Skip to main content
SpringerLink
Log in

Digital Transformation for Intelligent Road Condition Assessment

  • Chapter
  • First Online:
Intelligent Systems in Digital Transformation

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 549))

Abstract

Recently, governments have been resorting to cutting-edge artificial intelligence technologies to facilitate the digital transformation of smart cities. Remarkable progress has been made to strengthen smart city governance and sustainability, especially in road condition assessment. Road data acquisition and defect detection, two major processes of intelligent road condition assessment, play an important role in ensuring road maintainability while providing maximum traffic security and driving comfort. Traditional manual visual inspection is inefficient and lacks objectivity. Therefore, intelligent road condition assessment systems developed based on data-driven techniques have received increasing attention. This chapter presents the state-of-the-art intelligent road condition assessment systems, the existing challenges, and future development trends.

S. Guo and Y. Bai—Joint first authors of this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bohnsack R et al (2022) Sustainability in the digital age: intended and unintended consequences of digital technologies for sustainable development, pp 599–602

    Google Scholar 

  2. Elliott A (2021) Contemporary Social Theory: An Introduction. Routledge, London

    Google Scholar 

  3. Piepponen A et al (2022) Digital transformation of the value proposition: a single case study in the media industry. J Bus Res 150:311–325

    Article  Google Scholar 

  4. Lasi H et al (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242

    Article  Google Scholar 

  5. Nandico OF (2016) A framework to support digital transformation. In: El-Sheikh E, Zimmermann A, Jain LC (eds) Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures, vol 111. ISRL. Springer, Cham, pp 113–138. https://doi.org/10.1007/978-3-319-40564-3_7

    Chapter  Google Scholar 

  6. Henriette E et al (2015) The shape of digital transformation: a systematic literature review. In: MCIS 2015 Proceedings, vol 10, pp 431–443

    Google Scholar 

  7. Goran J et al (2017) Culture for a digital age. McKinsey Q 3(1):56–67

    Google Scholar 

  8. Davenport TH et al (2019) Artificial Intelligence: The Insights You Need From Harvard Business Review. Harvard Business Press, Boston

    Google Scholar 

  9. Kodama M (2020) Digitally transforming work styles in an era of infectious disease. Int J Inf Manag 55:102172

    Article  Google Scholar 

  10. Haggerty E (2017) Healthcare and digital transformation. Netw Secur 2017(8):7–11

    Article  Google Scholar 

  11. Gardy A et al (2016) Digital trends & opportunities for airports. In: ACI-NA World Annual Conference

    Google Scholar 

  12. Scriney M, Roantree M (2016) Efficient cube construction for smart city data. In: EDBT/ICDT Workshops, vol 2016

    Google Scholar 

  13. Fan R, Wang H, Bocus MJ, Liu M (2020) We learn better road pothole detection: from attention aggregation to adversarial domain adaptation. In: Bartoli A, Fusiello A (eds) ECCV 2020, vol 12538. LNCS. Springer, Cham, pp 285–300. https://doi.org/10.1007/978-3-030-66823-5_17

    Chapter  Google Scholar 

  14. Yesner R (2017) Accelerating the digital transformation of smart cities and smart communities, Microsoft, October 2017

    Google Scholar 

  15. Bordeleau F-È, Felden C (2019) Digitally transforming organisations: a review of change models of industry 4.0. In: European Conference on Information Systems

    Google Scholar 

  16. Schmarzo B (2017) What is digital transformation. Accessed 8 Mar 2018

    Google Scholar 

  17. Ebert C, Duarte CHC (2018) Digital transformation. IEEE Softw 35(4):16–21

    Article  Google Scholar 

  18. Erol T (2020) Digital transformation revolution with digital twin technology. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp 1–7. IEEE

    Google Scholar 

  19. Tao F et al (2019) Digital Twin Driven Smart Manufacturing. Academic Press, Cambridge

    Google Scholar 

  20. Catarci T (2019) A conceptual architecture and model for smart manufacturing relying on service-based digital twins. In: 2019 IEEE International Conference on Web Services (ICWS), pp 229–236. IEEE

    Google Scholar 

  21. The digital twin IoT platform. https://www.digitaltwincorporation.com/. Accessed 12 May 2022

  22. Batty M et al (2012) Smart cities of the future. Eur Phys J Spec Top 214(1):481–518

    Article  Google Scholar 

  23. Zhao F et al (2021) Smart city research: a holistic and state-of-the-art literature review. Cities 119:103406

    Article  Google Scholar 

  24. Forward N (2016) Building a smart city, equitable city

    Google Scholar 

  25. Sarker IH (2022) Smart city data science: towards data-driven smart cities with open research issues. Internet Things 19:100528

    Article  Google Scholar 

  26. Zhang Y (2010) Interpretation of smart planet and smart city [j]. China Inf Times 10:38–41

    Google Scholar 

  27. Nam T, Pardo TA (2011) Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp 282–291

    Google Scholar 

  28. Yovanof GS, Hazapis GN (2009) An architectural framework and enabling wireless technologies for digital cities & intelligent urban environments. Wirel Pers Commun 49(3):445–463

    Article  Google Scholar 

  29. Chu Z et al (2021) A smart city is a less polluted city. Technol Forecast Soc Chang 172:121037

    Article  Google Scholar 

  30. Xue Q (2010) Smart healthcare: applications of the internet of things in medical treatment and health. Inf Constr 2010(5):56–58

    Google Scholar 

  31. Su K (2011) Smart city and the applications. In: 2011 International Conference on Electronics, Communications and Control (ICECC), pp 1028–1031. IEEE

    Google Scholar 

  32. Fan R et al (2018) Road surface 3D reconstruction based on dense subpixel disparity map estimation. IEEE Trans Image Process 27(6):3025–3035

    Article  MATH  MathSciNet  Google Scholar 

  33. Fan R et al (2019) Real-time dense stereo embedded in a UAV for road inspection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society, pp 535–543

    Google Scholar 

  34. Fan R et al (2021) Rethinking road surface 3-D reconstruction and pothole detection: from perspective transformation to disparity map segmentation. In: IEEE Transactions on Cybernetics, 2021. https://doi.org/10.1109/TCYB.2021.3060461

  35. De Zoysa K, Keppitiyagama C, Seneviratne GP, Shihan W (2007) A public transport system based sensor network for road surface condition monitoring. In: Proceedings of the 2007 Workshop on Networked Systems for Developing Regions, pp 1–6

    Google Scholar 

  36. Eriksson J, Girod L, Hull B, Newton R, Madden S, 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

    Google Scholar 

  37. Ma N et al (2022) Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms. In: Transportation Safety and Environment. https://doi.org/10.1093/tse/tdac026

  38. Fan R (2021) Long-awaited next-generation road damage detection and localization system is finally here. In: 2021 29th European Signal Processing Conference (EUSIPCO), pp 641–645. IEEE

    Google Scholar 

  39. Mahler DS et al (1991) Pavement distress analysis using image processing techniques. Comput Aided Civ Infrastruct Eng 6(1):1–14

    Article  Google Scholar 

  40. Jahanshahi MR et al (2013) Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor. J Comput Civ Eng 27(6):743–754

    Article  Google Scholar 

  41. Woodham RJ (1980) Photometric method for determining surface orientation from multiple images. Opt Eng 19(1):191139

    Article  Google Scholar 

  42. Barsky S, Petrou M (2003) The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Trans Pattern Anal Mach Intell 25(10):1239–1252

    Article  Google Scholar 

  43. Fujimoto JG et al (2000) Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2(1–2):9–25

    Article  Google Scholar 

  44. Walecki WJ, Van P (2006) Determining thickness of slabs of materials by inventors, 3 Oct 2006, uS Patent 7,116,429

    Google Scholar 

  45. Scharstein D, Szeliski R (2003) High-accuracy stereo depth maps using structured light. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol 1, p I. IEEE

    Google Scholar 

  46. Muzet V et al (2009) Surface deflection measurement using structured light. Testing in Civil Engineering, Nantes, France

    Google Scholar 

  47. Oggier T et al (2004) An all-solid-state optical range camera for 3d real-time imaging with sub-centimeter depth resolution (SwissRanger). In: Optical Design and Engineering, vol 5249, pp 534–545. International Society for Optics and Photonics

    Google Scholar 

  48. Anderson D et al (2005) Experimental characterization of commercial flash ladar devices. In: 2005 International Conference of Sensing and Technology, vol 2, pp 17–23. Citeseer

    Google Scholar 

  49. Mathavan S et al (2015) A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Trans Intell Transp Syst 16(5):2353–2362

    Article  Google Scholar 

  50. Koch C, Brilakis I (2011) Pothole detection in asphalt pavement images. Adv Eng Inform 25(3):507–515

    Article  Google Scholar 

  51. Chang K et al (2005) Detection of pavement distresses using 3D laser scanning technology. Comput Civ Eng 2005:1–11

    Google Scholar 

  52. Lin J, Liu Y (2010) Potholes detection based on SVM in the pavement distress image. In: 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp 544–547. IEEE

    Google Scholar 

  53. Fan R et al (2019) Pothole detection based on disparity transformation and road surface modeling. IEEE Trans Image Process 29:897–908

    Article  MATH  MathSciNet  Google Scholar 

  54. Koutsopoulos HN et al (1993) Primitive-based classification of pavement cracking images. J Transp Eng 119(3):402–418

    Article  Google Scholar 

  55. Fan R et al (2018) Real-time stereo vision for road surface 3-D reconstruction. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST), pp 1–6. IEEE

    Google Scholar 

  56. Laurent J et al (1997) Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces. In: Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No. 97TB100134), pp 303–310. IEEE

    Google Scholar 

  57. Tsai Y-C et al (2018) Pothole detection and classification using 3D technology and watershed method. J Comput Civ Eng 32(2):04017078

    Article  Google Scholar 

  58. IIT: Lasers in quality controlline. http://www.iitk.ac.in/celt/lecture_laser/. Accessed 2 Apr 2022

  59. Joubert D et al (2011) Pothole tagging system. In: 2011 th Robotics and Mechatronics Conference of South Africa, CSIR International Conference Centre, Pretoria, pp 23–25

    Google Scholar 

  60. Moazzam I et al (2013) Metrology and visualization of potholes using the microsoft kinect sensor. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp 1284–1291. IEEE

    Google Scholar 

  61. Andrew, AM (2001) Multiple view geometry in computer vision, Kybernetes

    Google Scholar 

  62. Jog G et al (2012) Pothole properties measurement through visual 2D recognition and 3D reconstruction. Comput Civ Eng 2012:553–560

    Article  Google Scholar 

  63. Ullman S (1979) The interpretation of structure from motion. Proc R Soc Lond Ser B. Biol Sci 203(1153):405–426

    Google Scholar 

  64. Wang H et al (2021) CoT-AMFlow: adaptive modulation network with co-teaching strategy for unsupervised optical flow estimation. In: Conference on Robot Learning (CoRL), pp 143–155. PMLR

    Google Scholar 

  65. Schonberger JL, Frahm J-M (2016) Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4104–4113

    Google Scholar 

  66. B. Triggs et al., “International workshop on vision algorithms,” Bundle adjustment–a modern synthesis, pp. 298–372, 1999

    Google Scholar 

  67. Fleet D, Weiss Y (2006) Optical flow estimation. In: Paragios N, Chen Y, Faugeras O (eds) Handbook of Mathematical Models in Computer Vision, pp 237–257. Springer, Boston, MA. https://doi.org/10.1007/0-387-28831-7_15

  68. Hirschmuller H (2007) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341

    Article  Google Scholar 

  69. Sun J et al (2003) Stereo matching using belief propagation. IEEE Trans Pattern Anal Mach Intell 25(7):787–800

    Article  Google Scholar 

  70. Wang H et al (2021) PVStereo: pyramid voting module for end-to-end self-supervised stereo matching. IEEE Robot Autom Lett 6(3):4353–4360

    Article  Google Scholar 

  71. Wang H et al (2021) Co-Teaching: an ark to unsupervised stereo matching. In: 2021 IEEE International Conference on Image Processing (ICIP), pp 3328–3332. IEEE

    Google Scholar 

  72. Danzl R et al (2009) Focus variation–a new technology for high resolution optical 3D surface metrology. In: The 10th International Conference of the Slovenian Society for Non-destructive Testing, pp 484–491. Citeseer

    Google Scholar 

  73. Vilaça JL et al (2010) 3D surface profile equipment for the characterization of the pavement texture-TexScan. Mechatronics 20(6):674–685

    Article  Google Scholar 

  74. Pertuz S et al (2013) Analysis of focus measure operators for shape-from-focus. Pattern Recogn 46(5):1415–1432

    Article  MATH  Google Scholar 

  75. Conrad J (2006) Depth of field in depth. Large Format Photography, pp 1–45

    Google Scholar 

  76. Sundaram H, Nayar S (1997) Are textureless scenes recoverable? In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 814–820. IEEE

    Google Scholar 

  77. Buza E et al (2013) Pothole detection with image processing and spectral clustering. In: Proceedings of the 2nd International Conference on Information Technology and Computer Networks, vol 810, p 4853

    Google Scholar 

  78. Ryu S-K et al (2015) Image-based pothole detection system for its service and road management system. Math Probl Eng 2015

    Google Scholar 

  79. Zack GW et al (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25(7):741–753

    Article  Google Scholar 

  80. Fan R, Liu M (2019) Road damage detection based on unsupervised disparity map segmentation. IEEE Trans Intell Transp Syst 21(11):4906–4911

    Article  Google Scholar 

  81. Beylkin G et al (2009) Fast wavelet transforms and numerical algorithms. In: Fundamental Papers in Wavelet Theory, pp 741–783. Princeton University Press

    Google Scholar 

  82. Najman L, Schmitt M (1994) Watershed of a continuous function. Signal Process 38(1):99–112

    Article  Google Scholar 

  83. LeCun Y et al (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  84. Zhang L (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp 3708–3712. IEEE

    Google Scholar 

  85. Hu Y, Furukawa T (2019) A self-supervised learning technique for road defects detection based on monocular three-dimensional reconstruction. In: 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol 59216, p V003T01A021. American Society of Mechanical Engineers

    Google Scholar 

  86. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 618–626

    Google Scholar 

  87. Fan J et al (2021) Deep convolutional neural networks for road crack detection: qualitative and quantitative comparisons. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE

    Google Scholar 

  88. Hoffman J et al (2018) Cycada: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp 1989–1998. PMLR

    Google Scholar 

  89. Chattopadhay A (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 839–847. IEEE

    Google Scholar 

  90. Girshick R et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580–587

    Google Scholar 

  91. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448

    Google Scholar 

  92. Ren S et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99

    Google Scholar 

  93. Redmon J et al (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788

    Google Scholar 

  94. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263–7271

    Google Scholar 

  95. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement, CoRR

    Google Scholar 

  96. Bochkovskiy A et al (2020) Yolov4: optimal speed and accuracy of object detection, CoRR

    Google Scholar 

  97. Uijlings JR et al (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

    Article  Google Scholar 

  98. Wang W (2018) Road damage detection and classification with faster R-CNN. In: 2018 IEEE International Conference on Big Data (Big Data), pp 5220–5223. IEEE

    Google Scholar 

  99. Suong LK, Kwon J (2018) Detection of potholes using a deep convolutional neural network. J Univ Comput Sci 24(9):1244–1257

    Google Scholar 

  100. Camilleri N, Gatt T (2020) Detecting road potholes using computer vision techniques. In: 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pp 343–350. IEEE

    Google Scholar 

  101. Ukhwah EN (2019) Asphalt pavement pothole detection using deep learning method based on yolo neural network. In: 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp 35–40. IEEE

    Google Scholar 

  102. Dhiman A et al (2019) Pothole detection using computer vision and learning. IEEE Trans Intell Transp Syst 21(8):3536–3550

    Article  Google Scholar 

  103. He K et al (2017) Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2961–2969

    Google Scholar 

  104. Fan R et al (2020) Computer stereo vision for autonomous driving, CoRR

    Google Scholar 

  105. Fan R et al (2021) Learning collision-free space detection from stereo images: homography matrix brings better data augmentation. IEEE/ASME Trans Mechatron 27(1):225–233

    Article  Google Scholar 

  106. Fan R, Wang H, Cai P, Liu M (2020) SNE-RoadSeg: incorporating surface normal information into semantic segmentation for accurate freespace detection. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) ECCV 2020, vol 12375. LNCS. Springer, Cham, pp 340–356. https://doi.org/10.1007/978-3-030-58577-8_21

    Chapter  Google Scholar 

  107. Wang H et al (2021) SNE-RoadSeg+: rethinking depth-normal translation and deep supervision for freespace detection. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1140–1145. IEEE

    Google Scholar 

  108. Wang H et al (2021) Dynamic fusion module evolves drivable area and road anomaly detection: a benchmark and algorithms. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3064089

  109. Fan R et al (2021) Three-filters-to-normal: an accurate and ultrafast surface normal estimator. IEEE Robot Autom Lett 6(3):5405–5412

    Article  Google Scholar 

  110. Ozgunalp U (2016) Vision based lane detection for intelligent vehicles, Ph.D. dissertation, University of Bristol

    Google Scholar 

  111. Zhang Z (2013) Advanced stereo vision disparity calculation and obstacle analysis for intelligent vehicles, Ph.D. dissertation, University of Bristol

    Google Scholar 

  112. Wu R et al (2021) Scale-adaptive road pothole detection and tracking from 3d point clouds. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE

    Google Scholar 

  113. Hast A, Nysjö J (2013) Optimal RANSAC-towards a repeatable algorithm for finding the optimal set. J WSCG 21(1):21–30

    Google Scholar 

  114. Kang B-H, Choi S-I (2017) Pothole detection system using 2D lidar and camera. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp 744–746. IEEE

    Google Scholar 

  115. Clohessy T et al (2014) Smart city as a service (SCaaS): a future roadmap for e-government smart city cloud computing initiatives. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 836–841. IEEE

    Google Scholar 

  116. ThoughtLab E (2018) Smarter cities 2025 building a sustainable business and financing plan. https://econsultsolutions.com/wp-content/uploads/2018/11, ESI-ThoughtLab_Smarter-Cities-2025_ebook_FINAL. pdf (letöltve: 2019.07. 26.)

  117. Yoon SY et al (2020) Smart city pathways for developing Asia: an analytical framework and guidance, ADB Sustainable Development Working Paper Series

    Google Scholar 

  118. Zhe W et al (2015) Traffic patterns in the silk road economic belt and construction modes for a traffic economic belt across continental plates. J Resour Ecol 6(2):79–86

    Google Scholar 

  119. Tsakalidis A et al (2020) Digital transformation supporting transport decarbonisation: technological developments in EU-funded research and innovation. Sustainability 12(9):3762

    Article  Google Scholar 

  120. Parti K, Szigeti A (2021) The future of interdisciplinary research in the digital era: obstacles and perspectives of collaboration in social and data sciences-an empirical study. Cogent Soc Sci 7(1):1970880

    Google Scholar 

  121. Singh S et al (2020) Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustain Urban Areas 63:102364

    Google Scholar 

  122. Leduc G et al (2008) Road traffic data: collection methods and applications. Work Pap Energy Transp Climate Chang 1(55):1–55

    Google Scholar 

  123. Sidek O, Quadri S (2012) A review of data fusion models and systems. Int J Image Data Fusion 3(1):3–21

    Article  Google Scholar 

  124. Jeong S et al (2020) City data hub: implementation of standard-based smart city data platform for interoperability. Sensors 20(23):7000

    Article  Google Scholar 

  125. Al-Turjman F et al (2022) An overview of security and privacy in smart cities’ IoT communications. Trans Emerg Telecommun Technol 33(3):e3677

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tongji University, Shanghai, China

    Sicen Guo, Yue Bai & Rui Fan

  2. University of Bristol, Bristol, England

    Mohammud Junaid Bocus

Authors
  1. Sicen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammud Junaid Bocus
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sicen Guo .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Maçka, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey

    Elif Haktanır

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Guo, S., Bai, Y., Bocus, M.J., Fan, R. (2023). Digital Transformation for Intelligent Road Condition Assessment. In: Kahraman, C., Haktanır, E. (eds) Intelligent Systems in Digital Transformation. Lecture Notes in Networks and Systems, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-031-16598-6_22

Download citation

Publish with us

Policies and ethics

Search

Navigation