Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (2): 512-519.doi: 10.13229/j.cnki.jdxbgxb.20230477

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Evaluate the validity of traffic congestion dispersion based on random forest method

Yao SUN1,2(),Bao-zhen YAO1(),Zi-jian BAI2   

  1. 1.School of Mechanical Engineering，Dalian University of Technology，Dalian 116024，China
    2.Tianjin Municipal Engineering Design & Research Institute Co. ，Ltd. ，Tianjin 300051，China
  • Received:2023-05-12 Online:2025-02-01 Published:2025-04-16
  • Contact: Bao-zhen YAO E-mail:sunyao@mail.dlut.edu.cn;yaobaozhen@dlut.edu.cn

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Abstract:

To evaluate the effectiveness of short video dissemination on traffic congestion alleviation， this paper takes Tik Tok short video dissemination as the research object， and through the analysis of the dissemination characteristics of short videos， selects 5 major elements and 13 dimensions to construct an indicator system for short video characteristics. Based on above， a random forest model of machine learning is constructed to predict effective probabilistic of traffic short video dissemination and compare it with traditional prediction models. In particular， this paper proposes the concept of traffic diversion validity index for the first time， and quantitatively analyzes the effectiveness of short video on traffic diversion. Taking the road in the Five Avenue of Tianjin as an example， the results show that the six dimensions of the source， theme， material， purpose， communication power and comment content guide of the short traffic video have a strong correlation with their effective communication probability， and the average effective communication probability is in the range of 0.19-0.28； the final calculation results of the short video traffic guidance effectiveness index of Line 1 and Line 2 are 0.008 pcu/person-time and 0.011 pcu/person-time， respectively. Generally speaking， it is considered that the effect of traffic congestion relief is obvious. This paper provides new ideas for road traffic control in the new media era.

Key words: urban traffic, traffic dispersion, random forest, short video, TikTok

CLC Number: 

  • U491.4

Table 1

Feature elements of short video"

评价分类特征要素具体维度
内容生产来源原创；政务号；媒体号；娱乐号
时长<10 s；10~60 s；>60 s
题材情景型；内容型
主题服务类；宣传类；娱乐类；报道类
素材图片；音频；字幕合成；监控画面；二次加工
用途怀念；记录；警示；感想；娱乐
内容组织话题标签有关联；没有关联
主题策划有；没有
内容传播传播力点赞；评论；分享
评论内容指向与主题相关；不相关
内容消费评论态度肯定；质疑；悲伤；愤怒等
内容运营定位有上线视频；无上线视频；界面有定位；无定位
功能有功能介绍；无功能介绍

Fig. 1

Framework of the random forest model"

Fig. 2

Gini coefficients of short video characteristic elements"

Fig. 3

Prediction accuracy verification of random forest model"

Table 2

Comparing the results of different prediction models"

预测模型预测精度/%平均绝对百分比误差
ARMA79.060.458
BPNN84.390.395
SVM92.510.157
RF95.360.128

Fig. 4

Example verification"

Table 3

Observations of road traffic flow before and after the short video dissemination"

车流量/

（pcu·h-1）

星期一星期二星期三星期四星期五

7：30~

8：30

17：00~18：00

7：30~

8：30

17：00~18：00

7：30~

8：30

17：00~18：00

7：30~

8：30

17：00~18：00

7：30~

8：30

17：00~18：00
线路一1 8451 9521 7341 8901 6231 6621 6041 6471 7591 803
线路二1 4301 6341 3881 5241 6961 6471 7321 7901 82618 68

Table 4

Results of t-Test"

项目

对照组1

（6~10日）

对照组2

（13~17日）

实验组

（20~24日）

平均1 751.91 776.91 653.5
方差14 259.6512 109.6626 666.94
观测值101010

泊松相关

系数

—0.995 494-0.321 81
假设平均差—00
df—99
t Stat.—-5.505 191.345 582

P（T<=t）

单尾

—0.000 1890.105 677

P（T<=t）

双尾

—0.000 3780.211 354

Table 5

Effectiveness analysis of short video short videos dissemination on road traffic dispersion"

日期时间

累计播放量

/人次

有效传播

概率/%

累计有效传播

频次/人次

线路一有效性

指数/（pcu·人次-1）

线路二有效性

指数/（pcu·人次-1）

星期三7：30~8：3033 9520.3511 8830.0240.020
17：00~18：0046 7200.3215 9690.0140.011
星期四7：30~8：3060 2380.3620 8350.0150.013
17：00~18：0065 3910.3022 3810.0110.014
星期五7：30~8：3073 9440.3325 2040.0040.014
17：00~18：0076 2080.2625 7920.0020.015
平均—76 2080.3425 7920.0080.011
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