信控交叉口逆向可變車道最優(yōu)分配模型(英文)
發(fā)布時(shí)間:2024-12-26 05:19
為了提高左轉(zhuǎn)交通量較大的交叉口的運(yùn)行效率,提出了逆向可變車道(EFL)最優(yōu)分配模型.通過分析設(shè)置逆向可變車道的約束條件,包括逆向可變車道的數(shù)量和長度、流量流向約束、信號約束等,將約束條件及控制變量組合在一個(gè)統(tǒng)一的框架中同步優(yōu)化.將交叉口的車均延誤和左轉(zhuǎn)通行能力作為目標(biāo)函數(shù),通過非支配排序的遺傳算法(NSGA-Ⅱ)求解模型.結(jié)果表明,對逆向可變車道進(jìn)行最優(yōu)分配后,交叉口的車均延誤可以減少14.9%,左轉(zhuǎn)通行能力可以提高19.3%,驗(yàn)證了逆向可變車道最優(yōu)分配模型的有效性.
【文章頁數(shù)】:7 頁
【文章目錄】:
1 Model Formulation
1.1 Optimization objective selection
1.2 Input data
1) Lane allocation variables
2) Minimum permitted movement on approach lanes
3) Cycle length
4) Lane signal setting
5) Start of the green time setting
6) Duration of green
7) Order of signal displays
8) Flow conservation
9) Mixed-usage-area constraint
10) Flow factor
11) Average delay of the vehicle
1.3 Optimization formulations
2 Model Solution Based on Genetic Algorithm
3 Numerical Studies
4 Conclusions
本文編號:4020592
【文章頁數(shù)】:7 頁
【文章目錄】:
1 Model Formulation
1.1 Optimization objective selection
1.2 Input data
1) Lane allocation variables
2) Minimum permitted movement on approach lanes
3) Cycle length
4) Lane signal setting
5) Start of the green time setting
6) Duration of green
7) Order of signal displays
8) Flow conservation
9) Mixed-usage-area constraint
10) Flow factor
11) Average delay of the vehicle
1.3 Optimization formulations
2 Model Solution Based on Genetic Algorithm
3 Numerical Studies
4 Conclusions
本文編號:4020592
本文鏈接:http://sikaile.net/kejilunwen/jiaotonggongchenglunwen/4020592.html
最近更新
教材專著