Evaluation of the Utilization of Smartphone Applications in Active Probe Vehicle Traffic Data Collection

Öz

Probe vehicle data has been widely used as a mean of traffic monitoring, specifically for travel time, delay and speed measures. Technological developments in the last decade have increased the availability of technologies and tools used in probe vehicle data collection. One of the most common methods is obtaining necessary data from GPS equipped vehicles. Transportation agencies can utilize fleet data for continuous monitoring of a study area or assign a certain number of vehicles to perform data collection on a specific corridor/area. However, if the number of probe vehicles is low, the location accuracy becomes more critical. The purpose of this study is to evaluate the possibility of using existing smartphone applications in the market for collecting travel time and delay data in probe vehicles and compare with high-end GPS product. With this goal, the study aims to reduce the cost of data collection and test the accuracy and reliability of limited probe vehicle data. The data has been collected simultaneously on 102 segments in different speed, density and environmental conditions on major roadways in Delaware. The mean and variance of the travel time and delay measures are compared with statistical methods and the results revealed that there is no significant difference between smartphone application data and high-end GPS product data for travel time and delay measures. Therefore, it is emphasized that the smartphones are capable of collecting probe vehicle data for management and operation of the roadways even in specific data collections where the number of probe vehicles is limited.

Anahtar Kelimeler

• Travel Time,Delay,Probe Vehicle Data,Smartphone Applications,Traffic Data

Kaynakça

1. Antoniou, C., Balakrishna, R., & Koutsopoulos, H. N. (2011). A Synthesis of emerging data collection technologies and their impact on traffic management applications. European Transport Research Review, 3(3), 139–148. http://doi.org/10.1007/s12544-011-0058-1
2. Choi, K., & Chung, Y. (2002). A Data Fusion Algorithm for Estimating Link Travel Time. Journal of Intelligent Transportation Systems, 7(3–4), 235–260. http://doi.org/10.1080/714040818
3. Djuknic, G. M., & Richton, R. E. (2001). Geolocation and assisted GPS. Computers, 34(2), 123–125. http://doi.org/10.1109/2.901174
4. Faghri, A., & Hamad, K. (2002). Application of GPS in Traffic Management Systems. GPS Solutions, 5(3), 52–60. http://doi.org/10.1007/PL00012899
5. Faouzi, N. E. El, & Klein, L. A. (2016). Data Fusion for ITS: Techniques and Research Needs. In Transportation Research Procedia (Vol. 15, pp. 495–512). Elsevier B.V. http://doi.org/10.1016/j.trpro.2016.06.042
6. Faouzi, N. E. El, Leung, H., & Kurian, A. (2011). Data fusion in intelligent transportation systems: Progress and challenges - A survey. Information Fusion, 12(1), 4–10. http://doi.org/10.1016/j.inffus.2010.06.001
7. Kaplan, E. D., & Hegarty, C. J. (2006). Understanding GPS: Principles and Applications (2nd ed.). Norwood, MA: Artech House Inc.
8. Leduc, G. (2008). Road Traffic Data: Collection Methods and Applications. JRC Technical Notes (Vol. JRC 47967). Luxembourg. Retrieved from http://ftp.jrc.es/EURdoc/EURdoc/JRC47967.TN.pdf

9. Trimble Store. (2018). Retrieved March 3, 2018, from https://store.trimble.com/OA_HTML/ibeCCtdMinisites.jsp?language=US
10. Turner, S. M. (1996). Advanced techniques for travel time data collection. Transportation Research Record: Journal of the Transportation Research Board, 1551, 51–58. http://doi.org/10.1109/VNIS.1995.518816
11. Turner, S. M., Eisele, W. L., Benz, R. J., & Douglas, J. (1998). Travel Time Data Collection Handbook.