Purpose-Express freight transportation is in rapid development currently.Owing to the higher speed of express freight train,the deformation of the bridge deck worsens the railway line condition under the action of win...Purpose-Express freight transportation is in rapid development currently.Owing to the higher speed of express freight train,the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge.Besides,the blunt car body of vehicle has poor aerodynamic characteristics,bringing a greater challenge on the running stability in the crosswind.Design/methodology/approach-In this study,the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test.The dynamic model of the train-long-span steel truss bridge coupling system is established,and the dynamic response as well as the running safety of vehicle are evaluated.Findings-The results show that wind speed has a significant influence on running safety,which is mainly reflected in the over-limitation of wheel unloading rate.The wind speed limit decreases with train speed,and it reduces to 18.83 m/s when the train speed is 160 km/h.Originality/value-This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems.It also provides a new theoretical basis for the safety assessment of express freight trains.展开更多
响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选...响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选快递三轮车轨迹数据;然后,引入停留时长阈值作为二次筛选条件;最后,合并临近的聚集点,形成完整的停留点集。采用人工校验识别结果的准确性,并借助熵率法计算停留链的熵率,量化评估不同识别方法的精确度。以苏州市顺丰速运快递网点的快递三轮车配送轨迹数据为实证对象,将所提出的方法与货运卡车停留点识别中常用的基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行对比分析。结果表明,DBSCAN算法易将交通信号灯等待误判为配送停留点,而本文所提出的方法则有效规避了该问题,实现高达98%的精确率和召回率;同时,熵率法的应用进一步验证了所提方法在准确率上的有效性。在此基础上,扩大研究范围并识别配送停留点后,分析快递三轮车的出行链与配送时空分布特征。结果表明,8:00左右的高峰期配送车辆数显著多于16:00左右的高峰期;住宅区为配送热点,车辆数最多,且出行距离和工作时长最长;酒店类配送呈现停留时长较短的特点;此外,停留点空间分布亦揭示了部分配送距离偏远的情况。展开更多
基金supported by the Research Major Project of China Academy of Railway Sciences Group Co.,Ltd(Grant No.2021YJ270)the China National Railway Group Science and Technology Program(Grant No.N2022T001).
文摘Purpose-Express freight transportation is in rapid development currently.Owing to the higher speed of express freight train,the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge.Besides,the blunt car body of vehicle has poor aerodynamic characteristics,bringing a greater challenge on the running stability in the crosswind.Design/methodology/approach-In this study,the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test.The dynamic model of the train-long-span steel truss bridge coupling system is established,and the dynamic response as well as the running safety of vehicle are evaluated.Findings-The results show that wind speed has a significant influence on running safety,which is mainly reflected in the over-limitation of wheel unloading rate.The wind speed limit decreases with train speed,and it reduces to 18.83 m/s when the train speed is 160 km/h.Originality/value-This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems.It also provides a new theoretical basis for the safety assessment of express freight trains.
文摘响应需求的末端配送方案可显著提升顾客满意度,识别并提取末端配送快递三轮车配送停留点特征是分析配送时空分布和动态需求的基础。因此,本文提出结合兴趣点(POI)与停留时长规则的停留点识别方法。首先,利用POI信息和瞬时速度初步筛选快递三轮车轨迹数据;然后,引入停留时长阈值作为二次筛选条件;最后,合并临近的聚集点,形成完整的停留点集。采用人工校验识别结果的准确性,并借助熵率法计算停留链的熵率,量化评估不同识别方法的精确度。以苏州市顺丰速运快递网点的快递三轮车配送轨迹数据为实证对象,将所提出的方法与货运卡车停留点识别中常用的基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行对比分析。结果表明,DBSCAN算法易将交通信号灯等待误判为配送停留点,而本文所提出的方法则有效规避了该问题,实现高达98%的精确率和召回率;同时,熵率法的应用进一步验证了所提方法在准确率上的有效性。在此基础上,扩大研究范围并识别配送停留点后,分析快递三轮车的出行链与配送时空分布特征。结果表明,8:00左右的高峰期配送车辆数显著多于16:00左右的高峰期;住宅区为配送热点,车辆数最多,且出行距离和工作时长最长;酒店类配送呈现停留时长较短的特点;此外,停留点空间分布亦揭示了部分配送距离偏远的情况。
