Paper Info
Title
SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation
Abstract
Data generated by GPS-equipped probe vehicles, especially public transit vehicles can be a reliable source for traffic speed estimation. Traditionally, this estimation is done by learning the parameters of a model that describes the relationship between the speed of the probe vehicle and the actual traffic speed. However, such approaches typically suffer from data sparsity issues. Furthermore, most state of the art approaches does not consider the effect of weather and the driver of the probe vehicle on the parameters of the learned model. In this paper, we describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.
Year
DOI
Venue
2017
10.1109/SMARTCOMP.2017.7947048
2017 IEEE International Conference on Smart Computing (SMARTCOMP)
Keywords
Field
DocType
SpeedPro,urban traffic speed estimation,GPS-equipped probe vehicles,public transit vehicles,multivariate predictive multimodel approach,clusters,anonymized driver identifier
Data modeling,Identifier,Multivariate statistics,Computer science,Simulation,Floating car data,Public transport,Real-time computing,Weather data
Conference
ISBN
Citations 
PageRank 
978-1-5090-6518-9
2
0.40
References 
Authors
12
3
Name
Order
Citations
PageRank
Chinmaya Samal132.83
Fangzhou Sun252.89
Abhishek Dubey339357.92