References
- Abay, K. A. (2015). Investigating the nature and impact of reporting bias in road crash data. Transportation Research Part A: Policy and Practice, 71, 31–45. doi:https://doi.org/10.1016/j.tra.2014.11.002
- Aldred, R. (2016). Cycling near misses: Their frequency, impact, and prevention. Transportation Research Part A: Policy and Practice, 90, 69–83. doi:https://doi.org/10.1016/j.tra.2016.04.016
- Aldred, R. (2018). Inequalities in self-report road injury risk in Britain: A new analysis of National Travel Survey data, focusing on pedestrian injuries. Journal of Transport & Health, 9, 96–104. doi:https://doi.org/10.1016/j.jth.2018.03.006
- Aldred, R., & Crosweller, S. (2015). Investigating the rates and impacts of near misses and related incidents among UK cyclists. Journal of Transport & Health, 2(3), 379–393. doi:https://doi.org/10.1016/j.jth.2015.05.006
- Aldred, R., & Goodman, A. (2018). Predictors of the frequency and subjective experience of cycling near misses: Findings from the first two years of the UK near miss project. Accident Analysis & Prevention, 110, 161–170. doi:https://doi.org/10.1016/j.aap.2017.09.015
- Alvarez, L., Deriche, R., Papadopoulo, T., & Sánchez, J. (2007). Symmetrical dense optical flow estimation with occlusions detection. International Journal of Computer Vision, 75(3), 371–385. doi:https://doi.org/10.1007/s11263-007-0041-4
- Andrade, E. L., Blunsden, S., & Fisher, R. B. (2006). Modelling crowd scenes for event detection. 18th International Conference on Pattern Recognition (ICPR’06) (pp. 175–178). https://doi.org/https://doi.org/10.1109/ICPR.2006.806
- Ayvaci, A., Raptis, M., & Soatto, S. (2012). Sparse occlusion detection with optical flow. International Journal of Computer Vision, 97(3), 322–338. doi:https://doi.org/10.1007/s11263-011-0490-7
- Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1–31. doi:https://doi.org/10.1007/s11263-010-0390-2
- Beck, B., Chong, D., Olivier, J., Perkins, M., Tsay, A., Rushford, A., … Johnson, M. (2019). How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance. Accident Analysis & Prevention, 128, 253–260. doi:https://doi.org/10.1016/j.aap.2019.03.007
- Beck, B., Stevenson, M., Newstead, S., Cameron, P., Judson, R., Edwards, E. R., … Gabbe, B. (2016). Bicycling crash characteristics: An in-depth crash investigation study. Accident Analysis & Prevention, 96, 219–227. doi:https://doi.org/10.1016/j.aap.2016.08.012
- Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic image networks for action recognition. 2016 IEEE conference on Computer Vision and Pattern Recognition (CVPR), (pp. 3034–3042). https://doi.org/https://doi.org/10.1109/CVPR.2016.331
- Bíl, M., Bílová, M., & Müller, I. (2010). Critical factors in fatal collisions of adult cyclists with automobiles. Accident Analysis and Prevention, 5, 1632–1636.
- Blaizot, S., Papon, F., Haddak, M. M., & Amoros, E. (2013). Injury incidence rates of cyclists compared to pedestrians, car occupants and powered two-wheeler riders, using a medical registry and mobility data, Rhône County, France. Accident Analysis & Prevention, 58, 35–45. doi:https://doi.org/10.1016/j.aap.2013.04.018
- Branion-Calles, M., Nelson, T., & Winters, M. (2017). Comparing crowdsourced near-miss and collision cycling data and official bike safety reporting. Transportation Research Record: Journal of the Transportation Research Board, 2662(1), 1–11. doi:https://doi.org/10.3141/2662-01
- Broach, J., Dill, J., & Gliebe, J. (2012). Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transportation Research Part A: Policy and Practice, 46(10), 1730–1740. doi:https://doi.org/10.1016/j.tra.2012.07.005
- Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012). A naturalistic open source movie for optical flow evaluation. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Computer vision – ECCV 2012 (Vol. 7577, pp. 611–625). Springer Berlin Heidelberg. https://doi.org/https://doi.org/10.1007/978-3-642-33783-3_44
- Cao, Y., Wu, Z., & Shen, C. (2017). Estimating depth from monocular images as classification using deep fully convolutional residual networks. ArXiv:1605.02305 [Cs]. http://arxiv.org/abs/1605.02305
- Chaurand, N., & Delhomme, P. (2013). Cyclists and drivers in road interactions: A comparison of perceived crash risk. Accident Analysis and Prevention, 9, 1176–1184.
- Cho, G., Rodríguez, D. A., & Khattak, A. J. (2009). The role of the built environment in explaining relationships between perceived and actual pedestrian and bicyclist safety. Accident Analysis & Prevention, 41, 692–702. doi:https://doi.org/10.1016/j.aap.2009.03.008
- de Geus, B., Vandenbulcke, G., Int Panis, L., Thomas, I., Degraeuwe, B., Cumps, E., … Meeusen, R. (2012). A prospective cohort study on minor accidents involving commuter cyclists in Belgium. Accident Analysis & Prevention, 45, 683–693. doi:https://doi.org/10.1016/j.aap.2011.09.045
- de Hartog, J. J., Boogaard, H., Nijland, H., & Hoek, G. (2010). Do the health benefits of cycling Outweigh the Risks? Environmental Health Perspectives, 118(8), 1109–1116. doi:https://doi.org/10.1289/ehp.0901747
- Department for Transport. (2019). Reported road casualties in Great Britain: 2018 annual report (p. 47). UK Department for Transport. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/834585/reported-road-casualties-annual-report-2018.pdf
- De Rome, L., Boufous, S., Georgeson, T., Senserrick, T., Richardson, D., & Ivers, R. (2014). Bicycle crashes in different riding Environments in the Australian capital territory. Traffic Injury Prevention, 15(1), 81–88. doi:https://doi.org/10.1080/15389588.2013.781591
- DiGioia, J., Watkins, K. E., Xu, Y., Rodgers, M., & Guensler, R. (2017). Safety impacts of bicycle infrastructure: A critical review. Journal of Safety Research, 61, 105–119. doi:https://doi.org/10.1016/j.jsr.2017.02.015
- Dozza, M., Bianchi Piccinini, G. F., & Werneke, J. (2016). Using naturalistic data to assess e-cyclist behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 41, 217–226. doi:https://doi.org/10.1016/j.trf.2015.04.003
- Dozza, M., Schindler, R., Bianchi-Piccinini, G., & Karlsson, J. (2016). How do drivers overtake cyclists? Accident Analysis & Prevention, 88, 29–36. doi:https://doi.org/10.1016/j.aap.2015.12.008
- Dozza, M., Schwab, A., & Wegman, F. (2017). Safety science special issue on cycling safety. Safety Science, 92, 262–263. doi:https://doi.org/10.1016/j.ssci.2016.06.009
- Dozza, M., & Werneke, J. (2014). Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world? Transportation Research Part F: Traffic Psychology and Behaviour, 24, 83–91. doi:https://doi.org/10.1016/j.trf.2014.04.001
- Dozza, M., Werneke, J., & Fernandez, A. (2012). Piloting the Naturalistic Methodology on Bicycles. 11.
- El-Nouby, A., & Taylor, G. W. (2018). Real-time end-to-end action detection with two-stream networks. ArXiv:1802.08362 [Cs]. http://arxiv.org/abs/1802.08362
- Enkelmann, W. (n.d.). Obstacle detection by evaluation of optical flow fields from image sequences. 5.
- Fuller, D., Gauvin, L., Morency, P., Kestens, Y., & Drouin, L. (2013). The impact of implementing a public bicycle share program on the likelihood of collisions and near misses in Montreal, Canada. Preventive Medicine, 57(6), 920–924. doi:https://doi.org/10.1016/j.ypmed.2013.05.028
- Gatersleben, B., & Haddad, H. (2010). Who is the typical bicyclist? Transportation Research Part F: Traffic Psychology and Behaviour, 13(1), 41–48. doi:https://doi.org/10.1016/j.trf.2009.10.003
- Gustafsson, L., & Archer, J. (2013). A naturalistic study of commuter cyclists in the greater Stockholm area. Accident Analysis & Prevention, 58, 286–298. doi:https://doi.org/10.1016/j.aap.2012.06.004
- He, L., Wang, G., & Hu, Z. (2018). Learning depth from single images with deep neural network embedding focal length. IEEE Transactions on Image Processing, 27(9), 4676–4689. doi:https://doi.org/10.1109/TIP.2018.2832296
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. ArXiv:1512.03385v1. https://arxiv.org/pdf/1512.03385.pdf
- Huang, G., Liu, Z., Weinberger, K. Q., & van der Maaten, L. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1, 3.
- Ibrahim, M. R., Haworth, J., & Cheng, T. (2019). URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision. Environment and Planning B: Urban Analytics and City Science, 239980831984651. doi:https://doi.org/10.1177/2399808319846517
- Ibrahim, M. R., Haworth, J., & Cheng, T. (2019). Weathernet: Recognising weather and visual conditions from street-level images using deep residual learning. ISPRS International Journal of Geo-Information, 8(12), 549. doi:https://doi.org/10.3390/ijgi8120549
- Ibrahim, M. R., Haworth, J., & Cheng, T. (2020). Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities, 96, 102481. doi:https://doi.org/10.1016/j.cities.2019.102481
- Imprialou, M., & Quddus, M. (2017). Crash data quality for road safety research: Current state and future directions. Accident Analysis & Prevention, doi:https://doi.org/10.1016/j.aap.2017.02.022
- Jestico, B., Nelson, T., & Winters, M. (2016). Mapping ridership using crowdsourced cycling data. Journal of Transport Geography, 52, 90–97. doi:https://doi.org/10.1016/j.jtrangeo.2016.03.006
- Johnson, M., Charlton, J., Oxley, J., & Newstead, S. (2010). Naturalistic cycling study: Identifying risk factors for on-road commuter cyclists. Annals of Advances in Automotive Medicine / Annual Scientific Conference, 54, 275–283.
- Johnson, M., Newstead, S., Oxley, J., & Charlton, J. (2013). Cyclists and open vehicle doors: Crash characteristics and risk factors. Safety Science, 59, 135–140. doi:https://doi.org/10.1016/j.ssci.2013.04.010
- Johnson, M., Oxley, J., Newstead, S., & Charlton, J. (2014). Safety in numbers? Investigating Australian driver behaviour, knowledge and attitudes towards cyclists. Accident Analysis & Prevention, 70, 148–154. doi:https://doi.org/10.1016/j.aap.2014.02.010
- Jones, S., Kirchsteiger, C., & Bjerke, W. (1999). The importance of near miss reporting to further improve safety performance. Journal of Loss Prevention in the Process Industries, 9, 59–67.
- Juhra, C., Wieskötter, B., Chu, K., Trost, L., Weiss, U., Messerschmidt, M., … Raschke, M. (2012). Bicycle accidents – Do we only see the tip of the iceberg? Injury, 43(12), 2026–2034. doi:https://doi.org/10.1016/j.injury.2011.10.016
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Proceeding NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097–1105.
- Lacherez, P., Wood, J. M., Marszalek, R. P., & King, M. J. (2013). Visibility-related characteristics of crashes involving bicyclists and motor vehicles – responses from an online questionnaire study. Transportation Research Part F: Traffic Psychology and Behaviour, 20, 52–58. doi:https://doi.org/10.1016/j.trf.2013.04.003
- Lawson, A. R., Pakrashi, V., Ghosh, B., & Szeto, W. Y. (2013). Perception of safety of cyclists in Dublin city. Accident Analysis & Prevention, 50, 499–511. doi:https://doi.org/10.1016/j.aap.2012.05.029
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi:https://doi.org/10.1038/nature14539
- Lehtonen, E., Havia, V., Kovanen, A., Leminen, M., & Saure, E. (2016). Evaluating bicyclists’ risk perception using video clips: Comparison of frequent and infrequent city cyclists. Transportation Research Part F: Traffic Psychology and Behaviour, 41, 195–203. doi:https://doi.org/10.1016/j.trf.2015.04.006
- Levi, G., & Hassncer, T. (2015). Age and gender classification using convolutional neural networks. 2015 IEEE conference on computer vision and pattern recognition Workshops (CVPRW), (pp. 34–42). https://doi.org/https://doi.org/10.1109/CVPRW.2015.7301352
- Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, 21–37.
- Loo, B. P. Y., & Tsui, K. L. (2010). Bicycle crash casualties in a highly motorized city. Accident Analysis & Prevention, 42(6), 1902–1907. doi:https://doi.org/10.1016/j.aap.2010.05.011
- Mallot, H. A., Bülthoff, H. H., Little, J. J., & Bohrer, S. (1991). Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biological Cybernetics, 64(3), 177–185. doi:https://doi.org/10.1007/BF00201978
- Minaee, S., & Abdolrashidi, A. (2019). Deep-emotion: Facial expression recognition using attentional convolutional network. ArXiv:1902.01019 [Cs]. http://arxiv.org/abs/1902.01019
- Narang, N., & Bourlai, T. (2016). 2016 International conference on biometrics (ICB). Gender and ethnicity classification using deep learning in heterogeneous face recognition, (pp. 1–8). https://doi.org/https://doi.org/10.1109/ICB.2016.7550082
- Nelson, T. A., Denouden, T., Jestico, B., Laberee, K., & Winters, M. (2015). Bikemaps.org: A global tool for collision and near miss mapping. Frontiers in Public Health, 3. doi:https://doi.org/10.3389/fpubh.2015.00053
- Orsi, C., Ferraro, O. E., Montomoli, C., Otte, D., & Morandi, A. (2014). Alcohol consumption, helmet use and head trauma in cycling collisions in Germany. Accident Analysis & Prevention, 65, 97–104. doi:https://doi.org/10.1016/j.aap.2013.12.019
- Pai, C.-W. (2011). Overtaking, rear-end, and door crashes involving bicycles: An empirical investigation. Accident Analysis & Prevention, 43(3), 1228–1235. doi:https://doi.org/10.1016/j.aap.2011.01.004
- Parkin, J., & Meyers, C. (2010). The effect of cycle lanes on the proximity between motor traffic and cycle traffic. Accident Analysis & Prevention, 42(1), 159–165. doi:https://doi.org/10.1016/j.aap.2009.07.018
- Paschalidis, E., Basbas, S., Politis, I., & Prodromou, M. (2016). ‘“Put the blame on…others!”’: The battle of cyclists against pedestrians and car drivers at the urban environment. A cyclists’ perception study. 18.
- Poulos, R. G., Hatfield, J., Rissel, C., Grzebieta, R., & McIntosh, A. S. (2012). Exposure-based cycling crash, near miss and injury rates: The safer cycling prospective cohort study protocol: Figure 1. Injury Prevention, 18(1), e1–e1. doi:https://doi.org/10.1136/injuryprev-2011-040160
- PRISMA. (2015). Transparent reporting of systematic reviews and meta-analyses. PRISMA. http://prisma-statement.org/
- Pucher, J., Dill, J., & Handy, S. (2010). Infrastructure, programs, and policies to increase bicycling: An international review. Preventive Medicine, 50, S106–S125. doi:https://doi.org/10.1016/j.ypmed.2009.07.028
- Redmon, J., & Farhadi, A. (2017). YOLO9000: better, Faster, Stronger. 2017 IEEE conference on Computer Vision and Pattern Recognition (CVPR), (pp. 6517–6525). https://doi.org/https://doi.org/10.1109/CVPR.2017.690
- Saha, S., Singh, G., Sapienza, M., Torr, P. H. S., & Cuzzolin, F. (2016). Deep learning for detecting multiple space-time action tubes in videos. ArXiv:1608.01529 [Cs]. http://arxiv.org/abs/1608.01529
- Sanders, R. L. (2015). Perceived traffic risk for cyclists: The impact of near miss and collision experiences. Accident Analysis & Prevention, 75, 26–34. doi:https://doi.org/10.1016/j.aap.2014.11.004
- Savan, B., Cohlmeyer, E., & Ledsham, T. (2017). Integrated strategies to accelerate the adoption of cycling for transportation. Transportation Research Part F: Traffic Psychology and Behaviour, 46, 236–249. doi:https://doi.org/10.1016/j.trf.2017.03.002
- Schleinitz, K., Petzoldt, T., Franke-Bartholdt, L., Krems, J., & Gehlert, T. (2017). The German naturalistic cycling study – Comparing cycling speed of riders of different e-bikes and conventional bicycles. Safety Science, 92, 290–297. doi:https://doi.org/10.1016/j.ssci.2015.07.027
- Schleinitz, K., Petzoldt, T., Franke-Bartholdt, L., Krems, J. F., & Gehlert, T. (2015). Conflict partners and infrastructure use in safety critical events in cycling – results from a naturalistic cycling study. Transportation Research Part F: Traffic Psychology and Behaviour, 31, 99–111. doi:https://doi.org/10.1016/j.trf.2015.04.002
- Schlögl, M., & Stütz, R. (2017). Methodological considerations with data uncertainty in road safety analysis. Accident Analysis & Prevention, doi:https://doi.org/10.1016/j.aap.2017.02.001
- Shinar, D., Valero-Mora, P., van Strijp-Houtenbos, M., Haworth, N., Schramm, A., De Bruyne, G., … Tzamalouka, G. (2018). Under-reporting bicycle accidents to police in the COST TU1101 international survey: Cross-country comparisons and associated factors. Accident Analysis & Prevention, 110, 177–186. doi:https://doi.org/10.1016/j.aap.2017.09.018
- Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556.
- Soomro, K., & Shah, M. (2017). Unsupervised action discovery and localization in videos. 2017 IEEE International Conference on Computer Vision (ICCV), (pp. 696–705). https://doi.org/https://doi.org/10.1109/ICCV.2017.82
- Steinbach, R., Green, J., Datta, J., & Edwards, P. (2011). Cycling and the city: A case study of how gendered, ethnic and class identities can shape healthy transport choices. Social Science & Medicine, 72(7), 1123–1130. doi:https://doi.org/10.1016/j.socscimed.2011.01.033
- Strauss, J., Miranda-Moreno, L. F., & Morency, P. (2013). Cyclist activity and injury risk analysis at signalized intersections: A Bayesian modelling approach. Accident Analysis & Prevention, 59, 9–17. doi:https://doi.org/10.1016/j.aap.2013.04.037
- Sun, D., Roth, S., & Black, M. J. (2010). Secrets of optical flow estimation and their principles. 2010 IEEE computer society conference on Computer Vision and Pattern Recognition, (pp. 2432–2439), https://doi.org/https://doi.org/10.1109/CVPR.2010.5539939
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., & Reed, S. (2015). Going deeper with convolutions. https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
- Teschke, K., Frendo, T., Shen, H., Harris, M. A., Reynolds, C. C., Cripton, P. A., … Winters, M. (2014). Bicycling crash circumstances vary by route type: A cross-sectional analysis. BMC Public Health, 14, 1. doi:https://doi.org/10.1186/1471-2458-14-1205
- TfL. (2018). Cycling action plan: Making London the world’s best big city for cycling (p. 59).
- Vanparijs, J., Int Panis, L., Meeusen, R., & de Geus, B. (2015). Exposure measurement in bicycle safety analysis: A review of the literature. Accident Analysis & Prevention, 84, 9–19. doi:https://doi.org/10.1016/j.aap.2015.08.007
- Vansteenkiste, P., Zeuwts, L., Cardon, G., & Lenoir, M. (2016). A hazard-perception test for cycling children: An exploratory study. Transportation Research Part F: Traffic Psychology and Behaviour, 41, 182–194. doi:https://doi.org/10.1016/j.trf.2016.05.001
- Walker, I., Garrard, I., & Jowitt, F. (2014). The influence of a bicycle commuter’s appearance on drivers’ overtaking proximities: An on-road test of bicyclist stereotypes, high-visibility clothing and safety aids in the United Kingdom. Accident Analysis & Prevention, 64, 69–77. doi:https://doi.org/10.1016/j.aap.2013.11.007
- Wang, L., Qiao, Y., & Tang, X. (2015). Action recognition with trajectory-pooled deep-convolutional descriptors. 2015 IEEE conference on Computer Vision and Pattern Recognition (CVPR), (pp. 4305–4314). https://doi.org/https://doi.org/10.1109/CVPR.2015.7299059
- Weinzaepfel, P., Martin, X., & Schmid, C. (2016). Human action localization with sparse spatial supervision. ArXiv:1605.05197 [Cs]. http://arxiv.org/abs/1605.05197
- Winters, M., & Branion-Calles, M. (2017). Cycling safety: Quantifying the under reporting of cycling incidents in Vancouver, British Columbia. Journal of Transport & Health, 7, 48–53. doi:https://doi.org/10.1016/j.jth.2017.02.010
- Zangenehpour, S., Miranda-Moreno, L. F., & Saunier, N. (2015). Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application. Transportation Research Part C: Emerging Technologies, 56, 161–176. doi:https://doi.org/10.1016/j.trc.2015.04.003
- Zangenehpour, S., Strauss, J., Miranda-Moreno, L. F., & Saunier, N. (2016). Are signalized intersections with cycle tracks safer? A case–control study based on automated surrogate safety analysis using video data. Accident Analysis & Prevention, 86, 161–172. doi:https://doi.org/10.1016/j.aap.2015.10.025
- Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2016). Real-time action recognition with enhanced motion vector CNNs. ArXiv:1604.07669 [Cs]. http://arxiv.org/abs/1604.07669