论文标题
基于生物地理学的优化和支持矢量回归，用于高速公路旅行时间预测和特征选择
Biogeography-Based Optimization and Support Vector Regression for Freeway Travel Time Prediction and Feature Selection
论文作者
论文摘要
随着旅行者根据旅行时间做出选择，其先前的信息可能有助于他们做出更明智的旅行决策。为了实现这一目标，文献中已经提出了旅行时间预测模型，但是对重要预测变量的识别并没有得到太多关注。重要预测因素的识别降低了输入数据的维度，这不仅减少了计算负载，而且还可以更好地理解重要的预测变量和旅行时间之间的潜在关系。此外，只有重要预测因素的收集可以导致数据收集的大量设备节省。因此，本研究提出了一种混合方法选择特征选择（确定重要的预测因子），并开发强大的高速公路旅行时间预测模型。已经开发了一个集成基于生物地理学的优化（BBO）和支持向量回归（SVR）的框架。通过预测36.1公里长的国家台湾高速公路1号的旅行时间来验证它。拟议的混合方法能够开发一个只有六个预测变量的预测模型，该预测因子的准确度与独立的SVR预测模型相当于所有四十三个预测因子。
As travelers make their choices based on travel time, its prior information can be helpful for them in making more informed travel decisions. To achieve this goal, travel time prediction models have been proposed in literature, but identification of important predictors has not received much attention. Identification of important predictors reduces dimensions of input data, which not only lessens computational load, but also provides better understanding of underlying relationship between important predictors and travel time. Moreover, collection of only important predictors can lead to a significant equipment savings in data collection. Therefore, this study proposes a hybrid approach for feature selection (identifying important predictors) along with developing a robust freeway travel time prediction model. A framework integrating biogeography-based optimization (BBO) and support vector regression (SVR) has been developed. It was validated by predicting travel time at 36.1 km long segment of National Taiwan Freeway No. 1. The proposed hybrid approach is able to develop a prediction model with only six predictors, which is found to have accuracy equivalent to a stand-alone SVR prediction model developed with all forty three predictors.
