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Studying Individualized Transit Indicators Using a New Low-Cost Information System

Studying Individualized Transit Indicators Using a New Low-Cost Information System

P. A. Castillo, A. Fernández-Ares, P. García-Fernández, P. García-Sánchez, M. G. Arenas, A. M. Mora, V. M. Rivas, J. J. Asensio, G. Romero, J. J. Merelo
Copyright: © 2014 |Pages: 20
ISBN13: 9781466661943|ISBN10: 1466661941|EISBN13: 9781466661950
DOI: 10.4018/978-1-4666-6194-3.ch016
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MLA

Castillo, P. A., et al. "Studying Individualized Transit Indicators Using a New Low-Cost Information System." Handbook of Research on Embedded Systems Design, edited by Alessandra Bagnato, et al., IGI Global, 2014, pp. 388-407. https://doi.org/10.4018/978-1-4666-6194-3.ch016

APA

Castillo, P. A., Fernández-Ares, A., García-Fernández, P., García-Sánchez, P., Arenas, M. G., Mora, A. M., Rivas, V. M., Asensio, J. J., Romero, G., & Merelo, J. J. (2014). Studying Individualized Transit Indicators Using a New Low-Cost Information System. In A. Bagnato, L. Indrusiak, I. Quadri, & M. Rossi (Eds.), Handbook of Research on Embedded Systems Design (pp. 388-407). IGI Global. https://doi.org/10.4018/978-1-4666-6194-3.ch016

Chicago

Castillo, P. A., et al. "Studying Individualized Transit Indicators Using a New Low-Cost Information System." In Handbook of Research on Embedded Systems Design, edited by Alessandra Bagnato, et al., 388-407. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6194-3.ch016

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Abstract

Current information systems used for data collection and to generate information on the state of the roads have two drawbacks: the first is that they have no ability to identify target-detected vehicles; the second is their high cost, which makes them expensive to cover the secondary road network, so they are usually located just on main routes. Thus, a new low-cost information system to monitor the traffic in real-time is proposed in this chapter. This system is based on scanning Bluetooth devices that are near the detection node. A large amount of data from passes of Bluetooth devices by different nodes (movements or displacements) have been collected. From this data, the frequency of appearance, average speed, or the number of devices that pass a certain site each day (on both working or non-working days) can be determined. The analysis of collected data has given statistics and indicators about the use of vehicles by the population of the monitored area. Specifically, the authors have obtained information about the total number of vehicles that each node has detected, on weekdays or holidays, information on traffic density by time range, on individual movements, the average speed on a section delimited by two consecutive nodes, and what demonstrates the power and features of the developed system.

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