基于差分进化算法SVM的公交通勤乘客识别

Public Traffic Passenger Recognition Based on Differential Evolution Algorithm SVM

通勤乘客是在早晚高峰出行并具有一定出行规律的人群,准确地从公交刷卡数据中识别通勤人群,对采取措施缓解早晚高峰交通拥堵和整个城市公交线网规划和调整具有重要意义。本文以珠海市公交IC卡数据为依托,提出一种基于差分进化算法优化支持向量机(SVM)的公交通勤识别方法。首先通过通勤乘客调查和实际刷卡数据相结合,分析出通勤乘客出行的特征属性;然后采用SVM算法构建分类识别模型,并利用差分进化算法(DE)对SVM进行参数寻优,得到最优识别模型,其识别准确率高达94.28%,优于其他算法模型;最后利用该模型对珠海公交IC卡数据中的通勤人群进行识别,结果显示其公交通勤人数为178 259人,占公交出行总人数的21.47%。

Commuter passengers are people who travel during the rush hours in the morning and evening and have a regular pattern of travel.Accurate identification of commuter crowds from bus credit card data is of great significance for taking measures to alleviate traffic congestion during the rush hours in morning and evening and for overall urban line network planning and adjustment.Based on the data of Zhuhai bus IC card,this paper proposes a public transportation identification method based on differential evolutionary algorithm to optimize support vector machine(SVM).Firstly,the commuter passenger survey data are combined with the actual swipe data to analyze the characteristic attributes of commuter passenger travel.Then the SVM algorithm is used to build the classification recognition model,and a differential evolution algorithm(DE)is used to optimize the parameters of the SVM to obtain the optimal identification model,whose recognition accuracy is as high as94.28%,better than other algorithm models.Finally,the model is used to identify the commuters in the Zhuhai bus IC data.The results show that the number of public transportation personnel is178,259,accounting for21.47%of the total number of bus trips.