TY - JOUR
T1 - An introduction to urban mobility
T2 - Data, visualization, artificial intelligent approaches, and its foundations
AU - Alatrista-Salas, Hugo
AU - Cuenca, Erick
AU - Fonseca-Delgado, Rigoberto
AU - Infante, Saba
AU - Manzanilla, Raúl
AU - Morales-Navarrete, Diego
AU - Hernandez, Aracelis
AU - Nunez-Del-prado, Miguel
AU - Pineda, Israel
AU - Poncelet, Pascal
AU - Sallaberry, Arnaud
N1 - Publisher Copyright:
© 2024, Bull. Comput. Appl. Math. All rights reserved.
PY - 2024
Y1 - 2024
N2 - In the last years, the scientific community has increasingly studied urban mobility since around 55% of the world population live in urban areas. Thus, individuals living in urban areas have to deal with phenomena like traffic jams, commute time, pollution, among others, which are difficult to understand and solve. Therefore, new innovative approaches such as mobility models, artificial intelligence, or visualization applied to urban mobility analysis problems shed new light on understanding cities’ behavior. In this work, we survey the current state of the mathematical and computational tools we have at our disposal to better understand the current situation of urban areas. Our work presents datasets, discusses relevant artificial intelligence and visualization techniques, and reviews mathematical tools to analyze urban data. We hope our work offers a valuable summary of these ideas and provides the base for future investigations.
AB - In the last years, the scientific community has increasingly studied urban mobility since around 55% of the world population live in urban areas. Thus, individuals living in urban areas have to deal with phenomena like traffic jams, commute time, pollution, among others, which are difficult to understand and solve. Therefore, new innovative approaches such as mobility models, artificial intelligence, or visualization applied to urban mobility analysis problems shed new light on understanding cities’ behavior. In this work, we survey the current state of the mathematical and computational tools we have at our disposal to better understand the current situation of urban areas. Our work presents datasets, discusses relevant artificial intelligence and visualization techniques, and reviews mathematical tools to analyze urban data. We hope our work offers a valuable summary of these ideas and provides the base for future investigations.
KW - Data adjustment
KW - Data interpolation
KW - Data visualization
KW - Urban mobility
UR - http://www.scopus.com/inward/record.url?scp=85210453289&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85210453289
SN - 2244-8659
VL - 12
SP - 119
EP - 143
JO - Bulletin of Computational Applied Mathematics
JF - Bulletin of Computational Applied Mathematics
IS - 1
ER -