A real time multi-objective cyclists route choice model for a bike-sharing mobile application
(2017) 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 p.645-650- Abstract
The attractiveness of cycling-and in particular of bike-sharing systems-as a sustainable alternative of transportation is constantly growing, given the undeniable benefits associated with it. The aim of this paper is to present a multi-objective model based on a Fuzzy Inference System to be embedded in a mobile application that could assist cyclists in the selection of the smartest route to follow to reach their destination, in terms of travel costs (distance or time), level of air pollution and road safety. The features of the bike-sharing system (both traditional and free-floating) are considered in the generation of the final path, and also the starting and final stations to prefer (or the closest bike to pick up for the... (More)
The attractiveness of cycling-and in particular of bike-sharing systems-as a sustainable alternative of transportation is constantly growing, given the undeniable benefits associated with it. The aim of this paper is to present a multi-objective model based on a Fuzzy Inference System to be embedded in a mobile application that could assist cyclists in the selection of the smartest route to follow to reach their destination, in terms of travel costs (distance or time), level of air pollution and road safety. The features of the bike-sharing system (both traditional and free-floating) are considered in the generation of the final path, and also the starting and final stations to prefer (or the closest bike to pick up for the free-floating option) are provided. The proposed optimization model is dynamic, as it is synchronized with geolocated real time data regarding level of congestion and flows on the network, and availability of bikes/racks in the bike-sharing system. The mobile app gives bike users the possibility to plan, personalize and execute their trip with turn-by-turn guidance, allowing them to select the default optimal path, or to choose the desired travel time among the available route options, each of them accompanied by the related air pollution and safety. An application of the model is carried out through a test case to evaluate the proposed approach. Furthermore, a first study regarding the graphic interface of the mobile platform is presented to recommend some guidelines to follow to have a final product effective and bike users-friendly. The final goal is to improve the cycling experience, encouraging at the same time more people to elect the bike as their preferred mode of transportation.
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- author
- Caggiani, Leonardo ; Camporeale, Rosalia LU and Ottomanelli, Michele
- publishing date
- 2017-08-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Advanced Traveller Information System (ATIS), bike-sharing systems (BSS), cycle route planner, Fuzzy Inference System (FIS), mobile-app, multi-objective optimization, smart path choice model
- host publication
- 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
- article number
- 8005593
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017
- conference location
- Naples, Italy
- conference dates
- 2017-06-26 - 2017-06-28
- external identifiers
-
- scopus:85030233270
- ISBN
- 9781509064847
- DOI
- 10.1109/MTITS.2017.8005593
- language
- English
- LU publication?
- no
- id
- c9785927-a1f7-49ac-874c-83645f60e596
- date added to LUP
- 2018-09-25 10:20:28
- date last changed
- 2022-04-25 17:21:54
@inproceedings{c9785927-a1f7-49ac-874c-83645f60e596,
abstract = {{<p>The attractiveness of cycling-and in particular of bike-sharing systems-as a sustainable alternative of transportation is constantly growing, given the undeniable benefits associated with it. The aim of this paper is to present a multi-objective model based on a Fuzzy Inference System to be embedded in a mobile application that could assist cyclists in the selection of the smartest route to follow to reach their destination, in terms of travel costs (distance or time), level of air pollution and road safety. The features of the bike-sharing system (both traditional and free-floating) are considered in the generation of the final path, and also the starting and final stations to prefer (or the closest bike to pick up for the free-floating option) are provided. The proposed optimization model is dynamic, as it is synchronized with geolocated real time data regarding level of congestion and flows on the network, and availability of bikes/racks in the bike-sharing system. The mobile app gives bike users the possibility to plan, personalize and execute their trip with turn-by-turn guidance, allowing them to select the default optimal path, or to choose the desired travel time among the available route options, each of them accompanied by the related air pollution and safety. An application of the model is carried out through a test case to evaluate the proposed approach. Furthermore, a first study regarding the graphic interface of the mobile platform is presented to recommend some guidelines to follow to have a final product effective and bike users-friendly. The final goal is to improve the cycling experience, encouraging at the same time more people to elect the bike as their preferred mode of transportation.</p>}},
author = {{Caggiani, Leonardo and Camporeale, Rosalia and Ottomanelli, Michele}},
booktitle = {{5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings}},
isbn = {{9781509064847}},
keywords = {{Advanced Traveller Information System (ATIS); bike-sharing systems (BSS); cycle route planner; Fuzzy Inference System (FIS); mobile-app; multi-objective optimization; smart path choice model}},
language = {{eng}},
month = {{08}},
pages = {{645--650}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{A real time multi-objective cyclists route choice model for a bike-sharing mobile application}},
url = {{http://dx.doi.org/10.1109/MTITS.2017.8005593}},
doi = {{10.1109/MTITS.2017.8005593}},
year = {{2017}},
}