Name
Abstract | ||
|---|---|---|
As urban populations grow, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. However, despite great promise, researchers and policy makers lack adequate tools to assess the tradeoffs and benefits of various ride-sharing strategie... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TBDATA.2016.2627223 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Public transportation,Roads,Urban areas,Real-time systems,Big data,Computational modeling,Vehicles | Computer science,Taxis,Public transport,Artificial intelligence,Traffic congestion,Simulation,Operations research,Urban computing,TRIPS architecture,Big data,Machine learning,Scalability,Computational complexity theory | Journal |
Volume | Issue | ISSN |
3 | 3 | 2332-7790 |
Citations | PageRank | References |
4 | 0.43 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masayo Ota | 1 | 11 | 1.08 |
| Huy T. Vo | 2 | 1035 | 61.10 |
| Cláudio T. Silva | 3 | 5054 | 290.90 |
| Juliana Freire | 4 | 3956 | 270.89 |