Collaborative Data Analysis in Hyperconnected Transportation Systems
Taxi trip duration affects the efficiency of operation, the satisfaction of drivers, and, mainly, the satisfaction of the customers, therefore, it is an important metric for the taxi companies. Especially, knowing the predicted trip duration beforehand is
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INESC TEC, Faculdade de Engenharia, Universidade do Porto (FEUP), Rua Dr. Roberto Frias, 378, Porto, Portugal {mohammad.nozari,csoares}@fe.up.pt
Abstract. Taxi trip duration affects the efficiency of operation, the satisfaction of drivers, and, mainly, the satisfaction of the customers, therefore, it is an impor‐ tant metric for the taxi companies. Especially, knowing the predicted trip duration beforehand is very useful to allocate taxis to the taxi stands and also finding the best route for different trips. The existence of hyperconnected network can help to collect data from connected taxis in the city environment and use it collabora‐ tively between taxis for a better prediction. As a matter of fact, the existence of high volume of data, for each individual taxi, several models can be generated. Moreover, taking into account the difference between the data collected by taxis, this data can be organized into different levels of hierarchy. However, finding the best level of granularity which leads to the best model for an individual taxi could be computationally expensive. In this paper, the use of metalearning for addressing the problem of selection of the right level of the hierarchy and the right algorithm that generates the model with the best performance for each taxi is proposed. The proposed approach is evaluated by the data collected in the DriveIn project. The results show that metalearning helps the selection of the algorithm with the best performance. Keywords: Hyperconnected world · Machine learning · Metalearning · Data mining · Intelligent transportation systems · Collaborative data analysis
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Introduction
Hyperconnectivity is used to define the interconnectedness of people, organizations, and objects which result from different technology innovations like the Internet, mobile technology and the Internet of Things (IoT) [1]. The hyperconnectivity exists not only in the communication between people but also in the connectivity of cars [2]. In addition, to make the travel and transportation more efficient and more comfortable, the hyper‐ connectivity is the main driver of innovation [3]. On the other hand, the transportation system is clearly overloaded by congestion in the major cities. For example in the city center of London, the average speed of cars is 14 km/ h [4] while the car’s speed in the city center of Moscow is around 6 km/h [5]. Positively, this can be an opportunity to improve interconnectedness of cars in the city environment. These cars can be parts of the communication infrastructure for the Intelligent © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved H. Afsarmanesh et al. (Eds.): PRO-VE 2016, IFIP AICT 480, pp. 13–23, 2016. DOI: 10.1007/978-3-319-45390-3_2
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M.N. Zarmehri and C. Soares
Transportation Systems (ITS) and also offer various opportunities for gathering data about a city by continually sensing events from streets and process sensed data. Therefore, utilizing vehicular networks
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