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Prediction of bus passenger trip flow based on artificial neural network:

机译:基于人工神经网络的公交客流预测:

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The bus passenger trip flow is the base data for transit route design and optimization, and the characteristic of urban land use is the important factor for transit trip. However, the standard land use data are difficult to reflect the intensity of transit trip. This research proposed a method based on each zone building, land use situation, and bus accessibility to forecast the bus passenger trip flow in future period. Traffic zone is divided into three categories in accordance with the purpose of the residents travel: residential, commercial, and industrial. Then, by artificial neural network model, the three categories of the traffic zone bus passenger trip flow are forecasted. The method is assessed with the data of Dalian developing zone in China and results show its feasibility and reliability. Finally, the future research direction is discussed.
机译:公交客流是公交路线设计和优化的基础数据,城市土地利用特征是公交出行的重要因素。但是,标准的土地利用数据很难反映出过境旅行的强度。这项研究提出了一种基于每个区域的建设,土地使用情况和公交可达性的方法,以预测未来一段时间的公交客流。交通区根据居民出行的目的分为三类:住宅区,商业区和工业区。然后,通过人工神经网络模型,预测了交通区公交客流的三类。利用中国大连开发区的数据对该方法进行了评估,结果表明了该方法的可行性和可靠性。最后,讨论了未来的研究方向。

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