Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis
The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours
Short‐term traffic forecasting is becoming more important in intelligent transportation
systems. The k‐nearest neighbour (kNN) method is widely used for short‐term traffic …
systems. The k‐nearest neighbour (kNN) method is widely used for short‐term traffic …
Neural networks in transportation research–recent applications
T Pamuła - Transport problems, 2016 - yadda.icm.edu.pl
Neural networks'(NNs) capability of mapping the nonlinear functions of variables describing
the behaviour of objects and the simplicity of designing their configuration favours their …
the behaviour of objects and the simplicity of designing their configuration favours their …
A taxonomy of traffic forecasting regression problems from a supervised learning perspective
One contemporary policy to deal with traffic congestion is the design and implementation of
forecasting methods that allow users to plan ahead of time and decision makers to improve …
forecasting methods that allow users to plan ahead of time and decision makers to improve …
Forecasting short-term traffic speed based on multiple attributes of adjacent roads
Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic
control and trip planning, which however still remains a challenging task when the high …
control and trip planning, which however still remains a challenging task when the high …
Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach
Understanding the complex heterogeneity of traffic flow for the road network is of great
importance to relieving traffic congestion and designing traffic control strategies. This study …
importance to relieving traffic congestion and designing traffic control strategies. This study …
An adaptive cluster-based sparse autoregressive model for large-scale multi-step traffic forecasting
Traffic forecasting has been extensively studied due to its importance for the design and
development of Intelligent Transportation Systems (ITS). Most of the existing relevant …
development of Intelligent Transportation Systems (ITS). Most of the existing relevant …
High‐performance traffic speed forecasting based on spatiotemporal clustering of road segments
Traffic speed prediction is an indispensable element of intelligent transportation systems.
Numerous studies have devoted to high‐precision prediction models. However, most …
Numerous studies have devoted to high‐precision prediction models. However, most …
Hybrid short‐term prediction of traffic volume at ferry terminal based on data fusion
Ferry traffic prediction is an important consideration for urban transportation authorities. A
novel methodology which considers cyclical patterns in ferry on‐board vehicle traffic and …
novel methodology which considers cyclical patterns in ferry on‐board vehicle traffic and …
Highway travel time estimation using multiple data sources
Travel time is considered the most useful travel related information as it is the best indicator
of the level of service on the road stretch and is completely understandable to all users …
of the level of service on the road stretch and is completely understandable to all users …