Name
Abstract | ||
|---|---|---|
Real time system for vehicle travel time and traffic flow is an essential part of Intelligent Transportation Systems. In many Chinese cities, the interactions among buses, bicycles and cars bring difficulty to travel time prediction and traffic safety management. The aim of this paper is to develop a new model to estimate car travel time near bus stops in developing countries by data mining techniques and survival analysis methods. The travel time data under mixed traffic conditions are collected by video camera. Four influential factors including car volume, nonmotorized volume, bus departure volume and free ratio of bus stop are chosen by using data mining techniques. A proportional hazard-based duration model is proposed to analyze the factors related to car travel time. The results indicate that mixed traffic flow impacts the car travel time significantly. In addition, various factors can modify the travel time distribution in different degrees and the model can be used to estimate the travel time under assumed conditions. It is hoped to help improve the planning and designing of proper facilities with mixed traffic flow. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1080/18756891.2011.9727885 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | Field | DocType |
time prediction, data mining, survival data, bus stop, mixed traffic flow, traffic safety | Traffic wave,Mathematical optimization,Traffic flow,Simulation,Transport engineering,Floating car data,Real-time operating system,Intelligent transportation system,Travel time,Video camera,Traffic conditions,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 6 | 1875-6891 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobao Yang | 1 | 5 | 2.61 |
| Mei Huan | 2 | 0 | 1.01 |
| Hongwei Guo | 3 | 0 | 0.34 |
| Liang Gao | 4 | 19 | 2.14 |