Volume 43 Issue 5
Oct.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(5): 169-179
LU Shuibo, LIU Zhizhen, TANG Feng, HAO Wei, LI Shuxin, ZHANG Zhaolei. A Travel Time Prediction Model for Rescue Vehicles Based on Tensor Decomposition[J]. Journal of Transport Information and Safety, 2025, 43(5): 169-179. doi: 10.3963/j.jssn.1674-4861.2025.05.016
Citation: LU Shuibo, LIU Zhizhen, TANG Feng, HAO Wei, LI Shuxin, ZHANG Zhaolei. A Travel Time Prediction Model for Rescue Vehicles Based on Tensor Decomposition[J]. Journal of Transport Information and Safety, 2025, 43(5): 169-179. doi: 10.3963/j.jssn.1674-4861.2025.05.016
shu

A Travel Time Prediction Model for Rescue Vehicles Based on Tensor Decomposition

doi: 10.3963/j.jssn.1674-4861.2025.05.016

  • School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • Received Date: 2024-12-05
    Available Online: 2026-03-05
    Abstract
  • Rescue vehicles have the right of way when driving on urban roads, and predicting their travel time in the urban road network can provide support for rescue activities according to the driving characteristics of rescue vehicles, which can effectively improve rescue efficiency. This paper proposes a model for predicting the travel time of rescue vehicles based on tensor decomposition considering congestion, termed the rescue vehicles travel time prediction model based on tensor decomposition (RTPT). The RTPT model integrates tensor decomposition algorithm, travel characteristics extraction, and a travel time prediction algorithm, all considering road congestion states. The tensor decomposition algorithm fused with congestion state constructs an urban road travel time tensor based on vehicle trajectory data, applying congestion-informed Tucker tensor decomposition to complete missing data. The travel characteristics extraction method examines the distinct driving patterns of rescue vehicles in contrast to social vehicles, constructing a travel time tensor for rescue vehicles in the urban road network. In the travel time prediction algorithm, a congestion probability tensor is constructed to weight the road congestion probabilities for predicting rescue vehicles travel time across varying data sparsity and time intervals. Experimental results show that RTPT achieves a substantial reduction in average absolute error, outperforming traditional methods: driver-based road trip time estimation (DRTE), moving average (MA), and historical average (HA) by 32.44%, 70.66% and 74.50%, respectively. Additionally, the model reduces the root mean square error by 24.28%, 69.73% and 74.67%, compared to DRTE, MA, and HA, respectively, exhibiting minimal error across all prediction scenarios and data conditions. With the increase of data sparsity and prediction period, the variation of the prediction error range of RTPT is basically kept within 1 s, showing its good stability and robustness. The integration of the congestion probability tensor significantly enhances the model ability to reflect the unique driving characteristics of rescue vehicles while incorporating comprehensive traffic network information, resulting in improved prediction accuracy.

     

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    • References(28)
  • [1]
    TANG C, HU W, HU S, et al. Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(5): 2956-2968. doi: 10.1109/TITS.2020.2978227
    [2]
    BRENDAN M, LAWRENCE, JENNIFER A. OXLEY. You say one route, we observe four: Using naturalistic observation to understand route-choices in cyclists[J]. Safety Science, 2019, 119: 207-213. doi: 10.1016/j.ssci.2019.01.004
    [3]
    ALALI Z T, ALABADY S A. Techniques and methods for managing disasters and critical situations[J]. Nat Hazards, 2024, 120: 6943-6989. doi: 10.1007/s11069-024-06573-6
    [4]
    TAN Z Y, ZHANG Q, WEI L, et al. A simulation-based optimization for deploying multiple kinds road rescue vehicles in urban road networks[J]. Computers & Industrial Engineering, 2023, 181: 109333.
    [5]
    WEN H Y, LIN Y F, WU J B. Co-Evolutionary optimization algorithm based on the future traffic environment for emergency rescue path planning[J]. IEEE ACCESS, 2020(8): 148125-148135.
    [6]
    HUANG L P, YANG Y J, CHEN H C, et al. Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data[J]. Knowledge-Based Systems, 2022, 245: 108596. doi: 10.1016/j.knosys.2022.108596
    [7]
    XING Y, HAO Y Q. Research on travel time prediction of expressway in peak period based on Greenberg model[J]. International Journal of Simulation and Process Modelling, 2022, 19. 1-2: 54-61.
    [8]
    MARTÍNEZ DÍAZ M, SORIGUERA F. Short-term prediction of freeway travel times by fusing input-output vehicle counts and GPS tracking data[J]. Transportation Letters, 2021, 13. 3: 193-200.
    [9]
    WANG Q Q, LI H M, XIONG W X. Research on expressway travel time prediction based on exclusive disjunctive soft set[J]. MATEC Web of Conferences, 2020, 308: 02005. doi: 10.1051/matecconf/202030802005
    [10]
    金广垠, 沙恒宇, 张金雷, 等. 基于路段-路口联合建模的对偶图卷积网络的行程时间估计方法[J]. 地球信息科学学报, 2023, 25(7): 1500-1513.

    JIG G Y, SHA H Y, ZHANG J L, et al Travel time estimation method based on dual graph convolutional networks via joint modeling of road segments and intersections[J]. Journal of Geo-information Science, 2023, 25(7): 1500-1513. (in Chinese)
    [11]
    CHEN T, CAI Y, CHEN L, et al. Trajectory and velocity planning method of emergency rescue vehicle based on segmented three-dimensional quartic bezier curve[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (3): 3461-3475. doi: 10.1109/TITS.2022.3224785
    [12]
    SHAO Z, MA Z, LIU S, et al. Optimization of a traffic control scheme for a post-disaster urban road network[J]. Sustainability, 2018, 10(1): 68.
    [13]
    LU K, LIU J, ZHOU X, et al. A review of big data applications in urban transit systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(5): 2535-2552. doi: 10.1109/TITS.2020.2973365
    [14]
    祝浩祈, 王甫红, 张万威, 等. 一种城市环境下GNSS/ MEMS IMU车载实时精密定位方法[J]. 武汉大学学报(信息科学版), 2023, 48(7): 1232-1240. (in Chinese)

    ZHU H Q, WANG F H, ZHANG W W, et al. Real-Time precise positioning method for vehicle-borne GNSS/MEMS IMU integration in urban environment[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1232-1240. (in Chinese)
    [15]
    ZHANG Z W, WANG H J, FAN Z P, et al. Missing road condition imputation using a multi-view heterogeneous graph network from GPS trajectory[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(5): 4917-4931. doi: 10.1109/TITS.2023.3243087
    [16]
    赵亚伟, 陈艳晶, 管伟. 基于多维时间序列的ETC短时交通流量预测模型[J]. 交通运输系统工程与信息, 2016, 16 (4): 191-198.

    ZHAO Y W, CHEN Y J, GUAN W. ETC short-term traffic flow prediction model based on multi-dimensional time series[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(04): 191-198. (in Chinese)
    [17]
    钟颖, 邵毅明, 吴文文, 等. 基于XGBoost的短时交通流预测模型[J]. 科学技术与工程, 2019, 19(30): 337-342.

    ZHONG Y, SHAO Y M, WW W W, et al. Short-Term traffic flow prediction based on XGBoost[J]. Science Technology and Engineering, 2019, 19(30): 337-342. (in Chinese)
    [18]
    许淼, 刘宏飞, 苏岳龙. 考虑交通事件影响的城市道路行程时间预测[J]. 中国公路学报, 2021, 34(12): 229-238.

    XU M, LIU H F, SU Y L. Urban road travel time prediction considering impact of traffic event[J]. China Journal of Highway and Transport, 2021, 34(12): 229-238. (in Chinese)
    [19]
    PAN Z Y, WANG Z Y, WANG W F, et al. Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction[C]. The 28th ACM International Conference on Information and Knowledge Management. Beijing, China: Association for Computing Machinery, 2019.
    [20]
    邴其春, 赵盼盼, 任参政, 等. 基于时序数据分解重构的短时交通流预测方法[J]. 交通信息与安全, 2024, 42(6): 112-122. doi: 10.3963/j.jssn.1674-4861.2024.06.012

    BING Q C, ZHAO P P, REN C Z, et al. A short-term traffic flow prediction method based on time series data decomposition and reconstruction[J]. Journal of Transport Information and Safety, 2024, 42(6): 112-122. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2024.06.012
    [21]
    TANG K, CHEN S Y, A J. Khattak, et al. Deep architecture for citywide travel time estimation incorporating contextual information[J]. Journal of Intelligent Transportation Systems, 2021, 25: 313-329. doi: 10.1080/15472450.2019.1617141
    [22]
    TANG K, CHEN S Y, LIU Z Y, et al. A tensor-based Bayesian probabilistic model for citywide personalized travel time estimation[J]. Transportation Research Part C: Emerging Technologies, 2018, 90: 260-280. doi: 10.1016/j.trc.2018.03.004
    [23]
    GAO C, GUO H L, SHENG W D. GP4: Gaussian process proactive path planning for the stochastic on time arrival problem[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 9849-9862. doi: 10.1109/TVT.2021.3109169
    [24]
    WANG Y L, ZHENG Y, XUE Y X. Travel time estimation of a path using sparse trajectories[C]. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York USA: Association for Computing Machinery, 2014.
    [25]
    TANG K, CHEN S Y, KHATTAK A J. Personalized travel time estimation for urban road networks: a tensor-based context-aware approach[J]. Expert Syst. Appl, 2018, 103: 118-132. doi: 10.1016/j.eswa.2018.02.033
    [26]
    YU Q, JIAN Y. TransBigData: A python package for transportation spatio-temporal big data processing analysis and visualization[J]. Open Source Softw, 2022, 7: 4021. doi: 10.21105/joss.04021
    [27]
    YUAN J, ZHENG Y, ZAHNG C Y, et al. An interactive-voting based map matching algorithm[C]. The 11th International Conference on Mobile Data Management, Kansas City, USA: IEEE Computer Society, 2010.
    [28]
    GHOSAL S S. Travel time prediction of taxis using tensor factorization[C]. The ACM India Joint International Conference on Data Science and Management of Data, Goa, India: Association for Computing Machinery, 2019.
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