浅析数字化校园一卡通系统

本文介绍了数字化校园一卡通系统的总体设计目标,详述了各子系统的功能,介绍了系统的安全性和突发事件的防范措施。校园一卡通系统实现全校范围的资源整合和信息共享,构筑数字化的教学、科研、管理和校园生活环境。     

城市公共交通一卡多用系统经济评价研究

为了对城市公共交通一卡多用系统拟建项目的财务可行性和经济合理性进行分析论证,为项目的科学决策提供依据,本丈对城市公共交通一卡多用系统的经济评价基础进行了研究。首先以随机效用理论建立了一卡多用系统业务量预测模型,对系统成本和效益进行了详细的分析,然后讨论了系统的经济评价指标,并提出不同应用环境中评价指标的确定和选取方法,为项目的科学决策提供了坚实的理论基础。     

基于存储论的高速公路通行卡库存管理研究

为了提高高速公路通行卡的流转效率,提出了一种基于存储论的高速公路通行卡库存管理模型。利用高速公路复合通行卡CPC历史流转数据,基于自回归移动平均模型（Autoregressive Integrated Moving Average Model, ARIMA),对CPC卡需求量及流转量进行预测。该模型结合存储论的方法与思想,提出库存管理周期以及最大库存、预警值及最佳订购/调拨量等相关参数,实现了库存管理的动态调整。最后进行实例分析,得出基于存储论的库存管理模型对通行卡需求量与流转量的预测误差分别为7.4%和11.5%,表明该模型预测精度较高且能够根据通行卡流转情况动态调整各相关变量,减少了收费站库存成本,提高了通行卡使用效率以及高速公路服务水平。     

基于IC卡数据的公交客流特征分析 ——以北京市为例

公交客流特征分析是公共汽(电)车线网规划和优化布局的基础.首先简要介绍基于公交IC卡数据分析公交客流特征的技术.进而以北京市为例,对公交客流的时空特征进行宏观、中观分析,对公交服务水平与城市发展的协调适配性做出评估,指出公共汽(电)车系统面临的挑战.最后,结合公交线网布局、运力配置不合理等问题,提出实现公共交通可持续发展的相关建议.     

对地铁自动售检票系统单程票解决方案的探讨

对地铁自动售检票系统(AFC)单程票的解决方案进行了技术和应用方面的分析讨论,阐述了选择可靠的、经济的收费媒体(票卡)对系统的重要性。     

Recognizing metro-bus transfers from smart card data

Transfer points between metro and bus services remain an elusive, yet critical junction for transportation practitioners. Based on massive Smart Card (SC) data, previous studies apply a one-size-fits-all criterion to discriminate between transfers. However, this is not sufficiently convincing for different transfer pairs. To counter this problem, this study applies an association rules algorithm and cluster analysis to recognize metro-to-bus transfers using SC data, and demonstrates transfer recognition in a case study based on SC data collected during a week in Nanjing, China. It is shown that 85% of the transfer-recognition results are quite stable through the whole week, and the median transfer time between metro and bus is below 20?min. The method proposed in this study can be used to identify the busiest transfer points and to obtain average transfer times, which facilitates a smarter and more efficient public transit network.     

基于IC 卡数据的公交通勤熵变模型的构建与应用

建立利用公交IC 卡数据计算公交过剩通勤熵变的框架,对优化城市居民通勤出行距离的难易程度进行定量分析. 结合公交出行的特点,构建基于Wilson 熵模型和双约束重力模型的公交通勤熵变模型;以广州市居民公共交通IC 卡数据为例,对通勤筛选、单元问题及过剩通勤熵变评价等进行探讨. 结果发现,采用约1 km × 1 km的单元格划分是城市公交通勤熵变模型中适合的单元划分方式,公交过剩通勤的熵变能够有效衡量平均通勤距离减少的难易程度和检验改变通勤行为相关政策的有效性.     

内循环厌氧反应器处理脂肪酸废水

研究了厌氧内循环反应器处理脂肪酸废水的启动和运行效果,化学需氧量（Chemical Oxygen De-mand,COD）与容积负荷的关系、容积负荷与出水挥发性脂肪酸（Volatile Fatty Acid,VFA）的关系.结果表明,反应器25 d即可完成启动,达到设计运行负荷25 kg COD/（m3.d）;反应器运行负荷为25 kg COD/（m3.d）时,处理效果最佳,出水pH值为6.80~7.23,VFA约为130 mg/L,COD去除率达到85%以上;pH值变化滞后,VFA的变化比pH值能更好表征反应器内部的运行状况.     

城市轨道交通AFC系统票卡结构及固定流程设计

基于现有技术,AFC(自动售检票)系统很难实现票种的快速设计和发行。针对现有车票卡片结构设计以及票种处理方式的不足,提出了全新的卡片类型结构设计思路和车票交易处理流程设计原则。将票卡类型参数划分为6个区组,并通过各区组的信息组合实现票种信息描述。避免了新增票种带来的各类开发问题和成本压力,可帮助运营公司节约大量的开发费用,以有效缩短新增票种上线时间。     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.