Multigranular spatio-temporal exploration: An application to on-street parking data

Traffic congestions cost billions of dollars to the society every year and are often aggravated by road users looking for parking. One way of alleviating the parking problem is providing decision makers of smart cities with powerful exploratory tools to analyse the data and find more effective solutions. This paper proposes a novel visual analytics tool for decision makers that allows multigranular spatio-temporal on-street parking data exploration. Even if the tool has been designed to deal with on-street parking data, it relies on a generic logic that makes it adaptable to more general spatio-temporal datasets

Improving sensing coverage of probe vehicles with probabilistic routing

Modern cars are pervasively equipped with multiple sensors meant to improve in-vehicle quality of life, efficiency and safety. The aggregation on a remote back-end of the information collected from these sensors may give rise to one of the biggest and most pervasive sensor networks around the world, making possible to extract new knowledge, or contextual awareness, in a detail never experienced before. Anyhow, an open issue with probe vehicles is the achievable spatio-temporal sensing coverage, since vehicles are not uniformly distributed over the road network, because drivers mostly select a shortest time path to destination. In this paper we present an evolution of the standard A* algorithm, where the route is chosen in a probabilistic way, with the goal to maximize the spatio-temporal coverage of probe vehicles. The proposed algorithm has been empirically evaluated by means of a public dataset of more than 320.000 real taxi trajectories, showing promising performances in terms of achievable sensing coverage

Predicting the Spatial Impact of Planned Special Events

While it is widely acknowledged that Planned Special Events (PSEs), such as concerts, soccer games, etc., have a strong impact on road traffic, very few studies have quantitatively investigated this phenomenon. In this paper we present the preliminary results of a technique to quantify the impact of PSEs on traffic around the venue of the events. In particular, our goal was to automatically identify all those road segments around a venue that show a different traffic behavior on event days than on non-event days. To this aim, we defined a specific pipeline, including a K-Nearest Neighbor classifier, trained on traffic data of event and non-event days for each road, using the Dynamic Time Warp (DTW) as distance metric. The proposed solutions has been empirically evaluated on four PSE venues in Germany. Two of them hosted only soccer matches of the German First League, while the other two had mixed types of PSEs, including sport, concerts and other categories of events. Results are very positive for the soccer stadiums, while more research is needed for the venues hosting mixed types of PSEs

Adapting the A* Algorithm to Increase Vehicular Crowd-Sensing Coverage

Current vehicles are incorporating an even wider number of environmental sensors, mainly needed to improve safety, efficiency and quality of life for passengers. These sensors bring a high potential to significantly contribute also to urban surveillance for Smart Cities by leveraging opportunistic crowd-sensing approaches. In this context, the achievable spatio-temporal sensing coverage is an issue that requires more investigations, since usually vehicles are not uniformly distributed over the road network, as drivers mostly select a shortest time path to destination. In this paper we present an evolution of the standard A∗∗ algorithm to enhance vehicular crowd-sensing coverage. In particular, with our solution, the route is chosen in a probabilistic way, among all those satisfying a constraint on the total length of the path. The proposed algorithm has been empirically evaluated by means of a public dataset of real taxi trajectories, showing promising performances in terms of achievable sensing coverage