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The Essence of Multimodal Travel and Key Standards for Data Exchange in Public Transport

Ejdys Stanisław, Dzik Renata, Kowal Barbara, Dudzicz Martyna, Piasecki Adam, Górka Wojciech, Nowak Elżbieta, Markowska Ewelina, Witkowski Dominik

Rocznik Ochrona Środowiska

2024

Tom 26

691--699

The concept of multimodal transport has recently attracted much interest. It is user-oriented, i.e. the passenger planning his trip. To properly plan and prepare a mobility offer, it is necessary to know the mobile behaviour of travellers or their changes. Both the travel history and communication preferences of passengers can contribute to the design of a mobility distribution model in which various modes of transport offered by different operators are integrated into one service provided within one digital platform. The study aims to present the concept of multimodal travel and show the added value for the traveller: the synergy of combining ticket purchase and payment channels and a wide range of transport options. The authors highlight two key public transport data exchange standards: Open Journey Planner (OJP) and General Transit Feed Specification (GTFS). Their goal is to improve the quality of public transport services and provide travellers with better access to information. The article was developed as part of the Network Management Planning and Control & Mobility Management in a Multimodal Environment and Digital Enablers project – an acronym of the MOTIONAL project – implemented by the Research and Development Ecosystem created at PKP S.A. The article's content is part of the WP20 work package Development: Integration of Railways with other means of transport. In particular, it refers to Task 20.1, Improving railway integration using B2B intermodal services, which includes Subtask 20.1.1, Providing MaaS platforms for B2B intermodal services.

Accuracy of hourly demand forecasting of micro mobility for effective rebalancing strategies

Boonjubut Kanokporn, Hasegawa Hiroshi

Management Systems in Production Engineering

2022

nr 3 (30)

246--252

The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bikesharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process.