本文選題：實時公交 + 公交車到達時間預(yù)測��； 參考：《東北師范大學(xué)》2015年碩士論文

【摘要】：為了促進城市交通的可持續(xù)發(fā)展,必須優(yōu)先發(fā)展公共交通。為候車乘客提供公交車到達時間預(yù)報服務(wù)有利于提高公共交通服務(wù)水平。因此本文利用公交車的GPS數(shù)據(jù)對公交到達時間預(yù)測技術(shù)做了研究。由于公交車的行駛受到了很多環(huán)境因素的影響,實現(xiàn)公交車到達時間的精確預(yù)測是一個復(fù)雜而且困難的問題。很多研究者為了解決該難題,提出了很多關(guān)于公交車到達時間預(yù)測模型,總結(jié)當(dāng)前這些模型主要存在的問題有：未考慮實時路況、模型設(shè)計依賴于經(jīng)驗,過擬合,易陷入局部最優(yōu),基于一定假設(shè)而不能滿足實際情況,計算時間復(fù)雜度較高而不能滿足實時應(yīng)用需求,模型考慮因素過少,不合理的模型結(jié)構(gòu)使得出現(xiàn)誤差累加的情況�；诋�(dāng)前預(yù)測模型的研究現(xiàn)狀,本文選擇支持向量機作為預(yù)測模型的理論基礎(chǔ)。支持向量機作為一種較新的機器學(xué)習(xí)算法能完成對復(fù)雜的非線性關(guān)系的建模,模型設(shè)計不依賴于經(jīng)驗和任何假設(shè),不存在過擬合及陷入局部最優(yōu)的問題。支持向量機在利用已訓(xùn)練好的模型提供預(yù)測服務(wù)的時間復(fù)雜度很低,能滿足實時應(yīng)用需求。由于原始訓(xùn)練集過大導(dǎo)致支持向量機的模型訓(xùn)練不具有可計算性,故本文將對原始訓(xùn)練集進行劃分,并以一種多叉樹結(jié)構(gòu)組織這些劃分后的小訓(xùn)練集,同時這也避免了誤差累加的問題。為了提高預(yù)測模型的精度,本文考慮了多種影響因素,包括當(dāng)前時刻的路況信息。本文首先對公交車的運行耗時做了適當(dāng)?shù)姆治?并提出了一種公交線路分段化處理的方案；其次,充分考慮節(jié)假日、高峰期、天氣、實時路段等因素,提出了一個簡單的公交車到達時間預(yù)測模型和一個相對復(fù)雜的基于E-SVR的公交車到達時間預(yù)測模型；最后利用真實的GPS數(shù)據(jù)并對預(yù)測模型的預(yù)測精度進行了評估,結(jié)果顯示提出的預(yù)測模型具有較高的預(yù)測精度；另外,本文還提出了數(shù)據(jù)預(yù)處理階段的關(guān)鍵算法：確定公交站點邊界、確定車輛進出站時間、確定車輛發(fā)車時間。本文以深圳公交為背景,基于本文的研究內(nèi)容,實現(xiàn)了一個可提供公交車到達時間預(yù)報服務(wù)的軟件系統(tǒng),并且該系統(tǒng)已經(jīng)對外提供服務(wù),產(chǎn)生了應(yīng)用價值。
[Abstract]:In order to promote the sustainable development of urban transportation, priority must be given to the development of public transport. Providing bus arrival time forecast service for waiting passengers is helpful to improve the service level of public transport. In this paper, the GPS data of buses are used to predict the arrival time of buses. Due to the influence of many environmental factors, it is a complicated and difficult problem to realize the accurate prediction of bus arrival time. In order to solve this problem, many researchers put forward a lot of bus arrival time prediction models. The main problems of these models are: not considering the real-time traffic conditions, the model design depends on experience, over-fitting. It is easy to fall into local optimum, based on certain assumptions but can not meet the actual situation, high computational time complexity can not meet the needs of real-time applications, model considerations are too few, unreasonable model structure makes the case of error accumulation. Based on the current situation of prediction model, support vector machine (SVM) is chosen as the theoretical basis of prediction model. As a new machine learning algorithm, support vector machine (SVM) can model complex nonlinear relations. Model design does not depend on experience and any assumptions, and there is no problem of over-fitting and falling into local optimum. Support vector machines (SVM) have low time complexity in providing prediction services using trained models and can meet the requirements of real-time applications. Because the model training of support vector machine is not computable because the original training set is too large, this paper will divide the original training set and organize these small training sets with a multi-tree structure. At the same time, this also avoids the problem of accumulation of errors. In order to improve the accuracy of the prediction model, this paper takes into account a variety of factors, including the traffic information at the current time. In this paper, the running time of the bus is analyzed properly, and a scheme of segmenting the bus line is put forward. Secondly, the factors such as holidays, peak hours, weather and real time sections are taken into full consideration. A simple bus arrival time prediction model and a relatively complex bus arrival time prediction model based on E-SVR are proposed. Finally, the prediction accuracy of the prediction model is evaluated by using the real GPS data. The results show that the proposed prediction model has high prediction accuracy. In addition, the key algorithms in the data preprocessing stage are proposed in this paper: to determine the bus stop boundary, to determine the time of the vehicle entering and leaving station, and to determine the vehicle departure time. In this paper, based on the research content of Shenzhen bus, a software system is implemented to provide bus arrival time forecast service, and the system has already provided the service to the outside, which has produced the application value.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495

【參考文獻】

相關(guān)期刊論文 前1條

1 左忠義;汪磊;;公交到站時間實時預(yù)測信息發(fā)布技術(shù)研究[J];交通運輸系統(tǒng)工程與信息;2013年01期

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本文編號：1858644