Sustainable public transportation using Markov Chains: Case study Hamburg public transportation

Konferenzbeitrag

Purpose: Intelligent public transportation systems have been largely focused on improving the planning, and monitoring the transportation flows during recent years. Advancements in public transportation systems increase service levels and encourage more usage of public transportation. The forecast of buses' arrival time to stations and having a dynamic system to anticipate the real-time possible events for users, significantly increase passenger satisfaction. This paper has studied the literature considering dynamic public transportation systems and also matters of environmental emissions. Methodology: The paper has developed a method to predict bus arrivals at stations by considering the buses' operation parameters and variables with stochastic characteristics by applying Markov Chains. The paper also applied the assignment problem technique and multi-objective planning to enable a framework for public transportation resource assignment considering the perspectives mentioned earlier. Findings: The real data of Hamburg public transportation has been used to verify the capabilities of the platform. The findings show that the model validity of the platform and enabled effective strategic planning for public resource assignment. Originality: This paper has studied the related literature and discussed the considerable gap for proposing a dynamic public transportation system that brings satisfaction from the side of the users and also mutually minimizing environmental emissions.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: hdl:10419/249609

Klassifikation und Themen

• Klassifikation: Management
• Thema: City Logistics

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Sodachi, Majid ; Valilai, Omid Fatahi
• Ereignis: Veröffentlichung
• (wer): epubli GmbH
• (wo): Berlin
• (wann): 2021

Weitere Informationen

• DOI: doi:10.15480/882.3998
• Handle: http://hdl.handle.net/10419/249648
• URN: urn:nbn:de:gbv:830-882.0162248
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ