Vehicle electrification presents challenges and opportunities across multiple sectors, including the automotive, energy and infrastructure domains. Battery charging and swapping are the two primary technologies for refuelling electric vehicles (EVs). However, the involvement of multiple participants and various factors makes EV refuelling a complex and multi-domain issue. Since conventional conductive charging requires vehicles to remain stationary for a period of time, parking naturally provides opportunities for EV charging. Therefore, parking and EV charging are intrinsically connected in how they are organised and planned. This paper presents a comprehensive literature review on the features of EV refuelling demand and its relation to parking patterns. The review focuses on key study issues related to the interaction between EVs and the power grid, namely forecasting, planning, and scheduling. These issues are examined at three different scales: the individual, station, and regional levels. Based on the findings from the literature, an integrated framework is provided to capture the features and linkages between refuelling demand and parking patterns across the different study issues and scales. Finally, the paper proposes several open issues that could be explored in future studies from the perspective of integrating parking and refuelling analysis.

Effectively leveraging data and domain knowledge remains a significant challenge in controlling the Internet of Unmanned Agent (IUA). This article proposes a novel multiagent deep reinforcement learning-based cooperative control model called MARL-CTV to efficiently control two key IUA agents: 1) connected and automated vehicle (CAV) and 2) controllable traffic signal light (TSL). The CAV agents are controlled by the deep deterministic policy gradient (DDPG) algorithm, and the TSL agent is controlled by a dueling double deep Q-network (D3QN). To reduce the control burden and ensure the cumulative reward converges, the actor and critic networks are pretrained by the expert dataset, and the expert dataset initializes the experience replay buffer of DDPG. This dataset is generated by multiple velocity profiles derived from a genetic algorithm (GA) based on various random initial states of CAVs. Numerical experiments conducted using a joint simulation platform composed of SUMO and CARLA and real-world data from CitySim demonstrate the effectiveness of MARL-CTV. Specifically, when the market penetration rate (MPR) of CAV is 35%, MARL-CTV enables most CAVs to pass through the signalized intersection without stop-and-go behavior, reducing average travel time by 24.2%, fuel consumption by 22.7%, and the traffic conflicts by 68.3%.

Increases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.

The widespread development of digital technology facilitates the emergence of new entrepreneurial modes, of which crowdfunding digital platforms are one. In the digital environment of crowdfunding platforms, digital entrepreneurs can obtain the essential resources necessary for their startups' rapid and cost-effcient development. However, the information asymmetry derived from the digital nature of crowdfunding platforms leads to a lower chance of success for entrepreneurial ventures in this market, especially those in digital technology, limiting the important role that crowdfunding platforms can play in digital entrepreneurship. To this end, we focus on the risk disclosure section introduced by crowdfunding platforms to alleviate information asymmetry and explore the influence mechanism of the content of risk disclosure on entrepreneurial resource acquisition in crowdfunding digital platforms. By employing a novel text mining technique structural topic modelling, we analyse the risk disclosure texts of 4,284 digital technology crowdfunding projects and successfully identify various factors that constrain the development of digital technology ventures in crowdfunding platforms. Furthermore, we find that the risk topics digital entrepreneurs disclose negatively affect entrepreneurial resource acquisition. However, this relationship is moderated by the reward structure setting in the context of reward-based crowdfunding. The findings of this study not only enrich the literature on crowdfunding and digital entrepreneurship but also provide valuable practical implications on how crowdfunding digital platforms can be used to promote the development of digital entrepreneurship.

Interval-valued Fermatean fuzzy sets play a significant role in modelling decision-making problems with incomplete information more accurately than intuitionistic fuzzy sets. Various decision-making methods have been introduced for the different classes IFSs. In this study, we aim to introduce a novel two-phase interval-valued Fermatean fuzzy dominance method which suits the decision-making problems modelled under the IVFFS environment well and study its applications in the adoption of energy consumption in Urban mobility considering digital carbon footprint. The proposed method considers the importance and performance of one alternative with respect to all others, which is not the case with many available decision-making algorithms introduced in the literature. Transportation is one of the most significant sources of global greenhouse gas (GHG) emissions. Numerous potential remedies are proposed to reduce the quantity of GHG generated by transportation activities, including regulatory measures and public transit digitalization initiatives. Decision-makers, however, should consider the digital carbon footprint of such projects. This study proposes three alternatives for reducing GHG emissions from transportation activities: incremental adoption of digital technologies to reduce energy consumption and greenhouse gases, disruptive digitalization technologies in urban mobility, and redesign of urban mobility using regulatory approaches and economic instruments. The proposed novel two-phase interval-valued Fermatean fuzzy dominance method will be utilized to rank these alternative projects in order of advantage. First, the problem is converted into a multi-criterion group decision-making problem. Then a novel two-phase interval-valued Fermatean fuzzy dominance method is designed and developed to rank the alternatives. The importance and advantage of the proposed two-phase method over other existing methods are discussed by using sensitivity and comparative analysis. The results indicate that rethinking urban mobility through governmental policies and economic tools is the least advantageous choice, while incremental adoption of digital technologies is the most advantageous.

Automated container terminal (ACT) is considered as port industry development direction, and accurate kinematic data (speed, volume, etc.) is essential for enhancing ACT operation efficiency and safety. Port surveillance videos provide much useful spatial-temporal information with advantages of easy obtainable, large spatial coverage, etc. In that way, it is of great importance to analyze automated guided vehicle (AGV) trajectory movement from port surveillance videos. Motivated by the newly emerging computer vision and artificial intelligence (AI) techniques, we propose an ensemble framework for extracting vehicle speeds from port-like surveillance videos for the purpose of analyzing AGV moving trajectory. Firstly, the framework exploits vehicle position in each image via a feature-enhanced scale-aware descriptor. Secondly, we match vehicle position and trajectory data from the previous step output via Kalman filter and Hungarian algorithm, and thus we obtain the vehicular imaging trajectory in a frame-by-frame manner. Thirdly, we estimate the vehicular moving speed in real-world via the help of perspective projection theory. The experimental results suggest that our proposed framework can obtain accurate vehicle kinematic data under typical port traffic scenarios considering that the average measurement error of root mean square deviation is 0.675 km/h, the mean absolute deviation is 0.542 km/h, and the Pearson correlation coefficient is 0.9349. The research findings suggest that cutting-edge AI and computer vision techniques can accurately extract on-site vehicular trajectory related data from port videos, and thus help port traffic participants make more reasonable management decisions.

In this paper, an innovative fuzzy group decision-making model is designed for assessing regional transportation sustainability, focusing on the correlation between various attributes of the evaluation system. The focus of this model is the partitioned Maclaurin symmetric mean operator because of its better applicability when considering attribute correlation and attribute grouping. The modified spherical fuzzy partitioned Maclaurin symmetric mean operator is proposed, which has superior application scope. Its weighted form and special cases are discussed. Then, the extended statistical variance method and the evidence-based Bayes approximation method are used to obtain weight vectors of attributes and experts. In addition, a fuzzy assessment model of sustainable transportation is developed. Finally, a numerical example of regional transportation sustainability assessment and a comparison with previous studies are presented to illustrate the feasibility and universality of this method.

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI’s ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs.

The fast advancement of urban transportation systems in the recent decades has on one hand improved efficiency in traffic control and management, yet on the other hand brought new obstacles and interferences in audio related services in transportation systems, which is one of the dominating components in urban transportation systems, such as end-to-end Voice over Internet Protocol (VoIP) communications, risk alerting, and personalised recommendation services. The movement of vehicles/trains and the growing complexity of transportation infrastructures has become a big threat to the audio related services. Hence it is crucial to evaluate the Quality of Experience (QoE) of audio related services. Different from traditional algorithms which use digital signal processing to evaluate the QoE of mobile users, in this paper, we propose a two-stage brain-alike neural network aided graph learning algorithm to evaluate the QoE of audio signals with the aid of EEG feature extraction. The results are evaluated by newly-collected on-site data in public transportation environments and are examined by a branch of human experts to show that our algorithm outperforms other benchmark algorithms in term of human perception and accuracy of classification.

Ramp merging represents a bottleneck scenario that causes traffic congestion, accidents, and increases emissions. Connected and Automated Vehicles (CAVs) can realize the coordinated control of ramp merging through vehicle-to- infrastructure (V2I) for relieving above problems. Considering the previous studies on centralized ramp merging only involved single mainline, this paper proposes a multi-lane centralized collaborative control strategy using cooperative game. First, the merging rules of different lanes of vehicles in the merging area are defined, so that the vehicles can achieve the cooperative merging safely. Second, driving efficiency, comfort, and fuel consumption in the merging control zone are used as the cost function. The best merging sequences of vehicles in different lanes are solved by cooperative game. Finally, analytical solution of longitudinal optimal control for all vehicles is obtained by applying the Pontryagin principle. The effectiveness of the proposed method is verified through simulation under random traffic conditions. Compared to other centralized control algorithms, it can significantly improve driving efficiency and reduce fuel consumption. At the same time, the applicability of the method is verified by comparing with ExiD datasets, and some advantages are obtained in terms of fuel consumption.

With the rapid development of Industry 4.0, the industrial cyber-physical systems (ICPS) are expected to realize the digital sensing, automatic control, and refined management in smart factories. However, limited bandwidth resources and severe industrial interference make it difficult to meet the real-time and ultrahigh reliability in edge computing (EC)-based next-generation industrial automation networks. To tackle these challenges, in this article, we propose a real-time transmission optimization scheme to accelerate EC. First, we establish a hierarchical system model for smart manufacturing and automation scenarios. Then we present a power control optimization method based on noncooperative game to alleviate interference and reduce energy consumption. Finally, we propose a path optimization scheme based on Q-learning for low-latency and ultrahigh reliability transmission requirements. Extensive simulation results reveal that our proposals perform better in terms of transmission delay and packet-loss rate compared with traditional methods, and therefore, contributes to EC deployment in ICPS.

In ear of smart cities, intelligent medical image recognition technique has become a promising way to solve remote patient diagnosis in IoMT. Although deep learning-based recognition approaches have received great development during the past decade, explainability always acts as a main obstacle to promote recognition approaches to higher levels. Because it is always hard to clearly grasp internal principles of deep learning models. In contrast, the conventional machine learning (CML)-based methods are well explainable, as they give relatively certain meanings to parameters. Motivated by the above view, this paper combines deep learning with the CML, and proposes a hybrid intelligence-driven medical image recognition framework in IoMT. On the one hand, the convolution neural network is utilized to extract deep and abstract features for initial images. On the other hand, the CML-based techniques are employed to reduce dimensions for extracted features and construct a strong classifier that output recognition results. A real dataset about pathologic myopia is selected to establish simulative scenario, in order to assess the proposed recognition framework. Results reveal that the proposal that improves recognition accuracy about two to three percent.

The rapid progress in informatisation and electrification in transportation has gradually transferred public transport junctions such as metro stations into the nexus of pedestrian flows, information flows, computation flows and energy flows. These smart environments that are efficient in handling large volume passenger flows in routine circumstances can become even more vulnerable during emergency situations and amplify the losses in lives and property owing to power outage triggered service degradation and destructive crowed behaviours. On the bright side, the increasingly abundant resources contained in smart environments have enlarged the optimisation space of an evacuation process, yet little research has concentrated on the joint optimal resource allocation between transportation infrastructures and pedestrians. Hence, in the paper, we propose a queueing network based resource allocation model to comprehensively optimise various types of resources during emergency evacuations. Experiments are conducted in a simulated metro station environment with realistic settings. The simulation results show that the proposed model can considerably improve the evacuation efficiency as well as the robustness of the emergency response system during emergency situations.

The Covid-19 pandemic has hit hard on the highly-organised yet risk-vulnerable modern societies, and has introduced new characteristics to large-scale emergencies, which feature long persistence in duration, high frequency in occurrence, large sensitivity to individual behaviours, and extreme high hazard propagation rate owing to the highly-efficient transport networks. This has raised new challenges on long-term emergency preparedness of urban transportation systems in terms of safety, efficiency, robustness and sustainability. Non-cooperative behaviours of transport participants could result in severe performance degradation in emergency preparedness, and mandatory restrictions in human activities can be economical costly and difficult in operation. In addition, current arrangement models for the disaster financial assistance have not been elaborately designed for civilian behaviour optimisation although with great potential as an economical instrument. Hence, in this paper, we propose a reward based traffic control mechanism to generate cooperative behaviours and optimise resource allocation in an urban transportation system for emergency preparedness via distributing credit coins, which can also be treated as a financial assistance approach during long-term disasters. A queueing theory based analytic model is employed to mimic the behaviours of civilians in the transportation system under the emergency preparedness state and a probability choice model is utilised to optimise the emergency preparedness strategies of the system. The experimental results show that the introduction of the incentive based traffic control mechanism can significantly reduce hazard response time, travel delay as well as the energy usage of the urban transportation system at the expense of monetary rewards.