CoDriver ETA: Combine driver information in estimated time of arrival by driving style learning auxiliary task
Estimated time of arrival (ETA) is one of the most important services in intelligent
transportation systems (ITS). Precise ETA ensures proper travel scheduling of passengers …
transportation systems (ITS). Precise ETA ensures proper travel scheduling of passengers …
Urban travel time prediction using a small number of GPS floating cars
Predicting the travel time of a path is an important task in route planning and navigation
applications. As more GPS floating car data has been collected to monitor urban traffic, GPS …
applications. As more GPS floating car data has been collected to monitor urban traffic, GPS …
Travel time prediction using hybridized deep feature Space and machine learning based heterogeneous ensemble
Travel Time Prediction (TTP) has become an essential service that people use in daily
commutes. With the precise TTP, individuals, logistic companies, and transport authorities …
commutes. With the precise TTP, individuals, logistic companies, and transport authorities …
FMA-ETA: Estimating travel time entirely based on FFN with attention
Estimated time of arrival (ETA) is one of the most important services in intelligent
transportation systems (ITS) and becomes a challenging spatial-temporal (ST) data mining …
transportation systems (ITS) and becomes a challenging spatial-temporal (ST) data mining …
Road network metric learning for estimated time of arrival
Y Sun, K Fu, Z Wang, C Zhang… - 2020 25th International …, 2021 - ieeexplore.ieee.org
Recently, deep learning have achieved promising results in Estimated Time of Arrival (ETA),
which is considered as predicting the travel time from the origin to the destination along a …
which is considered as predicting the travel time from the origin to the destination along a …
A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
Travel time prediction is essential to intelligent transportation systems directly affecting smart
cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous …
cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous …
Bayesian mixture model to estimate freeway travel time under low-frequency probe data
This study develops a novel estimation method under low-frequency probe data using the
Bayesian approach. Given the challenges in estimating travel time under low-frequency …
Bayesian approach. Given the challenges in estimating travel time under low-frequency …
Alleviating data sparsity problems in estimated time of arrival via auxiliary metric learning
With millions of people using ride-hailing platforms for daily travel, estimated time of arrival
(ETA) has become a significant problem in intelligent transportation systems and attracted …
(ETA) has become a significant problem in intelligent transportation systems and attracted …
[HTML][HTML] Modelling of segment level travel time on urban roadway arterials using floating vehicle and GPS probe data
With the increasing traffic congestion levels on urban arterials, an essential step to tackling
this challenge is to effectively quantify it and understand how it relates to its contributing …
this challenge is to effectively quantify it and understand how it relates to its contributing …
The effect of information uncertainty in road transportation systems
SC Litescu, V Viswanathan, H Aydt, A Knoll - Journal of Computational …, 2016 - Elsevier
Abstract Developments in Intelligent Transportation Systems (ITS), navigation devices and
traffic sensors make it possible for traffic participants to not just access real time information …
traffic sensors make it possible for traffic participants to not just access real time information …