Feasibility Assessment of a Smartphone-Based Application to Estimate Road Roughness

다국어 초록 (Multilingual Abstract)

Transportation agencies spend significant resources to collect pavement roughness data using profiler vans. A potential alternativeto collect functionally equivalent data at a significantly lower cost and higher level of temporal resolution is to use existingaccelerometers in smartphones as the “set” of sensors. In this research, a prototype smartphone application was developed toinvestigate the feasibility of such an approach. Acceleration data were collected using a prototype application running on Androidtablets on two routes in Virginia. The analysis results show that the proposed smartphone application can generate consistent data setsfrom different data collecting runs. In addition, the average of the collected data sets is found to be highly correlated with theInternational Roughness Index data collected by the Virginia Department of Transportation using profiler vans. Also, a sample sizeanalysis revealed that most pavement sections require fewer than 12 data collecting trips at a 50 Hz sampling rate while fewer than 16trips are required for a rate of 10 Hz. Finally, a preliminary benefit assessment for Virginia showed that the proposed smartphoneapplication approach allows for collection of comparable roughness data for more roadways, more frequently with significantly less cost.

참고문헌 (Reference)

1 United State Department of Transportation, "Vehicle Based Data and Availability"

2 Mason, R., "Statistical design and analysis of experiments: With applications to engineering and science" John Wiley and Son, Inc. 1989

3 Virginia Department of Transportation, "State of the pavement 2010" 2010

4 Frankfort-Nachmias, C., "Social statistics for a diverse society (5th Edition)" Pine Forge Press, SAGE Publication, Inc. 2009

5 Robinson, R., "Slippery road detection and evaluation" 2012

6 Cochran, W., "Sampling techniques (3rd Edition)" Wiley India Pvt. Limited. 2007

7 Cantisani, G., "Road roughness and whole body vibration: Evaluation tools and comfort limits" ASCE 136 (136): 818-826, 2010

8 Ahlin, K., "Relating road roughness and vehicle speeds to human body vibration and exposure limits" 3 (3): 207-216, 2002

9 Flintsch, G., "Quality management of pavement condition data collection, NCHRP Synthesis 401" Transportation Research Board 2009

10 Flintsch, G. W., "Probe vehicles used to measure road ride quality: Pilot demonstration" (2304) : 158-165, 2012

11 Virginia Department of Transportation, "Pavement management program"

12 Richardson, J. K., "Network stratification method by travel time variation" (2256) : 1-9, 2011

13 Sauerwein, P. M., "Investigation of the implementation of a probe-vehicle based pavement roughness estimation system" 2011

14 Dawkin, J., "Investigation of pavement maintenance applications of $intellidrive^{SM}$, CTS PFS Report" 2010

15 Minnesota Department of Transportation, "Introduction to the international roughness index"

16 Sayers. M.W., "Guidelines for conducting and calibrating road roughness measurements, World Bank Technical Paper Number 46" The World Bank 1986

17 Mucka, P., "Correlation among road unevenness indicators and vehicle vibration response" ASCE 139 (139): 771-786, 2013

18 Michigan Department of Transportation, "Connected vehicle technology industry delphi study" 2012

19 Bridgelall, R. A., "Connected vehicle approach for pavement roughness" ASCE 20 (20): 2014

20 Ong, G., "Automated pavement condition data collection quality control, quality assurance, and reliability, Publication FHWA/IN/JTRP=2009/17" Indiana Department of Transportation and Purdue University 2010

21 Google, "Android API Guides: Motion sensors"

22 American Association of State Highway and Transportation Officials (AASHTO), "A policy on geometric design of highways and streets, 6th Edition" 2011