Name
Abstract | ||
|---|---|---|
In many metropolitan areas, traffic congestion is an escalating problem which causes a significant waste of money and time. Nowadays, cars equipped with GPS devices become widespread. The location information of those cars is very useful for estimate traffic condition in the complex city road network. Using the accurate and real time traffic condition, we can provide dynamic route guidance to ease traffic congestion. In this paper, we proposed a speed pattern model, called two phase piecewise linear speed model (2PEED), to estimate traffic condition and represent speed pattern in a road network using GPS data collected vehicles. With the estimated traffic condition and speed pattern, a proposed classification-based route guidance approach using machine learning technique provides dynamic routing for drivers. Using both current traffic data and the experience learned from history data, our route guidance approach is able to accurately predict the future traffic condition and selects a best route. We give simulation results to show that the proposed approach is able to select and dynamically update a route to prove drivers a best (e.g., less traffic and shortest travel time) route to their destination. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ASONAM.2011.46 | ASONAM |
Keywords | Field | DocType |
future traffic condition,gps data based urban guidance,traffic congestion,global positioning system,gps,dynamic route guidance,dynamic routing,learning (artificial intelligence),traffic engineering computing,pattern classification,current traffic data,estimate traffic condition,route guidance,road network,gps data,speed pattern,speed pattern estimation,traffic condition,real time traffic condition,traffic condition estimation,piecewise linear techniques,two phase piecewise linear speed model,road traffic,urban guidance,estimated traffic condition,machine learning,best route,speed pattern model,classification-based route guidance approach,accuracy,data collection,learning artificial intelligence,estimation,piecewise linear,routing | Data mining,Traffic generation model,Traffic flow,Simulation,Computer science,Floating car data,Real-time computing,Global Positioning System,Traffic congestion reconstruction with Kerner's three-phase theory,Metropolitan area,Piecewise linear function,Traffic congestion | Conference |
ISBN | Citations | PageRank |
978-0-7695-4375-8 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Hua Ho | 1 | 84 | 13.79 |
| Yao Chuan Wu | 2 | 4 | 0.78 |
| Meng Chang Chen | 3 | 995 | 298.75 |
| Tsun-Jui Wen | 4 | 0 | 0.34 |
| Yeali S. Sun | 5 | 316 | 33.78 |