不同测算方法下行程时间不确定性对可达性的影响分析

doi:10.12082/dqxxkx.2022.220219

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (11): 2102-2114.doi: 10.12082/dqxxkx.2022.220219

• 地球信息科学理论与方法 • 上一篇下一篇

不同测算方法下行程时间不确定性对可达性的影响分析

肖中圣¹(), 许奇¹^,², 毛保华²(), 魏润斌¹, 冯佳¹^,²

1.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
2.北京交通大学，中国综合交通研究中心，北京 100044

收稿日期:2022-04-23 修回日期:2022-05-16 出版日期:2022-11-25 发布日期:2023-01-25
通讯作者: *毛保华（1963- ），男，湖南祁阳人，博士，教授，主要从事综合交通研究。E-mail: bhmao@bjtu.edu.cm
作者简介:肖中圣（1995- ），男，重庆梁平人，博士研究生，主要从事公共交通与土地利用关系研究。E-mail: 18114047@bjtu.edu.cn
基金资助:
国家自然科学基金项目(71971021);国家自然科学基金项目(71901022)

The Impact of Travel Time Uncertainty on Accessibility under Different Measurements

XIAO Zhongsheng¹(), XU Qi¹^,², MAO Baohua²(), WEI Runbin¹, FENG Jia¹^,²

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
2. Integrated Transportation Research Centre of China, Beijing Jiaotong University, Beijing 100044, China

Received:2022-04-23 Revised:2022-05-16 Online:2022-11-25 Published:2023-01-25
Contact: MAO Baohua
Supported by:
National Natural Science Foundation of China(71971021);National Natural Science Foundation of China(71901022)

摘要/Abstract

摘要：

行程时间不确定性导致了可达性随时间的变化，相关研究表明忽略行程时间不确定性会高估可达性水平。既有可达性研究往往用行程时间可靠性表示行程时间不确定性，但未考虑不同可达性模型结果的差异以及行程时间可靠性价值。本文结合各OD之间的行程时间分布特征，构建方差型的行程时间可靠性来描述行程时间不确定性，并进一步将行程时间可靠性纳入到广义出行时间成本中，建立了时间距离模型、潜力模型、累计机会模型和高斯模型4种基于位置的可达性测算方法，以比较在不同测算方法下，行程时间不确定性对可达性的影响。深圳的案例研究表明：① 忽略行程时间不确定性会使全区域的可达性至少被高估5.04%，最大被高估95.04%。潜力模型、时间距离模型、累计机会模型和高斯模型的高估幅度由低到高；② 行程时间不确定性对可达性的影响存在阈值效应，阈值越高，可达性受影响的程度越小；③ 从空间分布来看，行程时间不确定性对可达性水平高和低的区域都有一定影响。若不考虑行程时间不确定性，可达性高的区域高估值大，而在可达性低的区域，可达性高估的百分比较大，高估百分比中位数的差异程度最大可达77.1%；④ 行程时间不确定性对潜力模型可达性分类的影响最小，对累计机会模型差异的影响最大。可达性使用者应充分考虑研究区域实际情况，结合可解释性与理论性偏好，进而选择合适的可达性模型和评判标准。

关键词: 城市交通, 可达性, 可达性差异, 行程时间不确定性, 开源大数据, 行程时间可靠性

Abstract:

Many scholars have focused on public transit accessibility, as the public transportation system plays more important role in commuting. Travel time uncertainty, which is caused by timetable and traffic condition changes, leads to uncertainty in accessibility. Ignoring travel time uncertainty may cause overestimation of accessibility. This overestimation may bring optimism about travel time to the commuter, and they may not reach their destinations on time. Therefore, it is essential to measure public transit accessibility realistically. Moreover, previous studies often use one measurement to calculate the influence of travel time uncertainty on accessibility and do not consider the differences between measurements. Different measures may be oriented to different infrastructure investment, therefore, understanding the results of different measures is also important. To fill these gaps, we first divided the study area (Shenzhen) into 1854 grids and collected millions of travel dataset and POI dataset from GaoDe Map. Then, this paper developed the travel time reliability by investigating the travel time distribution and standard deviation between OD pairs. We extended the generalized travel cost in accessibility measurements by incorporating travel time reliability. The static accessibility measurements use the generalized travel cost without considering travel time reliability. Furthermore, we developed four types of placed-based accessibility measurements to analyze the differences between accessibility with and without travel time uncertainty. The results in Shenzhen's case showed that travel time uncertainty increased accessibility at least by 5.80%, and at most by 95.04% in the whole area. The order of models from minimum to largest increase was that: the potential model, the time distance model, the cumulative opportunity model, and the Gaussian model. The accessibility of Gaussian model increased by 59%~553%, while the generalized travel cost threshold increased from 45 min to 120 min. Additionally, the impact of travel time uncertainty on accessibility existed threshold effect, the higher threshold, the smaller the degree of reduction. The results indicated that ignoring the travel time uncertainty may underestimate the effect of improving the transit system. From the perspective of spatial patterns, the influence of travel time uncertainty covers the whole region. The value of overestimation was high in the area with a high level of accessibility, the degree of overestimation was high in the area with a low level of accessibility when travel time uncertainty was ignored. The median of overestimation degree can reach 77.1%. The potential model had the minimal variation in accessibility classification and the cumulative model generated the maximum variation. The results provide the insights that users should pay more attention to the impact of travel time uncertainty on accessibility. Additionally, government should take care on the model choice and consider the results of different measurements comprehensively, as different models lead to different decisions.

Key words: urban traffic, accessibility, accessibility differences, travel time uncertainty, open-source big data, travel time reliability

肖中圣, 许奇, 毛保华, 魏润斌, 冯佳. 不同测算方法下行程时间不确定性对可达性的影响分析[J]. 地球信息科学学报, 2022, 24(11): 2102-2114.DOI:10.12082/dqxxkx.2022.220219

XIAO Zhongsheng, XU Qi, MAO Baohua, WEI Runbin, FENG Jia. The Impact of Travel Time Uncertainty on Accessibility under Different Measurements[J]. Journal of Geo-information Science, 2022, 24(11): 2102-2114.DOI:10.12082/dqxxkx.2022.220219

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模型类别	是否考虑行程时间不确定性(Y/N)	广义出行时间成本阈值/min
模型类别	是否考虑行程时间不确定性(Y/N)	45	60	75	90	115	120
时间距离模型	Y	0.3440	0.3440	0.3440	0.3440	0.3440	0.3440
时间距离模型	N	0.3748	0.3748	0.3748	0.3748	0.3748	0.3748
潜力模型	Y	31.92	31.92	31.92	31.92	31.92	31.92
潜力模型	N	33.84	33.84	33.84	33.84	33.84	33.84
高斯模型	Y	61.43	401.46	1179.76	2490.61	4611.32	7439.09
高斯模型	N	112.40	569.11	1554.64	3228.35	5722.88	9075.45
累计机会模型	Y	349	1685	4346	8542	14 902	22 803
累计机会模型	N	581	2252	5501	10 716	18 018	27 184

模型	成本阈值/min
模型	45	60	75	90	115	120
时间距离模型	7.51
潜力模型	6.33
高斯模型	150.6	50.6	27.4	47.5	27.7	29.6
累计机会模型	104.3	50.9	38.7	35.0	32.7	34.4

不同测算方法下行程时间不确定性对可达性的影响分析

The Impact of Travel Time Uncertainty on Accessibility under Different Measurements

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图/表 11

参考文献 46

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模型	是否考虑行程时间不确定性(Y/N)	成本阈值/min
模型	是否考虑行程时间不确定性(Y/N)	60&45	75&60	90&75	105&90	120&105
高斯模型	Y	553	194	111	85	61
高斯模型	N	406	173	108	77	59
累积机会模型	Y	382	158	97	74	53
累积机会模型	N	288	144	95	68	51

模型	成本阈值/min
模型	45	60	75	90	115	120
时间距离模型	7.85
潜力模型	5.80
高斯模型	95.04	51.19	37.67	35.11	26.54	23.58
累计机会模型	59.50	32.65	26.43	25.93	20.76	19.15

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模型	成本阈值/min						平均数量/个
模型	45	60	75	90	115	120
时间距离模型	103						103
潜力模型	96						96
高斯模型	344	338	171	169	236	129	231
累计机会模型	471	293	338	343	166	205	303