Name
Abstract | ||
|---|---|---|
Travel time estimation is a strategically important service in urban environments for personalized and eco-friendly route planning optimization, congestion avoidance, ridesharing and taxi dispatching. However, storing and retrieving traffic data in specific spatiotemporal regions is not an easy task as the data generated by these systems are typically very large and dynamic. In this paper we propose an efficient and scalable solution for real-time travel time estimation of trajectories. In our system buses are used as speed probes to obtain real-time traffic data information and spatio-temporal trajectories are stored in a dynamic indexing system optimized for efficiently retrieving spatiotemporal data in real-time. Our experimental evaluation illustrates the efficiency and scalability of our approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PERCOMW.2015.7133995 | Pervasive Computing and Communication Workshops |
Keywords | Field | DocType |
sensors,time measurement,velocity measurement,congestion avoidance,data traffic retrieval,dynamic indexing system,real-time traffic data information,ridesharing,route planning optimization,spatiotemporal trajectory,speed probe,system bus,taxi dispatching,travel time estimation | Data structure,Data information,Route planning,Computer science,Search engine indexing,Real-time computing,Travel time,Trajectory,Scalability,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitrios Tomaras | 1 | 3 | 2.47 |
| Ioannis Boutsis | 2 | 146 | 12.93 |
| Vana Kalogeraki | 3 | 1686 | 124.40 |