Name
Abstract | ||
|---|---|---|
In urban traffic network, the traffic flow within several adjacent intersections is strongly relevant and traffic congestion happened on one road section will probably result in cascade of regional congestion. In this paper, we try to optimize each traffic section state to a desired consensus state in traditional subarea. Because of the difficulty to execute real experiment, we apply ACP computational experiments method to realize the process. Firstly, we establish a regional road network traffic model and set up traffic state equation with traffic density as traffic state and leaving proportions of traffic flow on sections during effective green per cycle as control input. Secondly, we design a state feedback control strategy to regulate the leaving proportions of each section which indirectly reflects the effective green time, according to the difference between real-time traffic states and desired traffic consistency state. Thirdly, we apply computational experiments method to find the right value of leaving proportions indexes which will make section traffic state converge towards the desired consensus states and balance the distribution of traffic in overall regional traffic network. The MATLAB simulation results show the feasibility and efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ITSC.2013.6728450 | 2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC) |
Keywords | Field | DocType |
traffic network optimization, traffic state consistency, regional traffic network modeling, ACP computational experiment | Traffic optimization,Traffic generation model,Road traffic control,Traffic flow,Simulation,Floating car data,Traffic congestion reconstruction with Kerner's three-phase theory,Engineering,Traffic shaping,Network traffic control | Conference |
Volume | Issue | ISSN |
null | null | 2153-0009 |
Citations | PageRank | References |
1 | 0.38 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Wang | 1 | 11 | 3.06 |
| Dai Li | 2 | 2 | 0.73 |
| Xiaoming Liu | 3 | 1 | 0.38 |
| Zhengxi Li | 4 | 1 | 0.38 |