城市客车车厢内站立乘客对站立区域的选择偏好

闫晟煜; 白昭然; 温福华; 周继彪; 陈礼美

doi:10.19818/j.cnki.1671-1637.2023.05.016

城市客车车厢内站立乘客对站立区域的选择偏好

doi: 10.19818/j.cnki.1671-1637.2023.05.016

1.
长安大学汽车学院，陕西西安 710064
2.
同济大学交通运输工程学院，上海 201804

基金项目:

国家自然科学基金项目 52002282

交通运输战略规划政策项目 2020-23-3

陕西省自然科学基础研究计划项目 2021JQ-292

教育部人文社会科学研究项目 21YJC790137

详细信息

作者简介:
闫晟煜(1987-)，男，黑龙江绥化人，长安大学副教授，工学博士，从事城市公交运营理论研究

中图分类号: U491.17
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出版历程
- 收稿日期: 2023-04-26
- 网络出版日期: 2023-11-17
- 刊出日期: 2023-10-25

Standee preference for standing areas in urban buses

1.
School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China
2.
College of Transportation Engineering, Tongji University, Shanghai 201804, China

Funds:

National Natural Science Foundation of China 52002282

Transportation Strategic and Planning Policy Project 2020-23-3

Natural Science Basic Research Project of Shaanxi Province 2021JQ-292

Humanities and Social Sciences Research Project of Ministry of Education 21YJC790137

More Information

Author Bio:
YAN Sheng-yu(1987-), male, associate professor, PhD, Leo9574@163.com

摘要

摘要: 为揭示城市客车车厢内站立乘客对站立区域的选择偏好，基于随机效用理论，提出了站立乘客选择偏好的多项Logit(MNL)模型，考虑站立乘客流向和座椅布置模式，确定了影响选择偏好的3个关键独立要素，分别为站立宽松程度、下车便捷程度和获得座椅概率，并提出了要素的算法、阈值，以及在各站立区域内的分布规律与耦合关系；运用MATLAB中的牛顿迭代法标定了MNL模型的待估计参数，以典型的12 m城市客车车型为例，结合西安市22条公交线路的调查，提出了各站立区域的选择模型，分析了在不同车厢平均站立密度下站立乘客对站立区域的选择偏好。研究结果表明：标定的MNL模型能够有效反映站立乘客的选择偏好；站立乘客对3种独立要素的判断优先级依次为下车便捷程度、站立宽松程度、获得座椅概率；轴距内2个站立区域的站立宽松程度存在耦合关系；基准区域内站立乘客对站立宽松程度和获得座椅概率的偏好较弱；当车厢内每平方米的平均站立乘客数不超过2人时，乘客选择区域3的偏好降幅为35.95%，而选择区域2的偏好增幅为19.99%，二者互补现象显著；当车厢内每平方米的平均站立乘客数超过2人时，区域2将会显著地疏解区域1的客流，站立乘客对区域1的选择偏好始终高于区域4，乘客对站立区域的选择偏好呈现逐步收敛的特点。乘客的站立区域选择偏好研究对衡量座椅布置与客流量适配效果，指导客流应急疏散与分流和提升城市公交出行服务品质有重要价值。
- 交通规划 /
- 城市客车 /
- 多项Logit模型 /
- 选择偏好 /
- 站立乘客流 /
- 站立密度 /
- 座椅布置
Abstract: To reveal the standee preference for standing areas in urban buses, a multinomial logit (MNL) model of standee preferences was proposed based on the stochastic utility theory. According to standee preference flow and seat arrangement, three key independent factors that affected the preference were introduced, including the looseness of standing, the convenience of getting-off, and the probability of obtaining a seat. The algorithms and thresholds of three factors were proposed, and the distribution law and coupling relationship of each factor among all the standing areas were put forward. The parameters to be estimated in the MNL model were calibrated by Newton iteration method in MATLAB. The typical urban bus of 12 m was taken as a case, and the selection model of each standing area was given based on the investigation data of 22 bus lines in Xi'an. In addition, the standee preference for standing areas under different average standee densities in buses was analyzed. Analysis results show that the calibrated MNL model can effectively reflect standee preferences. The priority of standee for the three independent factors is the convenience of getting-off, the looseness of standing, and the probability of obtaining a seat. There is a coupling relationship of looseness of standing between the two standing areas in the wheelbase. The standee preference for the looseness of standing and the probability of obtaining a seat in the reference area is weaker than that in the non-reference area. When the average number of standing passengers per square meter in the bus is no more than 2, the preference decrease rate of area 3 is 35.95%, while the preference increase rate of area 2 is 19.99%, with a significant phenomenon of coupling. When the average number of standing passengers per square meter in the bus is more than 2, area 2 will significantly relieve the passenger flow in area 1, and the standee always prefers area 1 than area 4. Standee preference for standing areas presents a convergence feature. The study on the standee preference for standing areas is of great value to measure the effect of seat arrangement and passenger flow adaptation, guide the emergency evacuation and diversion of passenger flows, and improve the service quality of urban bus travel.
- traffic planning /
- urban bus /
- multinomial Logit model /
- choice preference /
- standee flow /
- standing density /
- seat arrangement

HTML全文

图 1 考虑站立乘客流分布规律的站立区域划分

Figure 1. Standing area division considering distribution law of standing passenger flow

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图 2 站立乘客对站立宽松程度的选择偏好

Figure 2. Standee choice preferences for looseness of standing

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图 3 各站立区域中心到基准点的移动距离

Figure 3. Distances between reference point and divided area centres

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图 4 m_j调查分布与理论值对比

Figure 4. Comparison of m_j between survey distributions and its theoretical values

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图 5 不同车厢平均站立密度下的乘客选择偏好

Figure 5. Passenger choice preferences under different standee densities

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表 1 k的调查结果

Table 1. Survey results of k

乘客性别	体重/kg	投影面积/m²	携带包裹等物品	样本量/个
男性	[50，80)	0.121	无	98
	[50，80)	0.129	有	47
	[80，100)	0.140	无	108
	[80，100)	0.145	有	65
女性	[50，65)	0.123	无	133
	[50，65)	0.126	有	82
	[65，80)	0.128	无	71
	[65，80)	0.138	有	61

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表 2 X_bnj的取值范围

Table 2. Value ranges of X_bnj

城市客车车型/m	X_bnj取值范围
城市客车车型/m	区域1	区域2	区域3	区域4
8.0	[0.512，0.533]	[0.739，0.771]	1.000	[0.648，0.681]
10.5	[0.371，0.409]	[0.712，0.733]	1.000	[0.388，0.420]
12.0	[0.249，0.292]	[0.641，0.682]	1.000	[0.321，0.369]

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表 3 乘客选择偏好的计算参数

Table 3. Calculating indicators for passenger choice preferences

站立区域	A_j	S_j/m²	D_nj/m	β_aj	β_bj	β_cj	m_j
1	8	2.02	4.820	-0.327	-0.043	-1.716	1.29
2	6	2.76	2.192	0.533	1.094	-1.638	1.14
3	2	2.52	1.175	-0.159	0.632	1.533	0.64
4	17	1.35	2.363	-0.047	-1.178	-0.467	1.94

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表 4 站立区域的选择模型描述与取值范围

Table 4. Choice model description and value range of standing area

站立区域	β_iX_ni计算结果	ρ_i取值范围	函数单调性
1	0.043ρ₁－0.336－1.096/ρ₁	ρ₁∈[0.495, 8]	增函数
2	－0.069ρ₂+0.958－0.676/ρ₂	ρ₂∈[0.362, 8]	增函数
3	0.021ρ₃+0.473+0.337/ρ₃	ρ₃∈[0.396, 8]	减函数
4	0.006ρ₄－0.476+0.671/ρ₄	ρ₄∈[0.741, 8]	减函数

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参考文献(30)

[1]	YAN Sheng-yu, CAO Jing, ZHAO Zhuan-zhuan. Seating provision and configuration of a 12 m city bus considering passenger crowding[J]. International Journal of Automotive Technology, 2020, 21(5): 1223-1231. doi: 10.1007/s12239-020-0116-6
[2]	沈景炎. 关于车辆定员与拥挤度的探析[J]. 都市快轨交通, 2007, 20(5): 14-18. doi: 10.3969/j.issn.1672-6073.2007.05.005 SHEN Jing-yan. On the carriage passenger capacity and the crowdedness involved[J]. Urban Rapid Rail Transit, 2007, 20(5): 14-18. (in Chinese) doi: 10.3969/j.issn.1672-6073.2007.05.005
[3]	BUNKER J M. High volume bus stop upstream average waiting time for working capacity and quality of service[J]. Public Transport, 2018, 10(2): 311-333. doi: 10.1007/s12469-018-0179-1
[4]	LIU Ling-bo, CHEN Jing-wen, WU He-feng, et al. Physical-virtual collaboration modeling for intra- and inter-station metro ridership prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(4): 3377-3391. doi: 10.1109/TITS.2020.3036057
[5]	刘映宏, 李海鹰, 王莹, 等. 考虑旅客选择行为的节假日普速旅客列车编组优化研究[J]. 铁道学报, 2020, 42(11): 1-7. doi: 10.3969/j.issn.1001-8360.2020.11.001 LIU Ying-hong, LI Hai-ying, WANG Ying, et al. Research on optimization of common-speed passenger train formation plan for holidays considering passenger choice behavior[J]. Journal of the China Railway Society, 2020, 42(11): 1-7. (in Chinese) doi: 10.3969/j.issn.1001-8360.2020.11.001
[6]	史芮嘉, 毛保华, 丁勇, 等. 地铁车厢内乘客站立位置选择行为研究[J]. 交通运输系统工程与信息, 2017, 17(2): 142-147, 159. SHI Rui-jia, MAO Bao-hua, DING Yong, et al. Pedestrian choice behavior analysis of standing position in subway carriage[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2): 142-147, 159. (in Chinese)
[7]	HAYWOOD L, KONING M. The distribution of crowding costs in public transport: new evidence from Paris[J]. Transportation Research Part A: Policy and Practice, 2015, 77: 182-201. doi: 10.1016/j.tra.2015.04.005
[8]	吴奇兵, 陈峰, 高永鑫, 等. 城市轨道交通车厢立席密度计算模型[J]. 交通运输工程学报, 2015, 15(4): 101-109. doi: 10.19818/j.cnki.1671-1637.2015.04.013 WU Qi-bing, CHEN Feng, GAO Yong-xin, et al. Computation model of standing-passenger density in urban rail transit carriage[J]. Journal of Traffic and Transportation Engineering, 2015, 15(4): 101-109. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2015.04.013
[9]	陈伟, 李宗平, 余大本, 等. 基于耐受性的城市轨道交通车厢立席密度研究[J]. 交通运输系统工程与信息, 2020, 20(2): 225-230, 243. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202002034.htm CHEN Wei, LI Zong-ping, YU Da-ben, et al. Standing passenger density of urban rail transit based on tolerance[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 225-230, 243. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202002034.htm
[10]	闫晟煜, 赵转转, 白鑫. 公交客车分区域站立密度分布特征[J]. 交通信息与安全, 2018, 36(2): 93-98. doi: 10.3963/j.issn.1674-4861.2018.02.013 YAN Sheng-yu, ZHAO Zhuan-zhuan, BAI Xin. An analysis of rider density distribution at different parts of public buses[J]. Journal of Transport Information and Safety, 2018, 36(2): 93-98. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.02.013
[11]	WANG Wen-si, TIAN Zhi-hui, LI Ke-wang, et al. Real-time short turning strategy based on passenger choice behavior[J]. Journal of Intelligent Transportation Systems, 2019, 23(6): 569-582. doi: 10.1080/15472450.2019.1573366
[12]	HÖRCHER D, GRAHAM D J, ANDERSON R J. The economics of seat provision in public transport[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 109(1): 277-292.
[13]	TIRACHINI A, HURTUBIA R, DEKKER T, et al. Estimation of crowding discomfort in public transport: results from Santiago de Chile[J]. Transportation Research Part A: Policy and Practice, 2017, 103(9): 311-326.
[14]	高毅. 基于人机工程学的客车内部布置研究[D]. 长春: 吉林大学, 2012. GAO Yi. Research on bus interior ergonomics layout[D]. Changchun: Jilin University, 2012. (in Chinese)
[15]	战飞飞. 高速列车车辆空间及布局参数对乘客应急撤离的影响[D]. 北京: 北京交通大学, 2017. ZHAN Fei-fei. The influence of high-speed train space and layout parameters on passenger emergency evacuation[D]. Beijing: Beijing Jiaotong University, 2017. (in Chinese)
[16]	章勇, 支锦亦, 刘峰, 等. 基于位置服务模型的高速列车二等车厢布置改进设计[J]. 机械设计, 2017, 34(9): 115-118. https://www.cnki.com.cn/Article/CJFDTOTAL-JXSJ201709022.htm ZHANG Yong, ZHI Jin-yi, LIU Feng, et al. Improved design of second class carriage layout of high-speed train based on location service model[J]. Journal of Machine Design, 2017, 34(9): 115-118. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXSJ201709022.htm
[17]	张鑫. 考虑舒适度的公交线网优化设计[D]. 广州: 华南理工大学, 2018. ZHANG Xin. Optimal design of transit network considering comfort[D]. Guangzhou: South China University of Technology, 2018. (in Chinese)
[18]	ISLAM M R, HADIUZZAMAN M, BANIK R, et al. Bus service quality prediction and attribute ranking: a neural network approach[J]. Public Transport, 2016, 8(2): 295-313.
[19]	DE PALMA A, KILANI M, PROOST S. Discomfort in mass transit and its implication for scheduling and pricing[J]. Transportation Research Part B: Methodological, 2015, 71(1): 1-18.
[20]	D'SOUZA C. Accessibility and user performance modeling for inclusive transit bus design[J]. SAE International Journal of Commercial Vehicles, 2014, 7(1): 50-58.
[21]	张文会, 秦佳琪, 李洪涛, 等. 考虑拥挤度的常规公交与地铁出行方式选择模型[J]. 吉林大学学报(工学版), 2021, 51(1): 200-205. ZHANG Wen-hui, QIN Jia-qi, LI Hong-tao, et al. Travel choice model between bus transit and subway considering crowding degree[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 200-205. (in Chinese)
[22]	陈绍宽, 狄月, 李芳, 等. 考虑心理压力的地铁站台乘客疏散模型[J]. 交通运输工程学报, 2017, 17(5): 113-120. http://www.cnki.com.cn/Article/CJFDTotal-JYGC201705011.htm CHEN Shao-kuan, DI Yue, LI Fang, et al. Passenger evacuation model of metro platform considering psychological stress[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 113-120. (in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-JYGC201705011.htm
[23]	KATZ D, GARROW L A. The impact of bus door crowding on operations and safety[J]. Journal of Public Transportation, 2012, 15(2): 71-93.
[24]	THOREAU R, HOLLOWAY C, BANSAL G, et al. Train design features affecting boarding and alighting of passengers[J]. Journal of Advanced Transportation, 2016, 50(8): 2077-2088.
[25]	胡为洋. 考虑多车型和多种运营模式的公交灵活调度研究[D]. 广州: 华南理工大学, 2020. HU Wei-yang. Research on flexible scheduling of public transport considering multiple vehicle types and operation models[D]. Guangzhou: South China University of Technology, 2020. (in Chinese)
[26]	HERBON A, HADAS Y. Determining optimal frequency and vehicle capacity for public transit routes: a generalized newsvendor model[J]. Transportation Research Part B: Methodological, 2015, 71(1): 85-99.
[27]	BIRAGO D, MENSAH S O, SHARMA S. Level of service delivery of public transport and mode choice in Accra, Ghana[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017, 46(4): 284-300.
[28]	霍月英, 李晓娟, 闫振英, 等. 基于乘客满意度的公交车站立乘客面积研究[J]. 交通运输系统工程与信息, 2019, 19(3): 157-162. HUO Yue-ying, LI Xiao-juan, YAN Zhen-ying, et al. Standing passenger space on a bus based on passenger satisfaction[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 157-162. (in Chinese)
[29]	房德威, 何东坡, 王立峰, 等. 城市轨道交通车厢内拥挤成本的估计方法[J]. 交通运输工程学报, 2018, 18(6): 121-130. doi: 10.19818/j.cnki.1671-1637.2018.06.013 FANG De-wei, HE Dong-po, WANG Li-feng, et al. Estimation method of crowding cost in urban rail transit carriages[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 121-130. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2018.06.013
[30]	BATARCE M, MUÑOZ J C, DE DIOS ORTÚZAR J. Valuing crowding in public transport: implications for cost-benefit analysis[J]. Transportation Research Part A: Policy and Practice, 2016, 91(4): 358-378.