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Journal of Intelligent Transportation Systems
Technology, Planning, and Operations
Volume 27, 2023 - Issue 3
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Articles

Vehicle trajectory extraction at the exit areas of urban freeways based on a novel composite algorithms framework

Ziyang LiuSchool of Transportation, Southeast University, Nanjing, P.R. China
,
Jie HeSchool of Transportation, Southeast University, Nanjing, P.R. ChinaCorrespondence[email protected]
,
Changjian ZhangSchool of Transportation, Southeast University, Nanjing, P.R. China
,
Xintong YanSchool of Transportation, Southeast University, Nanjing, P.R. China
,
Chenwei WangSchool of Transportation, Southeast University, Nanjing, P.R. China
&
Boshuai QiaoSchool of Transportation, Southeast University, Nanjing, P.R. China
Pages 295-313 | Received 29 Dec 2020, Accepted 16 Dec 2021, Published online: 03 Jan 2022

  • Azzam, R., Kemouche, M. S., Aouf, N., & Richardson, M. (2016). Efficient visual object detection with spatially global Gaussian mixture models and uncertainties. Journal of Visual Communication and Image Representation, 36, 90–106. https://doi.org/10.1016/j.jvcir.2015.11.009
  • Behbahani, H., Nadimi, N., & Naseralavi, S. (2015). New time-based surrogate safety measure to assess crash risk in car-following scenarios. Transportation Letters, 7(4), 229–238. https://doi.org/10.1179/1942787514Y.0000000051
  • Cepni, S., Atik, M. E., & Duran, Z. (2020). Vehicle detection using different deep learning algorithms from image sequence. Baltic Journal of Modern Computing, 8(2), 347–358. https://doi.org/10.22364/bjmc.2020.8.2.10
  • Chen, T., Shi, X., & Wong, Y. D. (2019). Key feature selection and risk prediction for lane-changing behaviors based on vehicles' trajectory data. Accident; Analysis and Prevention, 129, 156–169. https://doi.org/10.1016/j.aap.2019.05.017
  • Chen, Z., & Ellis, T. (2014). A self-adaptive Gaussian mixture model. Computer Vision and Image Understanding, 122, 35–46. https://doi.org/10.1016/j.cviu.2014.01.004
  • Chen, Z., Wu, C., Huang, Z., Lyu, N., Hu, Z., Zhong, M., Cheng, Y., & Ran, B. (2017). Dangerous driving behavior detection using video-extracted vehicle trajectory histograms. Journal of Intelligent Transportation Systems, 21(5), 409–421. https://doi.org/10.1080/15472450.2017.1305271
  • Cheng, Y., Qin, X., Jin, J., & Ran, B. (2012). An exploratory shockwave approach to estimating queue length using probe trajectories. Journal of Intelligent Transportation Systems, 16(1), 12–23. https://doi.org/10.1080/15472450.2012.639637
  • Dabiri, S., Marković, N., Heaslip, K., & Reddy, C. K. (2020). A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data. Transportation Research Part C: emerging Technologies, 116, 102644. https://doi.org/10.1016/j.trc.2020.102644
  • Gorry, P. A. (1990). General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry, 62(6), 570–573. https://doi.org/10.1021/ac00205a007
  • Ji, H., Gao, Z., Mei, T., & Ramesh, B. (2020). Vehicle detection in remote sensing images leveraging on simultaneous super-resolution. IEEE Geoscience and Remote Sensing Letters, 17(4), 676–680. https://doi.org/10.1109/LGRS.2019.2930308
  • Kaufmann, S., Kerner, B. S., Rehborn, H., Koller, M., & Klenov, S. L. (2018). Aerial observations of moving synchronized flow patterns in over-saturated city traffic. Transportation Research Part C: emerging Technologies, 86, 393–406. https://doi.org/10.1016/j.trc.2017.11.024
  • Kim, J., & Mahmassani, H. S. (2015). Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Transportation Research Procedia, 9, 164–184. https://doi.org/10.1016/j.trpro.2015.07.010
  • Lee, G., Mallipeddi, R., & Lee, M. (2017). Trajectory-based vehicle tracking at low frame rates. Expert Systems with Applications, 80, 46–57. https://doi.org/10.1016/j.eswa.2017.03.023
  • Li, Y., Li, Z., Wang, H., Wang, W., & Xing, L. (2017). Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways. Accident; Analysis and Prevention, 104, 137–145. https://doi.org/10.1016/j.aap.2017.04.025
  • Li, Z., Li, Y., Liu, P., Wang, W., & Xu, C. (2014). Development of a variable speed limit strategy to reduce secondary collision risks during inclement weathers. Accident; Analysis and Prevention, 72, 134–145. https://doi.org/10.1016/j.aap.2014.06.018
  • Liu, P., Wang, G., Yu, Z., Guo, X., & Lu, W. (2019). Vehicle tracking based on shape information and inter-frame motion vector. Computers & Electrical Engineering, 78, 22–31. https://doi.org/10.1016/j.compeleceng.2019.06.019
  • Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., & Kim, T.-K. (2021). Multiple object tracking: A literature review. Artificial Intelligence, 293, 103448. https://doi.org/10.1016/j.artint.2020.103448
  • Luo, X., Tian, X., Zhang, H., Hou, W., Leng, G., Xu, W., Jia, H., He, X., Wang, M., & Zhang, J. (2020). Fast automatic vehicle detection in uav images using convolutional neural networks. Remote Sensing, 12(12), 1994. https://doi.org/10.3390/rs12121994
  • Mao, Q.-C., Sun, H.-M., Zuo, L.-Q., & Jia, R.-S. (2020). Finding every car: A traffic surveillance multi-scale vehicle object detection method. Applied Intelligence, 50(10), 3125–3136. https://doi.org/10.1007/s10489-020-01704-5
  • Meng, Q., & Qu, X. (2012). Estimation of rear-end vehicle crash frequencies in urban road tunnels. Accident; Analysis and Prevention, 48, 254–263. https://doi.org/10.1016/j.aap.2012.01.025
  • Morris, B., & Trivedi, M. (2009). Learning trajectory patterns by clustering: Experimental studies and comparative evaluation [Paper presentation]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami [FL], USA. https://doi.org/10.1109/CVPR.2009.5206559
  • Morris, B. T., & Trivedi, M. M. (2011). Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), 2287–2301. https://doi.org/10.1109/TPAMI.2011.64
  • Nguyen, H. (2019). Improving faster R-CNN framework for fast vehicle detection. Mathematical Problems in Engineering, 2019, 1–11. (2019). https://doi.org/10.1155/2019/3808064
  • Niknejad, H. T., Takeuchi, A., Mita, S., & McAllester, D. (2012). On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation. IEEE Transactions on Intelligent Transportation Systems, 13(2), 748–758. https://doi.org/10.1109/TITS.2012.2187894
  • Osama, A., Sayed, T., Zaki, M. H., & Shaaban, K. (2015). Automated approach for a comprehensive safety assessment of roundabouts. https://trid.trb.org/view/1337647
  • Park, H., Oh, C., Moon, J., & Kim, S. (2018). Development of a lane change risk index using vehicle trajectory data. Accident; Analysis and Prevention, 110, 1–8. https://doi.org/10.1016/j.aap.2017.10.015
  • Qiu, Z., & Yao, D. (2006). Kalman filtering used in video-based traffic monitoring system. Journal of Intelligent Transportation Systems, 10(1), 15–21. https://doi.org/10.1080/15472450500455211
  • Raju, N., Arkatkar, S., & Joshi, G. (2020). Evaluating performance of selected vehicle following models using trajectory data under mixed traffic conditions. Journal of Intelligent Transportation Systems, 24(6), 617–634. https://doi.org/10.1080/15472450.2019.1675522
  • Sivaraman, S., & Trivedi, M. M. (2012). Real-time vehicle detection using parts at intersections [Paper presentation]. 2012 15th International Ieee Conference on Intelligent Transportation Systems, Anchorage [AK], USA. https://doi.org/10.1109/ITSC.2012.6338886
  • Tan, C., Liu, L., Wu, H., Cao, Y., & Tang, K. (2020). Fuzing license plate recognition data and vehicle trajectory data for lane-based queue length estimation at signalized intersections. Journal of Intelligent Transportation Systems, 24(5), 449–466. https://doi.org/10.1080/15472450.2020.1732217
  • Veeraraghavan, H., & Papanikolopoulos, N. P. (2009). Learning to recognize video-based spatiotemporal events. IEEE Transactions on Intelligent Transportation Systems, 10(4), 628–638. https://doi.org/10.1109/TITS.2009.2026440
  • Wu, Y., Abdel-Aty, M., Zheng, O., Cai, Q., & Zhang, S. (2020). Automated safety diagnosis based on unmanned aerial vehicle video and deep learning algorithm. Transportation Research Record: Journal of the Transportation Research Board, 2674(8), 350–359. https://doi.org/10.1177/0361198120925808
  • Xie, K., Ozbay, K., Yang, H., & Li, C. (2019). Mining automatically extracted vehicle trajectory data for proactive safety analytics. Transportation Research Part C: emerging Technologies, 106, 61–72. https://doi.org/10.1016/j.trc.2019.07.004
  • Xing, L., He, J., Abdel-Aty, M., Cai, Q., Li, Y., & Zheng, O. (2019). Examining traffic conflicts of up stream toll plaza area using vehicles' trajectory data. Accident; Analysis and Prevention, 125, 174–187. https://doi.org/10.1016/j.aap.2019.01.034
  • Xu, C., Wang, G., Yan, S., Yu, J., Zhang, B., Dai, S., Li, Y., & Xu, L. (2020). Fast vehicle and pedestrian detection using improved mask R-CNN. Mathematical Problems in Engineering, 2020, 1–15. https://doi.org/10.1155/2020/5761414
  • Yan, G., Yu, M., Yu, Y., & Fan, L. (2016). Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification. Optik, 127(19), 7941–7951. https://doi.org/10.1016/j.ijleo.2016.05.092
  • Yang, B., Tang, M., Chen, S., Wang, G., Tan, Y., & Li, B. (2020). A vehicle tracking algorithm combining detector and tracker. EURASIP Journal on Image and Video Processing, 2020(1), 1–20. https://doi.org/10.1186/s13640-020-00505-7
  • Zhao, Y., Zhou, X., Xu, X., Jiang, Z., Cheng, F., Tang, J., & Shen, Y. (2020). A novel vehicle tracking ID switches algorithm for driving recording sensors. Sensors, 20(13), 3638. https://doi.org/10.3390/s20133638
  • Zhuang, X., Kang, W., & Wu, Q. (2016). Real-time vehicle detection with foreground-based cascade classifier. IET Image Processing, 10(4), 289–296. https://doi.org/10.1049/iet-ipr.2015.0333

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