Name
Abstract | ||
|---|---|---|
Identification of traffic flow is very important since it can help provide dynamic navigation and optimize the performance of vehicular ad hoc networks (VANETs). The existing ways for estimating the traffic state mostly show the drawbacks of large computation and hard implementations. In this paper, we propose a self-organizing way to collect traffic information through VANET communications without any aid of infrastructures. Then, a method based on fuzzy logic is presented to predict the phase of traffic flow timely. This method shows the advantages of easier implementations and less computation, and it can adapt dynamically to take more traffic factors into consideration if needed. With the knowledge of current traffic status, a new routing algorithm for vehicular communication on highways is proposed, which can be adaptive to different phases of traffic flow and guarantee reliable transmission in different environments. The simulation results show that our self-organizing proposal gains satisfactory accuracy according to the real-time identification of traffic flow, and the network performance of the adaptive routing algorithm is improved compared with traditional routing algorithms in terms of packet delivery ratio and throughput; at the same time, the end-to-end delay is still within an acceptable level. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1186/1687-1499-2014-38 | EURASIP J. Wireless Comm. and Networking |
Keywords | Field | DocType |
VANETs, Routing, Traffic flow, Three-phase traffic model, Fuzzy logic | Traffic generation model,Traffic flow,Static routing,Internet traffic engineering,Computer science,Floating car data,Computer network,Real-time computing,InSync adaptive traffic control system,Traffic shaping,Traffic congestion reconstruction with Kerner's three-phase theory | Journal |
Volume | Issue | ISSN |
2014 | 1 | 1687-1499 |
Citations | PageRank | References |
2 | 0.37 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Wang | 1 | 78 | 18.83 |
| Fuqiang Liu | 2 | 270 | 24.48 |
| Dawei Li | 3 | 2 | 0.37 |
| Nguyen Van | 4 | 2 | 0.37 |