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Dataset: Spatiotemporal analysis of freight patterns in Southern California

Cite this dataset

Rivera-Royero, Daniel et al. (2020). Dataset: Spatiotemporal analysis of freight patterns in Southern California [Dataset]. Dryad.


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.


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).

Usage notes

The data set contains the 18 columns: Vehicle class, district, county, raw_wim, year, minGVW, maxGVW, meanGVW, minLTR, maxLTR, meanLTE, volume, Q1GVW, Q2GVW, Q3GVW, Q1LTR, Q2LTR, and Q3LTR. GVW means gross vehicle weight, max=maximum. min=minimum, Q1, Q2 and Q3= quartiles 1, 2 and 3, finally volume= number of vehicles. There are 5070 rows and 145076053 vehicles measured.


Pacific Southwest Region University Transportation Center