VMS行程时间诱导效益仿真算法

干宏程; 杨珍珍

doi:10.19818/j.cnki.1671-1637.2012.01.019

VMS行程时间诱导效益仿真算法

doi: 10.19818/j.cnki.1671-1637.2012.01.019

上海理工大学管理学院，上海 200093

基金项目:

国家自然科学基金项目 51008195

上海市重点学科建设项目 S30504

详细信息

作者简介:
干宏程（1978 -），男，浙江宁波人，上海理工大学副教授，工学博士，从事智能交通与交通规划研究

中图分类号: U491.112
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出版历程
- 刊出日期: 2012-02-25

Inductive benefit simulation algorithm of VMS travel time

School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China

More Information

Author Bio:
GAN Hong-cheng(1978-), male, associate professor, PhD, + 86-21-65679404, hongchenggan@126.com

摘要

摘要: 基于宏观交通仿真模型, 提出了可变情报板(VMS)行程时间诱导效益仿真算法, 分析了驾驶人的信息关注率和信息理解偏差系数对VMS行程时间诱导效益的影响。以行程时间计算模型和驾驶人信息响应模型与METANET仿真模型为理论基础, 以路网总耗时改善率为诱导效益目标, 在3种不同规模的路网上进行仿真试验。仿真结果表明: VMS行程时间对于改善路网运行效率通常具有正面的诱导效益; 信息关注率越高, 信息理解偏差系数越小, 诱导效益越显著; 当信息关注率为80%以上时, 小型、中型、大型3种路网的诱导效益分别达到28.89%、15.87%、10.53%以上。可见, 仿真算法有效。
- 宏观交通仿真 /
- 可变情报板 /
- 交通需求 /
- 行程时间 /
- 信息响应 /
- 诱导效益 /
- 路径选择
Abstract: Based on macroscopic traffic simulation model, the inductive benefit simulation method of variable message sign(VMS) travel time was proposed, and the influences of information concern ratio and information comprehension deviation coefficients for drivers on the inductive benefit of VMS travel time were analyzed. The travel time calculation model, driver information response model and METANET simulation model were taken as theory foundations, the improving ratio of total road network consuming time was taken as inductive benefit objective, and simulation tests on three road networks with different sizes were carried out. Simulation result shows that VMS travel time normally has positive inductive benefit on improving the running efficiency of road network. The higher the information concern ratio is, the smaller the information comprehension deviation coefficient is, and the more significant the inductive benefit is. While information concern ratio is over 80G, the inductive benefits of small, medium and large road networks are above 28. 89%, 15. 87% and 10. 53% respectively. So the simulation algorithm is effective.
- macroscopic traffic simulation /
- variable message sign /
- traffic demand /
- travel time /
- information response /
- inductive benefit /
- route choice

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图 1 分析方法

Figure 1. Analysis method

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图 2 交通路网

Figure 2. Traffic network

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图 3 检测线圈

Figure 3. Detecting coils

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图 4 交通需求

Figure 4. Traffic demands

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图 5 w为0.10时的TTS改善率曲线

Figure 5. Curves of TTS improvement rates while wis 0.10

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图 6 w为0.30时的TTS改善率曲线

Figure 6. Curves of TTS improvement rates while w is 0.30

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图 7 w为0.08时的TTS改善率曲线

Figure 7. Curves of TTS improvement rates while w is 0.08

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图 8 E为0.8时的TTS改善率曲线

Figure 8. Curves of TTS improvement rates while E is 0.8

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图 9 w为0.01时的TTS改善率曲线

Figure 9. Curve of TTS improvement rates while w is 0.01

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图 10 E为0.9时的TTS改善率曲线

Figure 10. Curves of TTS improvement rates while E is 0.9

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表 1 路网尺寸

Table 1. Road network sizes

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