Abstract
Cycling is associated with health, environmental and societal benefits. Urban infrastructure design catering to cyclists’ safety can potentially reduce cyclist crashes and therefore, injury and/or mortality. This research uses publicly available big data such as maps and satellite images to capture information of the environment of cyclist crashes. Deep learning methods, such as generative adversarial networks (GANs), learn from these datasets and explore factors associated with cyclist crashes. This assumes existing environmental patterns for roads at locations with and without cyclist crashes, and suggests a deep learning method is able to learn the hidden features from map and satellite images and model the road environments using GANs. Experiments validated the method by identifying factors associated with cyclist crashes that show agreement with existing literature. Additionally, it revealed the potential of this method to identify implicit factors that have not been previously identified in the existing literature. These results provide visual indications about what streetscapes are safer for cyclist and suggestions on how city streetscapes should be planned or reconstructed to improve it.
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References
Cheng, W., Gill, G.S., Ensch, J.L., Kwong, J., Jia, X.: Multimodal crash frequency modeling: multivariate space-time models with alternate spatiotemporal interactions. Accid. Anal. Prev. 113, 159–170 (2018)
Beck, B., Stevenson, M., Newstead, S., Cameron, P., Judson, R., Edwards, E.R., Bucknill, A., Johnson, M., Gabbe, B.: Bicycling crash char- acteristics: an in-depth crash investigation study. Accid. Anal. Prev. 96, 219–227 (2016)
DiGioia, J., Watkins, K.E., Xu, Y., Rodgers, M., Guensler, R.: Safety impacts of bicycle infrastructure: a critical review. J. Saf. Res. 61, 105–119 (2017)
Prato, C.G., Kaplan, S., Rasmussen, T.K., Hels, T.: Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region. J. Transp. Saf. Secur. 8, 346–360 (2016)
Silla, A., Leden, L., R¨am¨a, P., Scholliers, J., Van Noort, M., Bell, D.: Can cyclist safety be improved with intelligent transport systems? Accid. Anal. Prev. 105, 134–145 (2017)
Asgarzadeh, M., Verma, S., Mekary, R.A., Courtney, T.K., Christiani, D.C.: The role of intersection and street design on severity of bicycle-motor vehicle crashes. Inj. Prev. 23, 179–185 (2017)
De Rome, L., Boufous, S., Georgeson, T., Senserrick, T., Richardson, D., Ivers, R.: Bicycle crashes in different riding environments in the Australian capital territory. Traffic Inj. Prev. 15, 81–88. https://doi.org/10.1080/15389588.2013.781591
Thomas, B., DeRobertis, M., Board, T.R.: The safety of urban cycle tracks: a review of the literature. Accid. Anal. Prev. 52, 6p (2012)
Vandenbulcke, G., Thomas, I., Int Panis, L.: Predicting cycling acci- dent risk in Brussels: a spatial case-control approach. Accid. Anal. Prev. 62, 341–357 (2014)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks, pp. 1–9 (2014)
Sønderby, C.K., Caballero, J., Theis, L., Shi, W., Husz´ar, F.: Amortised map inference for image super-resolution. arXiv:1610.04490 (2016)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Wijnands, J., Nice, K., Thompson, J., Zhao, H., Stevenson, M.: Streetscape augmentation using generative adversarial networks: insights related to health and wellbeing. arXiv:1905.06464 (2019)
Naznin, F., Currie, G., Logan, D.: Exploring the key challenges in tram driving and crash risk factors on the Melbourne tram network: tram driver focus groups. Australas. Transp. Res. Forum 16, 1–15 (2016)
Leao, S.Z., Pettit, C.J.: RiderLog Anonymised Bicycling Data (2017)
Liu, M., Breuel, T., Kautz, J.: Unsupervised image-to-image transla- tion networks. arXiv:1703.00848 (2017)
Teschke, K., Dennis, J., Reynolds, C.C., Winters, M., Harris, M.A.: Bicycling crashes on streetcar (tram) or train tracks: mixed methods to identify prevention measures. BMC Public Health 16, 1–10 (2016)
Prati, G., Pietrantoni, L., Fraboni, F.: Using data mining techniques to predict the severity of bicycle crashes. Accid. Anal. Prev. 101, 44–54 (2017)
Juhra, C., Wiesk¨otter, B., Chu, K., Trost, L., Weiss, U., Messerschmidt, M., Malczyk, A., Heckwolf, M., Raschke, M.: Bicycle accidents - do we only see the tip of the iceberg?: A prospective multi-centre study in a large German city combining medical and police data. Injury 43, 2026–2034 (2012)
Vanparijs, J., Int, L., Meeusen, R., De Geus, B.: Exposure measure- ment in bicycle safety analysis: a review of the literature. Accid. Anal. Prev. 84, 9–19 (2015)
Saha, D., Alluri, P., Gan, A., Wu, W.: Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. Accid. Anal. Prev. 0–1 (2018). https://doi.org/10.1016/j.aap.2018.02.014
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Zhao, H. et al. (2019). Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety. In: Qu, X., Zhen, L., Howlett, R., Jain, L. (eds) Smart Transportation Systems 2019. Smart Innovation, Systems and Technologies, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-13-8683-1_16
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DOI: https://doi.org/10.1007/978-981-13-8683-1_16
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