In the last decades, the cities' capacity for generating digital information has grown exponentially. In this context, the successful implementation of smart cities' concept depends on the current possibility of handling the significant volumes of sensed data. This is particularly notorious in the case of urban mobility. Researchers in the field of urban planning have shown a great interest in urban mobility problems, proposing different route recommendation services towards making it easier and safer to move around the city. This paper addresses the development of an urban data platform and how to obtain and integrate information from sensors and other data sources to provide aggregated and intelligent views of raw data to support urban mobility. With the aim of evaluating the efficiency of the developed platform, we present an intelligent urban mobility solution, where the context-awareness, user preferences, and environmental factors play a significant role in the process of route planning. Finally, our work provides an experiment to assess different long-range wireless communication technologies to enable its implementation within an urban environment.

In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems.

Sustainable urban mobility refers to the sustainable transportation mode in terms of social, environmental, and climate impacts. Cycling has emerged as one of the most sustainable means of urban travel due to its flexibility, low costs, reduced carbon emissions, improved traffic, and mobility in cities. Vehicle-to-vehicle (V2V) communication is becoming a reality standard. Non-motorized vehicles, such as bikes, are expected to participate in V2V and Vehicle-To-Everything (V2X) networking alongside cars and trucks, although they have gotten significantly less attention. In this paper, we analyze and experimentally evaluate Bike-to-Bike (Bi2Bi) wireless communication in different urban scenarios, considering full topo-morphological characteristics of urban environments by means of deterministic 3D Ray Launching hybrid simulations. Communication and data exchange between bikes relies on the IEEE 802.15.4 standard, and specifically, on ZigBee. We analyze Bi2Bi communication and cooperation towards sustainable smart mobility, following a holistic approach providing a tool based on the city platform to monitor and understand the impact of mobility at both city and citizen level and to provide accurate information on carbon footprint reduction, for multiple city/system stakeholders.

Last-mile logistics is one of the most complicated operations in the whole logistic process. This concept describes the final leg of a product travel from a warehouse or hub to specific customers. One of the last-mile logistics challenges that courier delivery companies face is route planning. Ineffective route planning can cause operational delays that cascade and affect several last-mile deliveries. Thus, numerous factors need to be considered to plan and optimize effective delivery routes. These involve many extraordinary and unpredictable events, including weather, traffic conditions, and traffic regulations. A lack of accessible data hinders dynamic, efficient, and reliable route planning, leading to these factors being overlooked. In this paper, we propose the use of open data (OD) to overcome these limitations. OD are information available for anyone to access, reuse, and distribute for free with minimal attribution and sharing restrictions. Therefore, the aim of this work is to examine the impact of incorporating specific open data elements on the performance of the Clarke and Wright algorithm, particularly in calculating savings, and identifying optimal routes. The results we obtained showed the effect of considering OD with an increase rate of approximately 2% on the total distance compared to not considering them.