Technical report on "Decision support for tactical resource allocation in bike sharing systems"
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Patrick Vogel
Jun 17, 2014, 6:37:23 AM6/17/14
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Hi all,
we have a techincal report (submitted to a journal) regarding "Decision support for tactical resource allocation in bike sharing systems".
Abstract
This paper presents an integrated approach of intelligent data analysis and mathematical optimization to support tactical resource allocation in bike sharing systems. Bike sharing has recently enabled sustainable means of shared mobility by automated rental stations in metropolitan areas. Spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes causing full or empty stations in the course of a day. Ensuring the reliable provision of bikes and bike racks is crucial for the viability of these systems. This can be done within the scope of tactical resource allocation. Tactical planning requires the anticipation of operational decisions and a suitable aggregation of input data.
We present a linear programming formulation aiming at cost-efficient allocation of bikes to stations given a predefined service level for different scenarios of bike demand. In particular, tactical decisions regard the fulfilment of time-dependent target fill levels of bikes at stations. Target fill levels serve as input for the detailed planning of relocation tours on the operational level. Different scenarios of bike demand are considered as realizations of typical bike flows between stations in terms of time-dependent origin / destination matrices. We employ an intelligent data analysis approach to generate aggregated input data from individual trips recorded in Bike Sharing Systems. Intelligent data analysis produces spatio-temporal distributions of bike flows and helps determining typical trip purposes in combination with methods from the field of traffic modeling.
The proposed methodology is exemplified based on two years of trip data from Vienna’s “Citybike Wien”. Computational experiments show changing appropriate target fill levels for a potential increase of demand. Furthermore, determined characteristics of relocation regimes can support BSS operators in the planning and implementation of relocation.
Link
Feel free to contact me if you have any questions.
Best regards,
Patrick Vogel
we have a techincal report (submitted to a journal) regarding "Decision support for tactical resource allocation in bike sharing systems".
Abstract
This paper presents an integrated approach of intelligent data analysis and mathematical optimization to support tactical resource allocation in bike sharing systems. Bike sharing has recently enabled sustainable means of shared mobility by automated rental stations in metropolitan areas. Spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes causing full or empty stations in the course of a day. Ensuring the reliable provision of bikes and bike racks is crucial for the viability of these systems. This can be done within the scope of tactical resource allocation. Tactical planning requires the anticipation of operational decisions and a suitable aggregation of input data.
We present a linear programming formulation aiming at cost-efficient allocation of bikes to stations given a predefined service level for different scenarios of bike demand. In particular, tactical decisions regard the fulfilment of time-dependent target fill levels of bikes at stations. Target fill levels serve as input for the detailed planning of relocation tours on the operational level. Different scenarios of bike demand are considered as realizations of typical bike flows between stations in terms of time-dependent origin / destination matrices. We employ an intelligent data analysis approach to generate aggregated input data from individual trips recorded in Bike Sharing Systems. Intelligent data analysis produces spatio-temporal distributions of bike flows and helps determining typical trip purposes in combination with methods from the field of traffic modeling.
The proposed methodology is exemplified based on two years of trip data from Vienna’s “Citybike Wien”. Computational experiments show changing appropriate target fill levels for a potential increase of demand. Furthermore, determined characteristics of relocation regimes can support BSS operators in the planning and implementation of relocation.
Link
Feel free to contact me if you have any questions.
Best regards,
Patrick Vogel
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