MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Sci Total Environ. 2022 Feb 20;808:152057. doi: 10.1016/j.scitotenv.2021.152057. Epub 2021 Dec 6.
The existing literature has confirmed that extreme weather such as wind, rain, and snow have a negative impact on cross-sectional traffic flow. However, travel activities with different destination regions, travel distances and vehicle types may have different responses to severe weather. We employ the multilevel mixed-effects negative binomial (MENB) model to explore the interaction effect of severe weather and non-weather factors on intercity origin-destination (OD) demand, based on the data from freeway toll stations in Shandong Province from March 2011 to February 2012. The MENB model is superior to the SNB model in that the former has both smaller AIC (456,645.4 < 4,586,877.2) and BIC (456,963.4 < 458,975.8) values and log-likelihood ratio tests. The results indicate that weather impact on freeway travel to the same metropolitan area is homogeneous, and the impact in different metropolitan areas is heterogeneous. Besides, there is an interaction effect between severe weather (strong wind, fog, heavy rain, snow) and travel distance on freeway OD volume. With increasing travel distance, the impact of both strong wind and heavy rain decreases gradually, while the impact of both fog and snow increases. In addition, heat (0.0351 > 0.0201), strong wind (0.0930 > 0.0454), and heavy rain (0.1245 > 0.1044) have a greater impact on passenger car volume than on truck volume, while fog (0.4340 < 0.4802) and snow (0.4383 < 0.4884) have a less significant impact on passenger car volume. The findings of this study provide some deep insights into the relationship between severe weather factors and intercity travel demand, which suggests that different strategies of travel demand management should be adopted for different travel distances and different vehicle types under various weather conditions.
已有文献证实，风、雨、雪等极端天气会对横断面交通流量产生负面影响。然而，不同目的地区域、旅行距离和车型的出行活动可能对恶劣天气有不同的反应。我们使用多层次混合效应负二项式（MENB）模型，基于 2011 年 3 月至 2012 年 2 月山东省高速公路收费站的数据，探讨了恶劣天气和非天气因素对城际出行起讫（OD）需求的交互影响。与 SNB 模型相比，MENB 模型具有更小的 AIC（456,645.4 < 4,586,877.2）和 BIC（456,963.4 < 458,975.8）值和对数似然比检验。结果表明，恶劣天气对同一大都市区的高速公路出行影响是同质的，而不同大都市区的影响是异质的。此外，恶劣天气（强风、雾、大雨、雪）与旅行距离对高速公路 OD 量存在交互影响。随着旅行距离的增加，强风和大雨的影响逐渐减小，而雾和雪的影响则增加。此外，高温（0.0351 > 0.0201）、强风（0.0930 > 0.0454）和大雨（0.1245 > 0.1044）对乘用车的影响大于对卡车的影响，而雾（0.4340 < 0.4802）和雪（0.4383 < 0.4884）对乘用车的影响较小。本研究的结果为恶劣天气因素与城际出行需求之间的关系提供了一些深入的见解，这表明在不同天气条件下，应针对不同的旅行距离和不同的车辆类型采用不同的出行需求管理策略。
