It is well established that the transport sector is not an equalitarian sector. To develop a sustainable society, a more equalitarian and safe transport system for both users and transport sector employees is needed. This work prioritizes the needs and barriers previously identified as relevant among transport system users and employees for four different transport scenarios (railways, autonomous vehicles (AVs), bicycle-sharing services (BSSs), and employment). The aim of this paper is to prioritize the factors affecting women in these four transport scenarios with the help of a survey followed by the application of mathematical and computational algorithms based on the analytic hierarchy process (AHP) methodology. The identification of factors with higher influence in the fair participation of women in the transport sector will help transport planners, bike-sharing system owners, decision-makers, transport companies, and regulatory professionals to develop measures that could plausibly increase the proportion of women as users of BSSs, users of rail public transport, and AVs, as well as employees in the transport sector for a sustainable society. The results indicated that safety and security were the most challenging factors for railways. Weather, topography, and family responsibilities were shown to have a high influence on the use of BSSs. In the case of autonomous vehicles, the simultaneity and trust in the technology are the main opportunities to influence the acceptance of such vehicles. Finally, for transport employment, caring and parenting responsibilities were the factors that had the largest effect. Some differences in priorities were found for different profiles of women.

Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.

This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model.

Concerning the increase in the number of trips and tourists after the COVID-19 pandemic, TSPs (Transport Service Providers) and transport organizations are trying to improve their operability to answer the needs and expectations of passengers. This paper presents a methodology to assess and evaluate to what extent innovative technologies meet the needs of tourists and TSPs involved in the digital ecosystem for door-to-door trips in Europe, making railways and public transport more attractive and consequently encouraging people to use more intermodal solutions in public transport. In this study, two kinds of quantitative data are used: operational KPIs (Key Performance Indicators) and USI (User Satisfaction Index) surveys. The Effectiveness concept, as a metric of the capacity to meet these needs and expectations by the innovative technology, is calculated by merging both types of quantitative data. The method considers tourists’ socio-demographic profiles, allowing comparisons among TSPs and profiles for a specific technology, and it is extended to figure out correlations among variables through regression and Bayesian Networks analysis. In addition, specific socio-demographic data relevant to the needs and expectations were studied through the ANOVA test. This work belongs to the methodological framework of the IP4MaaS (Innovation Program 4 Mobility as a Service) project, which sets six demo sites on which this assessment method will be applied in a further stage. The concept of Effectiveness is applied in all the above-mentioned demo sites for the final assessment. Some IT innovations such as Location-Based Experience and Journey Planning have shown high Effectiveness. This work could be interesting for TSPs and IT (Information Technology) developers, researchers, policymakers, and organizations in the transport sector.

This paper presents a comprehensive and systematic analysis of the environmental impacts (EI) produced by novel nickel-zinc battery (RNZB) technology, which is a promising alternative for energy storage applications. The paper develops mathematical models for estimating the life cycle environmental impacts of RNZB from cradle to grave, based on an extensive literature review and the ISO standards for life cycle costing and life cycle analysis. The paper uses the ReCiPe 2016 method of life cycle analysis (LCA) to calculate the EI of RNZB in terms of eighteen Midpoint impact categories and three Endpoint impact categories: damage to human health, damage to ecosystem diversity, and damage to resource availability. The paper also compares the EI of RNZB with those of other battery technologies, such as lead-acid and lithium-ion LFP and NMC. The paper applies the models and compares results with those provided by the software openLCA (version 1.11.0), showing its reliability and concluding that NiZn batteries contribute approximately 14 MJ for CED and 0.82 kg CO₂ eq. for global warming per kWh of released energy, placing them between lithium-ion and lead-acid batteries. This study suggests that NiZn battery technology could benefit from using more renewable energy in end-use applications and adopting green recovery technology to reduce environmental impact. Further developments can use these models as objective functions for heuristic optimisation of the EI in the life cycle of RNZB.

The increasing demand for electricity and the electrification of various sectors require more efficient and sustainable energy storage solutions. This paper focuses on the novel rechargeable nickel–zinc battery (RNZB) technology, which has the potential to replace the conventional nickel–cadmium battery (NiCd), in terms of safety, performance, environmental impact, and cost. The paper aims to provide a comprehensive and systematic analysis of RNZBs by modeling their lifecycle cost (LCC) from cradle to grave. This paper also applies this LCC model to estimate costs along the RNZB’s lifecycle in both cases: per kilogram of battery mass and per kilowatt hour of energy released. This model is shown to be reliable by comparing its results with costs provided by recognized software used for LCC analysis. A comparison of LCCs for three widely used battery technologies: lead–acid, Li-ion LFP, and NMC batteries, which can be market competitors of NiZn, is also provided. The study concludes that the NiZn battery was found to be the cheapest throughout its entire lifecycle, with NiZn Formulation 1 being the cheapest option. The cost per unit of energy released was also found to be the lowest for NiZn batteries. The current research pain points are the availability of data for nickel–zinc batteries, which are in the research and development phase, while other battery types are already widely used in energy storage. This paper recommends taking into account the location factor of infrastructures, cost of machinery, storage, number of suppliers of raw materials, amount of materials transported in each shipment, and the value of materials recovered after the battery recycling process to further reduce costs throughout the battery’s lifecycle. This LCC model can be also used for other energy storage technologies and serve as objective functions for optimization in further developments.