本文選題：城市快速路 + 短時(shí)交通流預(yù)測(cè)��； 參考：《西南交通大學(xué)學(xué)報(bào)》2017年05期

【摘要】：為解決傳統(tǒng)的ALINEA(asservissement linéaire d'entrée autoroutière)匝道控制算法未考慮城市快速路入口匝道排隊(duì)溢出,造成關(guān)聯(lián)交叉口交通擁堵等問(wèn)題,在經(jīng)典的ALINEA匝道控制算法的基礎(chǔ)上,提出了一種新的基于主干道車流量預(yù)測(cè)的城市快速路入口匝道控制方法.該方法采用遺傳算法優(yōu)化的小波神經(jīng)網(wǎng)絡(luò)來(lái)預(yù)測(cè)城市快速路交通流量;引入主干道車流可插入間隙和匝道排隊(duì)分級(jí)控制原則,實(shí)現(xiàn)了對(duì)城市快速路入口匝道控制率的動(dòng)態(tài)調(diào)節(jié).通過(guò)微觀仿真實(shí)驗(yàn)比較兩種算法的控制效果.結(jié)果表明:與傳統(tǒng)的ALINEA匝道控制算法相比,新的控制方法不僅能夠有效保證主線交通通行能力,同時(shí)還使匝道平均旅行時(shí)間減少了24.8%.
[Abstract]:In order to solve the problem that the traditional ALINEA(asservissement lin 茅 aire d'entr 茅 e autorouti 貓 re ramp control algorithm does not take into account the queue overflow of the urban expressway ramp, resulting in traffic congestion at the associated intersections, the classical ALINEA ramp control algorithm is used to solve the problem. This paper presents a new approach to control the on-ramp of urban expressway based on the traffic flow prediction of the main road. In this method, the wavelet neural network optimized by genetic algorithm is used to predict the traffic flow of urban expressway, and the principle of interchangeable gap and ramp queuing grading control is introduced to realize the dynamic regulation of the on-ramp control rate of urban expressway. The control effects of the two algorithms are compared by microscopic simulation experiments. The results show that compared with the traditional ALINEA ramp control algorithm, the new control method can not only effectively guarantee the main line traffic capacity, but also reduce the average ramp travel time by 24. 8%.
【作者單位】： 西南交通大學(xué)交通運(yùn)輸與物流學(xué)院;西南交通大學(xué)綜合交通運(yùn)輸智能化國(guó)家地方聯(lián)合工程實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51578465,71402149) 重慶市應(yīng)用開發(fā)計(jì)劃重點(diǎn)資助項(xiàng)目(cstc2014yykfB30003,2015H01373)
【分類號(hào)】：U491

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本文編號(hào)：1789239