Car Use: A Matter of Dependency or Choice? The Case of Commuting in Noord-Brabant
Car Use: A Matter of Dependency or Choice? The Case of Commuting in Noord-Brabant
复制标题
汽车使用：依赖还是选择？
作者：
H. Dashtestaninejad;Paul van de Coevering;Joost de Kruijf
北布拉邦省广阔的城市地区的汽车使用量高于荷兰平均水平。这是否反映了由于缺乏有竞争力的替代模式而对汽车的依赖？还是还有其他因素在起作用，例如偏好差异？本文旨在确定该地区汽车使用的性质，并探讨这在多大程度上反映了汽车依赖性。该数据包含 3,244 名受访者，来自对埃因霍温高科技园区（2018 年）和埃因霍温理工大学/电子校园（2019 年）员工进行的两份在线调查问卷。根据受访者的居住地点计算开车、公共交通、骑自行车和步行上班的时间。汽车依赖性指标是根据自行车最大通勤时间阈值以及公共交通与汽车之间的最大出行时间比率制定的。根据这些阈值，大约 40% 的受访者被归类为汽车依赖者。在不依赖汽车的受访者中，31% 使用汽车上下班。二项式 Logit 模型显示，较高的住宅密度和靠近火车站会降低汽车通勤的几率。行程时间比率也对预期方向有重大影响。模式选择偏好（例如舒适度、灵活性等）也有显着且强烈的影响。这些结果凸显了将硬措施（例如，改善基础设施或公共交通供应）和软措施（信息和说服）相结合以减少通勤旅行中汽车使用和汽车依赖的重要性。
Car use in the sprawled urban region of Noord-Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High-Tech Campus (2018) and the TU/e-campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car-dependent. Of the non-car-dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provision) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.