Authors:
Leandro Ricardo
1
;
Susana Sargento
2
and
Ilídio C. Oliveira
3
Affiliations:
1
Instituto de Telecomunicações, Portugal
;
2
Universidade de Aveiro and Instituto de Telecomunicações, Portugal
;
3
Universidade de Aveiro and Instituto de Engenharia Electrónica e Informática de Aveiro, Portugal
Keyword(s):
Vehicular Networks, Long-Term Time Estimation, Prediction, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
A wide vehicular network has a huge potential to collect city-data, specially with respect to city mobility, one
of the top concerns of the municipalities. In this work, we propose the use of the mobility data generated by
the movement of the connected buses to deliver a new set of tools to support both the bus passengers and bus
fleet operator use cases. Considering the bus passengers, it is possible to build smart schedules, which deliver
an estimated time of arrival based on the city dynamics along time, and that can be accessed directly in the
smartphone. Considering the bus fleet operator, it is possible to characterize the behaviour of buses and bus
lines. Using the GPS trace of buses and map-matching algorithm, we are able to discover the line each bus
is assigned to. Estimated times of arrival and predictions are implemented recurring to time estimations and
predictions, using both data mining and machine learning approaches. Proof-of-concept applications were
implem
ented to demonstrate the real-life applicability, including a mobile app for the citizens, and a web
dashboard for the fleet operator.
(More)