Investigating the Situation of Urban Pathways Pavements Surface in Future Smart Cities Using Accelerometer Sensors and GPS Tools of Smartphones and GIS Application.

Document Type : Original Research

Authors
R. Jalalkamali 1
P. Kamali Sarvestani 2
1 Assistant Professor, Faculty of EngineeringShahid Bahonar University of Kerman, Kerman, Iran.
2 Faculty of EngineeringShahid Bahonar University of Kerman, Kerman, Iran.
10.22034/22.5.21
Abstract
In smart cities, with using lots of new technologies, while creating appropriate facilities in routine urban life, infrastructure problems are investigated and the necessary measures are taken in a targeted and systematic manner to solve these problems. One of the most important technologies for managing infrastructure in smart cities is IT technology. GPS and smartphone sensors are other technologies that can be widely used in these cities. Streets, roads, and pavements are important infrastructures in any city and the future smart cities. Proper supervision, repair, and improvement of pavements, streets, and urban pathways are the main factor in reducing the cost of depreciation of vehicles and providing comfort and safety for citizens. On the other hand, if timely action is taken to restore and improve the pavement, huge costs of repair and reconstruction will be avoided, and this can significantly reduce the costs of urban management. The first step to achieving this goal is to identify the location of the roughness and distortion of the surface of the streets and urban pathways and the severity of these failures in the shortest possible time. In this paper, road surface roughnesses and failures have been studied using accelerometer sensors and GPS smartphone devices. Location and vertical acceleration data have been entered into GIS software and a quantitative index based on the values of vertical acceleration has been introduced to determine the quality of each section of urban road pavement. In this research, Androsensor software, which is a useful application for using smartphone sensors, has been used. This software is installed on two smartphones, Huawei, P30 Lite, and BlackBerry, Priv STV100. To collect the data, the smartphones were placed in a fixed position on the right and left sides of the car, on the dashboard. The collected data for analysis is transferred to the computer in Excel files. This research has been done in Kerman city and to collect data, different routes with specific failures have been selected. Data collection was performed in 81 pieces with a length of 500 meters and 24 pieces with a length of 200 meters (105 pieces, 45300 meters in total). By analyzing the vertical acceleration data and calculating some proposed indexes and comparing them, the best index has been selected. This index is classified into different ranges according to the field inspection of the pavement condition in the routes in this study, that each of which indicates the quality status of the pavement. Each of these intervals is introduced with a specific color, and by examining the index obtained in each route and the corresponding interval, the studied routes are marked with different colors on the map. Finally, it was found that the accelerometer sensors and GPS of smartphones can be used with low cost, high speed, and appropriate accuracy to check the surface of pavement of urban roads and grading the quality of the pavement. It also seems that in the smart cities of the future, which are based on IT technology, the use of user data, high accuracy in locating, and speed of action in prevention, the proposed method in this research can be used more favorably.

Keywords

Subjects

[1]. Focus group on smart sustainable cities, 2020. [Online]. Available: https://www.itu.int/en/ITUT/focusgroups/ssc/Pages/default.aspx
[2]. Berglund, E.Z., Monroe, J.G., Ahmed, I., Noghabaei, M., Do, J., Pesantez, J.E., Khaksar Fasaee, M.A., Bardaka, E., Han, K., Proestos, G.T. and Levis, J., 2020. Smart infrastructure: A vision for the role of the civil engineering profession in smart cities. Journal of Infrastructure Systems, 26(2), p.03120001.
[3]. Nofriyanti, E., 2020. Evaluation of concretization of local road in Padang city using the importance satisfaction analysis and customer satisfaction index methods. In E3S Web of Conferences (Vol. 156, p. 05007). EDP Sciences.
[4]. Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G. and Selavo, L., 2011, June. Real time pothole detection using android smartphones with accelerometers. In 2011 International conference on distributed computing in sensor systems and workshops (DCOSS) (pp. 1-6). IEEE.
[5]. Hancke, G.P. and Hancke Jr, G.P., 2013. The role of advanced sensing in smart cities. Sensors, 13(1), pp.393-425.
[6]. Gao, J., Li, J., Cai, Z. and Gao, H., 2015, April. Composite event coverage in wireless sensor networks with heterogeneous sensors. In 2015 IEEE Conference on Computer Communications (INFOCOM) (pp. 217-225). IEEE.
[7]. Alvear, O., Calafate, C.T., Cano, J.C. and Manzoni, P., 2018. Crowdsensing in smart cities: Overview, platforms, and environment sensing issues. Sensors, 18(2), p.460.
[8].Ahmadinejad, M., Afandizadeh Zargari, S. and Jalalkamali, R., 2018, October. Are deceleration numbers a suitable index for road safety?. In Proceedings of the Institution of Civil Engineers-Transport (Vol. 171, No. 5, pp. 247-252). Thomas Telford Ltd.
[9]. Yu, J., Stettler, M.E., Angeloudis, P., Hu, S. and Chen, X.M., 2020. Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces. Transportation Research Part C: Emerging Technologies, 112, pp.136-152.
[10]. Douangphachanh, V. and Oneyama, H., 2014. A study on the use of smartphones under realistic settings to estimate road roughness condition. EURASIP Journal on Wireless Communications and Networking, 2014(1), pp.1-11.
[11]. Islam, S., Buttlar, W.G., Aldunate, R.G. and Vavrik, W.R., 2014. Measurement of pavement roughness using android-based smartphone application. Transportation Research Record, 2457(1), pp.30-38.
[12]. Medina, J.R., Noorvand, H., Shane Underwood, B. and Kaloush, K., 2020. Statistical validation of crowdsourced pavement ride quality measurements from smartphones. Journal of Computing in Civil Engineering, 34(3), p.04020009.
[13]. Harikrishnan, P.M. and Gopi, V.P., 2017. Vehicle vibration signal processing for road surface monitoring. IEEE Sensors Journal, 17(16), pp.5192-5197.
[14]. Allouch, A., Koubâa, A., Abbes, T. and Ammar, A., 2017. Roadsense: Smartphone application to estimate road conditions using accelerometer and gyroscope. IEEE Sensors Journal, 17(13), pp.4231-4238.
[15]. Aleadelat, W., Ksaibati, K., Wright, C.H. and Saha, P., 2018. Evaluation of pavement roughness using an android-based smartphone. Journal of Transportation Engineering, Part B: Pavements, 144(3), p.04018033.
[16]. Bidgoli, M.A., Golroo, A., Nadjar, H.S., Rashidabad, A.G. and Ganji, M.R., 2019. Road roughness measurement using a cost-effective sensor-based monitoring system. Automation in Construction, 104, pp.140-152.
[17]. Setiawan, E.B. and Nurdin, H., 2019. Road Quality Assessment Using International Roughness Index Method and Accelerometer on Android. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, pp.62-72.
[18]. Janani, L., Sunitha, V. and Mathew, S., 2020. Influence of surface distresses on smartphone-based pavement roughness evaluation. International Journal of Pavement Engineering, pp.1-14.
[19].Vemuri, V., Ren, Y., Gao, L., Lu, P. and Song, L., 2020, November. Pavement Condition Index Estimation Using Smartphone Based Accelerometers for City of Houston. In Construction Research Congress 2020: Infrastructure Systems and Sustainability (pp. 522-531). Reston, VA: American Society of Civil Engineers.
[20]. Nomura, T. and Shiraishi, Y., 2015. A method for estimating road surface conditions with a smartphone. International Journal of Informatics Society, 7(1), pp.29-36.
[21]. Yeganeh, S.F., Mahmoudzadeh, A., Azizpour, M.A. and Golroo, A., 2019. Validation of smartphone based pavement roughness measures. arXiv preprint arXiv:1902.10699
[22]. Aleadelat, W. and Ksaibati, K., 2017. Estimation of pavement serviceability index through android-based smartphone application for local roads. Transportation Research Record, 2639(1), pp.129-135.
Modares Civil Engineering journal
Volume 22, Issue 5
November and December 2022
Pages 21-36