… Short-term prediction of traffic conditions is one of the central … to select the most appropriate
traffic prediction method for one … It is concluded that apart from traffic simulation models only …

… the process of developing short‐term traffic forecasting algorithms into three essential clusters:
… flow that can be used as a framework for developing short‐term traffic forecasting models. …

… traffic prediction models … for traffic prediction ( Figure 1). Due to the broad ground covered,
the methods are described only qualitatively. Three (overlapping) strands of traffic prediction …

… review and related work about traffic prediction models. We explored modeldriven and data-…
considered when characterizing and developing new short-term traffic prediction methods. …

… Short term traffic forecasting has been a very important consideration in many areas of
transportation research for more than 3 decades. This interest is the direct result of an increasing …

… of traffic data is generated every day. This has led to rapid progress in short-term traffic
prediction (… In traffic networks with complex spatiotemporal relationships, deep neural networks (…

… short-term traffic forecast via deep learning approaches. A novel traffic forecast model based
on long short-term … authors proposed a novel short-term traffic forecast model. By combining …

… The development and implementation of the time series shortterm traffic prediction model
took place on the 62.5 km corridor of I-4 in Orlando, Florida. Along the corridor are 70 inductive …

… as significant determinants of traffic flow characteristics on freeways, … short-term traffic
forecasting models. We evaluate the effects of weather and traffic mix on the predictability of traffic …

… We compare our model against well-known existing machine/deep … prediction models. Our
results indicate that our ALLSCP model consistently achieves the most accurate predictions ( …