This research elucidates the influence of environmental and hedonic motivations on the adoption of electric vehicles (EVs). A framework was constructed to scrutinize the impacts of perceived environmental friendliness on an extended technology acceptance model (TAM), with an added element of perceived enjoyment. A sample of 391 residents in Rome was surveyed, and findings were extracted using structural equation modeling (SEM) in JASP, a statistical software. The results indicated a significant influence of perceived environmental friendliness on TAM factors. Moreover, the perceived enjoyment associated with using an EV significantly correlated with consumer intention to adopt such vehicles. These insights suggest that understanding and promoting the environmental advantages and enjoyment of EV usage could potentially stimulate consumer adoption. Strategies such as government procurement of EVs and expansion of charging infrastructure may also prove beneficial. This research augments existing literature by emphasizing the importance of environmental friendliness perceptions and hedonic motivations in consumer adoption of EVs, contributing unique insights into consumer mobility needs. To the best of our knowledge, such an extensive examination has not been previously undertaken.

One of the major challenges facing Indian cities today is the need to generate and manage local-level finances, improve the quality and frequency of bus services, and develop multimodal transport systems. This study examines the generation and management of local finances, the enhancement of bus services, and the development of multimodal transport systems in Indian cities, specifically Pune, Bangalore, and Indore. Selected for their similarity in size and high rankings on domestic indices—Ease of Living Index (EoLI), Municipal Performance Index (MPI), and Swachh Bharat Mission—these cities provide a representative analysis of broader urban transport issues. A ratio analysis of financial allocations for bus service management and procurement within city budgets was conducted, utilizing a descriptive methodology and secondary data from municipal documents, official websites, scholarly articles, and news reports. The findings reveal critical insights into the fiscal challenges and potential solutions for public transportation systems in India, highlighting the necessary financial commitments for improving bus service quality and ridership. Furthermore, the study suggests recommendations based on best practices for advancing sustainable urban bus transportation in line with Sustainable Development Goal 11.2, offering a valuable reference for policymakers in allocating resources effectively for public transit infrastructure.

The primary focus of this study is to quantify the transport and logistics potential that influences effective international trade. Utilizing an integral assessment method, an analysis of key indicators contributing to this potential was conducted, resulting in the computation of eight coefficients. The pivotal finding of the study reveals a decreasing trend in the transport and logistics potential of enterprises, with predictions suggesting substantial declines based on the novel methodological approach employed. A comprehensive evaluation of the transport and logistics potential of enterprises was executed, and the results are subsequently discussed. This evaluation led to the determination of an integral indicator of transport and logistics potential, forming a dependency matrix that underpins the research findings. The novelty of this research lies in the unique methodological approach to assess the efficacy of the transport system management within business structures, emphasizing the coordinating and integrative role of logistics. This pioneering approach marks one of the first applications for evaluating transport and logistics potential in the context of international trade. Future research should address the optimization of transport and logistics potential to attract new trade development investments. However, this study's scope was limited to Ukrainian enterprises, thus future research should consider expanding the sample to include enterprises from neighbouring countries engaged in international trade with Ukraine, such as Poland and Romania.

Road traffic collisions (RTCs) represent a significant public health challenge, particularly in countries with elevated mortality rates from such incidents. In Libya, the scarcity of digitized RTC data hampers robust analysis and subsequent intervention strategies. This study aims to bridge this gap by meticulously transforming over 2,300 hard-copy RTC reports from the Ajdabiya Traffic Police Department archives into a structured electronic database. For this analysis, 1,255 rural freeway incidents were scrutinized using a Binary logit model (BLM) to ascertain determinants of injury severity. It was found that head-on collisions, elevated speeds, the use of private cars, and weekend incidents markedly increased the likelihood of severe injuries. Examination of investigative reports disclosed a significant deficiency in traffic safety awareness among enforcement personnel, coupled with suboptimal law enforcement. To augment road safety in Libya, the enforcement of traffic laws, speed regulation, and activation of emergency medical services are identified as primary interventions. Additionally, the establishment of an integrated, multi-source database is imperative to advance traffic safety research and policy development.

Fulfilling norms is a way to respect all the safety properties embedded in norm specifications. Moreover, it provides interoperability qualities that are particularly relevant in the transport domain. The article proposes a modelling engineering approach using a semi-formal model phase to identify a multilayered decomposition of the system with domain experts. Then a transformation into formal models is used in order to verify and validate the behaviour with technical and safety experts. Propositions are illustrated on a case study from the transport domain: Automatic Train Operation (ATO) over European Train Control System (ETCS), also named AoE, for freight trains. ATO under the supervision of a human driver is sometimes presented as a first step toward autonomous train. This paper provides a system analysis of the available norms dealing with automatic train operation under driver supervision. The work focuses on the collaboration between an automatic software for braking and accelerating in the European normative and technological context, known as AoE. From the study of the available documents, we derive an architectural model of this global system containing on board automation and on track automated specific devices. The technical contribution is a proposition of an approach specifying a correct-by-construction software system. This software component respects the industrial norms of automated train. We explain how it is relevant to use a norm-based technical architecture, that allow drivers to identify various functioning phases where, depending on the overall context, they can let an automatic system drive the train or not.

Asphalt-paved Road junctions frequently encounter deformation and degradation challenges due to heavy vehicular traffic and varying climatic conditions, such as temperature fluctuations and precipitation. This study employs a multifaceted approach, incorporating a Multilayer Perceptron (MLP) model, ancillary machine learning techniques, and optimization methodologies, to address these challenges effectively. The primary objectives are the prediction and analysis of pavement deformation, the optimization of maintenance strategies, and the evaluation of road effectiveness. Our findings underscore the substantial contribution of heavy vehicles to road erosion and the profound impact of vehicular retention and braking at intersections. A Multilayer Perceptron (MLP) model is utilized to simulate future pavement degradation accurately at a specific intersection, leveraging real-time traffic flow data. This approach showcases the advantages of using real-world traffic data to model the lifecycle of asphalt dependencies dynamically at the intersection level. Mitigation of road deterioration is proposed via controlled traffic flow and optimization of relevant parameters, such as minimization of intersection wait times. The integration of machine learning substantially enhances road conditions and reduces vehicular waiting times at intersections. The implementation of this study's findings in pavement design and preservation practices could enable transportation authorities to improve road safety, reduce maintenance costs, and decrease the incidence of road accidents. Overall, this paper presents a comprehensive approach towards sustainable and efficient road infrastructure management, highlighting the potential of AI in tackling pressing infrastructure challenges.

Urban transportation systems and their integration with spatially distributed opportunities are pivotal for ensuring effective accessibility. This study aims to rigorously evaluate urban accessibility by scrutinizing established criteria and measurement approaches within the literature. A systematic literature review was executed, targeting articles selected for their pertinence and citation impact. Through meticulous analysis, four cardinal indicators of access and their respective subsets were distilled. Synthesizing data from 61 scholarly publications elucidated the key indicators of accessibility. The findings underscore the adaptability and utility of these criteria as evaluative instruments and in guiding policy decisions. On the other hand, availability and quality of data, greater attention to travel reliability and user preferences are among the factors that should be considered in the accessibility assessment. The study's insights advocate for a nuanced application of accessibility indicators, promoting their evolution as multifaceted tools in urban planning domains. These results serve as a foundation for future research and contribute to the refinement of methods for comprehensive accessibility analysis in urban settings.

Autonomous vehicles necessitate robust stability and safety mechanisms for effective navigation, relying heavily upon advanced perception and precise environmental awareness. This study addresses the object detection challenge intrinsic to autonomous navigation, with a focus on the system architecture and the integration of cutting-edge hardware and software technologies. The efficacy of various object recognition algorithms, notably the Single Shot Detector (SSD) and You Only Look Once (YOLO), is rigorously compared. Prior research has indicated that SSD, when augmented with depth estimation techniques, demonstrates superior performance in real-time applications within complex environments. Consequently, this research proposes an optimized SSD algorithm paired with a Zed camera system. Through this integration, a notable improvement in detection accuracy is achieved, with a precision increase to 87%. This advancement marks a significant step towards resolving the critical challenges faced by autonomous vehicles in object detection and distance estimation, thereby enhancing their operational safety and reliability.

Global road transport safety concerns are escalating, evidenced by an annual increase in traffic-related accidents, fatalities, and injuries. In response, numerous governmental road safety initiatives aim to mitigate crash incidences and consequent harm. Extant literature documents myriad datasets collated to address road safety challenges and bolster intelligent transport systems (ITS). These datasets are amassed via diverse measurement modalities, including cameras, radar sensors, and unmanned aerial vehicles (UAVs), commonly known as drones. This study delineates ITS datasets pertinent to transport issue resolution and elucidates the measurement methodologies employed in dataset accrual for ITS. A dual comparative analysis forms the core of this research: the first examination juxtaposes data source methodologies for dataset collection, while the second compares disparate datasets. Both examinations are conducted using the Weighted Scoring Model (WSM). Criteria germane to the comparison are meticulously defined, and respective weights are assigned, mirroring their significance. Findings reveal the UAV-based method as superior in amassing datasets pertinent to drivers and vehicles. Among the datasets evaluated, the SinD dataset secures the preeminent position. This methodical approach facilitates astute decisions regarding data source and dataset selection, augmenting the comprehension of their efficacy and relevance within the ITS domain.

A forecast by the India Brand Equation suggests that the Maintenance, Repair, and Overhaul (MRO) industry will burgeon to US$ 2.4 billion by 2028. This anticipated expansion necessitates the strategic allocation of airport land for essential airline support facilities, which is pivotal in augmenting non-aeronautical revenue. In this study, land allotment practices at twenty-three Indian airports were evaluated against proposed optimization strategies for fuel stations, ground servicing equipment (GSE), hangars, and porta-cabins. Goal Programming was employed to minimize discrepancies in achieving land use and revenue benchmarks. The optimization, considering various constraints, revealed a potential 77% enhancement in area utilization and a 95% increase in revenue. Additionally, a model was formulated to determine the optimal allocation for commercial outlets, utilizing hypothetical data. The findings advocate for land resource optimization at non-major airports, where traditional traffic-based revenue is limited. This paper presents a roadmap for airport operators and policymakers, ensuring efficient resource management amid the aviation sector's growth.