THE USE OF SMART PHONES TO ESTIMATE ROAD ROUGHNESS: A CASE STUDY IN TURKEY

Yıl 2018, Cilt: 13 Sayı: 3, 247 - 257, 23.07.2018

Metin Mutlu Aydın Mehmet Sinan Yıldırım Lars Farslöf

Öz

     Previous
studies have shown that road surface conditions are an important factor for
road quality. To provide quality on road surface, it should be observed
steadily and repaired as necessarily. There are many process to determine road
surface condition. Using a smart phone to collect data is an alternative and
simple application because of it’s low cost, wider population coverage property
and easy utilization. This paper explores the utilization of Roadroid, a simple
android application, as a low cost vehicle-based solution for road surface
condition monitoring with using sensors from smartphones. In the scope of this
study, site experiments have been conducted to collect data using acceleration
and GPS properties of a smartphone in a specific (passenger car) vehicle type.
This method was evaluated with 3259 km urban and rural road data collected from
the site experiments in Turkey, and it was seen from the results that average
84.4% of Turkish roads have good, 7.9% have satisfactory, 3.8 have
unsatisfactory and 3.8% have poor road roughness conditions. It shows that
approximately 4% of Turkish roads need maintenance urgently. Also experimental
study results confirm that Roadroid have a great potential to evaluate road
surface roughness condition correctly, even under obstacle like, potholes,
manholes and decelerating marks.

Anahtar Kelimeler

Road Surface Condition, Road Roughness, Smartphone Sensors, Mobile Sensing

Kaynakça

• [1] Ben-Edigbe, J. and Ferguson, N., (2005). Extent of Capacity Loss Resulting From Pavement Distress, Proceedings of the Institution of Civil Engineers–Transport, vol:158, no:1, pp:27–32.
• [2] Aydin, M.M. and Topal, A., (2016). Effect of Road Surface Deformations on Lateral Lane Utilization and Longitudinal Driving Behaviours, TRANSPORT, vol:31, no:2, pp:192-201.
• [3] Ben-Edigbe, J., (2010). Assessment of Speed–Flow–Density Functions under Adverse Pavement Condition, International Journal of Sustainable Development and Planning vol:5, no:3, pp:238–252.
• [4] Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., and Balakrishnan, H., (2008). The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring, In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, pp:29-39.
• [5] Mohan, P., Padmanabhan, V.N., and Ramjee, R., (2008). Nericell: Rich Monitoring of Road and Traffic Conditions Using Mobile Smartphones, In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp:323-336.
• [6] TRB, (2004). Automated Pavements Distress Collection Techniques: a Synthesis of Highway Practice. NCHRP Synthesis 334, National Cooperative Highway Research Program (NCHRP). Transportation Research Board (TRB), Washington, DC. 94 p.
• [7] Oloufa, A., Mahgoub, H., and Ali, H., (2004). Infrared Thermography for Asphalt Crack Imaging and Automated Detection, Transportation Research Record: Journal of the Transportation Research Board, vol:1889, pp:126–133.
• [8] Lee, H.D. and Kim, J.J., (2005). Development of a Manual Crack Quantification and Automated Crack Measurement System. Project TR-457 Final Report. University of Iowa, US. 21p.
• [9] Battiato, S., Rizzo, L., Stanco, F., Cafiso, S., and Di Graziano, A., (2006). Pavement Surface Distress by Using Non-Linear Image Analysis Techniques, in Proceedings of SIMAI 2006, pp:1-4.
• [10] Strazdins, G., Mednis, A., Kanonirs, G., Zviedris, R., and Selavo, L., (2011). Towards Vehicular Sensor Networks with Android Smartphones for Road Surface Monitoring, 2nd International Workshop on Networks of Cooperating Objects (CONET’11), Electronic Proceedings of CPS Week, Vol:11, p:2015.
• [11] Bychkovsky, V., Chen, K.M., Goraczko, H.H., Hull, B., Miu, A., Shih, E., Zhang, Y., Balakrishnan, H., and Madden, S., (2006). The Cartel Mobile Sensor Computing System, In SenSys’06, pp:383–384.
• [12] Yoon, J., Noble, B., and Liu, M., (2007). Surface Street Traffic Estimation, In MobiSys 07, pp:220–232.
• [13] Sen, R., Raman, B., and Sharma, P., (2010). Horn-Ok-Please, In MobiSys, pp. 137–150.
• [14] Sayers, M.W. and Karamihas, S., (1996). Interpretation of Road Roughness Profile Data, FHWA/RD-96/101 University of Michigan.
• [15] Douangphachanh, V. and Oneyama, H., (2013). Estimation of Road Roughness Condition From Smartphones under Realistic Settings, In: ITS Telecommunications (ITST), 2013 13th International Conference on. IEEE, pp. 433-439.
• [16] González, A., O’brien, E.J., Lia, Y.Y., and Cashell, K., (2008). The Use of Vehicle Acceleration Measurements to Estimate Road Roughness, Vehicle System Dynamics, vol:46, no:6, pp:483–499.
• [17] Traffic Sense, (2008). Rich Monitoring of Road And Traffic Conditions Using Mobile Smartphones, Microsoft Research, Tech. Rep. MSR-TR-2008-59.
• [18] Forslöf, L., (2012). Roadroid–Smartphone Road Quality Monitoring, Proceedings of the 19th ITS World Congres, pp:1-8.
• [19] Forslöf, L. and Jones, H., (2013). Roadroid: Continuous Road Condition Monitoring With Smart Phones, In IRF 17th World Meeting and Exhibition, Vol:24, pp:1-11.
• [20] Forslöf, L. and Jones, H., (2015). Roadroid: Continuous Road Condition Monitoring With Smart Phones, Journal of Civil Engineering and Architecture, vol:9, pp:485-496.
• [21] Guideline, R., (2013). Quick start ver 1.2.1., Sweden.
• [22] Souza, R.O., (2002). Influence of Longitudinal Roughness on the Evaluation of Pavement, M.S. Dissertation, Publication 625.8(043) S729i, University of Brasilia, Brasilia, Brazil. (In Portuguese)
• [23] Gamage, D., Pasindu, H.R., and Bandara, S., (2016). Pavement Roughness Evaluation Method for Low Volume Roads, Proc. of the Eighth Intl. Conf. on Maintenance and Rehabilitation of Pavements, pp:1-10.