停車格的可用性資料對於有效率的停車位偵測系統是很重要的，目前已經開發的室外停車系統是使用無線感測器、物聯網科技、及照相機組成的，但由於電磁場的干擾會使得調校偵測演算法變得複雜且使準確率只能有九成。在此研究中，我們利用磁力感測器、亮度感測器以及 LoRa 無線模組經由長達 13 個月的時間來感測車輛的暫態事件 (停車及開車) 來研究干擾的因素。藉由長期實地的實驗使得此停車位偵測系統在設置時只需要簡單的調校即可。

Data pertaining to the availability of parking slots is crucial to the efficientoperation of systems designed to monitor the state of parking spaces. Outdoorparking systems have been developed using wireless sensors, Internet of Things (IoT)technology, and cameras. Unfortunately, interference from electromagnetic fieldscomplicates the tuning of parameters for detection algorithms and limits accuracy to only 90 percent. In this study, we investigated these problems by collecting data from magnetic sensors, light sensors, and LoRa wireless modules used in the detection transient events (car arrivals and departures) over a period of 13 months.
This led to the design an adaptive occupancy detection system using a variety of
sensors, which can be deployed with only minimal calibration.

[1] “Accelerating sustainability: Demonstrating the benefts of transportation technology.” http://digitalenergysolutions.org/dotAsset/933052fc-0c81-43cf-a061-
6f76a44459d6.pdf.
[2] “The learning curve of smart parking.” http://www.nytimes.com/2012/12/23/
technology/smart-parking-has-a-learning-curve-too.html.
[3] K. Blumer, H. R. Halaseh, M. U. Ahsan, H. Dong, and N. Mavridis, “Costeffective single-camera multi-car parking monitoring and vacancy detection towards real-world parking statistics and real-time reporting,” in Proc. ICONIP
2012 - Volume Part V, pp. 506–515, Springer-Verlag, 2012.
[4] “Streetline Inc..” http://www.streetline.com.
[5] “SFpark project.” http://sfpark.org.
[6] “Fastprk.” http://www.fastprk.com/.
[7] “The project - FastPrk2.” http://www.fastprk2.eu/the-project/.
[8] “Fastprk | Sigfox Partner Network.” https://partners.sigfox.com/products/fas
tprk.
[9] “Fastprk | Worldsensing.” http://www.worldsensing.com/solutions/mobility/te
chnology/fastprk-en.html.
[10] “Periodic Reporting for period 1 - FASTPRK-2.” http://cordis.europa.eu/resul
t/rcn/186387_en.html.
[11] “Smart Parking Should Be High on List of Priorities for City Managers.” https://machinaresearch.com/report/smart-parking-should-be-high-on
-list-of-priorities-for-city-managers/.
[12] “Urbiotica.” http://www.urbiotica.com/en/.
[13] San Francisco Municipal Transportation Agency, “SFpark: Putting Theory Into
Practice - Post-launch Implementation Summary and Lessons Learned,” tech.
rep., August 2011.
[14] “Streetline and Cisco Introduce Camera-Based Smart Parking Sensing and
Integrated Network Solution.” http://www.streetline.com/2014/05/streetline
-cisco-introduce-camera-based-smart-parking-sensing-integrated-network-sol
ution-2/.
[15] “ADEC Technologies.” http://www.adec-technologies.ch.
[16] “Worldsensing starts Fastprk-2 project.” http://www.fastprk.com/media-news
/fp-news.html?wsdetail=%20284.
[17] M. Y. Idris, Y. Y. Leng, E. M. Tamil, N. M. Noor, and Z. Razak, “Car Park
System: A Review of Smart Parking System and its Technology,” Information
Technology Journal, vol. 8, no. 2, pp. 101–113, 2009.
[18] S. Mathur, T. Jin, N. Kasturirangan, J. Chandrasekaran, W. Xue, M. Gruteser,
and W. Trappe, “Parknet: Drive-by sensing of road-side parking statistics,” in
Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys ’10, (New York, NY, USA), pp. 123–136, ACM,
2010.
[19] S. Nawaz, C. Efstratiou, and C. Mascolo, “Parksense: A smartphone based
sensing system for on-street parking,” in Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, MobiCom ’13,
(New York, NY, USA), pp. 75–86, ACM, 2013.
[20] “TI MSP430 ultra-low-power Microcontrollers.” http://www.ti.com/lsds/ti/m
icrocontrollers_16-bit_32-bit/msp/overview.page.
[21] “Honeywell HMC5883 3-Axis Digital Compass IC.” https://www.seeedstudio.
com/wiki/images/4/42/HMC5883.pdf.
[22] “TAOS TSL2561 Light-to-Digital Converter.” https://cdn-shop.adafruit.com/
datasheets/TSL2561.pdf.
[23] “ARCT TM1276 LoRa Transceiver module.” http://www.arct.com.tw/web/.
[24] “AN218: Vehicle Detection Using AMR Sensor.” http://www51.honeywell.co
m/aero/common/documents/myaerospacecatalog-documents/Defense_Broch
ures-documents/Magnetic__Literature_Application_notes-documents/AN
218_Vehicle_Detection_Using_AMR_Sensors.pdf.
[25] “Taoglas 915MHz Embedded Ceramic Patch Antenna.” https://www.taoglas.
com/wp-content/uploads/2015/06/ISPC.91A.09.0092E.pdf.
[26] H. Wang, W. Fan, P. S. Yu, and J. Han, “Mining concept-drifting data streams
using ensemble classifers,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, (New
York, NY, USA), pp. 226–235, ACM, 2003.
[27] “Kruskal-Wallis H Test using SPSS Statistics.” https://statistics.laerd.com/sps
s-tutorials/kruskal-wallis-h-test-using-spss-statistics.php.
[28] “Kolmogorov-Smirnov test.” https://www.encyclopediaofmath.org/index.php
/Kolmogorov%E2%80%93Smirnov_test.
[29] “Hellinger distance.” https://www.encyclopediaofmath.org/index.php/Helling
er_distance.
[30] P. Legendre and E. D. Gallagher, “Ecologically meaningful transformations for
ordination of species data,” Oecologia, vol. 129, no. 2, pp. 271–280, 2001.
[31] D. Brain and G. I. Webb, “On the effect of data set size on bias and variance
in classifcation learning,” in Proceedings of the Fourth Australian Knowledge
Acquisition Workshop (AKAW-99), pp. 117–128, The University of New South
Wales, 1999.