Predictive Simulation of Public Transportation Using Deep Learning
| Autor: | Gary Tan, Muhammad Shalihin Bin Othman |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
050210 logistics & transportation education.field_of_study SIMPLE (military communications protocol) Operations research Computer science business.industry Deep learning 05 social sciences Population Neural network regression Predictive simulation 03 medical and health sciences 030104 developmental biology Traffic congestion Order (exchange) Public transport 0502 economics and business Artificial intelligence business education |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811328527 |
| Popis: | Traffic congestion has been one of the most common issues faced today as the rising population and growing economy calls for higher demands in efficient transportation. Looking into the public transport system in Singapore, we investigate its efficiency through a simple simulation and introduced predictive travel times to simulate ahead in future so as to identify congestion issues well in advance. Public transport operators can then utilize the reports to apply strategic resolutions in order to mitigate or avoid those issues beforehand. A deep neural network regression model was proposed to predict congestion, which is then used to compute future travel times. Experiments showed that the proposed methods are able to inject a better sense of realism into future simulations. |
| Databáze: | OpenAIRE |
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