| 引用本文: | 马艳丽,张鹏,朱洁玉,姜健锋.势能场影响区域车辆交互速度变化模型[J].哈尔滨工业大学学报,2020,52(9):51.DOI:10.11918/201904266 |
| MA Yanli,ZHANG Peng,ZHU Jieyu,JIANG Jianfeng.Vehicle interaction velocity-changing model in the region affected by potential field[J].Journal of Harbin Institute of Technology,2020,52(9):51.DOI:10.11918/201904266 |
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| 摘要: |
| 为定量分析交通流中车辆间的交互影响,开展了主干路车辆交互速度变化模型研究,通过类比势能场理论中的引力和斥力,界定了势能场影响区域的概念,将目标车运行时与周围车辆的吸引和排斥作用归结为势能场影响区域交互面积的变化,提出考虑势能场影响区域的车辆交互分析方法,建立了目标车速度变化量与势能场影响区域交互面积的线性模型. 采用P3-DT北斗高精度定位测向机采集车辆坐标和速度,标定模型参数. 用该模型计算目标车速度变化量,并与实测数据进行对比. 结果表明:模型计算值与实际值之间的误差小于15%,车道变换时间越短,目标车与目标车道后方车辆间的交互作用越明显,后车的减速操作越迅速,验证了模型的有效性. 该模型将传统微观交通流分析中的车速与车辆间距两大因素归一为势能场影响区域交互面积,可为微观交通流中的多车交互研究提供方法,并为自动驾驶车辆提供速度控制策略. |
| 关键词: 交通工程 微观交通分析 势能场 车速变化 车辆间距 |
| DOI：10.11918/201904266 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2017YFC0803901)； 黑龙江省公路勘察设计院科技项目(2018006) |
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| Vehicle interaction velocity-changing model in the region affected by potential field |
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MA Yanli1,ZHANG Peng1,ZHU Jieyu1,JIANG Jianfeng2
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(1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China; 2. Heilongjiang Provincial Highway Survey and Design Institute, Harbin 150080, China)
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| Abstract: |
| To quantitatively analyze the interaction between vehicles in traffic flow, the vehicle interaction velocity-changing model of arterial road was studied. The concept of potential field influence region was defined through the analogy of gravitation and repulsion in the potential field theory. The attraction and repulsion between the target vehicle and the surrounding vehicles were attributed to the changes of the interaction area affected by the potential field. A vehicle interaction analysis method considering the influence area of potential field was proposed, and a linear model of the interaction area between target vehicle velocity variation and potential field was established. Vehicle coordinates and speed data collected by the P3-DT BeiDou high-precision positioning direction finder were used to calibrate model parameters. The speed changes of the target vehicle were calculated by the proposed method and compared with the measured data. Results show that the error values between the calculation results and the measured data were less than 15%. When the lane-changing time became shorter, the interaction between the lane-changing vehicle and the rear vehicle on the target lane was stronger, and the deceleration of the rear vehicle was operated more quickly, which verifies the validity of the model. The model normalizes the vehicle speed and spacing in traditional microscopic traffic flow analysis into the interaction area of potential field, which can provide method for the study of multi-vehicle interactions in microscopic traffic flow and speed control strategy for automatic driving vehicles. |
| Key words: traffic engineering microscopic traffic analysis potential field vehicle speed changing vehicle spacing |
