Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios

On-street Parking Availaibilty Data in San Francisco, from Stationary Sensors and High-Mileage Probe Vehicles

This dataset contains records of the measured on-street parking availability in San Francisco, obtained from the public API of the SFpark project. In 2011, the San Francisco Municipal Transportation Agency (SFMTA) started a project on smart parking, called SFpark, whose goal was the improvement of on-street parking management in San Francisco, mostly by means of demand-responsive price adjustments. One of the key points of the project was the collection of information about on-street parking availability. To this aim, about 8,000 parking spaces were equipped with specific sensors in the asphalt, periodically broadcasting availability information. The SFpark project made available a public REST API, returning the number of free parking spaces and total number of provided parking spaces per road segment, for 5,314 parking spaces on 579 road segments in the pilot area. We collected parking availability data from 2013/06/13 until 2013/07/24, by querying this API at approximately 5-minute intervals. As a result, we obtained in total about 7 million observations of parking availability on the road segments. These observations represent the first dataset we are providing. In addition, we simulated the achievable sensing coverage of on-street parking availability that could be achieved by a fleet of taxis, if they were equipped with sensors able to detect free parking spaces, like side-scanning ultrasonic sensors, or windshield-mounted cameras [4]. In particular, by exploiting real taxi trajectories in San Francisco from the Cabspotting project, we first computed the frequencies of taxi visits for each road segment covered by the SFpark sensors. Then, we downsampled the first dataset, in order to have a parking availability information for a road segment at a given time only in presence of a transit of a taxi on that segment at that time. This step was replicated for 5 different sizes of taxi fleets, namely 100, 200, 300, 400, and 486. Consequently, in total six datasets are available for further research in the field of on-street parking dynamics

HappyParking

There are estimations that indicate that about half of the vehicles on the move are searching for parking and that more than 40% of the total fuel consumption is spent while looking for an available parking space. This also contributes to significant urban traffic congestion. So, it would be interesting to have software tools that can help drivers to park easily. For the application challenge, our group proposed a HappyParking application, which would offer some interesting benefits: It ackowledges the importance of considering parking in the context of a displacement between a source location and a target location. This implies that the final target location has to be considered when deciding an appropriate parking space. Moreover, the application can be integrated into existing GPS-based navigation applications. It considers multimodality, that is, that parking a car could be just a leg within a longer trip using different modes of transportation. It exploits real-time constraints (e.g., time-based parking restrictions). It can accommodate a variety of methods to capture information about available parking spaces (e.g., magnetic sensors on the parkings, crowdsourcing information provided by drivers releasing a spot, cars with different types of sensors able to detect free places, etc.). It supports different types of parking spaces: on-street parking, private parkings and garages, home parking available for rental during specific time periods, etc...

An analysis of the link between high speed transport and tourists\u27 behaviour

The focus of this manuscript is on the analysis of the impacts of the High Speed Rail system in Italy on the tourism market. An analysis has been carried out for 77 Italian cities. Results show that the effects of High Speed on the number of tourists and the number of overnights spent at destination are positive in all the cities served by the High Speed Rail. On the other hand, other factors, such as the attractions at destinations and the Gross Domestic Product, affect tourists\u27 choices for the case study of cities not served by the High Speed Rail

Web and Wireless Geographic Information Systems

FEATURE SECTION EDITORIA

On-street parking availaibilty data in San Francisco, from stationary sensors and high-mileage probe vehicles

This dataset contains records of the measured on-street parking availability in San Francisco, obtained from the public API of the SFpark project. In 2011, the San Francisco Municipal Transportation Agency (SFMTA) started a project on smart parking, called SFpark, whose goal was the improvement of on-street parking management in San Francisco, mostly by means of demand-responsive price adjustments [1]. One of the key points of the project was the collection of information about on-street parking availability. To this aim, about 8,000 parking spaces were equipped with specific sensors in the asphalt, periodically broadcasting availability information. The SFpark project made available a public REST API, returning the number of free parking spaces and total number of provided parking spaces per road segment, for 5,314 parking spaces on 579 road segments in the pilot area. We collected parking availability data from 2013/06/13 until 2013/07/24, by querying this API at approximately 5-min intervals. As a result, we obtained in total about 7 million observations of parking availability on the road segments. These observations represent the first dataset we are providing. In addition, we simulated the achievable sensing coverage of on-street parking availability that could be achieved by a fleet of taxis, if they were equipped with sensors able to detect free parking spaces, like side-scanning ultrasonic sensors [3], or windshield-mounted cameras [4]. In particular, by exploiting real taxi trajectories in San Francisco from the Cabspotting project [5], we first computed the frequencies of taxi visits for each road segment covered by the SFpark sensors. Then, we downsampled the first dataset, in order to have a parking availability information for a road segment at a given time only in presence of a transit of a taxi on that segment at that time. This step was replicated for 5 different sizes of taxi fleets, namely 100, 200, 300, 400, and 486. Consequently, in total six datasets are available for further research in the field of on-street parking dynamics. All these datasets can be downloaded at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YLWCSU

Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forerast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the interest of many users has shifted from static views of geospatial phenomena, which capture its “spatiality” only, to more advanced means of discovering dynamic relationships among the patterns and events contained in the data as well as understanding the changes occurring in spatial data over time

Web application testing: Using tree kernels to detect near-duplicate states in automated model inference

Background: In the context of End-to-End testing of web applications , automated exploration techniques (a.k.a. crawling) are widely used to infer state-based models of the site under test. These models, in which states represent features of the web application and transitions represent reachability relationships, can be used for several model-based testing tasks, such as test case generation. However, current exploration techniques often lead to models containing many near-duplicate states, i.e., states representing slightly different pages that are in fact instances of the same feature. This has a negative impact on the subsequent model-based testing tasks, adversely affecting, for example, size, running time, and achieved coverage of generated test suites. Aims: As a web page can be naturally represented by its tree-structured DOM representation, we propose a novel near-duplicate detection technique to improve the model inference of web applications, based on Tree Kernel (TK) functions. TKs are a class of functions that compute similarity between tree-structured objects, largely investigated and successfully applied in the Natural Language Processing domain. Method: To evaluate the capability of the proposed approach in detecting near-duplicate web pages, we conducted preliminary classification experiments on a freely-available massive dataset of about 100k manually annotated web page pairs. We compared the classification performance of the proposed approach with other state-of-the-art near-duplicate detection techniques. Results: Preliminary results show that our approach performs better than state-of-the-art techniques in the near-duplicate detection classification task. Conclusions: These promising results show that TKs can be applied to near-duplicate detection in the context of web application model inference, and motivate further research in this direction to assess the impact of the technique on the quality of the inferred models and on the subsequent application of model-based testing techniques

An analysis of the link between high speed transport and tourists\u27 behaviour

The focus of this manuscript is on the analysis of the impacts of the High Speed Rail system in Italy on the tourism market. An analysis has been carried out for 77 Italian cities. Results show that the effects of High Speed on the number of tourists and the number of overnights spent at destination are positive in all the cities served by the High Speed Rail. On the other hand, other factors, such as the attractions at destinations and the Gross Domestic Product, affect tourists\u27 choices for the case study of cities not served by the High Speed Rail

Phenotypical heterogeneity linked to adipose tissue dysfunction in patients with type 2 diabetes

Adipose tissue (AT) inflammation leads to increased free fatty acid (FFA) efflux and ectopic fat deposition, but whether AT dysfunction drives selective fat accumulation in specific sites remains unknown. The aim of the present study was to investigate the correlation between AT dysfunction, hepatic/pancreatic fat fraction (HFF, PFF) and the associated metabolic phenotype in patients with Type 2 diabetes (T2D). Sixty-five consecutive T2D patients were recruited at the Diabetes Centre of Sapienza University, Rome, Italy. The study population underwent clinical examination and blood sampling for routine biochemistry and calculation of insulin secretion [homoeostasis model assessment of insulin secretion (HOMA-β%)] and insulin-resistance [homoeostasis model assessment of insulin resistance (HOMA-IR) and adipose tissue insulin resistance (ADIPO-IR)] indexes. Subcutaneous (SAT) and visceral (VAT) AT area, HFF and PFF were determined by magnetic resonance. Some 55.4% of T2D patients had non-alcoholic fatty liver disease (NAFLD); they were significantly younger and more insulin-resistant than non-NAFLD subjects. ADIPO-IR was the main determinant of HFF independently of age, sex, HOMA-IR, VAT, SAT and predicted severe NAFLD with the area under the receiver operating characteristic curve (AUROC)=0.796 (95% confidence interval: 0.65-0.94, P=0.001). PFF was independently associated with increased total adiposity but did not correlate with AT dysfunction, insulin resistance and secretion or NAFLD. The ADIPO-IR index was capable of predicting NAFLD independently of all confounders, whereas it did not seem to be related to intrapancreatic fat deposition; unlike HFF, higher PFF was not associated with relevant alterations in the metabolic profile. In conclusion, the presence and severity of AT dysfunction may drive ectopic fat accumulation towards specific targets, such as VAT and liver, therefore evaluation of AT dysfunction may contribute to the identification of different risk profiles among T2D patients