By: Marissa Milam on June 22nd, 2018

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Trucks and the Port of Virginia: An HRTPO Case Study

Big Data | Commercial Trucks | Transportation

The Port of Virginia is one of the largest ports on the East Coast, with over 1.6 million containers moving through the port in 2017. The port is only a day’s drive from two-thirds of the US population, making Hampton Roads, Virginia a hot spot for commercial trucking activity. With over 60% of cargo transported out of the port by commercial trucks, understanding the travel behavior of trucks is critical to manage the impact of commercial trucking and maintain the regional highway infrastructure. In this blog article, I will share how StreetLight Data's transportation analytics are helping the local planning agency address this issue.

The Hampton Roads Transportation Planning Organization (HRTPO) is the local MPO in the region. For this project, Robert E. Case, Chief Transportation Engineer at HRTPO, teamed up with StreetLight to use Big Data analytics to learn about the travel patterns of commercial trucks at the Port of Virginia. HRTPO has access to StreetLight Data’s on-demand platform StreetLight InSight® via Virginia Department of Transportation’s Regional Subscription, which allows for unlimited use of the platform within the state of Virginia.


Figure 1: Location of the Port of Virginia


Big Data Methodology for Commercial Trips

In recent years, Big Data has disrupted the transportation industry by providing the location records from hundreds of millions of smartphones, connected cars and trucks that create a greater representative sample of travel behavior than traditional data collection methods. For this project, HRTPO ran analytics derived from navigation-GPS data. The navigation-GPS data is provided with commercial trucks tagged by weight class, and also has very high spatial precision of up to 5 meters, which creates an accurate picture of commercial truck travel behavior related to the Port of Virginia.

StreetLight sources the data from their partner INRIX, which has access to GPS- based fleet management data for commercial trucks. We estimate the sample covers about 12% of all commercial truck trips in the US; however, it is important to note that sample size varies regionally. (Please see our blog article on measuring sample size for commercial trips for more details.)

Analyzing Commercial Truck Travel Patterns

HRTPO’s purpose for the project was to measure the highway gateway usage by trucks coming from the Port of Virginia. By understanding which routes were most commonly used by commercial trucks, it could inform HRTPO’s project prioritization, as well as current studies, and support federal funding applications.


Figure 2: HRTPO’s defined highway gateways for StreetLight InSight  (Images courtesy of HRTPO)

Using StreetLight InSight, HRTPO first analyzed the total volume of trucks trips originating at the 4 main terminals of the Port of Virginia. They found that 91% of all commercial truck trips leaving the Port of Virginia originated in the two international terminals, Virginia International Gateaway (VIG) and Norfolk International Terminal (NIT). It was a result that they expected, but were unable to quantify before the use of StreetLight InSight.


Figure 3: HRTPO truck volume results from StreetLight InSight (Images courtesy of HRTPO)

To figure out which highway gateways the trucks used upon departing the Port of Virginia, HRTPO used an O-D Analysis, which describes trips between Origin and Destination Zones. In this case, the Origin Zone was the Port of Virginia and the Destinations were different highway gates. The analysis should show which routes are most commonly used; however, due to StreetLight’s definition of a trip end, which defines a trip end once the truck is turned off or is stationary for 5 minutes, the results didn’t meet HRTPO’s needs. This initial test showed trips ending prematurely before reaching the regional highways, which prevented analysis of the commercial truck usage of the highway gateways.

Results: Commercial Truck Transportation Analytics

To remedy this issue, HRTPO came up with a smart solution. HRTPO obtained the location of all distribution centers in the region that utilized the Port of Virginia. Since the distribution centers are spread across Hampton Roads, the new O-D analysis using the distribution centers as the origins resulted in accurate trips that reflected the share of commercial trucks at each highway gateway. The path of the red dots on the graphic below represent commercial trucks traveling along each regional highway.

truck-transportation-gateways-hrtpoFigure 4: O-D Analysis with Port Distribution Centers as Origins (Image courtesy of HRTPO)

Using Metrics from StreetLight InSight, HRTPO was able to determine the share of commercial trucks that used each highway gateway, and therefore each route. From the travel analytics, we see that I-64 is used 57% of the time, a much greater percentage than any other route.


Figure 5: The above chart shows highway gateways share of usage by truck distribution center trucks on an average weekday from July 2016 through June 2017 (Image courtesy of HRTPO) 

HRTPO was able to implement these results in a number of different ways. Potential projects in the region are scored via the HRTPO Prioritization Tool, which tries to reward projects that increase access to port facilities, but until now, HRTPO has not been able to quantify which projects would actually improve port access. Having quantifiable results will help HRTPO choose projects that will be the most effective in managing commercial trucking impacts. Also, the gateway usage information is being provided to current corridor studies, which will assist HRTPO in developing accurate and reasonable plans based on real-world data. The results have also been used to validate HRTPO’s selection of past projects for regional highway maintenance and infrastructure improvements.


How Do StreetLight’s Truck Metrics Compare?

Finally, for model validation, HRTPO compared the StreetLight InSight results to truck counts obtained from Virginia Department of Transportation (VDOT). The data from VDOT contained total number of trucks at the highway gateways, but they had no way to determine which of those trucks were affiliated with the Port of Virginia. After comparing the StreetLight InSight results with VDOT’s data, HRTPO determined that the commercial truck counts were fairly similar, but StreetLight’s results showed slightly higher numbers. This difference is important, as it shows that commercial trucks affiliated with the port have different travel behavior than average trucks, and highlights the importance of interstate highways to trucks leaving the Port of Virginia. The StreetLight InSight platform is able to enable users to obtain provide Metrics like the highway gateway usage for HRTPO efficiently by using the right algorithmic processing techniques, but it hasn’t always been this easy to visualize and understand travel behavior patterns.

HRTPO said that prior to StreetLight, they attempted to use a research institute’s commercial truck GPS data, but had no way to process the raw data effectively, and found that the data’s sample size was too small to be representative of the trucking population. Traditional data collection methods for commercial trucking rely on surveying drivers, which has poor results due to very low response rates and human error in reporting answers.

This commercial truck analysis could also have been performed with StreetLight’s commercial truck tour Metrics. These custom Metrics create truck tours by combining all trips a commercial truck makes in 24 hours, which provides information about the truck’s final destination, as well as Metrics such as number of stops and average stop duration.

If you would like to learn more about how StreetLight and HRTPO analyzed commercial truck behavior at the Port of Virginia, watch our webinar featuring Robert Case of HRTPO here!

About the author: Marissa Milam is a summer intern at StreetLight Data. She is a rising senior at UC Berkeley, studying civil engineering and specializing in transportation.


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