Jurica Babic

Anomaly detection algorithms are widely used across various domains, but they often suffer from high false positive rates and lack of interpretability. This paper introduces xAAD, a novel approach that combines Active Anomaly Discovery (AAD) with the Assist-Based Weighting Scheme (AWS) explainability metric for Isolation Forest-based anomaly detection. Our method enhances model interpretability and reduces false positives by incorporating expert feedback and providing post-feedback feature importance values. We evaluate xAAD on both synthetic and real-world datasets, demonstrating improved performance in terms of binary classification accuracy and root mean square error (RMSE) compared to traditional AWS. The results show that xAAD consistently outperforms the baseline in both simulated and real-world scenarios, suggesting a positive impact on the interpretability of anomaly detection systems across multiple industries.

The transport sector is one of the main contributors to gas emissions and the greenhouse effect. Mobility as a Service seeks to combine different means of transport (public transport, bicycles, cars, etc.) in an integrated way to solve this problem. Raising drivers’ awareness and advising them to drive more efficiently and economically can help reduce environmental impact. Ultimately, eco-driving can help save fuel, money and provide higher passenger satisfaction during the trip. The research uses a contextually enriched automotive data set collected via OBD, during passenger transportation in private cars. A multilevel analysis was conducted using a variety of approaches. With AI-based analysis, using unsupervised learning, we identified five groups of similarities as a function of speed and acceleration. The efficiency of each driver was examined and speed and acceleration/deceleration were noted as the most influential factors. Also, passengers’ perception of driving quality is negatively affected by sudden acceleration and deceleration. Passengers have a poor perception of the driving efficiency and additional efforts should be made to educate them about the environmental aspect of driving. Finally, the main contribution is the eco-aware framework, which covers and describes the entire process from data collection to analysis. The collected data are publicly available and include 110 trips represented by more than 65,000 data points.

In the last decade, electric vehicles (EVs) have emerged as a sustainable transportation alternative to traditional internal combustion engine (ICE) cars, with automotive software as the key driver behind the advancements. A well-defined information and communication technology (ICT) architecture, comprised of electronics and software, can increase an EV’s energy and cost efficiency, safety and comfort. With connected and autonomous electric vehicles (CAEVs) fast becoming a reality, the importance of software in vehicles increases tenfold. This paper serves as an introduction into the field of electromobility and automotive software. It provides an overview of the software and ICT architecture found in CAEVs and identifies future trends and challenges in automotive software development.

Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r) evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

Besides the smart grid, future sustainable energy systems will have to employ a smart market approach where consumers are able choose one of many different energy providers. The Power Trading Agent Competition (Power TAC) provides an open source, smart grid simulation platform where brokers compete in power brokerage. This paper presents CrocodileAgent, which competed in the Power TAC 2018 finals as a broker agent. The main focus in the design and development of CrocodileAgent 2018 was the creation of smart time-of-use tariffs to reduce peak-demand charges. CrocodileAgent 2018 was ranked third in Power TAC 2018 Finals, with a positive final profit and a positive result in each of three game types. In addition, CrocodileAgent 2018 had the highest percentage of “profitable games” (91%) from among all competing agents, the second highest level of “net profit per standard deviation” (0.48) and the third highest “net profit per subscriber” (79 monetary units).

The recent advent of electric vehicles (EVs) marks the beginning of a new positive era in the transportation sector. Although the environmental benefits of EVs are well-known today, planning and managing EV charging infrastructure are activities that are still not well-understood. In this paper, we are investigating how the so-called EV-enabled parking lot (EVPL), a parking lot that is equipped with a certain number of chargers, can define an appropriate parking policy in such a way that satisfies two challenges: EV owners’ needs for recharging as well as the parking lot operator’s goal of profit maximization. Concretely, we present three parking policies that are able to simultaneously deal with both EVs and internal combustion engine vehicles. Detailed sensitivity analysis, based on realworld data and simulations, evaluates the proposed parking policies in a case study concerning parking lots in Melbourne, Australia. Our study produces results that are highly prescriptive in nature because they inform a decision maker under which circumstances a certain parking policy operates optimally. Most notably, we find that the dynamic parking policy, which takes the advantage of advanced information technology (IT) and charging infrastructure by dynamically changing the role of parking spots with chargers, often outperforms the other two parking policies because it maximizes the profit and minimizes the chance of cars being rejected by the parking lot. We also discuss how making a few parking spots EV-exclusive might be a good policy when the number of available chargers is small and/or the required IT infrastructure is not in place for using the dynamic policy. We conclude our work by proposing a technology roadmap for transforming parking lots into smart EV-enabled parking lots based on the three studied parking policies.