Open Access
The impact of integrating open data in smart last-mile logistics: the example of Pamplona open data catalog
(MDPI, 2025-01-08) Al-Rahamneh, Anas; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
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.
Open Access
Agent-based modelling and simulation for hub and electric last mile distribution in Vienna
(Elsevier, 2023) Ballano Biurrun, Aitor; Al-Rahamneh, Anas; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA26-2022
With the rise of e-commerce and door-to-door sales, last-mile deliveries are gaining more and more importance. As a result, last-mile distribution has become one of the most sensitive logistics processes due to its uniqueness, difficulties in meeting schedules, and high costs. Therefore, this work explores the use of urban consolidation centers to ease these last-mile difficulties. For that purpose, a hub in the city center of Vienna has been selected to deliver up to 150 clients disseminated by the city. This suitability is assessed by defining convenient simulation settings in order to replicate parcel demands in the city. Experiments are based in different hub-based fleets (traditional internal combustion vehicles or electric cargo bikes), demand patterns, and delivery frequency strategies by means of a biased randomization vehicle routing optimization heuristic. Results quantify the effects of having an urban consolidation center and highlight the use of electric cargo bikes for the last-mile distribution.
Open Access
Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data
(MDPI, 2025-07-11) Al-Rahamneh, Anas; Izco Berastegui, Irene; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC
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.

Faulín Fajardo, Javier

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