考虑出行行为的机场轨道交通服务水平优化方法

张锐; 薛紫薇; 刘昕睿

doi:10.3963/j.jssn.1674-4861.2024.06.015

考虑出行行为的机场轨道交通服务水平优化方法

doi: 10.3963/j.jssn.1674-4861.2024.06.015

张锐^{1, 2, ,},
薛紫薇¹,
刘昕睿¹

1.
长安大学运输工程学院西安 710064
2.
北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室北京 100044

基金项目:

国家自然科学基金项目 72371035

陕西省重点研发计划项目 2023YBGY143

综合交通运输大数据应用技术交通运输行业重点实验室（北京交通大学）开放基金 ZHJTDSJ202203

详细信息

通讯作者:
张锐（1991—），博士，讲师. 研究方向：交通规划与管理. E-mail：zhangrui@chd.edu.cn

中图分类号: U121
计量
- 文章访问数: 37
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-07
- 网络出版日期: 2025-03-08

Level of Service Optimization Method for Airport Rail Transit Considering Travel Behavior

ZHANG Rui^{1, 2
, ,},
XUE Ziwei¹,
LIU Xinrui¹

1.
School of Transportation Engineering, Chang' an University, Xi' an 710064, China
2.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China

摘要

摘要: 为实现机场轨道交通运能利用率与服务水平、运营收益之间的有效平衡，提出了考虑机场旅客出行方式选择行为的机场轨道交通服务水平优化方法。融合反映行为惯性的显示性偏好（revealed preference，RP）依赖与陈述性偏好（stated preference，SP）依赖变量，以及与行为惯性相关的其他变量（如常住人口、换乘难度等），构建考虑行为惯性的嵌套Logit（nested Logit，NL）模型模拟旅客前往机场过程中的陆侧出行方式选择行为。在NL模型的基础上构建机场轨道交通服务水平优化模型探讨轨道交通票价、发车间隔、高峰小时满载率等服务水平要素与轨道交通分担率、轨道交通运营企业收益之间的互动关系，提出相应的机场轨道交通服务水平优化方案。以西安市为例进行实证研究，结果表明：①受访者的SP问卷调查结果受行为惯性的影响，即受访者实际发生过的出行方式选择行为和先前的SP问卷调查选择结果会对受访者当下的SP问卷调查结果产生正向影响，且前者的影响程度大于后者；②考虑行为惯性的NL模型相较传统不考虑行为惯性的多项Logit（multinomial Logit，MNL）模型，可有效提升模型精度40.86%；③优化模型可经枚举法求解，若将案例城市当前机场轨道交通的拥挤程度（“有些拥挤”）优化至“临界状态”评价标准，为使轨道交通分担率不降低的同时客票收入最大化，则发车间隔缩小1 min前提下，地铁票价增幅建议为3元。
- 轨道交通 /
- 机场旅客 /
- 服务水平优化 /
- 嵌套Logit模型 /
- 行为惯性
Abstract: This paper aims to balance the utilization rate of transport capacity, level of service and operational profit for airport rail transit, a level of service optimization method for airport rail transit is proposed, considering travel behavior. Specifically, a nested Logit (NL) model considering behavior inertia is established to simulate the landside travel mode choice behavior of passengers accessing the airport. The model includes variables related to behavioral inertia, such as revealed preference (RP) dependence, stated preference (SP) dependence, local residency, and transfer difficulty. On this basis, a level of service optimization model for airport rail transit is built to investigate the relationships among level of service factors, such as ticket price, departure interval, peak hour load factor, as well as market share of rail transit and operation profit of rail transit. Corresponding level of service optimization proposals are then presented. The results of case study conducted in Xi' an city show that: ① SP survey results are influenced by behavioral inertia. Specifically, the current SP questionnaire survey results of respondents are positively dependent on their actual travel choice behaviors and the previous SP survey results, while the dependence on actual travel choice behavior is higher than that on previous SP survey results. ②The proposed NL model, which considers behavioral inertia, outperforms the conventional multinomial Logit (MNL) model that does not account for behavioral inertia. The R-squared value of the NL model is improved by 40.86% compared to the MNL model. ③The proposed optimization model can be solved using enumeration method. If the congestion degree of the current airport rail transit ("somewhat crowded") in the case city is expected to reach the "critical state" threshold, to avoid a decrease in the market share of rail transit and maximize ticket revenue, it is recommended that the ticket price be increased by 3 yuan, provided that the departure interval is reduced by 1 minute.
- rail transit /
- airport passenger /
- level of service optimization /
- nested Logit model /
- behavior inertia

HTML全文

图 1 出行方式选择模型结构

Figure 1. Travel mode choice model structure

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图 2 票价变化对轨道交通分担率的影响

Figure 2. Influence of fare change on market share of rail transit

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图 3 发车间隔对轨道交通分担率的影响

Figure 3. Influence of departure interval on market share of rail transit

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图 4 票价与发车间隔组合变化对轨道交通分担率的影响

Figure 4. Combined influence of fare and departure interval on market share of rail transit

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图 5 票价与发车间隔组合变化对轨道交通高峰小时满载率的影响

Figure 5. Combined influence of fare and departure interval on peak hour load factor of rail transit

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表 1 受访者的个人属性、家庭属性和出行属性统计信息

Table 1. Statistics of respondents' personal, family and travel attributes

变量		类别	样本量/人	比例%
个人属性	性别	男	2 208	57.62
	性别	女	1 624	42.38
	年龄/岁	≤18	50	1.25
		＞18~25	1 182	30.90
		＞25~35	1 480	38.62
		＞35~45	664	17.33
		＞45~55	320	8.35
		＞55~60	89	2.32
		＞60	47	1.23
	最高学历	初中及以下	328	8.56
		高中	408	10.65
		大专	840	21.92
		本科	1 832	47.81
		硕士及以上	424	11.06
	是否常驻西安	是	792	20.67
	是否常驻西安	否	3 040	79.33
家庭属性	家庭月收入/元	≤5 000	595	15.53
		＞5 000~10 000	1 709	44.60
		＞10 000~20 000	1 096	28.60
		＞20 000	432	11.27
	家庭成员人数/人	1	232	6.05
		2	552	14.41
		≥3	3 048	79.54
	家庭私家车保有量/辆	0	816	21.29
		1	2 256	58.87
		≥2	760	19.83
出行属性	出行目的	公务	1 072	27.97
	出行目的	私务	2 760	72.03
	出行方式	机场大巴	432	11.27
		轨道交通	1 104	28.81
		出租/网约车	1 528	39.87
		自驾	320	8.35
		其他	440	11.69
	候车时间/min	≤5	2 032	53.03
		＞5~10	928	24.22
		＞10~20	312	8.14
		＞20~30	196	5.11
		＞30	364	9.50
	出行时间/h	≤1	2 000	52.19
		＞1~2	1 346	35.13
		＞2~3	318	8.30
		＞3~4	80	2.09
		＞4	88	2.30
	换乘次数/次	0	2 552	66.60
		1	720	18.79
		2	392	10.23
		≥3	168	4.38
	行李件数/件	0	328	8.56
		1	2 280	59.50
		2	952	24.84
		≥3	272	7.10
	出行费用/元	≤10	885	23.09
		＞10~30	915	23.88
		＞30~50	530	13.83
		＞50~100	766	19.99
		＞100	736	19.21

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表 2 模型参数标定结果

Table 2. Model parameter calibration results

出行方式	模型参数	考虑行为惯性的NL模型		不考虑行为惯性的MNL模型
出行方式	模型参数	预测值	t值	预测值	t值
机场大巴	出行时间/min	-0.008 80	-5.11	-0.016 1	-9.83
	票价/元	-0.002 97	-4.86	-0.012 6	-17.94
	换乘难度/（次·件）	-0.272	-9.07	-0.271	-9.01
	年龄/岁	0.124	3.68	0.087 3	2.53
	家庭规模/人	-0.696	-15.67	-0.785	-17.08
	家庭月收入/元	0.193	5.22	0.274	7.21
	RP依赖	0.609	4.24
	SP依赖	0.015 7	1.68
轨道交通	出行时间/min	-0.008 80	-5.11	-0.016 1	-9.83
	票价/元	-0.002 97	-4.86	-0.012 6	-17.94
	换乘难度/（次·件）	-0.272	-9.07	-0.271	-9.01
	出行目的	-0.466	-5.85	-0.610	-7.58
	常住人口	0.202	2.09	0.164	1.69
	RP依赖	0.609	4.24
	SP依赖	0.015 7	1.68
出租车/网约车	出行时间/min	-0.008 80	-5.11	-0.016 1	-9.83
	出行费用/元	-0.002 97	-4.86	-0.012 6	-17.94
	性别	-0.312	-5.31	-0.218	-3.66
	家庭月收入/元	0.193	5.22	0.274	7.21
	RP依赖	0.609	4.24
	SP依赖	0.015 7	1.68
自驾	出行时间/min	-0.008 80	-5.11	-0.016 1	-9.83
	出行费用/元	-0.002 97	-4.86	-0.012 6	-17.94
	性别	-0.312	-5.31	-0.218	-3.66
	家庭月收入/元	0.193	5.22	0.274	7.21
尺度系数		0.370
样本量		373 8		373 8
调整R²		0.131		0.093

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表 3 直接弹性与交叉弹性计算结果

Table 3. Results of direct and cross elastic coefficients

出行方式	出行时间		出行费用		换乘难度
出行方式	直接弹性	交叉弹性	直接弹性	交叉弹性	直接弹性	交叉弹性
机场大巴	-0.879	0.234	-0.120	0.015	-1.132	0.365
轨道交通	-0.708		-0.047		-1.104
出租车/网约车	-0.870	0.916	-0.330	0.061		1.429
自驾	-1.574	0.916	-0.783	0.061		1.429

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表 4 票价与发车间隔组合变化下轨道交通分担率和轨道交通高峰小时满载率

Table 4. Values of market share of rail transit and peak hour load factor of rail transit under different combinations of fare and departure interval

票价增幅/元	不同发车间隔下的轨道交通分担率/%							不同发车间隔下的高峰小时满载率/%
票价增幅/元	9 min	8 min	7 min	6 min	5 min	4 min	3 min	9 min	8 min	7 min	6 min	5 min	4 min	3 min
0	24.84	24.90	24.96	25.02	25.08	25.14	25.21	106.19	94.62	83.00	71.31	59.57	47.77	35.92
1	24.82	24.88	24.94	25.00	25.06	25.12	25.19	106.10	94.54	82.93	71.25	59.52	47.74	35.89
2	24.80	24.86	24.92	24.98	25.04	25.10	25.16	106.01	94.46	82.86	71.20	59.47	47.70	35.86
3	24.78	24.84	24.90	24.96	25.02	25.08	25.14	105.93	94.39	82.79	71.14	59.43	47.66	35.83
4	24.76	24.82	24.88	24.94	25.00	25.06	25.12	105.84	94.31	82.72	71.08	59.38	47.62	35.80
5	24.74	24.80	24.86	24.92	24.98	25.04	25.10	105.75	94.23	82.65	71.02	59.33	47.58	35.77
6	24.72	24.78	24.84	24.90	24.96	25.02	25.08	105.66	94.15	82.59	70.96	59.28	47.54	35.74
7	24.70	24.76	24.82	24.88	24.94	25.00	25.06	105.58	94.08	82.52	70.90	59.23	47.50	35.71
8	24.68	24.74	24.80	24.86	24.92	24.98	25.04	105.49	94.00	82.45	70.84	59.18	47.46	35.68
9	24.66	24.72	24.78	24.84	24.90	24.96	25.02	105.40	93.92	82.38	70.78	59.13	47.42	35.65
10	24.63	24.70	24.76	24.82	24.88	24.94	25.00	105.31	93.84	82.31	70.73	59.08	47.38	35.62
11	24.61	24.67	24.73	24.80	24.86	24.92	24.98	105.23	93.76	82.24	70.67	59.03	47.34	35.59
12	24.59	24.65	24.71	24.77	24.84	24.90	24.96	105.14	93.69	82.17	70.61	58.98	47.30	35.56
13	24.57	24.63	24.69	24.75	24.82	24.88	24.94	105.05	93.61	82.11	70.55	58.94	47.26	35.54
14	24.55	24.61	24.67	24.73	24.80	24.86	24.92	104.97	93.53	82.04	70.49	58.89	47.22	35.51
15	24.53	24.59	24.65	24.71	24.78	24.83	24.90	104.88	93.46	81.97	70.43	58.84	47.19	35.48
16	24.51	24.57	24.63	24.69	24.76	24.81	24.88	104.79	93.38	81.90	70.37	58.80	47.15	35.45
17	24.49	24.55	24.61	24.67	24.73	24.79	24.86	104.70	93.30	81.84	70.32	58.74	47.11	35.42
18	24.47	24.53	24.59	24.65	24.71	24.77	24.83	104.62	93.22	81.77	70.26	58.69	47.07	35.39

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