Electric Vehicles (EVs) currently provide a major opportunity to decarbonize urban areas and improve their quality of life, however, the mass transition towards electric mobility requires understanding and solving the potential issues that they might cause to users. In this work, we propose a process that, through a mix of mobility data analytics, efficient trip planning, and simulation heuristics, is able to analyze the current fuel-based mobility of a user and quantitatively describe the impact of switching to EVs on their mobility lifestyle. We apply our process to a large dataset of real trips, analyzing both the impact of EVs on the collectivity and on the individuals, providing a case study with insights at the level of single users.

An Individual Mobility Network (IMN) is a graph representation of the mobility history of an individual that highlights the relevant locations visited (nodes of the graph) and the movements across them (edges), also providing a rich set of annotations of both nodes and edges. Extracting representative features from an IMN has proven to be a valuable task for enabling various learning applications. However, it is also a demanding operation that does not guarantee the inclusion of all important aspects from the human perspective. A vast recent literature on graph embedding goes in a similar direction, yet typically aims at general-purpose methods that might not suit specific contexts. In this paper, we discuss the existing approaches to graph embedding and the specificities of IMNs, trying to find the best matching solutions. We experiment with representative algorithms and study the results in relation to IMN characteristics. Tests are performed on a large dataset of real vehicle trajectories.

This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

The ever-increasing volume of trajectories of moving objects and the diversity of intelligent transportation systems and location-based services that rely on spatio-temporal data of moving objects highlight the need for more efficient indexing techniques. The state-of-the-art methods index moving objects at three time modes of past (historical data), present, and future. An integrated method called “PCI” was proposed (past current indexing) to index and store spatial-temporal data of the past and present simultaneously. The method can handle queries in both the time modes and it processes and generates both the past and present indices using an integrated set of processing resources. Two interconnected data structures were utilized to store indices of both the time modes. Connecting the index of different time modes enforces efficiency challenges due to difference in updating costs. Since the method stores the indices in the main memory, the way the structures are connected to each other makes it possible to transfer the current data to the section responsible for historical data. This method indexes them in the trajectory of moving objects at a minimum time expense. As the quality of data inevitably affects the performance of applications, map matching methods was used in PCI to remove noises-e.g., stationary state noises-in the data received from the moving objects. This feature adds to the accuracy and reliability of query results. The effects of data reduction techniques on accelerating indexing, query processing, and reducing memory consumption (in voluminous data sets) were examined. Results of the comparisons, made based on the experiments, showed the higher efficiency of the indexing structure.