Department of Transport and Planning, Delft University of Technology, Delft, Netherlands.
Transport and Logistics Group, Delft University of Technology, Delft, Netherlands.
PLoS One. 2022 Mar 3;17(3):e0264805. doi: 10.1371/journal.pone.0264805. eCollection 2022.
Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers' confidence it is critical to assess their risk determinants and trade-offs.
To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers' COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively.
We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact-nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization.
Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk.
与以往的大流行不同，COVID-19 持续时间相对较长，出现了周期性的感染浪潮和众多变体。公共交通的客流量受到了特别严重的打击。为了恢复旅行者的信心，评估他们的风险决定因素和权衡取舍至关重要。
为此，我们在荷兰对火车旅行者进行了调查，以：（i）量化特定行程、基于政策和大流行相关属性对旅行者 COVID-19 风险感知的影响；（ii）评估这种风险感知与其他旅行属性之间的权衡取舍。采用分层信息整合方法，在两阶段的陈述偏好实验中，要求受访者首先对他们认为不同旅行情况的风险程度进行评分，然后在包含他们自己的感知风险评分作为属性的不同旅行选择之间进行选择。使用线性回归和混合多项逻辑回归模型分别对感知风险评分和旅行选择之间的关系进行建模。
我们发现，车上拥挤程度和感染率是风险感知的最重要因素。在个人特征中，家庭和朋友的脆弱性影响最大——几乎是个人健康风险的两倍。桥接选择实验表明，虽然时间价值与大流行前的估计相似，但旅行者更有可能选择 COVID-19 风险较低的路线（例如，由于拥挤程度较低）。乘坐火车长途旅行的受访者对风险的重视程度是短途旅行受访者的四倍。通过结合这两个模型，我们还报告了对缓解因素的支付意愿：减少拥挤、佩戴口罩和增加消毒。
由于我们评估了大量变量对路线选择行为的影响，因此我们可以使用估计模型来预测在详细的大流行情景下的行为。此外，除了强调 COVID-19 风险感知在公共交通路线选择中的重要性外，这项研究的结果还提供了有关各种政策对感知风险的缓解影响的宝贵信息。
