Potential applications of connected vehicles in pavement condition evaluation: a brief review
Source abstract
Abstract
Road authorities are concerned with maintaining the road condition at a high level of service to minimise user and agency costs. For this purpose, they have to monitor the road condition regularly. Pavement condition monitoring with automated data collection vehicles is costly. One emerging technology that has been widely applied in transportation is connected vehicles. They have various sensors to monitor their ambient condition and transfer the data to infrastructure or other vehicles. They could be employed as a cost-effective method in pavement condition evaluation which has been taken for granted. This study aims to review research studies investigating the potential of connected vehicles collected data in assessing pavement conditions. Moreover, the sensors used in these vehicles, data types collected, and analysis methods utilised are being scrutinised. It is concluded that the connected vehicles have excellent potential to be applied as a crowd-sourced-based and cost-effective approach in pavement data evaluation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Traffic Prediction and Management Techniques · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
References (96)
Show source-provided references
1. 2008 IEEE 68th Vehicular Technology Conference. (2008). IEEE.
2. 2016 18th Mediterranean Electrotechnical Conference (MELECON). (2016). IEEE.
3. Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. Corrado G. S. Davis A. Dean J. Devin M. Ghemawat S. Goodfellow I. Harp A. Irving G. Isard M. Jia Y. Jozefowicz R. Kaiser L. Kudlur M. … Zheng X. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. http://arxiv.org/abs/1603.04467
4. 10.1080/10298436.2021.194605910.1080/10298436.2021.1946059 ↗
5. 10.3141/2639-1610.3141/2639-16 ↗
6. 10.1061/JPEODX.000005810.1061/JPEODX.0000058 ↗
7. Anand S. Gupta S. Darbari V. & Kohli S. (2018). Crack-pot: Autonomous road crack and pothole detection. http://arxiv.org/abs/1810.0510710.1109/DICTA.2018.8615819 ↗
8. 10.1007/978-94-009-0279-410.1007/978-94-009-0279-4 ↗
9. Badrinarayanan V. Handa A. & Cipolla R. (2015). SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. http://arxiv.org/abs/1505.07293
10. 10.1109/TPAMI.2016.264461510.1109/TPAMI.2016.2644615 ↗
11. Bisconsini, D., Nicoletti, R., Yesid, J., Nuñez, M., Fernandes, J. L., Rinaldi Bisconsini, D., Yesid Mahecha Núñez, J., Leomar, J., & Júnior, F. (2018). Pavement roughness evaluation with smartphones pavement roughness evaluation with smartphones view project comportamento de misturas asfálticas com incorporação de borracha moída de pneu (BMP) view project pavement roughness evaluation with smartphones. International Journal of Science and Engineering Investigations, 7. www.IJSEI.com
12. 10.1061/(ASCE)IS.1943-555X.000016710.1061/(ASCE)IS.1943-555X.0000167 ↗
13. Bridgelall R. (2015a). Pavement performance evaluation using connected vehicles.
14. 10.1061/(ASCE)IS.1943-555X.000023410.1061/(ASCE)IS.1943-555X.0000234 ↗
15. 10.1080/10298436.2022.206924310.1080/10298436.2022.2069243 ↗
16. 10.1080/10298436.2017.136676810.1080/10298436.2017.1366768 ↗
17. 10.1088/0957-0233/27/2/02501210.1088/0957-0233/27/2/025012 ↗
18. 10.1080/10298436.2016.116230710.1080/10298436.2016.1162307 ↗
19. 10.3141/2589-1310.3141/2589-13 ↗
20. 10.1080/10298436.2017.131664510.1080/10298436.2017.1316645 ↗
21. Castro J. Cruz J. F. & Castro J. T. (n.d.). Developing a low-cost road roughness survey methodology using smartphones and geographic information system (GIS) “Planning guidance for disaster response and rehabilitation via humanitarian logistics management” view project Dost-BBB view project developing a low-cost road roughness survey methodology using smartphones and geographic information system (GIS). https://www.researchgate.net/publication/324586130
22. 10.1061/(ASCE)CP.1943-5487.000092510.1061/(ASCE)CP.1943-5487.0000925 ↗
23. 10.1177/0361198121101973510.1177/03611981211019735 ↗
24. 10.1109/IVCNZ51579.2020.929054710.1109/IVCNZ51579.2020.9290547 ↗
25. 10.1080/23311916.2017.137482210.1080/23311916.2017.1374822 ↗
26. 10.5194/isprs-archives-XLII-1-W1-333-201710.5194/isprs-archives-XLII-1-W1-333-2017 ↗
27. Digitalcommons@usu D. & Vasquez C. A. (2011). Pavement management systems on a local level. https://digitalcommons.usu.edu/gradreports
28. 10.1016/j.aei.2020.10122610.1016/j.aei.2020.101226 ↗
29. 10.14453/isngi2013.proc.1610.14453/isngi2013.proc.16 ↗
30. 10.1007/978-3-030-00220-6_7210.1007/978-3-030-00220-6_72 ↗
31. 10.1088/1742-6596/1004/1/01202910.1088/1742-6596/1004/1/012029 ↗
32. 10.1155/2016/841314610.1155/2016/8413146 ↗
33. 10.1016/j.conbuildmat.2019.01.14210.1016/j.conbuildmat.2019.01.142 ↗
34. 10.3390/coatings1002015210.3390/coatings10020152 ↗
35. 10.3390/s11100962810.3390/s111009628 ↗
36. Girshick R. (2015). Fast R-CNN. https://github.com/rbgirshick/10.1109/ICCV.2015.169 ↗
37. 10.1080/0042311070148505010.1080/00423110701485050 ↗
38. 10.3141/2381-0810.3141/2381-08 ↗
39. 10.3390/s1804121210.3390/s18041212 ↗
40. Hadjidemetriou G. M. & Christodoulou S. (2016). Patch Defects Detection for Pavement Assessment using Smartphones and support vector machines road anomaly detection and classification using smartphones and artificial neural networks (“ROADIE”) view project fully integrated and automated project processes (FIAPP) view project. https://www.researchgate.net/publication/312154691
41. 10.1061/(ASCE)CP.1943-5487.000083610.1061/(ASCE)CP.1943-5487.0000836 ↗
42. 10.1109/MELCON.2016.749546010.1109/MELCON.2016.7495460 ↗
43. 10.1061/JPEODX.000020510.1061/JPEODX.0000205 ↗
44. 10.3390/infrastructures602002810.3390/infrastructures6020028 ↗
45. 10.1016/j.eng.2020.07.03010.1016/j.eng.2020.07.030 ↗
46. 10.1155/2021/557359010.1155/2021/5573590 ↗
47. 10.1080/10298436.2020.183320410.1080/10298436.2020.1833204 ↗
48. IEEE Singapore Section. RFID Chapter IEEE Singapore Section. ITSS Chapter & Institute of Electrical and Electronics Engineers. (n.d.-a). Proceedings of the 2018 IEEE international conference on service operations and logistics and informatics (SOLI 2018): 31 July–2 August 2018 Singapore.
49. IEEE Singapore Section. RFID Chapter IEEE Singapore Section. ITSS Chapter & Institute of Electrical and Electronics Engineers. (n.d.-b). Proceedings of the 2018 IEEE international conference on service operations and logistics and informatics (SOLI 2018): 31 July–2 August 2018 Singapore.
50. Institute of Electrical and Electronics Engineers. (n.d.-a). 2013 13th international conference on ITS telecommunications (ITST).
51. Institute of Electrical and Electronics Engineers. (n.d.-b). 2018 IEEE international smart cities conference (ISC2): 16–19 September 2018.
52. 10.1016/j.proeng.2017.04.42510.1016/j.proeng.2017.04.425 ↗
53. 10.1061/(ASCE)CP.1943-5487.000061810.1061/(ASCE)CP.1943-5487.0000618 ↗
54. 10.1117/12.40378810.1117/12.403788 ↗
55. Kendall A. Badrinarayanan V. & Cipolla R. (2015). Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. http://arxiv.org/abs/1511.02680
56. 10.1145/306538610.1145/3065386 ↗
57. 10.1007/s40890-021-00130-710.1007/s40890-021-00130-7 ↗
58. 10.1109/ACCESS.2020.298902810.1109/ACCESS.2020.2989028 ↗
59. 10.1007/s42947-020-0302-y10.1007/s42947-020-0302-y ↗
60. 10.1109/MWC.2019.180054110.1109/MWC.2019.1800541 ↗
61. 10.1109/TITS.2020.303205510.1109/TITS.2020.3032055 ↗
62. 10.3141/2551-0810.3141/2551-08 ↗
63. 10.1109/MetroCAD48866.2020.0001410.1109/MetroCAD48866.2020.00014 ↗
64. 10.3390/su903045310.3390/su9030453 ↗
65. Lu P. Bridgelall R. Tolliver D. Chia L. & Bhardwaj B. (n.d.). Intelligent transportation systems approach to railroad infrastructure performance evaluation: Track surface abnormality identification with smartphone-based app (MPC-19-384).
66. 10.1109/BigData.2018.862232710.1109/BigData.2018.8622327 ↗
67. 10.1080/10298436.2019.160967310.1080/10298436.2019.1609673 ↗
68. 10.1109/CONCAPANXXXIX47272.2019.897703410.1109/CONCAPANXXXIX47272.2019.8977034 ↗
69. 10.1109/TITS.2015.242865510.1109/TITS.2015.2428655 ↗
70. 10.1080/10298436.2020.175543410.1080/10298436.2020.1755434 ↗
71. 10.1016/j.autcon.2018.07.00810.1016/j.autcon.2018.07.008 ↗
72. 10.25103/jestr.126.2010.25103/jestr.126.20 ↗
73. 10.3141/2243-1410.3141/2243-14 ↗
74. PART 2 Pavement Monitoring Evaluation and Data Storage. (n.d.).
75. 10.1016/j.autcon.2020.10333610.1016/j.autcon.2020.103336 ↗
76. 10.1007/s13042-014-0240-610.1007/s13042-014-0240-6 ↗
77. Quintana M. Torres J. & Menéndez J. M. (n.d.). A simplified computer vision system for road surface inspection and maintenance.
78. 10.1061/(asce)cp.1943-5487.000062310.1061/(asce)cp.1943-5487.0000623 ↗
79. 10.3390/infrastructures304005810.3390/infrastructures3040058 ↗
80. 10.1016/j.measurement.2019.01.09310.1016/j.measurement.2019.01.093 ↗
81. Saiprasert C. Pholprasit T. & Pattara-Atikom W. (n.d.). Detecting driving events using smartphone.
82. Sauerwein & Peter. (n.d.). Investigation of the implementation of a probe-vehicle based pavement roughness estimation system.
83. 10.1016/j.neunet.2014.09.00310.1016/j.neunet.2014.09.003 ↗
84. 10.1016/j.sbspro.2013.11.12610.1016/j.sbspro.2013.11.126 ↗
85. 10.1016/j.autcon.2022.10419010.1016/j.autcon.2022.104190 ↗
86. Sinha, S., & Kumar Suman, S. (2012). Pavement condition forecasting through artificial neural network modelling preparation of CMP and alternative analysis for patna metro rail project view project application of nanotechnology. Prevention of Moisture Damage of Bituminous Pavements View Project International Journal of Emerging Technology and Advanced Engineering Pavement Condition Forecasting Through Artificial Neural Network Modelling, 2(11). www.ijetae.com
87. Siponen M. Mantila H. Kostamovaara J. Senior V. Timo A. Communications Officer L. & Stjerna E. (n.d.). Electrical properties of road materials and subgrade soils and the use of ground penetrating radar in traffic infrastructure surveys scientiae rerum naturalium humaniora technica medica scientiae rerum socialium scripta academica oeconomica editor in chief editorial secretary.
88. 10.1016/j.ndteint.2013.11.00610.1016/j.ndteint.2013.11.006 ↗
89. 10.14456/ITJEMAST.2019.11410.14456/ITJEMAST.2019.114 ↗
90. 10.3390/infrastructures512010710.3390/infrastructures5120107 ↗
91. 10.1109/MVT.2015.239092010.1109/MVT.2015.2390920 ↗
92. Wang X. & Hu Z. (n.d.). Grid-based pavement crack analysis using deep learning.
93. Wendt C. İlişkilerde U. İnşacılık S. & Kıran A. (n.d.). Pattern recognition and machine learning phat le Journal of Electronic Imaging.
94. Yeganeh S. F. Mahmoudzadeh A. Azizpour M. A. & Golroo A. (2019). Validation of smartphone based pavement roughness measures. http://arxiv.org/abs/1902.10699
95. Zeng X. Balke K. & Songchitruksa P. (2012). Potential connected vehicle applications to enhance mobility safety and environmental security 5. Report Date 13. Type of Report and Period Covered Unclassified. http://www.ntis.gov
96. Zhang L. Yang F. Zhang D. & Zhu Y. J. (n.d.). Road crack detection using deep convolutional neural network.