Multimodal integration is an important issue in public transport systems due to its influence on both passenger experience and overall network efficiency. In most countries in the global South, achieving integration is particularly problematic because of the informal nature of most public transport. Decentralised service planning and demand responsiveness lead to often uncoordinated, highly variable service patterns, which are not optimised from a passenger perspective. Efforts to promote integration are also hampered by a lack of planning data on routes, service frequencies, and transfer locations. This research asks whether GPS data supplied by passengers as they move through the network can be used to help form a better understanding of the extent and quality of the transfer experience. The data was collected in the City of Tshwane, South Africa, among informal minibus-taxi passengers. Post-processing involved the use of a machine learning algorithm to identify in-vehicle, wait and walk segments, which were used to identify transfers between one vehicle and another. The results showed that many transfers are spatially efficient with short walk and wait times, but that a minority of transferring passengers may experience very long transfers. Transfers encompass a diverse range of behaviours including pacing, shopping and browsing, and typically involve much more walking than waiting. Transfers also occur across a wide range of locations, but tend to be concentrated in certain nodes and along street segments. Strategies to improve transfer facilities as well as general walkability might be targeted at such locations. The study demonstrated that volunteered GPS data is a promising source of information to help planners understand the transfer experience in multimodal networks in data-poor environments.

中文翻译：

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使用自愿提供的 GPS 数据研究非正规交通网络中的换乘


多式联运集成是公共交通系统中的一个重要问题，因为它会影响乘客体验和整体网络效率。在南半球的大多数国家，由于大多数公共交通的非正式性质，实现一体化尤其成问题。分散的服务规划和需求响应导致服务模式往往不协调、高度可变，从乘客的角度来看，这些模式没有得到优化。由于缺乏有关路线、服务频率和换乘地点的规划数据，促进整合的努力也受到了阻碍。这项研究询问了乘客在网络中移动时提供的 GPS 数据是否可用于帮助更好地了解转乘体验的范围和质量。该数据是在南非茨瓦内市的非正式小巴出租车乘客中收集的。后处理涉及使用机器学习算法来识别车内、等待和步行路段，这些路段用于识别一辆车与另一辆车之间的换乘。结果表明，许多换乘在空间上是高效的，步行和等待时间短，但少数转乘乘客可能会经历非常长的换乘。换乘包括各种行为，包括踱步、购物和浏览，通常涉及更多的步行而不是等待。转移也发生在广泛的位置，但往往集中在某些节点和街段沿线。改善换乘设施和一般步行性的策略可能针对这些地点。 研究表明，自愿提供的 GPS 数据是一个很有前途的信息来源，可以帮助规划者了解在数据匮乏环境中多式联运网络中的传输体验。