This paper assesses the vehicle dynamics of a new cargo bike concept developed for euro pallet sized cargo. The cargo bike developed is for last-mile delivery. Different aspects of manoeuvrability and stability are ex...This paper assesses the vehicle dynamics of a new cargo bike concept developed for euro pallet sized cargo. The cargo bike developed is for last-mile delivery. Different aspects of manoeuvrability and stability are examined using a series of manoeuvres based on tests from the automotive industry combined with bicycle industry regulations. These manoeuvres objectively evaluate and determine the handling capabilities of the cargo bike concept. Those tests can be compared using the results of the benchmark vehicles. The results conclude the new cargo bike has proper vehicle dynamics above the majority of benchmark vehicles but there is still room for improvement.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
Freight transportation in urban areas has increased significantly in a shorter period due to the widespread use of e-commerce, fast delivery, and population growth. Recently, a noticeable government initiative aimed a...Freight transportation in urban areas has increased significantly in a shorter period due to the widespread use of e-commerce, fast delivery, and population growth. Recently, a noticeable government initiative aimed at creating an effective, acceptable, and sustainable city logistics policy. This paper examines freight consolidation as a transportation strategy for optimizing last-mile delivery costs. Freight consolidation involves combining smaller shipments from various origins into a single, larger shipment for more efficient transportation to a common destination. This approach is particularly beneficial for last-mile delivery, where frequent deliveries of smaller quantities are frequently visible. Finally, we provide an illustrative example targeting urban freight stakeholders for practicing possible consolidation methodology. The result in the illustrative example shows that freight with 3-day consolidation, despite the delay penalty, is cheaper than daily shipping, and both are cheaper than 2-day consolidated shipping. The study will benefit urban businesses and freight services.展开更多
最后一公里路径优化是提高物流企业配送效率的关键问题。本研究将深度强化学习中求解组合优化的方法(Learning to Optimize,L2O)与遗传算法相结合,提出一种混合算法,以求解最后一公里路径优化问题。在L2O模块中,扩展了已有框架,引入时...最后一公里路径优化是提高物流企业配送效率的关键问题。本研究将深度强化学习中求解组合优化的方法(Learning to Optimize,L2O)与遗传算法相结合,提出一种混合算法,以求解最后一公里路径优化问题。在L2O模块中,扩展了已有框架,引入时间和剩余容量编码器,有效反映了问题的时间和容量约束。同时,遗传算法模块采用重启策略和采样概率调控,更充分地利用了L2O的网络信息。基于亚马逊实际业务数据构建测试集,计算结果表明,在同样的求解时间内,该算法优于Gurobi求解器和扩展的指针网络算法。展开更多
文摘This paper assesses the vehicle dynamics of a new cargo bike concept developed for euro pallet sized cargo. The cargo bike developed is for last-mile delivery. Different aspects of manoeuvrability and stability are examined using a series of manoeuvres based on tests from the automotive industry combined with bicycle industry regulations. These manoeuvres objectively evaluate and determine the handling capabilities of the cargo bike concept. Those tests can be compared using the results of the benchmark vehicles. The results conclude the new cargo bike has proper vehicle dynamics above the majority of benchmark vehicles but there is still room for improvement.
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。
文摘Freight transportation in urban areas has increased significantly in a shorter period due to the widespread use of e-commerce, fast delivery, and population growth. Recently, a noticeable government initiative aimed at creating an effective, acceptable, and sustainable city logistics policy. This paper examines freight consolidation as a transportation strategy for optimizing last-mile delivery costs. Freight consolidation involves combining smaller shipments from various origins into a single, larger shipment for more efficient transportation to a common destination. This approach is particularly beneficial for last-mile delivery, where frequent deliveries of smaller quantities are frequently visible. Finally, we provide an illustrative example targeting urban freight stakeholders for practicing possible consolidation methodology. The result in the illustrative example shows that freight with 3-day consolidation, despite the delay penalty, is cheaper than daily shipping, and both are cheaper than 2-day consolidated shipping. The study will benefit urban businesses and freight services.
文摘最后一公里路径优化是提高物流企业配送效率的关键问题。本研究将深度强化学习中求解组合优化的方法(Learning to Optimize,L2O)与遗传算法相结合,提出一种混合算法,以求解最后一公里路径优化问题。在L2O模块中,扩展了已有框架,引入时间和剩余容量编码器,有效反映了问题的时间和容量约束。同时,遗传算法模块采用重启策略和采样概率调控,更充分地利用了L2O的网络信息。基于亚马逊实际业务数据构建测试集,计算结果表明,在同样的求解时间内,该算法优于Gurobi求解器和扩展的指针网络算法。
