本文選題：OD預測 + 非負矩陣分解　； 參考：《西南大學》2017年碩士論文

【摘要】：隨著中國城鎮(zhèn)化進程日益加快,城市規(guī)模急劇擴張,居民出行方式不斷變化,居民出行范圍逐步擴大,但由于城市地理位置的限制及交通基礎設施的建設滯后,從而導致一系列制約城市經濟發(fā)展和居民生活水平提高的城市交通問題,包括交通擁堵、資源分配不均等。研究城市居民出行模式和交通流量波動現象為解決出租車空載率過高、居民出行需求無法滿足、交通管理效率較低等交通問題提供可能性。鑒于此,本文通過對出租車GPS數據的分析,挖掘居民出行規(guī)律及區(qū)域交通流的波動現象,并提出基于非負矩陣分解的自回歸預測模型和實現對交通流波動現象的定量分析,為交通使用者提供實時有效的出行信息和有效的交通流量波動演化規(guī)律,從而對緩解目前的交通問題提供實質性地幫助及建議。面對日益嚴重的城市交通問題,本文通過挖掘和分析海量出租車GPS軌跡數據,基于矩陣分解算法和時間序列模型實現對起訖(Origin-Destination,OD)矩陣的預測。并通過對流量粗�；＜皬碗s系統(tǒng)波動理論分析,本文已實現對區(qū)域網絡單節(jié)點流量的動態(tài)分析、全節(jié)點流量的波動規(guī)律以及區(qū)域網絡系統(tǒng)內外部流量的分離分析。本文主要貢獻如下:(1)提出基于非負矩陣分解的自回歸模型,主要通過對OD矩陣的非負特征和可模擬的用戶出行特征,本文引入非負矩陣分解算法對用戶出行特征進行分析和宏觀描述;同時,在此基礎上利用自回歸模型對OD矩陣進行預測和估計。(2)基于北京市出租車交通數據,實現基于非負矩陣分解的自回歸(Nonnegative Matrix Factorization-Auto Regressive,NMF-AR)模型對OD矩陣的預測�；诔鲎廛嘒PS數據,通過NMF-AR模型挖掘和預測用戶出行信息,同時與引入的短時交通流預測模型進行對比分析,并且對預測精度、模型參數、數據敏感度等問題進行深入分析,驗證模型的預測能力。通過對OD矩陣實時預測分析可為居民提供實時有效的出行信息,可有效降低空載率,提高運營效益。(3)實現基于流量的粗粒化建模和深入分析交通流波動情況。在北京市區(qū)域棋盤式劃分策略和區(qū)域交通網絡的基礎上,本研究對北京市出租車GPS數據進行適當的數據預處理工作。一方面,針對單個區(qū)域的交通流量波動情況,本文利用粗粒化方法處理流量變化構建對應的網絡,同時分析節(jié)點車流量的波動情況。另一方面,本文對區(qū)域網絡交通流量波動的規(guī)律和演化進行重點分析,并對多個區(qū)域交通流量的總體特性進行考察,并實證分析該波動規(guī)律�；诒本┦谐鲎廛嘒PS軌跡數據,本文從真實區(qū)域出租車流量實證分析其基于時間的流量均值和標準差之間的關系。同時通過對區(qū)域網絡內外部流量的分解分析,發(fā)現網絡系統(tǒng)流量波動的演化規(guī)律。從而為交通管理部門優(yōu)化和制定交通管理策略有效信息。綜上,通過用戶出行OD矩陣建立NMF-AR預測模型、基于區(qū)域流量的粗�；治黾敖煌鲀缏涩F象定量分析,可為交通運營提供實時的出行規(guī)律,為交通監(jiān)管部門提供有效的建議,從而有助于降低出租車空載率、優(yōu)化交通應急管理、提升城市交通運行效率。
[Abstract]:With the rapid urbanization process in China, the urban scale is expanding rapidly, the mode of resident travel is changing, and the residents' travel scope is gradually expanding. However, because of the limitation of urban geographical location and the lagging of the construction of traffic infrastructure, a series of urban traffic problems which restrict the development of urban economy and the improvement of the living standard of residents are caused. Including traffic congestion and uneven distribution of resources. The study of urban residents' travel mode and traffic flow fluctuation is a possibility to solve the problem of overloading of taxi no-load, the inability to meet the needs of the residents and the low efficiency of traffic management. In view of this, this paper makes an analysis of the GPS data of the taxi and excavates the laws and areas of the residents' travel. The fluctuation phenomenon of the traffic flow is presented, and the autoregressive prediction model based on the non negative matrix decomposition and the quantitative analysis of the fluctuation of traffic flow are put forward to provide the traffic users with real time effective travel information and the effective evolution law of the traffic flow fluctuation, thus providing substantial help and suggestion to alleviate the current traffic problems. Facing the increasingly serious urban traffic problems, this paper realizes the prediction of the Origin-Destination (OD) matrix based on the matrix decomposition algorithm and the time series model by mining and analyzing the mass taxi GPS trajectory data. Through the rough modeling of the traffic and the analysis of the wave theory of the complex system, this paper has realized the regional network single. The dynamic analysis of node flow, the fluctuation law of the full node flow and the separation and analysis of the internal and external flow of the regional network system. The main contributions of this paper are as follows: (1) the autoregressive model based on the non negative matrix decomposition is proposed. The non negative matrix decomposition algorithm is introduced in this paper mainly through the non negative characteristics of the OD matrix and the simulated user travel characteristics. The characteristics of user travel are analyzed and macroscopically described; at the same time, the OD matrix is predicted and estimated by the autoregressive model. (2) based on the Beijing taxi traffic data, the prediction of the OD matrix based on the Nonnegative Matrix Factorization-Auto Regressive (NMF-AR) model based on the non negative matrix decomposition is based on the prediction of the OD matrix. Taxi GPS data, through NMF-AR model mining and prediction of user travel information, and compared with the introduction of short-term traffic flow prediction model, and in-depth analysis of the prediction accuracy, model parameters, data sensitivity and other issues to verify the prediction ability of the model. Through the real-time prediction analysis of the OD matrix can provide the residents with real information. The effective travel information can effectively reduce the no-load rate and improve the operation efficiency. (3) realize the rough modeling based on the traffic flow and analyze the fluctuation of traffic flow. On the basis of the regional chessboard division strategy and regional traffic network in Beijing, this study carries out the appropriate data preprocessing to the taxi GPS data in Beijing. In view of the fluctuation of traffic flow in a single region, this paper uses the coarse graining method to deal with the flow change and constructs the corresponding network, and analyzes the fluctuation of the node traffic flow. On the other hand, this paper focuses on the law and evolution of the regional network traffic flow fluctuation, and the overall characteristics of the traffic flow in multiple regions. Based on the taxi GPS trajectory data of Beijing City, this paper empirically analyses the relationship between the mean and standard deviation of the time based traffic flow from the real area taxi traffic flow. At the same time, through the analysis of the internal and external flow of the regional network, the evolution of the flow fluctuation of the network system is found. On the other hand, the traffic management department optimizes and establishes the effective information of traffic management strategy. To sum up, the NMF-AR prediction model is established by the OD matrix of user travel. Based on the roughing analysis of regional flow and the quantitative analysis of the power law phenomenon of traffic flow, it can provide real time travel rules for traffic operation and provide effective suggestions for traffic supervision departments. It helps to reduce the empty load rate of taxis, optimize traffic emergency management and improve the efficiency of urban traffic operation.

【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491

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本文編號：1838978