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2495. Transit Estimation Models for Transportation Planning
Invited abstract in session WC-57: Forecasting for the middle mile, stream Optimization at Amazon.
Wednesday, 12:30-14:00Room: S06 (building: 101)
Authors (first author is the speaker)
| 1. | Carlos Sanchez Sanchez
|
| Amazon | |
| 2. | Mounir Boujrad
|
| 3. | Tim Forbes
|
| 4. | Olle Green
|
| Machine Learning & Engineering, Amazon | |
| 5. | Madhavan Sriram
|
| Amazon Transportation Services, Amazon | |
| 6. | Chris George
|
| ATS, Amazon |
Abstract
Decision making in Transportation, including network design, reactive adjustments during execution, or post-run analysis, requires an understanding of the vehicle transit time, carbon impact, and risks. In this talk, we explore a unified approach for the estimation of such factors that leverages the information available from vehicle sensors and other geospatial data at each time horizon. We propose a model that combines routing and predictive algorithms to provide transit estimates for both connections between Amazon locations as well as unseen routes between third-party vendor connections. We describe how we deal with noisy telematic signals and sparse ground truth, the machine learning approaches to work with road-level data, and the mechanisms to represent the variability inherent to a prediction that depends on the road conditions and driver behaviour.
Keywords
- Transportation
- Machine Learning
- Vehicle Routing
Status: accepted
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