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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

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Rodrigues, Diego O. [1, 2] ; Maia, Guilherme [3] ; Braun, Torsten [2] ; Loureiro, Antonio A. F. [3] ; Peixoto, Maycon L. M. [1, 4] ; Villas, Leandro A. [1]
Total Authors: 6
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas - Brazil
[2] Univ Bern, Inst Comp Sci, CH-3012 Bern - Switzerland
[3] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG - Brazil
[4] Univ Fed Bahia, Dept Comp Sci, BR-40170110 Salvador, BA - Brazil
Total Affiliations: 4
Document type: Journal article
Source: APPLIED SCIENCES-BASEL; v. 11, n. 10 MAY 2021.
Web of Science Citations: 0

Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode. (AU)

FAPESP's process: 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS)
Grantee:Antonio Alfredo Ferreira Loureiro
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/23126-3 - Data Orchestration for Urban Computing through Fog Computing
Grantee:Maycon Leone Maciel Peixoto
Support Opportunities: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 18/19639-5 - Solutions for intelligent and cooperative transportation systems based on urban computing
Grantee:Leandro Aparecido Villas
Support Opportunities: Regular Research Grants
FAPESP's process: 18/12447-3 - Platform for location-based user-centric service provision
Grantee:Diego Oliveira Rodrigues
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
Grantee:Nelson Luis Saldanha da Fonseca
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/11259-9 - Analysis of mobile applications and mobility patterns in urban environments
Grantee:Diego Oliveira Rodrigues
Support Opportunities: Scholarships abroad - Research Internship - Doctorate