Data underlying the master thesis: Tradable Mobility Credits for Long-Distance Travel in Europe – Impacts on the Modal Split between Air, Rail and Car

Long-distance travels cause a large share of total greenhouse gas emissions. Environmental policy instruments aim to direct people’s travel behaviour, but at present, they are not sufficiently effective. As an alternative to existing policies, there is growing attention to market-based pricing instruments. Such an innovative policy instrument is the Tradable Mobility Credit Scheme (TMC). It allows for the direct internalisation of externalities into the price of mobility. This study specifies a TMC which shall be internationally implemented in Europe. The objective of the study is to estimate the impact of such a TMC on the modal split for long-distance leisure travel in Europe. Therefore, first, a mode choice model is created to estimate the current modal split of long-distance routes in Europe. Second, the TMC is incorporated into the mode choice model, and the modal split under TMC is estimated. A case study with 73 European cities and 2,998 OD-pair connections is conducted. Flight and train ticket prices are obtained by web scraping. The dataset includes the Python TMC mode choice model, raw data (flight and train ticket prices collected by web scraping) and output data. The output data includes both the output assuming inelastic demand and the output assuming elastic demand.

长途出行在全球温室气体总排放量中占据相当大的份额。现有环境政策工具旨在引导人们的出行行为，但目前其效果尚未达到足够理想的水平。作为现有政策的替代方案，基于市场的定价工具正受到越来越多的关注。此类创新性政策工具即为可交易出行信用计划（Tradable Mobility Credit Scheme, TMC），其可将外部性直接内部化至出行成本之中。本研究明确了一套拟在欧洲范围内国际推行的TMC方案，研究目标为评估该TMC对欧洲长途休闲出行方式分担率的影响。为此，研究首先构建了方式选择模型，以估算欧洲长途出行线路当前的方式分担率；其次将TMC方案嵌入该方式选择模型，进而估算实施TMC后的方式分担率。本研究开展了一项包含73座欧洲城市、共计2998个起讫点（Origin-Destination, OD）对连接的案例研究，并通过网络爬取获取机票与火车票票价数据。本数据集包含基于Python实现的TMC方式选择模型、原始数据（通过网络爬取采集的机票与火车票票价）以及输出数据；其中输出数据涵盖需求无弹性假设下的结果与需求有弹性假设下的结果。