Dataset: Spatiotemporal analysis of freight patterns in Southern California

There has been general trend to shift the location of warehouses and distribution facilities away from consumer markets (logistics sprawl) in Southern California. This shift has a negative impact on cost and the environment because freight vehicles have to travel longer to reach their destinations. However, during the last decade, this trend has not continued at the same pace, and it may have even reversed. Two main factors potentially explain this phenomenon: the 2008-2009 economic slow-down, and an increase in e-commerce activity. E-commerce impacts are relevant for freight planning because of the changes in vehicle size to distribute smaller shipments at higher frequencies, consumer proximity requirements to improve delivery times, and the redistribution of freight activity and supply chain configurations.

This research conducted spatio-temporal analyses of Caltrans Weigh-in-Motion data to validate some of these assumptions. There is evidence that during 2003-2015, the short-haul volume has increased by 69%, whereas long-haul 59%. The analyses identified changes in concentrations of trip origins by vehicle evidencing changes in long-haul versus last-mile distribution patterns. The results can help estimate changes in vehicles miles traveled, and more importantly, identify the geographical areas of the most impacted communities.

Methods
The data was collected from Caltrans Weigh-in-Motion (WIM) database. The California Department of Transportation (Caltrans) has installed WIM devices in about 150 sites. Several stations are at PrePass locations, but the majority are spread throughout the transportation network as WIM data stations. The Caltrans WIM database contains the information of the WIM data stations in 13 years (2003-2015).  California has twelve districts, but this analysis was filtered the data base with the four districts in southern California and their 39 stations, it also incluedes analysis of  raw_wim and vehicle class using those vehicles with maxgvw>0 and gvw>0. The database also contains information of twelve vehicle classes (VC), from VC 4 to VC 15. Vehicle class 4 is bus service and vehicle class 15 is unclassified. Therefore, the analysis excludes these two classes (Class 7 is another particular case). Finally, the variables recorded are Gross vehicle weight (GVW) and Maximum gross vehicle weight (maxGVW).

美国南加州曾普遍出现将仓库与配送设施迁出消费市场的趋势，即物流蔓延（logistics sprawl）。该趋势会对运输成本与环境造成负面影响，因为货运车辆需行驶更长距离才能抵达目的地。然而近十年来，这一趋势的发展速度已放缓，甚至可能出现逆转。有两大潜在因素可解释这一现象：2008-2009年的经济衰退，以及电子商务活动的增长。电子商务对货运规划具有重要意义，因其会带来诸多变化：货运车辆需调整车型以高频配送小批量货物、需满足贴近消费者的配送要求以缩短交付时长，以及货运活动与供应链架构的重新配置。

本研究针对加州交通局（California Department of Transportation, Caltrans）的动态称重（Weigh-in-Motion, WIM）数据开展时空分析，以验证上述部分假设。数据显示，2003年至2015年间，短途货运量增长69%，长途货运量则增长59%。分析通过车辆出行起点的集中度变化，揭示了长途配送与末端配送模式的转变。研究结果可用于估算车辆行驶里程的变化，更重要的是，能够定位受影响最严重的社区地理区域。

研究方法
本研究的数据源自加州交通局动态称重数据库。加州交通局已在约150个站点部署动态称重设备，其中部分站点设于PrePass节点，其余多数作为动态称重数据站遍布于交通网络中。该数据库涵盖2003年至2015年共13年的动态称重站点数据。加州共划分为12个交通辖区，但本次分析仅筛选了南加州的4个辖区及其下辖的39个站点；同时，分析仅纳入总车重（gross vehicle weight, GVW）与最大总车重（maximum gross vehicle weight, maxGVW）均大于0的车辆，并使用原始WIM数据与车辆分类信息开展处理。数据库共包含12种车辆类别（VC 4至VC 15），其中VC 4为公交车辆，VC 15为未分类车辆，因此本分析剔除了这两类（此外第7类车辆亦为特殊个案，同样予以排除）。最终记录的变量包括总车重（GVW）与最大总车重（maxGVW）。