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Explaining the growth in light electric vehicles in city logistics

Research finds that the global market value of cargo bikes will hit 2.4 billion euros by 2031. Analysts with Future Market Insights assessing the growth of cargo bikes have placed the parcel courier industry as a key buyer of electric cargo bikes, forecasting that 43 per cent of sales could go to this industry. This growth is driven by city logistics trends, particularly as studies emerge showing the high efficiency and cost saving of the cargo bike versus the delivery van. It will not solely be direct incentives that drive uptake, however. The policy that restricts motoring and emissions is expected to be a key driver for businesses that seek profitability, with three-wheeled electric cargo bikes making up nearly half the market. The advance of e-bike technology has seen a strong rise in market share for assisted cargo bikes, now accounting for a 73 per cent market share. Potentially limiting the growth is the legislation governing the output and range of electric cargo bikes (FMI, 2021).To deal with the issues of faster delivery, clean delivery (low/zero emission) and less space in dense cities, the light electric freight vehicle (LEFV) can be–and is used more and more as–an innovative solution. The way logistics in urban areas is organized is being challenged, as the global growth of cities leads to more jobs, more businesses and more residents. As a result, companies, workers, residents and visitors demand more goods and produce more waste. More space for logistics activities in and around cities is at odds with the growing need for accommodation for people living and working in cities. Book: Innovations in Transport: Success, Failure and Societal Impacts

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.

Analyzing airport capacity by simulation

Air transportation has grown in an unexpected way during last decades and is expected to increase even more in the next years. Traffic growth tendencies forecast an expansion in the demand and greater aviation connectivity, but also higher workload to the different airspace users, especially for airport and services. Therefore, it is essential to employ strategies designed to use efficiently valuable corporate resource. Airport authorities around the world are investing in large capital projects, including new or improved runways, terminal expansions, and entirely new airports. However, this effort is sometimes limited due to their geographic location. In this work, two main objectives are pursued: first, to highlight the importance of the industry by exposing the current situation and future trends all over the world focusing in the Mexican industry; and second, to introduce a simulation model which can be used as a decision making tool for the upcoming demand. The analysis of the scenarios illustrates how to develop strategies to cope with the different airspace user's needs.

Individual intuition and communicative charisma as enablers of futures envisioning: ‘genius forecasting’ vs ‘forecasting geniuses’.

Demand Forecasting

Here, we delve into Demand Forecasting via Machine Learning, dissecting how to predict future demand using time-sensitive data. Westveer highlights key forecasting models, from the basic Simple Exponential Smoothing to the advanced SARIMA, applied to an electricity production dataset. The session, encapsulating the essence of data-driven forecasting, culminates in a compelling three-year predictive outlook, illustrating the transformative potential of machine learning in strategic planning and decision-making.