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Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm

机译：评估基于移动性的信息干预发展的亲环境潜力：一种数据驱动算法

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摘要

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual's mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.
机译：信息干预对于使个人的态度和观念发生积极变化非常重要。关于个人的流动行为，习惯，态度和看法很难改变。因此，至关重要的是要确定出行行为相对较软的方面，以减少交通对环境和个人健康的负面影响。本文提供了一种方法框架，并描述了有助于识别出行行为的软变化的计算算法的开发。该算法基于各种不同的数据源，例如活动旅行日记和相关约束信息，气象条件，自行车和公共交通供应数据以及排放物和空气污染物浓度数据。从运输建模文献中得出了算法中的各种规则，在这些文献中，针对活动旅行决策检查了约束和因素。在评估有利于环境的潜力时，应考虑活动-旅行行为的三个主要方面，例如减少用车，冷启动汽车发动机和参加非强制性户外活动。该算法适用于从哈瑟尔特（比利时），博洛尼亚（意大利）和吉尔福德（英国）的公民收集的小型数据集。已发现在3公里以内的自行车旅行和公共交通工具的汽车旅行具有巨大的可替代潜力。还发现，过度冷启动和参与非强制性户外活动的可替代潜力在一定程度上带来了环境的积极变化。在未来的研究中，这些确定出的潜力会作为基于移动性的信息干预的一部分，反馈给个人，其结果将作为回报。

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