Name
Abstract | ||
|---|---|---|
This paper proposes a novel approach to traffic state estimation using mobile phones. In this work, a real-time traffic data collection policy based on the so-called "3R" philosophy, a unique vehicle classification method, and a reasonable traffic state quantification model are proposed. The "3R" philosophy, in which the (R) under bar ight data are collected by the (R) under bar ight mobile devices at the (R) under bar ight time, helps to improve not only the effectiveness but also the scalability of the traffic state estimation model. The vehicle classification method using the simple data collected by mobile phones makes the traffic state estimation more accurate. The traffic state quantification model integrates both the mean speed capacity and the density of a traffic flow to improve the comprehensibility of the traffic condition. The experimental results reveal the effectiveness as well as the robustness of the proposed solutions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1587/transcom.E94.B.3447 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | Field | DocType |
mobile probes, "3R" philosophy, vehicle classification, pedestrian recognition, traffic state quantification model | Traffic generation model,Data collection,Traffic flow,Computer science,Simulation,Floating car data,Real-time computing,Robustness (computer science),Mobile device,Traffic conditions,Scalability,Distributed computing | Journal |
Volume | Issue | ISSN |
E94B | 12 | 0916-8516 |
Citations | PageRank | References |
3 | 0.45 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quang Tran Minh | 1 | 97 | 22.78 |
| Eiji Kamioka | 2 | 96 | 21.65 |