交通运输研究
交通運輸研究
교통운수연구
Transportation Standardization
2015年
2期
90-96
,共7页
张国胜%贾正文%魏海霞%苏二磊%韩宁宁
張國勝%賈正文%魏海霞%囌二磊%韓寧寧
장국성%가정문%위해하%소이뢰%한저저
订票系统%BP神经网络%学生客流%出行管理%校园
訂票繫統%BP神經網絡%學生客流%齣行管理%校園
정표계통%BP신경망락%학생객류%출행관리%교완
booking system%BP neural network%students passenger flow%travel management%cam-pus
针对学生群体出行计划性强、组织性好、客流高度集中的特点,基于B/S体系结构、Ajax等技术,并运用BP神经网络客流预测模型,开发出校园公路客运订票系统。通过该订票系统,可以实现学生客运车票的订购,并且学校管理部门能够查询学生出行的实时数据,便于学校管理部门及时了解学生出行动态,实现学校对学生安全出行及外出集中实习教学的管理。客运部门应用本系统可及时处理学生客流所引发的客流高峰问题,并通过对学生订票信息数据的挖掘和预测分析,可提高信息化管理水平和服务质量。应用该订票系统对客运量进行预测,结果表明预测数据与实际客运量误差较小,系统可提供较准确的客运信息。
針對學生群體齣行計劃性彊、組織性好、客流高度集中的特點,基于B/S體繫結構、Ajax等技術,併運用BP神經網絡客流預測模型,開髮齣校園公路客運訂票繫統。通過該訂票繫統,可以實現學生客運車票的訂購,併且學校管理部門能夠查詢學生齣行的實時數據,便于學校管理部門及時瞭解學生齣行動態,實現學校對學生安全齣行及外齣集中實習教學的管理。客運部門應用本繫統可及時處理學生客流所引髮的客流高峰問題,併通過對學生訂票信息數據的挖掘和預測分析,可提高信息化管理水平和服務質量。應用該訂票繫統對客運量進行預測,結果錶明預測數據與實際客運量誤差較小,繫統可提供較準確的客運信息。
침대학생군체출행계화성강、조직성호、객류고도집중적특점,기우B/S체계결구、Ajax등기술,병운용BP신경망락객류예측모형,개발출교완공로객운정표계통。통과해정표계통,가이실현학생객운차표적정구,병차학교관리부문능구사순학생출행적실시수거,편우학교관리부문급시료해학생출행동태,실현학교대학생안전출행급외출집중실습교학적관리。객운부문응용본계통가급시처이학생객류소인발적객류고봉문제,병통과대학생정표신식수거적알굴화예측분석,가제고신식화관리수평화복무질량。응용해정표계통대객운량진행예측,결과표명예측수거여실제객운량오차교소,계통가제공교준학적객운신식。
For students travel was strongly planed, well organized, and highly centralized, a campus booking system for highway passenger was developed based on B/S architecture, Ajax technology, and the BP neural network prediction model. Through the booking system, students can order tickets. And the administrative department of schools can search real-time data about students′ travel at the same time, grasp the dynamic status of students′travel, achieve the management of students′safety travel and centralized internship teaching. The system was easy to help passenger transport departments timely han?dle problems caused by students′peak flow. Moreover, through digging and forecasting data about ticket?ing information, passenger transport department can improve the level of informatization management and the quality of service. The passenger volume was predicted by the booking system. The results showed that the error between predicted data and actual passenger volume was small, and more accurate information of passenger transport was provided by the booking system.