考虑多维属性的混合路网路径选择行为预测

doi:10.13229/j.cnki.jdxbgxb.20250035

Abstract

Abstract:

To explore the underlying causes of uneven path utilization in urban mixed road networks， revealed preference （RP） and stated preference （SP） joint surveys were adopted to systematically collect data on drivers' attributes and path choices in urban mixed road networks. Path choice models based on random regret theory were developed， incorporating multidimensional path attributes and individual travel characteristics， with scale effects introduced to enhance predictive accuracy. The results show that path attributes such as distance， number of traffic lights， tolls， historical average travel time， additional time during congestion， and the likelihood of congestion significantly influence path selection. Personal travel frequency and historical path preferences also play critical roles in decision-making. The improved model considering path attributes achieves a hit rate of 0.703， and the improved model integrated drivers' attributes has a hit rate of 0.872， demonstrating superior fitting performance and predictive capability compared to traditional models. The study results provides a quantitative basis for refined urban traffic management strategies and practical insights for optimizing personalized navigation services.

Key words: transportation planning and management, route selection behavior prediction, stochastic regret minimization model, mixed road network, travel characteristics, route attributes

CLC Number:

U491.2

Yan-li WANG,Chen-xi WANG,Xin-ran ZHAO,Bing WU. Prediction of route selection behavior for mixed road network considering multi⁃class attributes[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(8): 2067-2076.

Figures/Tables 10

Fig.1

Table 1

Fig.2

Table 2

Fig.3

Table 3

Fig.4

Table 4

Table 5

Model results considering path attributes"

变量和评价指标	RRM-C		RRM		MNL
变量和评价指标	系数	p值	系数	p值	系数	p值
距离	-1.406	0.226	0.026	0.416	0.035	0.666
高快速路	-13.839	0.000	-0.487	0.000	-0.967	0.278
高快速路和普通地面道路	-6.206	0.000	-0.244	0.006	-0.449	0.261
红绿灯数量	-0.162	0.239	-0.040	0.000	-0.074	0.250
过路费	-96.912	0.000	-0.032	0.459	-0.052	0.455
路线历史平均时间	-8.115	0.000	-0.015	0.174	-0.020	0.491
发生拥堵时额外所需时间	-2.150	0.000	-0.042	0.118	-0.066	0.170
发生拥堵可能性	-0.262	0.032	-1.613	0.000	-2.345	0.003
功率因数 $γ$	0.959	0.000	-	-	-	-
Mcfadden R²	0.220		0.216		0.189
去掉不显著变量后Mcfadden R²	0.215		0.206		0.188
HitR	0.703		0.495		0.495
HitR₁	0.758		0.242		0.242
HitR₂	0.797		0.797		0.797
HitR₃	0.555		0.445		0.445

Table 5

Table 6

Model results of the group integrating travel characteristics"

变量	RRM-C		RRM		MNL
变量	系数	p值	系数	p值	系数	p值
距离	-0.396	0.000	0.780	0.000	1.139	0.017
高快速路	-4.043	0.000	-0.302	0.000	-0.531	0.000
高快速路和普通地面道路	-2.369	0.000	-0.096	0.005	-0.164	0.056
红绿灯数量	-4.339	0.000	-0.379	0.000	-0.688	0.000
过路费	-0.153	0.000	-0.064	0.111	-0.107	0.112
路线历史平均时间×出行频率	-1.143	0.000	0.004	0.350	0.005	0.291
发生拥堵时额外所需时间	-0.669	0.000	-0.081	0.023	-0.118	0.003
发生拥堵可能性×高/快速路选择比例	-0.541	0.000	-0.011	0.886	-0.023	0.863
功率因数 $γ$	0.989	0.000	-	-	-	-
Mcfadden R²	0.222		0.201		0.173
去掉不显著变量后Mcfadden R²	0.222		0.195		0.178
HitR	0.872		0.615		0.417
HitR₁	0.808		0.594		0.192
HitR₂	0.874		0.614		0.126
HitR₃	0.934		0.637		0.934

Table 6

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类别	问题	类别	问题
社会经济特征	性别	历史出行特征	一周驾车出行频率
	驾龄		工作日日均驾车出行频率
	年龄段		周末日均驾车出行频率
	家庭汽车总数		节假日日均驾车出行频率
	受教育程度		出行目的
	月均收入		预估时间差异
	家庭总人数		历史单程出行距离
	家庭年收入		最近高/快速路出入口距离
			高/快速路选择比例

属性列名	编码情况	变量形式
个体编号	受访者编号	个体标识
场景编号	一个路径选择即为一个场景，共计48个场景	场景标识
路径编号	该条路径在场景中的编号，对应1、2、3	路径标识
选择行为	选择该条路径编码为1，否则为0	选择标识
路径属性（共7列）	道路类型（只包含高快速路则为1；包含高快速路和地面普通道路为2；只包含地面普通道路为3）、	分类变量
路径属性（共7列）	距离、红绿灯数量、过路费、发生拥堵可能性、路线历史平均时间、发生拥堵时额外所需时间	连续变量
社会经济特征（共8列）	性别、驾龄、年龄段、家庭汽车总数、受教育程度、月均收入、家庭总人数、家庭年收入	分类变量
历史出行特征（共9列）	一周驾车出行频率、工作日日均驾车出行频率、周末日均驾车出行频率、节假期日均驾车出行频率、出行目的、时间预估差异、历史单程出行距离、与高/快速路出入口距离、高/快速路选择比例	分类变量

Prediction of route selection behavior for mixed road network considering multi⁃class attributes

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