【摘要】：隨著智慧化城市的蓬勃發(fā)展，智能交通正受到人們?cè)絹碓蕉嗟年P(guān)注，而公交到站時(shí)間預(yù)測(cè)也成為了近年來的研究熱點(diǎn)。實(shí)時(shí)準(zhǔn)確的預(yù)測(cè)公交到站時(shí)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(duì)公交到站時(shí)間預(yù)測(cè)問題和換乘問題進(jìn)行了研究，提出了公交到站時(shí)間動(dòng)態(tài)預(yù)測(cè)模型和基于公交到站時(shí)間預(yù)測(cè)的公交換乘模型，具體的研究?jī)?nèi)容包括以下三個(gè)方面： 首先，探究了影響公交到站時(shí)間的各種靜態(tài)因素和動(dòng)態(tài)因素，建立了基于多種靜態(tài)因素和動(dòng)態(tài)因素的公交到站時(shí)間動(dòng)態(tài)預(yù)測(cè)模型。實(shí)驗(yàn)結(jié)果表明，相對(duì)于基準(zhǔn)模型，動(dòng)態(tài)預(yù)測(cè)模型有效提高了預(yù)測(cè)準(zhǔn)確度。 其次，為了提高動(dòng)態(tài)預(yù)測(cè)模型的效率和準(zhǔn)確度，本文提出了基于波動(dòng)性的自適應(yīng)預(yù)測(cè)模型，該模型對(duì)歷史數(shù)據(jù)波動(dòng)性的統(tǒng)計(jì)主要包括以下3個(gè)方面：1）不同日期不同時(shí)間段到站時(shí)間波動(dòng)性；2）不同日期不同路段到站時(shí)間波動(dòng)性；3）不同時(shí)間段不同路段到站時(shí)間波動(dòng)性。模型基于相鄰路段到站時(shí)間波動(dòng)相似性的考慮，將預(yù)測(cè)路線分成不同的路段組合進(jìn)行分段預(yù)測(cè)。實(shí)驗(yàn)結(jié)果顯示，自適應(yīng)模型成功減少了模型的計(jì)算時(shí)間，并進(jìn)一步提高了預(yù)測(cè)準(zhǔn)確度。 最后，針對(duì)現(xiàn)有公交換乘模型實(shí)時(shí)性和可靠性較低問題，，本文提出基于公交到站時(shí)間預(yù)測(cè)的公交換乘模型，該模型更多地考慮了時(shí)間段對(duì)換乘等車時(shí)間及換乘方式的影響，從而增強(qiáng)了換乘模型的實(shí)時(shí)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;U491.17

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