Name
Abstract | ||
|---|---|---|
Activity-based travel demand models are becoming essential tools used in transportation planning and regional development scenario evaluation. They describe travel itineraries of individual travelers, namely, what activities they are participating in, when they perform these activities, and how they choose to travel to the activity locales. However, data collection for activity-based models is per... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2695438 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Hidden Markov models,Data models,Transportation,Context modeling,Activity recognition,Global Positioning System,Analytical models | Data science,Data modeling,Data collection,Activity recognition,Simulation,Traffic simulation,Engineering,Mobile phone,Transportation planning,Big data,Generative model | Journal |
Volume | Issue | ISSN |
19 | 6 | 1524-9050 |
Citations | PageRank | References |
10 | 0.61 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mogeng Yin | 1 | 10 | 0.61 |
| Madeleine Sheehan | 2 | 10 | 0.61 |
| Sidney Feygin | 3 | 10 | 0.61 |
| Jean-Francois Paiement | 4 | 10 | 0.61 |
| Alexei Pozdnoukhov | 5 | 216 | 18.87 |