- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | JIANG Yangsheng, LIU Meng, WANG Sichen, GAO Kuan, YAO Zhihong. Trajectory Reconstruction for Traffic Flow Mixed withConnected Automated Vehicles Based on Car-Following Characteristics[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1135-1142. doi: 10.3969/j.issn.0258-2724.20200735
|
Vehicle trajectory data contains massive spatial-temporal traffic information, which is one of the necessary data for traffic state estimation. To solve the problem that it's difficult to obtain the fully sampled vehicular trajectory in the existing data collection environment, oriented to the connected and automated environment, a fully sampled trajectory reconstruction model of mixed traffic flow is proposed . Firstly, vehicle composition and trajectory data collection environment of mixed traffic flow with the connected automated vehicle (CAV) are analyzed. Then, a vehicle trajectory reconstruction model is proposed based on intelligent driver car-following model. Based on this, the number of inserted trajectories, trajectory position and speed are estimated. Finally, numerical simulation is designed to investigate the influence of traffic density and penetration rate of CAVs. Results show that, when the penetration rates of CAV and connected vehicle (CV) at 8% and 20%, respectively, the model can reconstruct more than 84% vehicular trajectories under different traffic densities. The accuracy of the reconstructed trajectories increases with the increase in penetration rates of CAV and CV. Besides, when the traffic density is 70 veh/km and the penetration rate of CAV at a low level of 4%, the proposed model can reconstruct 82% vehicular trajectories.
| [1] |
BANSAL P, KOCKELMAN K M. Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies[J]. Transportation Research Part A:Policy and Practice, 2017, 95: 49-63. doi: 10.1016/j.tra.2016.10.013
|
| [2] |
PUNZO V, BORZACCHIELLO M T, CIUFFO B. On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data[J]. Transportation Research Part C:Emerging Technologies, 2011, 19(6): 1243-1262. doi: 10.1016/j.trc.2010.12.007
|
| [3] |
COIFMAN B. Estimating travel times and vehicle trajectories on freeways using dual loop detectors[J]. Transportation Research Part A:Policy and Practice, 2002, 36(4): 351-364. doi: 10.1016/S0965-8564(01)00007-6
|
| [4] |
RAO W M, WU Y J, XIA J X, et al. Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data[J]. Transportation Research Part C:Emerging Technologies, 2018, 95: 29-46. doi: 10.1016/j.trc.2018.07.002
|
| [5] |
HAO P, BORIBOONSOMSIN K, WU G Y, et al. Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(3): 701-711. doi: 10.1109/TITS.2016.2584388
|
| [6] |
WAN N F, VAHIDI A, LUCKOW A. Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates[J]. Transportation Research Part C:Emerging Technologies, 2016, 65: 16-30. doi: 10.1016/j.trc.2016.01.010
|
| [7] |
SHAN X N, HAO P, CHEN X H, et al. Vehicle energy/emissions estimation based on vehicle trajectory reconstruction using sparse mobile sensor data[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(2): 716-726. doi: 10.1109/TITS.2018.2826571
|
| [8] |
LI Z, KLUGER R, HU X B, et al. Reconstructing vehicle trajectories to support travel time estimation[J]. Transportation Research Record:Journal of the Transportation Research Board, 2018, 2672(42): 148-158. doi: 10.1177/0361198118772956
|
| [9] |
XIE X, VAN LINT H, VERBRAECK A. A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters[J]. Transportation Research Part C:Emerging Technologies, 2018, 92: 364-391. doi: 10.1016/j.trc.2018.05.009
|
| [10] |
MEHRAN B, KUWAHARA M, NAZNIN F. Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data[J]. Transportation Research Part C:Emerging Technologies, 2012, 20(1): 144-163. doi: 10.1016/j.trc.2011.05.006
|
| [11] |
唐克双,徐天祥,潘昂,等. 基于定点检测数据的城市干道车辆轨迹重构[J]. 同济大学学报(自然科学版),2016,44(10): 1545-1552.
TANG Keshuang, XU Tianxiang, PAN Ang, et al. Signal timing and detector data-based reconstruction of vehicle trajectories on urban arterials[J]. Journal of Tongji University (Natural Science), 2016, 44(10): 1545-1552.
|
| [12] |
王昊, 金诚杰. 交通流理论及应用[M]. 北京: 人民交通出版社, 2020: 50-79.
|
| [13] |
WANG Y P, WEI L, CHEN P. Trajectory reconstruction for freeway traffic mixed with human-driven vehicles and connected and automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2020, 111: 135-155. doi: 10.1016/j.trc.2019.12.002
|
| [14] |
TALEBPOUR A, MAHMASSANI H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 143-163. doi: 10.1016/j.trc.2016.07.007
|
| [15] |
YAO Z H, XU T R, JIANG Y S, et al. Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time[J]. Physica A:Statistical Mechanics and Its Applications, 2021, 561: 125218.1-125218.15. doi: 10.1016/j.physa.2020.125218
|
| [16] |
MILANÉS V, SHLADOVER S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C:Emerging Technologies, 2014, 48: 285-300. doi: 10.1016/j.trc.2014.09.001
|
Figures(8) / Tables(5)
DownLoad:
