污染天气居民通勤模式选择影响因素的链式效用

doi:10.13229/j.cnki.Jdxbgxb.20230411

Abstract

Abstract:

To explore the complete commuting mode choice behavior when residents are aware of PM_2.5 inhalation and risk under different polluted weather， a revised ordering-based max-relevance and min-redundancy greedy （OMRMRG） Bayesian Network model was established to analyze the chain-effect utility of various variables. A questionnaire was designed to obtain residents' travel intentions in polluted weather after informing them of the risk level of their current commuting patterns and the pollutant inhalation of different travel modes. An OMRMRG structural learning algorithm with the introduction of mutual information theory was proposed to explore the chain-effect utility based on the causal relationship between variables. The results of analyzing the survey data of Xi'an residents show that： the variable chain with the greatest influence under light pollution is age-the presence of private car-daily commuting mode-polluted weather commuting mode， and 39% of residents reselect a commuting mode with a lower risk level than the daily commuting mode. The chain-effect utility of the influencing factors under heavy pollution is shown as age-income-the presence of private car-polluted weather commuting mode， and the correlations between the adjacent variables are positive， positive， positive and negative， respectively. The study reveals the chain-effect utility of different pollution conditions affecting residents' commuting patterns， which is beneficial to develop more constructive induced strategies to help residents travel healthily.

Key words: transportation planning and management, complete commuting process, chain-effect utility, Bayesian network, OMRMRG model, PM_2.5 inhalation volume

CLC Number:

U268.6

Yu-ran LI,Fei WANG,Cai-hua ZHU,Fei HAN,Yan LI. Chain-effect utility of factors influencing residents' commuting mode choice in polluted weather[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 577-590.

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References 23

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方式	年龄/岁	轻度污染		中度污染		重度污染		方式	年龄/岁	轻度污染		中度污染		重度污染
方式	年龄/岁	男	女	男	女	男	女	方式	年龄/岁	男	女	男	女	男	女
步行	<22	2.69	1.74	3.58	2.31	4.67	3.01	步行	31~45	2.30	1.60	3.06	1.60	3.99	2.78
步行	22~30	2.47	1.67	3.28	2.22	4.28	2.89	步行	46~60	2.21	1.57	2.94	1.57	3.83	2.73
骑行	<22	4.45	2.83	5.92	3.76	7.72	4.90	骑行	31~45	3.66	2.54	4.87	2.54	6.34	4.41
骑行	22~30	4.00	2.65	5.32	3.53	6.93	4.59	骑行	46~60	3.50	2.49	4.65	2.49	6.06	4.32
公交	<22	2.52	1.55	3.35	2.07	4.37	2.69	公交	31~45	1.96	1.35	2.61	1.35	3.41	2.33
公交	22~30	2.21	1.44	2.94	1.92	3.83	2.49	公交	46~60	1.83	1.31	2.44	1.31	3.17	2.26
地铁	<22	2.58	1.76	3.43	2.34	4.47	3.05	地铁	31~45	2.10	1.59	2.79	1.59	3.64	2.76
地铁	22~30	2.30	1.67	3.06	2.22	3.99	2.89	地铁	46~60	2.00	1.53	2.67	1.53	3.47	2.66
汽车	<22	1.57	0.80	2.09	1.07	2.73	1.39	汽车	31~45	0.82	0.52	1.09	0.52	1.43	0.91
汽车	22~30	1.14	0.66	1.52	0.88	1.98	1.14	汽车	46~60	0.66	0.46	0.88	0.46	1.14	0.80

空气质量指数类别	AQI 指数值	PM_2.5浓度限值/（μg·m^-3）	PM_2.5吸入量限值/ （μg·min^-1）	等级	表示颜色	对健康影响情况
优	0~50	0~35	0~0.55	Ⅰ	绿色 ■	基本无空气污染
良	51~100	36~75	0.56~1.17	Ⅱ	黄色 ■	空气质量可接受，但可能对极少数异常敏感人群健康有较小影响
轻度污染	101~150	76~115	1.18~1.80	Ⅲ	橙色 ■	易感人群症状有轻度加剧，健康人群出现刺激症状
中度污染	151~200	116~150	1.81~2.34	Ⅳ	红色 ■	进一步加剧易感人群症状，可能对健康人群心脏、呼吸系统有影响
重度污染	201~300	151~250	2.35~3.91	Ⅴ	紫色 ■	心脏病和肺病患者症状加剧，运动耐受力降低，健康人群普遍出现症状
严重污染	>300	>250	>3.91	Ⅵ	褐红色 ■	健康人群运动耐受力降低，有明显强烈症状，提前出现某些疾病

属性	变量	符号	定义
通勤者个人属性	性别	Gen	0：女性 1：男性
	年龄/岁	Age	1：<22 2：22~30 3：31~45 4：46~60
	受教育程度	Edu	1：小学及以下 2：初中 3：高中（中专）4：本科（大专）5：研究生
	家庭月收入/元	Inc	1：<2 000 2：2 000~5 000 3：5 000~10 000 4：10 000~20 000 5：>20 000
	吸烟状况	Smo	0：无 1：有
	是否有心脏病或呼吸系统疾病史	Sick	0：无 1：有
	私家车拥有情况	Car	0：无车 1：有车
	非机动车拥有情况	Bike	0：无车 1：有车
通勤模式	通勤距离	Dis	1：0~3 km 2：3~5 km 3：5~10 km 4：>10 km
	完整通勤过程	Mode	需填写完整通勤链（如步行5 min-骑行10 min-地铁30 min-步行6 min），不足5 min的通勤方式忽略不计
	早上通勤时间	MT	6：00~7：00（X_MT1）； 7：00~8：00（X_MT2）； 8：00~9：00（X_MT3）；其他时间（X_MT4）（若超过1 h，以整个通勤过程中的时间占比最长的时间段为准）
	晚上通勤时间	ET	17：00~18：00（X_ET1）； 18：00~19：00（X_ET2）； 19：00~20：00（X_ET3）；其他（X_ET4）（若超过1 h，以整个通勤过程中的时间占比最长的时间段为准）
污染感知	空气质量预报关注程度	Wea	1：基本不；2：偶尔；3：经常
	PM_2.5的危害了解程度	Risk	1：不了解；2：了解一些；3：了解
	污染防护措施了解程度	Pro	1：不了解；2：了解一些；3：了解
	是否采取污染防护措施	Poll	1：从不；2：偶尔；3：经常；4：总是
告知被调查者在3个场景下采用日常通勤模式时的风险等级和潜在危险，同时告知采用不同出行方式的PM_2.5吸入量值和风险等级，具体空气质量指数类别和PM_2.5吸入量对应风险等级划分参照表2
得知污染信息下的通勤模式	场景一/二/三	Scene1/2/3	3个场景下的完整通勤过程

算法	场景一			场景二			场景三
算法	平均运行时间/s	平均识别率/%	平均BIC 评分	平均运行时间/s	平均识别率/%	平均BIC 评分	平均运行时间/s	平均识别率/%	平均BIC 评分
K2算法	13.53	83.45	-2 643.12	12.93	83.56	-2 758.95	14.73	82.74	-2 690.44
爬山法	14.12	82.64	-2 837.38	14.58	81.75	-2 842.43	14.96	84.15	-2 943.63
OMRMRG	5.64	90.56	-1 834.29	6.39	88.46	-1 958.53	5.34	90.70	-2 068.97
改进OMRMRG	3.88	93.53	-1 539.88	2.94	92.94	-1 746.86	3.24	94.29	-1 748.53

Chain-effect utility of factors influencing residents' commuting mode choice in polluted weather

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