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Towards Automatic Assessment of Perceived Walkability

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

We present a method for automatic assessment of perceived walkability by pedestrans, using a machine learning technique with deep convolutional neural networks (CNNs) trained on a dataset of georeferenced street-level images obtained from Google Street View. On a dataset of more than 17,000 human-assessed images used for training, validation and testing of CNN, out method yields an accuracy of 78% of correct and 99% of correct or 1-class-off predictions. These are quite promising, even encouraging results, paving the way for seamless large-scale applications of perceived walkability assessment on large metropolitan areas, and for a mass assessment and comparisons of walkability over many cities across regions.

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References

  1. Speck, J.: Walkable City. Farrar, Straus and Giroux, New York (2012)

    Google Scholar 

  2. Talen, E., Koschinsky, J.: The walkable neighborhood: a literature review. Int. J. Sustain. Land Use Urban Plan. 1, 42–63 (2013)

    Google Scholar 

  3. Forsyth, A.: What is a walkable place? The walkability debate in urban design. Urban Des. Int. 20, 274–292 (2015)

    Article  Google Scholar 

  4. Cervero, R., Duncan, M.: Walking, bicycling, and urban landscapes: evidence from the San Francisco bay area. Am. J. Public Health 93, 1478–1483 (2003)

    Article  Google Scholar 

  5. Livi Smith, A., Clifton, K.J.: Issues and methods in capturing pedestrian behaviours, attitudes and perceptions: experiences with a community based walkability survey. In: Transportation Research Board, Annual Meeting (2004)

    Google Scholar 

  6. Porta, S., Renne, J.L.: Linking urban design to sustainability: formal indicators of social urban sustainability field in perth, Western Australia. Urban Des. Int. 10, 51–64 (2005)

    Article  Google Scholar 

  7. Frank, L.D., Sallis, J.F., Conway, T.L., Chapman, J.E., Saelens, B.E., Bachman, W.: Many pathways from land use to health. associations between neighborhood walkability and active transportation, body mass index, and air quality. J. Am. Plan. Assoc. 72, 75–87 (2006)

    Article  Google Scholar 

  8. Saelens, B.E., Handy, S.L.: Built environment correlates of walking: a review. Med. Sci. Sports Exerc. 40, 550–566 (2008)

    Article  Google Scholar 

  9. Ewing, R., Cervero, R.: Travel and the built environment: a meta-analysis. J. Am. Plan. Assoc. 76, 265–294 (2010)

    Article  Google Scholar 

  10. Maghelal, P.K., Capp, C.J.: Walkability: a review of existing pedestrian indices. URISA J. 23, 5–19 (2011)

    Google Scholar 

  11. Sugiyama, T., Neuhaus, M., Cole, R., Giles-Corti, B., Owen, N.: Destination and route attributes associated with adults walking: a review. Med. Sci. Sports Exerc. 44, 1275–1286 (2012)

    Article  Google Scholar 

  12. Páez, A., Moniruzzamana, M., Bourbonnaisb, P.L., Morency, C.: Developing a web-based accessibility calculator prototype for the greater montreal area. Transp. Res. Part A: Policy Pract. 58, 103–115 (2013)

    Google Scholar 

  13. Blecic, I., Cecchini, A., Congiu, T., Fancello, G., Trunfio, G.A.: Evaluating walkability: a capability-wise planning and design support system. Int. J. Geogr. Inf. Sci. 29, 1350–1374 (2015)

    Article  Google Scholar 

  14. Vale, D.S., Saraiva, M., Pereira, M.: Active accessibility: a review of operational measures of walking and cycling accessibility. J. Transp. Land Use 9, 209–235 (2016)

    Google Scholar 

  15. Blečić, I., Canu, D., Cecchini, A., Congiu, T., Fancello, G.: Factors of perceived walkability: a pilot empirical study. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9789, pp. 125–137. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42089-9_9

    Chapter  Google Scholar 

  16. Blečić, I., Cecchini, A., Canu, D., Cappai, A., Congiu, T., Fancello, G.: Evaluating the effect of urban intersections on walkability. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9789, pp. 138–149. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42089-9_10

    Chapter  Google Scholar 

  17. Blecic, I., Cecchini, A., Trunfio, G.A.: Computer-aided drafting of urban designs for walkability. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10407, pp. 695–709. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62401-3_51

    Chapter  Google Scholar 

  18. Blecic, I., Canu, D., Cecchini, A., Congiu, T., Fancello, G.: Walkability and street intersections in rural-urban fringes: a decision aiding evaluation procedure. Sustainability 9, 883 (2017)

    Article  Google Scholar 

  19. Blečić, I., Cecchini, A., Congiu, T., Fancello, G., Trunfio, G.A.: Walkability explorer: an evaluation and design support tool for walkability. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8582, pp. 511–521. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09147-1_37

    Chapter  Google Scholar 

  20. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  21. Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 487–495. Curran Associates Inc. (2014)

    Google Scholar 

  22. Salesses, P., Schechtner, K., Hidalgo, C.A.: The collaborative image of the city: mapping the inequality of urban perception. PLoS ONE 8, e68400 (2013)

    Article  Google Scholar 

  23. Naik, N., Philipoom, J., Raskar, R., Hidalgo, C.: Streetscore - predicting the perceived safety of one million streetscapes. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. CVPRW 2014, Washington, DC, USA, pp. 793–799. IEEE Computer Society (2014)

    Google Scholar 

  24. Herbrich, R., Minka, T., Graepel, T.: Trueskill™: a Bayesian skill rating system. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 569–576. MIT Press (2007)

    Google Scholar 

  25. Ordonez, V., Berg, T.L.: Learning high-level judgments of urban perception. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 494–510. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_32

    Chapter  Google Scholar 

  26. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. CoRR abs/1310.1531 (2013)

    Google Scholar 

  27. Porzi, L., Rota Bulò, S., Lepri, B., Ricci, E.: Predicting and understanding urban perception with convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, MM 2015, pp. 139–148. ACM, New York (2015)

    Google Scholar 

  28. Dubey, A., Naik, N., Parikh, D., Raskar, R., Hidalgo, C.A.: Deep learning the city: quantifying urban perception at a global scale. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 196–212. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_12

    Chapter  Google Scholar 

  29. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  30. Liu, L., Silva, E.A., Wu, C., Wang, H.: A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Comput. Environ. Urban Syst. 65, 113–125 (2017)

    Article  Google Scholar 

  31. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, ICCV 1999, Washington, DC, USA, vol. 2, pp. 1150–1157. IEEE Computer Society (1999)

    Google Scholar 

  32. Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Washington, DC, USA, pp. 1717–1724. IEEE Computer Society (2014)

    Google Scholar 

  33. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)

    Google Scholar 

  34. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2018). https://doi.org/10.1109/TPAMI.2017.2723009

    Article  Google Scholar 

  35. Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., Murphy, K.: OpenImages: a public dataset for large-scale multi-label and multi-class image classification (2017). https://github.com/openimages

  36. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  37. Seresinhe, C.I., Preis, T., Moat, H.S.: Using deep learning to quantify the beauty of outdoor places. R. Soc. Open Sci. 4, 170170 (2017)

    Article  MathSciNet  Google Scholar 

  38. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115, 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  39. Lin, M., Chen, Q., Yan, S.: Network in network. CoRR abs/1312.4400 (2013)

    Google Scholar 

  40. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)

    Google Scholar 

  41. Crammer, K., Singer, Y.: Pranking with ranking. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 641–647. MIT Press (2002)

    Google Scholar 

  42. da Costa, J.P., Cardoso, J.S.: Classification of ordinal data using neural networks. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 690–697. Springer, Heidelberg (2005). https://doi.org/10.1007/11564096_70

    Chapter  Google Scholar 

  43. Gutirrez, P.A., Prez-Ortiz, M., Snchez-Monedero, J., Fernndez-Navarro, F., Hervs-Martnez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28, 127–146 (2016)

    Article  Google Scholar 

  44. Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras

  45. Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). tensorow.org

  46. Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012)

    Google Scholar 

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Acknowledgements

This study was supported by the research grants for the projects: “Healthy Cities and Smart Territories” (2016/17) funded by Fondazione di Sardegna and the Autonomous Region of Sardinia and “Large Scale Optimization of Computationally Expensive Objective Functions” funded by Fondazione di Sardegna (2015).

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering and Architecture, University of Cagliari, Cagliari, Italy

    Ivan Blečić

  2. Department of Architecture, Design and Urban Planning, University of Sassari, Alghero, Italy

    Arnaldo Cecchini & Giuseppe A. Trunfio

Authors
  1. Ivan Blečić
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  2. Arnaldo Cecchini
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  3. Giuseppe A. Trunfio
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Corresponding author

Correspondence to Ivan Blečić .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Blečić, I., Cecchini, A., Trunfio, G.A. (2018). Towards Automatic Assessment of Perceived Walkability. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_24

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