【摘要】：隨著智慧化城市的蓬勃發(fā)展，智能交通正受到人們越來越多的關(guān)注，而公交到站時間預(yù)測也成為了近年來的研究熱點。實時準(zhǔn)確的預(yù)測公交到站時間不僅能幫助出行者選擇更好的出行路線，而且還能為交通部門科學(xué)管理、合理調(diào)度提供依據(jù)。 本文針對公交到站時間預(yù)測問題和換乘問題進行了研究，提出了公交到站時間動態(tài)預(yù)測模型和基于公交到站時間預(yù)測的公交換乘模型，具體的研究內(nèi)容包括以下三個方面： 首先，探究了影響公交到站時間的各種靜態(tài)因素和動態(tài)因素，建立了基于多種靜態(tài)因素和動態(tài)因素的公交到站時間動態(tài)預(yù)測模型。實驗結(jié)果表明，相對于基準(zhǔn)模型，動態(tài)預(yù)測模型有效提高了預(yù)測準(zhǔn)確度。 其次，為了提高動態(tài)預(yù)測模型的效率和準(zhǔn)確度，本文提出了基于波動性的自適應(yīng)預(yù)測模型，該模型對歷史數(shù)據(jù)波動性的統(tǒng)計主要包括以下3個方面：1）不同日期不同時間段到站時間波動性；2）不同日期不同路段到站時間波動性；3）不同時間段不同路段到站時間波動性。模型基于相鄰路段到站時間波動相似性的考慮，將預(yù)測路線分成不同的路段組合進行分段預(yù)測。實驗結(jié)果顯示，自適應(yīng)模型成功減少了模型的計算時間，并進一步提高了預(yù)測準(zhǔn)確度。 最后，針對現(xiàn)有公交換乘模型實時性和可靠性較低問題，，本文提出基于公交到站時間預(yù)測的公交換乘模型，該模型更多地考慮了時間段對換乘等車時間及換乘方式的影響，從而增強了換乘模型的實時性和可靠性。
[Abstract]:With the vigorous development of intelligent cities, people pay more and more attention to intelligent transportation, and bus arrival time prediction has become a hot topic in recent years. The real-time and accurate prediction of bus arrival time can not only help travelers choose a better route, but also provide scientific management and reasonable scheduling basis for traffic departments. In this paper, the problem of bus arrival time prediction and transit transfer is studied, and a dynamic prediction model of bus arrival time and a bus transfer model based on bus arrival time prediction are proposed. The specific research contents include the following three aspects: firstly, the static and dynamic factors affecting bus arrival time are explored, and a dynamic prediction model of bus arrival time based on various static and dynamic factors is established. The experimental results show that the dynamic prediction model can effectively improve the prediction accuracy compared with the benchmark model. Secondly, in order to improve the efficiency and accuracy of the dynamic prediction model, an adaptive prediction model based on volatility is proposed in this paper. The statistics of historical data volatility in this model mainly include the following three aspects: 1) the arrival time volatility of different dates and different time periods; 2) fluctuation of arrival time of different sections on different dates, 3) fluctuation of arrival time of different sections in different time periods. Based on the similarity of arrival time fluctuation of adjacent sections, the model divides the predicted routes into different combinations of sections for segmental prediction. The experimental results show that the adaptive model can reduce the computation time and improve the prediction accuracy. Finally, aiming at the problem of low real-time and reliability of the existing bus transfer model, this paper proposes a bus transfer model based on bus arrival time prediction. The model takes more account of the influence of time period on the transfer time and transfer mode. Thus, the real-time and reliability of the transfer model are enhanced.
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;U491.17

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