电动自行车出行特性及骑行决策行为建模

doi:10.13229/j.cnki.jdxbgxb20210346

Abstract

Abstract:

Through questionnaire survey and cross-section traffic flow parameter survey， the characteristics of electric bicycle travel， traffic flow and speed are analyzed. Aiming at four kinds of decision-making behaviors of electric bike， such as normal riding， occupying motorway riding， interleaving between vehicles and reverse riding， a decision-making behavior model of electric bike riding based on Elman neural network and multiple logistic regression was established. Finally， taking Futian District of Shenzhen as an example， this paper makes an empirical study. The results show that： the prediction effect of electric bicycle riding decision-making behavior model based on Elman neural network is significantly better than that based on multiple logistic regression， with an average prediction accuracy of 91.62%， which is 9.93% higher than that of multiple logistic regression model； the established multiple logistic regression model can reveal the relationship between influencing factors and riding decision-making behavior. The relationship can provide theoretical support for the formulation of strategies and policies to reduce the unsafe riding behavior of electric bikes.

Key words: traffic engineering, urban traffic, electric bike, riding decision behavior, Elman neural network, multivariate logistic regression

CLC Number:

U491.1

Chun-jiao DONG,Dai-yue DONG,Cheng-xiang ZHU-GE,Li ZHEN. Trip characteristics and decision⁃making behaviors modeling of electric bicycles riding[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2618-2625.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Investigation results of hourly flow of electric bicycles"

道路等级	平均总流量/（辆? $h ? 1$ ）	正常骑行		占用机动车道骑行		在机动车之间穿插骑行		逆向骑行
道路等级	平均总流量/（辆? $h ? 1$ ）	数量	占比/%	数量	占比/%	数量	占比/%	数量	占比/%
主干路	298.00	35.00	11.15	166.00	55.20	3.00	1.35	94.00	32.30
次干路	464.33	157.17	36.28	216.33	48.65	54.00	7.43	24.67	7.60
支路	554.50	83.50	47.95	407.00	45.55	64.00	6.50	0.00	0.00
平均值	449.10	118.00	33.59	244.40	49.34	45.80	6.03	33.60	11.02

Table 1

Fig.3

Fig.4

Fig.5

Table 2

References 11

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决策行为	变量	β	显著性
占道骑行	常量	-0.575	0.456
	收入5万以下	-1.226	0.007
	收入5~10万	-1.138	0.006
	收入10~20万	-1.284	0.003
	收入20~50万	-1.308	0.005
	回家时间18∶00~19∶00	0.730	0.030
	快递/外卖用途	1.876	0.000
	车道过窄	0.784	0.000
	机动车较少	1.037	0.000
	公交车占道	-0.578	0.046
穿插骑行	常量	-1.848	0.001
	回家时间17∶00之前	-1.049	0.012
	回家时间19∶00~22∶00	-0.739	0.045
	出行时长	0.406	0.001
	快递/外卖用途	1.682	0.001
	路边停车	1.087	0.001
	公交车停靠	-0.566	0.009
	伺机转弯	0.827	0.002
逆向骑行	常量	-2.347	0.001
	回家时间17∶00~18∶00	0.904	0.017
	出行时长	0.257	0.005
	快递/外卖用途	1.187	0.001
	机动车流量大	1.321	0.001
	左侧流量少	0.579	0.006
	目的地在左侧	0.595	0.001

Trip characteristics and decision⁃making behaviors modeling of electric bicycles riding

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