Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Traffic volume propagating from upstream road link to downstream road link is the key
parameter for designing intersection signal timing scheme. Recent works successfully used …

Abstract Within Intelligent Transportation Systems (ITSs), modeling of traffic flow assumes a
pivotal role, as it is crucial for alleviating traffic congestion and reducing carbon emissions …

Correlated traffic data refers to a collection of time series recorded simultaneously in
different regions throughout the same transportation network route. Due to the presence of …

In the aftermath of natural disasters, efficient waste collection becomes a crucial challenge,
owing to the dynamic and unpredictable nature of waste generation, coupled with resource …

Short-time traffic flow prediction plays an essential role in Intelligent Transportation System.
Mixed flow is a common type of short-time traffic flow, with characteristics of small sample …

Traffic flow data (TFD) prediction is a hot research area in intelligent transportation system.
TFD is non-stationary and nonlinear, so it has become a challenge to predict it accurately. In …

Traffic flow forecasting is one of the essential means to realize smart cities and smart
transportation. The accurate and effective prediction will provide an important basis for …

To improve the management ability of expressway service area and formulate strategies for
traffic flow changes in time, a short-term traffic flow prediction model is proposed. Firstly …

Widespread traffic congestion significantly impacts the quality of life, posing several
problems and challenges. To reduce traffic congestion, it is necessary to have accurate …