achieved by the experiment converted to fuel consumption. The research is given as an example, and its principle can be applied in other specific localities. it can be applied to others, they may also apply to other transport routes.