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首页> 外文会议>International conference on transportation engineering >Prediction of Market Segmentation Based on Attitudes towards Bus Trips and Risk Preference in an Urban Transit System by Bayesian Network
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Prediction of Market Segmentation Based on Attitudes towards Bus Trips and Risk Preference in an Urban Transit System by Bayesian Network

机译：贝叶斯网络在城市公交系统中基于对公交出行的态度和风险偏好的市场细分预测

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Transportation policy can be more efficient in attracting a considerable number of people to choose public transit as their travel mode when decision-makers tend to develop specific policies considering different groups of people. The market segmentation method based on bus commuters' attitude towards bus trip and their own risk preference is a significant approach to characterize various demands from bus commuters. Traditional segmentation approaches, however, rarely attempted to reveal the connection between commuters' socioeconomics attributes and the result of segmentation due to the fact that classic market segmentation is conducted on the basis of commuters' attitude investigation and analysis. Bayesian Network, an advanced method to make fantastic prediction, can directly predict market segmentation based on commuters' socioeconomic attributes and risk preferences. In this way, the segmentation method can still be valid on the lack of original data of attitude and risk preference. It helps market segmentation to be more practical in demand forecasting. This paper applies Bayesian Network based on K2 and TAN structure learning algorithm respectively to predict market segmentation of attitude and risk preference on the basis of socioeconomics attributes. Traditional segmentation approach is used in this work to verify the precision of predicting segmentation results. Moreover, comparison between K2 and TAN Bayesian Network is made. The results show that the total relative error of TAN network is 29.5% while that of K2 network is 32.7%. Besides, TAN Bayesian Network takes more socioeconomic attributes into consideration than that of K.2 Bayesian Network, which means the structure of TAN network coincides with common sense better. It comes to the conclusion that using Bayesian Network to predict market segmentation based on attitude towards bus trip and risk preference is capable of making the segmentation method plays a more important role in traffic demand forecasting. TAN Bayesian Network, furthermore, owns much stronger effectiveness. The proposed approach is of great help to establish potent transit systems planning and management strategies.
机译：当决策者倾向于考虑不同人群而制定具体政策时，交通政策可以更有效地吸引大量人选择公共交通作为其出行方式。基于公共汽车通勤者对公共汽车出行的态度和他们自己的风险偏好的市场细分方法是表征公共汽车通勤者各种需求的重要方法。但是，由于传统的市场细分是在通勤者的态度调查和分析的基础上进行的，因此传统的细分方法很少尝试揭示通勤者的社会经济学属性与细分结果之间的联系。贝叶斯网络是一种出色的预测方法，可以根据通勤者的社会经济属性和风险偏好直接预测市场细分。这样，在缺乏态度和风险偏好的原始数据的情况下，分割方法仍然可以有效。它有助于市场细分在需求预测中更加实用。本文分别采用基于K2和TAN结构学习算法的贝叶斯网络，根据社会经济学属性预测态度和风险偏好的市场细分。在这项工作中使用传统的分割方法来验证预测分割结果的准确性。此外，还对K2和TAN贝叶斯网络进行了比较。结果表明，TAN网络的总相对误差为29.5％，而K2网络的总相对误差为32.7％。此外，TAN贝叶斯网络比K.2贝叶斯网络考虑了更多的社会经济属性，这意味着TAN网络的结构更符合常识。得出的结论是，利用贝叶斯网络基于对公交出行的态度和风险偏好的态度来预测市场细分，能够使细分方法在交通需求预测中发挥更重要的作用。此外，TAN贝叶斯网络拥有更强大的效力。所提出的方法对建立有效的公交系统规划和管理策略有很大的帮助。

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