Traffic prediction, data compression, abnormal data detection and missing data imputation: An integrated study based on the decomposition of traffic time series
This papers discusses the decomposition of road traffic time series and its benefits. The
purposes of this paper are trifold. First, we provide an integrated framework for studying …
purposes of this paper are trifold. First, we provide an integrated framework for studying …
Traffic dynamics exploration and incident detection using spatiotemporal graphical modeling
To discover the spatial and temporal traffic patterns, this paper proposes a spatiotemporal
graphical modeling approach, spatiotemporal pattern network (STPN), to explore traffic …
graphical modeling approach, spatiotemporal pattern network (STPN), to explore traffic …
Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering
V Mirge, K Verma, S Gupta - Advances in Data Analysis and Classification, 2017 - Springer
Due to the rapid growth of wireless communications and positioning technologies, trajectory
data have become increasingly popular, posing great challenges to the researchers of data …
data have become increasingly popular, posing great challenges to the researchers of data …
Mapping to cells: A simple method to extract traffic dynamics from probe vehicle data
In the era of big data, mining data instead of collecting data are a new challenge for
researchers and engineers. In the field of transportation, extracting traffic dynamics from …
researchers and engineers. In the field of transportation, extracting traffic dynamics from …
[HTML][HTML] Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets
A common task in traffic data analysis and management is categorizing different days based
on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is …
on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is …
Clustering of time series data with prior geographical information
Time Series data are broadly studied in various domains of transportation systems. Traffic
data area challenging example of spatio-temporal data, as it is multi-variate time series with …
data area challenging example of spatio-temporal data, as it is multi-variate time series with …
[HTML][HTML] Special issue on spatiotemporal big data analytics for transportation applications
In recent years, the development of information and communication technologies (ICTs) has
made it technically and economically feasible to collect huge amounts of spatiotemporal …
made it technically and economically feasible to collect huge amounts of spatiotemporal …
Pattern mining from historical traffic big data
Knowledge mining from the historical traffic big data is absolutely necessary for future
intelligent transportation system (ITS) and smart city. Mining traffic data is a challenging task …
intelligent transportation system (ITS) and smart city. Mining traffic data is a challenging task …
Deriving traffic flow patterns from historical data
F Soriguera - Journal of Transportation Engineering, 2012 - ascelibrary.org
The development and decreased cost of technology and communications have brought
about a huge increase in the availability of traffic data. With every passing day, traffic …
about a huge increase in the availability of traffic data. With every passing day, traffic …
Pattern recognition using clustering analysis to support transportation system management, operations, and modeling
There has been an increasing interest in recent years in using clustering analysis for the
identification of traffic patterns that are representative of traffic conditions in support of …
identification of traffic patterns that are representative of traffic conditions in support of …