城市快速路路段行程時間估計與預測方法研究
發(fā)布時間：2019-06-10 06:05
【摘要】：路段行程時間是描述道路交通狀態(tài)的重要參數(shù),它能夠較好地評價道路的通暢程度,能夠反映道路的運輸效率,在交通規(guī)劃、交通管理與交通控制中起著重要作用,在當前智能交通系統(tǒng)的研究和開發(fā)應用中也占據(jù)著重要地位。 針對城市快速路的路段行程時間估計問題,考慮到微波檢測器技術(shù)成熟、數(shù)據(jù)易獲取以及低成本的特點,本文提出了一種基于微波檢測數(shù)據(jù)的行程—時間域法進行路段行程時間估計。該方法首先假定微波檢測器實時檢測的速度即為路段單元在不同時間單元的空間平均車速,然后構(gòu)建車輛出行的行程—時間域,最后通過模擬虛擬車輛穿越行程—時間域的過程獲得車輛在該路段上的行程時間。該方法以北京市二環(huán)快速路上的微波檢測數(shù)據(jù)為基礎(chǔ)進行實例驗證,結(jié)果表明,相比于傳統(tǒng)靜態(tài)行程時間估計方法,該方法顯著提高了行程時間估計精度。 不僅獲得當前時刻的路段行程時間非常重要,預測未來時刻的路段行程時間也十分重要。本文以提高路段行程時間預測精度為目的,構(gòu)建了基于小波神經(jīng)網(wǎng)絡的路段行程時間預測模型。然后以行程—時間域法估計得到的北京市二環(huán)快速路路段行程時間為實驗數(shù)據(jù),根據(jù)不同參數(shù)選擇、不同樣本數(shù)據(jù)建立多個預測實例對該模型進行檢驗,并與BP神經(jīng)網(wǎng)絡模型的預測誤差進行比較。結(jié)果分析表明,所建立的小波神經(jīng)網(wǎng)絡模型能夠更好地描述輸入輸出的映射規(guī)律。最后,將各個預測實例的結(jié)果進行對比,結(jié)合以往的路段行程時間預測研究,進一步分析了誤差產(chǎn)生的原因以及本文所構(gòu)建的模型取得較高預測精度的原因。本文所構(gòu)建的路段行程時間預測模型及對模型進行的相關(guān)討論,對于交通參數(shù)預測領(lǐng)域的研究具有一定的創(chuàng)新意義和借鑒價值。
[Abstract]:The travel time of road section is an important parameter to describe the state of road traffic. It can better evaluate the unobstructed degree of the road, can reflect the transportation efficiency of the road, and plays an important role in traffic planning, traffic management and traffic control. It also occupies an important position in the research, development and application of intelligent transportation system. In view of the problem of road travel time estimation of urban expressway, considering the mature technology of microwave detector, easy to obtain data and low cost, In this paper, a travel-time domain method based on microwave detection data is proposed to estimate the travel time of road sections. The method first assumes that the speed detected by the microwave detector in real time is the spatial average speed of the section unit in different time units, and then constructs the travel-time domain of the vehicle. Finally, the travel time of the virtual vehicle on the road section is obtained by simulating the process of crossing the travel-time domain of the virtual vehicle. The method is verified by an example based on the microwave detection data on the second Ring Road Expressway in Beijing. The results show that compared with the traditional static travel time estimation method, this method significantly improves the accuracy of travel time estimation. It is very important not only to obtain the travel time of the current time, but also to predict the travel time of the road section in the future. In order to improve the accuracy of road travel time prediction, a wavelet neural network based travel time prediction model is constructed in this paper. Then, taking the travel time estimated by the travel-time domain method as the experimental data, several prediction examples are established to test the model according to the selection of different parameters and different sample data. The prediction error is compared with that of BP neural network model. The results show that the wavelet neural network model can better describe the mapping law of input and output. Finally, the results of each prediction example are compared, and combined with the previous research on road travel time prediction, the causes of errors and the reasons for the higher prediction accuracy of the model constructed in this paper are further analyzed. The road travel time prediction model constructed in this paper and the related discussion of the model have certain innovative significance and reference value for the research in the field of traffic parameter prediction.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.14
本文編號：2496230
[Abstract]:The travel time of road section is an important parameter to describe the state of road traffic. It can better evaluate the unobstructed degree of the road, can reflect the transportation efficiency of the road, and plays an important role in traffic planning, traffic management and traffic control. It also occupies an important position in the research, development and application of intelligent transportation system. In view of the problem of road travel time estimation of urban expressway, considering the mature technology of microwave detector, easy to obtain data and low cost, In this paper, a travel-time domain method based on microwave detection data is proposed to estimate the travel time of road sections. The method first assumes that the speed detected by the microwave detector in real time is the spatial average speed of the section unit in different time units, and then constructs the travel-time domain of the vehicle. Finally, the travel time of the virtual vehicle on the road section is obtained by simulating the process of crossing the travel-time domain of the virtual vehicle. The method is verified by an example based on the microwave detection data on the second Ring Road Expressway in Beijing. The results show that compared with the traditional static travel time estimation method, this method significantly improves the accuracy of travel time estimation. It is very important not only to obtain the travel time of the current time, but also to predict the travel time of the road section in the future. In order to improve the accuracy of road travel time prediction, a wavelet neural network based travel time prediction model is constructed in this paper. Then, taking the travel time estimated by the travel-time domain method as the experimental data, several prediction examples are established to test the model according to the selection of different parameters and different sample data. The prediction error is compared with that of BP neural network model. The results show that the wavelet neural network model can better describe the mapping law of input and output. Finally, the results of each prediction example are compared, and combined with the previous research on road travel time prediction, the causes of errors and the reasons for the higher prediction accuracy of the model constructed in this paper are further analyzed. The road travel time prediction model constructed in this paper and the related discussion of the model have certain innovative significance and reference value for the research in the field of traffic parameter prediction.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.14
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