Predictive Analytics of Bus and Streetcar Bunching Occurrence Time for Real-Time Applications

实时应用程序中公交车和有轨电车捆绑发生时间的预测分析

• 批准号: 515839-2017
• 负责人: Shalaby, Amer
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639844
• 关键词: Predictive; Analytics; Bus; Streetcar; Bunching

Transit vehicle bunching is a common and well-known operational problem for transit agencies. It has negativeimpacts on service quality and efficiency as well as on users' perception. In both research and practice,investigation efforts have mainly focused on bus bunching by examining the impacts of a range of factors or bydeveloping corrective actions to mitigate the effects of bunching once it occurs. Most of these studies weredeveloped for exploratory purposes. It is rare to find studies that focused on streetcar bunching or developedpredictive analytics of bunching for real-time applications. In response to this gap, this research aims atdeveloping predictive models of the time to bunching occurrence along bus and streetcar lines, enriching theknowledge and practice in this critical area of transit management. The ability to correctly predict the time tobunching in real-time would allow transit agencies to take pre-emptive actions towards preventing bunchingoccurrence or at least mitigating its effects. This can only result in improving the service efficiency and ridersatisfaction. This research will use Toronto's streetcar and bus systems as a case study, providing ready-to-useoutput that will have a direct impact on the reliability and efficiency of the surface network in Toronto.However, the research output is expected to be generic enough for transfer to other agencies elsewhere inCanada and across the globe.

公交车辆拥挤是公交机构普遍存在的运营问题。它对服务质量和效率以及用户的感知都有负面影响。在研究和实践中，调查工作主要集中在公交车拥挤方面，方法是检查一系列因素的影响，或制定纠正措施，以减轻拥挤一旦发生时的影响。这些研究中的大多数都是出于探索的目的而开发的。很少有研究聚焦于有轨电车聚集，或开发实时应用的聚集预测分析。针对这一差距，本研究旨在开发公交和有轨电车沿线集结发生时间的预测模型，丰富公交管理这一关键领域的知识和实践。能够准确地预测实时集结的时间，将使公交机构能够采取先发制人的行动，防止集结或至少减轻其影响。这只会提高服务效率和乘客满意度。这项研究将以多伦多的有轨电车和公交系统为例，提供直接影响多伦多地面网络可靠性和效率的现成输出。然而，研究成果预计将具有足够的通用性，可以转移到加拿大其他地方和全球各地的其他机构。