Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
A precise Estimated Time of Arrival (ETA) finds applications in various domains, such as navigation and logistics systems. This problem has gained a lot of attention from the research community. Machine learning has recently been applied and has shown promising results for ETA. Machine learning approaches can be divided into two categories, which are route-based and origin-destination-based methods. The first one divides the route into segments and predicts the ETA based on the information of these segments. The last one predicts ETA based on a few natural information, such as the origin, the estimation, and the departure time. In this paper, we aim to review recent studies of the mentioned machine learning approaches for ETA to determine the necessary input for an ETA forecasting model, the critical factors, and suitable approaches for ETA. Furthermore, we will discuss promising research directions to improve ETA, such as formulating ETA as a time series forecasting problem, including uncertainty or using ensemble learning models.
Recommended Citation
Pham, Truong Son; Nistor, Marian Sorin; Cao, Loi; Gerschberger, Markus; and Moll, Maximilian, "Machine Learning in Vehicle Travel Time Estimation: A Brief Technological Perspective and Review" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/da/supply_chain/3
Machine Learning in Vehicle Travel Time Estimation: A Brief Technological Perspective and Review
Hilton Hawaiian Village, Honolulu, Hawaii
A precise Estimated Time of Arrival (ETA) finds applications in various domains, such as navigation and logistics systems. This problem has gained a lot of attention from the research community. Machine learning has recently been applied and has shown promising results for ETA. Machine learning approaches can be divided into two categories, which are route-based and origin-destination-based methods. The first one divides the route into segments and predicts the ETA based on the information of these segments. The last one predicts ETA based on a few natural information, such as the origin, the estimation, and the departure time. In this paper, we aim to review recent studies of the mentioned machine learning approaches for ETA to determine the necessary input for an ETA forecasting model, the critical factors, and suitable approaches for ETA. Furthermore, we will discuss promising research directions to improve ETA, such as formulating ETA as a time series forecasting problem, including uncertainty or using ensemble learning models.
https://aisel.aisnet.org/hicss-57/da/supply_chain/3