Abstract
Natural disasters cause extensive damages to urban cities, demanding authorities to take urgent and effective measures to restore normalcy on transportation. During a disaster, restoring road transportation in a timely manner for rescue, supply and also prevent the risk of road accidents due to obstacles is of vital importance. Traditional post-disaster road obstacle work relies on a manual investigation which is time-consuming and labor-intensive. Predicting road risks can provide decision support for emergency management departments, reducing the damage caused by disasters. In this paper, we propose a three-phase framework for predicting road risks post-disaster leveraging heterogeneous urban data. Firstly, We use a clustering algorithm to extract and classify urban road networks based on the floating car data. Then we extract the spatiotemporal features of the urban roads. Through social network data, we collect historical risk-prone data using the crowdsensing method. To address the challenges of the small amount of labeled data, we train our model based on self-training. We verified the validity of this model by using a real dataset in Xiamen island which proves that our model accurately predicts road risk with precision and recall both more than 85% respectively.
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Adriana Kocornik-Mina, G.M., McDermott, T.K.J., Rauch, F.: Flooded cities. LSC Centre for Economic Performance, no. CEPCP463 (2016)
Bertozzi, M., Bombini, L., Cerri, P., Medici, P., Antonello, P.C., Miglietta, M.: Obstacle detection and classification fusing radar and vision. In: IEEE Intelligent Vehicles Symposium. IEEE 2008, pp. 608–613 (2008). https://doi.org/10.1109/IVS.2008.4621304
Broadhurst, A., Baker, S., Kanade, T.: A Prediction and Planning Framework for Road Safety Analysis, Obstacle Avoidance and Driver Information. Carnegie Mellon University, The Robotics Institute (2004)
Chen, L., Zhang, D. Pan, G., Ma, X., Yang, D., Kushlev, K., Zhang, W., Li, S.: Bike sharing station placement leveraging heterogeneous urban open data. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ser. UbiComp ’15. New York, NY, USA: ACM, 2015, pp. 571–575 (2015)
Chen, L., Fan, X., Wang, L., Zhang, D., Yu, Z., Li, J., Thi Mai Trang, N., Pan, G., Wang, C.: Radar: Road obstacle identification for disaster response leveraging cross-domain urban data. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, pp. 1–23 (2018)
Ewan, L., Al-Kaisy, A., Veneziano, D.: Remote sensing of weather and road surface conditions: Is technology mature for reliable intelligent transportation systems applications? Transp. Res. Rec. 2329(1), 8–16 (2013)
Ferguson, D., Darms, M., Urmson, C., Kolski, S.: Detection, prediction, and avoidance of dynamic obstacles in urban environments. In: IEEE Intelligent Vehicles Symposium. IEEE 2008, 1149–1154 (2008). https://doi.org/10.1109/IVS.2008.4621214
Gahr, B., Ryder, B., Dahlinger, A., Wortmann, F.: A Crowd sensing approach to video classification of traffic accident hotspots. International conference on machine learning and data mining in pattern recognition, July 2018, 183–197 (2018)
Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: European Conference on Computer Vision, June 2000, pp. 37–49 (2000). https://doi.org/10.1007/3-540-45053-X_3
Guo, T., Iwamura, K., Koga, M.: Towards high accuracy road maps generation from massive gps traces data. In: 2007 IEEE International Geoscience and Remote Sensing Symposium, July 2007, pp. 667–670 (2007). https://doi.org/10.1109/IGARSS.2007.4422884
Guo, S., Chen, C., Wang, J., Ding, Y., Liu, Y., Ke, X., Yu, Z., Zhang, D.: A force-directed approach to seeking route recommendation in ride-on-demand service using multi-source urban data. IEEE Trans. Mob. Comput., 1 (2020)
Kalinke, T., Tzomakas, C., von Seelen, W.: A texture-based object detection and an adaptive model-based classification. In: Procs. IEEE Intelligent Vehicles Symposium, vol. 98. Citeseer, 1998, pp. 341–346 (1998)
Kelly, C.: Assessing environmental impacts during natural disaster: the development of a rapid environmental assessment methodology. J. Environ. Assess. Policy Manag. 4, 11 (2011)
Kuehnle, A.: Symmetry-based vehicle location for ahs. In: Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, vol. 2902. International Society for Optics and Photonics, pp. 19–27 (1997)
Kuo, Y., Leung, J.M.Y., Meng, H.M., Tsoi, K.K.F.: A real-time decision support tool for disaster response: a mathematical programming approach. In: 2015 IEEE International Congress on Big Data, 9, 2015, pp. 639–642 (2015)
Levi, D., Garnett, N., Fetaya, E., Herzlyia, I.: Stixelnet: a deep convolutional network for obstacle detection and road segmentation. BMVC 1(2), 4 (2015)
Li, L., Li, D., Xing, X., Yang, F., Rong, W., Zhu, H.: Extraction of road intersections from gps traces based on the dominant orientations of roads. ISPRS Int. J. Geo-Inf. 6(12), 403 (2017)
Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and terrain classification for autonomous off-road navigation. Auton. Robots 18(1), 81–102 (2005)
Muller, U., Ben, J., Cosatto, E., Flepp, B., Cun, Y.L.: Off-road obstacle avoidance through end-to-end learning. In: Weiss, Y., Schölkopf, B., Platt, J.C. (eds.) Advances in Neural Information Processing Systems 18, pp. 739–746. MIT Press (2006)
Perrollaz, M., Labayrade, R., Royere, C., Hautiere, N., Aubert, D.: Long range obstacle detection using laser scanner and stereovision. In: IEEE intelligent vehicles symposium. IEEE 2006, pp. 182–187 (2006). https://doi.org/10.1109/IVS.2006.1689625
Prabhakar, G., Kailath, B., Natarajan, S., Kumar, R.: Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving. In: 2017 IEEE Region 10 Symposium (TENSYMP), 2017, pp. 1–6 (2017). https://doi.org/10.1109/TENCONSpring.2017.8069972
Rozsa, Z., Sziranyi, T.: Obstacle prediction for automated guided vehicles based on point clouds measured by a tilted lidar sensor. IEEE Trans. Intell. Transport. Syst. 19(8), 2708–2720 (2018)
Ryder, B., Wortmann, F.: Autonomously detecting and classifying traffic accident hotspots. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, ser. UbiComp ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 365-370 (2017). https://doi.org/10.1145/3123024.3123199
Tamerius, J.D., Zhou, X., Mantilla, R., Greenfield-Huitt, T.: Precipitation effects on motor vehicle crashes vary by space, time, and environmental conditions. Weather Clim. Soc. 8(4), 399–407 (2016)
Torres-Ruiz, M., Lytras, M.: Urban computing and smart cities applications for the knowledge society. Int. J. Knowl. Soc. Res. 7, 113–119 (2016)
Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: AAAI/IAAI, July 2000, pp. 866–871 (2000). https://doi.org/10.5555/647288.721755
US Department of Transportation: Recovering from disasters: The national transportation recovery strategy, US Department of Transportation, September 2009, p. 9–25 (2009)
Wang, Z.: Analysis and prediction of urban traffic congestion based on big data. Int. J. Data Sci. Technol. 4, 100 (2018)
Wang, T., Zheng, N., Xin, J., Ma, Z.: Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications. Sensors 11(9), 8992–9008 (2011)
Wang, Y., Liu, X., Wei, H., Forman,, G. Zhu, Y.: Crowdatlas: self-updating maps for cloud and personal use, p. 469–470 (2013)
Wang, X., Xu, L., Sun, H., Xin, J., Zheng, N.: Bionic vision inspired on-road obstacle detection and tracking using radar and visual information. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 39–44 (2014)
Wolf, K., Bratton, N.: Urban trees and traffic safety: Considering U.S. roadside policy and crash data. Arboricult. Urban For. 32, 07 (2006)
Wu, F., Wang, H., Li, Z.: Interpreting traffic dynamics using ubiquitous urban data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(SIGSPACIAL '16), pp. 1–4 (2016). https://doi.org/10.1145/2996913.2996962
Xie, M., Trassoudaine, L., Alizon, J., Gallice, J.: Road obstacle detection and tracking by an active and intelligent sensing strategy. Mach. Vis. Appl. 7(3), 165–177 (1994)
Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X., Hu, C.: Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput. 8(2), 387–397 (2016)
Yamada, R., Shibao, M., Kajiwara, A.: Radar cross section measurement of road debris in 79ghz-band. IEICE Commun. Express 10, 11 (2020)
Yang, C., Su, G., Chen, J.: Using big data to enhance crisis response and disaster resilience for a smart city. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), March 2017, pp. 504–507 (2017)
Yu, B., Wang, Z., Mu, H., Sun, L., Fengning, H.: Identification of urban functional regions based on floating car track data and poi data. Sustainability 11, 6541 (2019)
Zeng, J., Tang, B., Mining heterogeneous urban data for retail store placement. In: Proceedings of the ACM Turing Celebration Conference - China (ACM TURC '19), p. 1–5 (2019). https://doi.org/10.1145/3321408.3322834
Zhang, D., Guo, B., Yu, Z.: The emergence of social and community intelligence. Computer 44, 21–28 (2011)
Zheng, Y., Liu, F., Hsieh, H.-P.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’13. New York, NY, USA: ACM, 2013, pp. 1436–1444 (2013)
Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 1–55 (2014)
Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., Li, T.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’15. New York, NY, USA: ACM, 2015, pp. 2267–2276 (2015)
Acknowledgements
We would like to thank the reviewers and editors for their constructive suggestions. This research is supported by NSF of China No. 61802325 and No. 61872306.
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You, J., Muhammad, A.S., He, X. et al. PANDA: predicting road risks after natural disasters leveraging heterogeneous urban data. CCF Trans. Pervasive Comp. Interact. 4, 393–407 (2022). https://doi.org/10.1007/s42486-022-00095-5
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DOI: https://doi.org/10.1007/s42486-022-00095-5