Intercity travel mode choice behavior of travelers in large urban regions during emergencies
YUAN Yali1, YANG Xiaobao1, LI Honghui2, SI Bingfeng1
1. MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Highway Monitoring & Response Center, Ministry of Transport, Beijing 100029, China
摘要以城市群内旅客城际出行为研究对象，分析心理潜变量、城市群属性和突发事件等因素对城际出行方式选择行为的影响。基于京津冀旅客城市群内部城际出行问卷调查数据，构建突发事件下考虑心理潜变量的综合选择（integrated choice and latent variable，ICLV）模型进行实证分析。结果表明：考虑便捷性和乘车体验潜变量的混合选择模型对居民城际出行行为的拟合优度更高；城市群属性方面，起讫点城市类型、出行距离和起讫点间铁路车次数量对出行方式选择有显著影响；突发事件方面，雾霾天气下选择铁路出行的概率提高25.69%，雨天选择小汽车出行的概率提高31.63%，雪天选择小汽车出行的概率相对降低，发生阻断事件时选择小汽车出行的概率升高。研究结果有助于深入理解城市群内城际出行方式选择行为，为突发事件下城际出行需求的差异化出行诱导和需求管控提供科学依据。
Abstract：This study analyzed the influence of psychological latent variables, urban agglomeration, emergencies and other factors on intercity travel mode choices in large cities. Data from the Beijing-Tianjin-Hebei urban region was used to estimate an integrated choice and latent variable (ICLV) model for the empirical analysis. The results show that the goodness-of-fit of the ICLV model, which included convenience and ride experience, is better than that of the traditional discrete choice model. Origin and destination, travel distance and frequency of train services all significantly impact the choice of travel mode. The probability of train use in hazy weather increased by 25.69%, the probability of car use in rainy weather increased by 31.63%, the probability of car use in snowy weather decreased, and the probability of car use when the trains were blocked increased. These results are helpful for understanding intercity travel mode choices and provide a scientific basis for predicting travel volume and demand control for intercity travel during emergencies.
原雅丽, 杨小宝, 李虹慧, 四兵锋. 突发事件下城市群内旅客城际出行方式选择行为[J]. 清华大学学报（自然科学版）, 2022, 62(7): 1142-1150.
YUAN Yali, YANG Xiaobao, LI Honghui, SI Bingfeng. Intercity travel mode choice behavior of travelers in large urban regions during emergencies. Journal of Tsinghua University(Science and Technology), 2022, 62(7): 1142-1150.
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2022, Vol. 62 
