Abstract
Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.
Highlights
Transportation provides several benefits to the world in terms of worldwide cargo delivery, providing access to people, enhancing the quality of life and the overall well-being of the economy and environment
Convolutional neural network (CNN) (6%), long short-term memory (LSTM) (6%) and linear regression (LR) (6%) are competing algorithms, used to a similar extent for TTP, while other papers used each of the remaining techniques for TTP (37%)
We report the rate at which the factors are used based on the five main groups of the factors: Temporal Factors (TF), Traffic Information (TrI), Spatial Factors (SF), Augmented Factors (AF) and Personalized Information (PI)
Summary
Transportation provides several benefits to the world in terms of worldwide cargo delivery, providing access to people, enhancing the quality of life and the overall well-being of the economy and environment. It presents significant challenges including fuel consumption, traffic congestion, carbon emissions, economic and environmental costs. The availability of a large transportation dataset and the ability to process a significant amount of data has attracted lots of attention in recent years to research in the area of transportation modelling for. A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities.
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