基于随机参数Logit的中小城市居民出行方式选择建模

doi:10.13229/j.cnki.jdxbgxb.20220312

Abstract

Abstract:

In order to provide new thoughts and new methods for traffic planning and management of small and medium-sized cities， the travel characteristics of residents in small and medium-sized cities was analyzed. Considering the heterogeneity of residents' travel mode choices， a travel mode choice model of based on random parameters Logit was constructed. Factors of psychological latent variables such as the requirement of comfort were introduced， and the structural equation model was used to calibrate the parameters， which reflects the relationship between latent variables and index variables. The results show that the prediction accuracy of the stochastic parameter logit model calibrated by structural equation is higher than 95%， which indicates that the structural equation model has good parameter calibration effect， and the constructed stochastic parameter logit model has good fitting degree. In the utility function of the model， the coefficient of travel time is a random parameter， which is related to the individual characteristic variables， travel characteristic variables and psychological latent variables of travelers. Among them， the walking time to the bus stop has the greatest impact on the travel utility， and the value of time decreases by 48 yuan for each additional unit. In addition， the marginal utility of different travel modes on driving time is different. The marginal utility of bus turns from increase to decrease after 45 min. Therefore， the random parameters Logit model considering the heterogeneity of individual psychological latent variables can describe the actual travel mode choice behavior of residents in small and medium-sized cities， and has a stronger explanation for the mechanism of travel characteristics.

Key words: urban transportation, travel mode selection, random parameter, small and medium cities, structural equation model, marginal effect

CLC Number:

U491.1

Yan ZHUANG,Chun-jiao DONG,Xue-yu MI,Xiao-yu ZHANG,Jing WANG. Travel mode choice in small and media sized city based on random parameters Logit model[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 461-468.

Figures/Tables 6

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References 15

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	变量	符号	含义
个人属性	性别	X_Gen	0：女性1：男性
	职业	X_Job	1：学生2：工人3：公务员及事业单位4：公司职员5：私营及个体劳动者6：离退休7：无业8：其他
	年龄	X_Age	6~18岁（X_Age1）；19~29岁（X_Age2）；30~49岁（X_Age3）；50~59岁（X_Age4）；60岁以上（X_Age5）
	受教育程度	X_Edu	1：初中及其以下2：高中及中专3：大专及本科4：硕士及以上
	月收入	X_Inc	<5000元（X_Inc1）；5000~10000元（X_Inc2）；＞10000元（X_Inc3）
	私家车拥有情况	X_Car	0、1分别表示无车、有车
	非机动车拥有情况	X_Non	0、1分别表示无车、有车
出行属性	每周乘坐公交次数	X_Freq	①<2次②2~5次③6~10次④11~15次⑤>5次
出行属性	步行时间	X_Time	到公交站台的步行时间（X_Time1）；在公交站台的等车时间（X_Time2）；从公交站台到目的地的步行时间（X_Time3）
心理潜变量	舒适性	X_Comf	我会在意出行方式的环境（光线、气味、噪声等）c₁；我会在意出行方式的速度c₂；我会在意出行方式的拥挤程度c₃；如果某种出行方式不舒适，我会选择更舒适的出行方式，即使要多花钱（超过2元）c₄；如果某种出行方式不舒适，我会选择更舒适的出行方式，即使要多时间（超过10 min）c₅
	可靠性	X_Reli	我会在意站台等车时间的长短c₆；我会在意公交到站的准时性c₇；我会在意公交到达目的地时间估计的准确性c₈
	便捷性	X_Conv	我会在意步行到公交站台的时间c₉；我会在意公交换乘次数的多少c₁₀；我会在意公交行驶的线路途经的站点c₁₁

潜变量	指标变量符号	因素荷载值	Cronbach's α值	适配系数
L_comfort 舒适性	c₁	0.63	0.616	0.194
	c₂	0.71		0.219
	c₃	0.55		0.170
	c₄	0.66		0.204
	c₅	0.69		0.213
L_reliability 可靠性	c₆	0.74	0.648	0.372
	c₇	0.52		0.261
	c₈	0.73		0.367
L_convenience 便捷性	c₉	0.67	0.595	0.294
	c₁₀	0.82		0.360
	c₁₁	0.79		0.346
因子分析	Cronbach's α系数：0.768；KMO测度值：0.815；Bartlett检验显著性值：0
模型评价参数	卡方自由度比（NC）：1.987；适配度（GFI）0.906；调整适配度（AGFI）：0.927；均方根残差（RMR）：0.026

参数		系数	标准差	z值	p值	95%置信区间
随机参数（相关个人、出行属性及心理潜变量参数）	β_T	-0.265***	0.017	1.47	0.0000	-0.298	-0.232
	σ_T	0.042***	0.035	2.57	0.0004	-0.161	0.245
	T←X_Age3	-0.133***	0.104	-5.69	0.0005	-0.451	0.185
	T←X_Edu	-0.147***	0.082	-5.34	0.0000	-0.545	0.251
	T←X_Car	-0.138**	0.107	-1.92	0.0000	-0.220	-0.056
	T←X_Time1	0.224***	0.036	-7.87	0.0000	0.022	0.426
	T←X_Comf	-0.108***	0.304	-1.36	0.0000	-0.312	0.096
固定参数	d_car	-0.098***	0.162	-4.12	0.0000	-0.259	0.063
	d_bus	-0.143***	0.203	-5.21	0.0000	-0.353	0.067
	β_C	-0.281***	0.042	-6.78	0.0102	-0.352	-0.210
	β_W	-0.137**	0.103	2.35	0.0001	-0.733	0.459
总体参数	McFadden's R2	0.278
	AIC	2323
	BIC	2146

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Travel mode choice in small and media sized city based on random parameters Logit model

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