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Alface, G., Ferreira, J. C. & Pereira, R. (2019). App guidance for parking occupation prediction. In Martins, A. L., Ferreira, J. C., and Kocian, A. (Ed.), Intelligent Transport Systems: From Research and Development to the Market Uptake. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. (pp. 172-191). Braga: Springer.

G. Alface et al.,  "App guidance for parking occupation prediction", in Intelligent Transport Systems: From Research and Development to the Market Uptake. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Martins, A. L., Ferreira, J. C., and Kocian, A., Ed., Braga, Springer, 2019, vol. 310, pp. 172-191

@inproceedings{alface2019_1715102827393,
	author = "Alface, G. and Ferreira, J. C. and Pereira, R.",
	title = "App guidance for parking occupation prediction",
	booktitle = "Intelligent Transport Systems: From Research and Development to the Market Uptake. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering",
	year = "2019",
	editor = "Martins, A. L., Ferreira, J. C., and Kocian, A.",
	volume = "310",
	number = "",
	series = "",
	doi = "10.1007/978-3-030-38822-5_12",
	pages = "172-191",
	publisher = "Springer",
	address = "Braga",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-38822-5"
}

TY  - CPAPER
TI  - App guidance for parking occupation prediction
T2  - Intelligent Transport Systems: From Research and Development to the Market Uptake. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
VL  - 310
AU  - Alface, G.
AU  - Ferreira, J. C.
AU  - Pereira, R.
PY  - 2019
SP  - 172-191
SN  - 1867-8211
DO  - 10.1007/978-3-030-38822-5_12
CY  - Braga
UR  - https://link.springer.com/book/10.1007/978-3-030-38822-5
AB  - This research work presents a prototype model, focused on an android application, to handle the problem of finding an available parking space during driving process for all type of road vehicles in a city using historical data and prediction methods, where there is not any type of real-time system to provide information about the current state of the parking lot. Different source data integration were performed to improve the process of prediction, namely events in the surrounding areas, traffic information on the vicinity of the park and weather conditions on the city of the parking lot. This type of system aims to help users on a daily basis to find an available parking space, such as recommending the best parking lot taking into account some heuristics used by the decision algorithm, and creating a route to it, this way removing some anxiety felt by drivers looking for available spaces.
ER  -