本文關(guān)鍵詞： 歷史數(shù)據(jù) 實時數(shù)據(jù) 公交運行指標(biāo) BP神經(jīng)網(wǎng)絡(luò) Neuroph　出處：《北京郵電大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著國家在大中城市越來越倡導(dǎo)綠色出行,并采取了一系列措施來鼓勵大家乘坐地鐵公交出行,從而緩減城市道路擁堵和減少污染物排放、降低PM2.5,更多的人選擇公交作為日常通勤的方式。除了合理布局和優(yōu)化公交線路外,通過挖掘公交運行數(shù)據(jù),給乘客推薦基于實時和歷史數(shù)據(jù),更加精確的乘車方案也是一種提升公交乘坐質(zhì)量的方式。 論文首先對當(dāng)前國內(nèi)外公交換乘方案查詢系統(tǒng)的發(fā)展?fàn)顩r做了描述,基于實時公交數(shù)據(jù)的應(yīng)用大多數(shù)都是應(yīng)用在公交到站時間的預(yù)測上,很少有把實時公交數(shù)據(jù)直接用在公交換乘方案的生成上。于是,論文提出了通過公交運行數(shù)據(jù)實現(xiàn)實時公交換乘方案查詢系統(tǒng)的方案,整個方案首先基于原始公交信息計算得到靜態(tài)換乘方案,然后對公交運行歷史數(shù)據(jù)和實時數(shù)據(jù)在預(yù)處理之后對神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練,通過訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)就可以快速地計算靜態(tài)換乘方案的動態(tài)代價值,基于新的代價值,由靜態(tài)換乘方案生成了動態(tài)換乘方案。 論文對動態(tài)換乘方案生成系統(tǒng)進行了需求分析和實現(xiàn)過程的詳細介紹,分析了系統(tǒng)對數(shù)據(jù)庫的需求、需要的功能模塊,設(shè)計了相關(guān)的數(shù)據(jù)庫表、功能模塊的程序流程、以及模塊調(diào)用接口。并對BP多層神經(jīng)網(wǎng)絡(luò)做了工程上的融合,將其集成到工程代碼中。 論文也介紹了動態(tài)公交換乘系統(tǒng)某些模塊所做的黑盒測試、白盒測試流程和測試結(jié)果,驗證了動態(tài)換乘方案查詢在不同查詢模式和查詢條件下的結(jié)果,以及在生成動態(tài)換乘方案過程中對公交運行指標(biāo)的選取策略。測試結(jié)果顯示,動態(tài)方案可以在生成過程中根據(jù)查詢條件選取實時或者歷史公交運行指標(biāo),根據(jù)不同查詢條件生成不同的動態(tài)換乘方案。另外,也對系統(tǒng)進行了性能測試,測試了系統(tǒng)的并發(fā)性能、方案查詢相應(yīng)時間和系統(tǒng)穩(wěn)定性。
[Abstract]:As the country increasingly advocates green travel in large and medium-sized cities, it has taken a series of measures to encourage people to travel by subway, thereby reducing urban road congestion and reducing pollutant emissions. Reducing PM2.5, more people choose public transport as the daily commuter. In addition to rational layout and optimization of bus routes, by mining transit operation data, recommend passengers based on real-time and historical data. More precise rides are also a way to improve the quality of public transportation. Firstly, the paper describes the development of the query system of bus transfer scheme at home and abroad. Most of the applications based on real-time bus data are applied to the prediction of bus arrival time. Few of the real-time bus data are directly used in the generation of bus transfer scheme. Therefore, the paper puts forward a scheme to realize the query system of real-time bus transfer scheme through transit operation data. The whole scheme is first based on the calculation of the original bus information to obtain the static transfer scheme, and then train the neural network after the pretreatment of the historical data and real-time data of the bus operation. The dynamic substitution value of the static transfer scheme can be calculated quickly by the trained neural network. Based on the new cost value, the dynamic transfer scheme is generated from the static transfer scheme. In this paper, the requirement analysis and implementation process of the dynamic transfer scheme generation system are introduced in detail, and the requirements of the system to the database, the required functional modules, the related database tables and the program flow of the functional modules are analyzed. And the module call interface. The BP multilayer neural network is integrated into the engineering code. The paper also introduces the black box test, the white box test flow and the test results of some modules of the dynamic bus transfer system, and verifies the results of the dynamic transfer scheme query under different query modes and query conditions. The test results show that the dynamic scheme can select real-time or historical bus operation index according to the query condition during the process of generating dynamic transfer scheme. Different dynamic transfer schemes are generated according to different query conditions. In addition, the performance of the system is tested, the concurrent performance of the system is tested, the query time of the scheme and the stability of the system are tested.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;U491.17

【共引文獻】

相關(guān)會議論文 前6條

1 閆法志;梁英慧;;大型綜合交通客運站客流組織動態(tài)仿真研究[A];公路交通與建設(shè)論壇（2011）[C];2003年

2 王q，

本文編號：1544094