By Rodrikas Jones, East Coast Director of Operations, Quality Counts
What’s an activity that weathers rain, snow, and tornado warnings to piece together an accurate story of how a city curbside is being used? It’s what I specialize in, and one of the most important steps to making good engineering and planning decisions: data collection. The importance of collecting holistic and trustworthy data cannot be overemphasized as cities seek to manage growing demands facing our curbs.
The Role of Data Collection in Curbside Management
Curbside space is a hub of competing transportation modes. Delivery trucks need space to pull up to drop off a package; cyclists and e-scooter riders need safe lanes to pass parked cars; drivers want to find the spot closest to their destination. Every block face has a unique set of users and needs, which means block-by-block data collection is required to understand how that curb is being used, how cities can regulate it for better safety and efficiency, and how it fits into the regional transportation network.
Biggest Challenges With Curbside Data Collection
The biggest challenge facing curbside data collection lies in the number and variety of data points to be collected, and the lack of standardization across agencies to do so. Processes are becoming increasingly automated, yet manual effort is still required, which makes it an employee management exercise as well as a data collection exercise.
It’s more than simply capturing how many vehicles use the space in a day and how quickly turnover occurs. The nuances of the collected data tell an important story about who is using the curb and patterns of behavior—we get this from the make and model of vehicle, what state the tag was from, whether or not the vehicle is registered, if there is a handicap placard in the window, and in special cases, where approval has been given by the governing DOT. It is impossible to capture this sort of detailed data without having people on the ground supplementing the technology with human judgment, and filling in the gaps in the story.
Another interesting challenge is distinguishing normal vehicles from ride share vehicles. More and more, agencies want to understand how ride share vehicles are using the curbside to pick up and drop off passengers. The distinction is made, of course, through capturing the decal on the car, but this can’t happen consistently without clearly visible decals, which requires action from the ride share companies.
Selecting a Data Collection Partner
Before jumping into data collection, you first need to understand how you plan to use it. Look for a company that has experience with the specific type of data you want to collect, and who is willing to put in the work to assemble a customized plan for getting you the information you need. A company that understands and utilizes new data collection technologies, but knows how to supplement the automated processes with human judgment and fact-finding. As data collectors, we’re detectives, and each case warrants a unique approach to understand curbside usage from every angle.
Of course, curbside data studies quickly become inefficient and costly when too much collection is performed manually, which is where technology comes in. One of our most successful approaches has been using a GoPro GPS camera strategically placed to capture a large amount of data in a small amount of time. We can rewind, stop, or go back and collect additional info our client needs. Using the video, we have done everything from sign inventory and occupancy to turnover and general observations.
Data collection is like assembling a puzzle, and experience can be the best teacher; even down to the practical details. For an example, we played a role in the parkDC: Penn Quarter/Chinatown project in Washington, D.C., which captures and predicts occupancy using a blend of data sources that eliminate the need to put a sensor in every parking space. We needed to simulate vehicles looking for parking around the Capital One Arena, so not thinking, we rented a set of bicycles to do the collection. Well, after about three hours of riding up and down the hills of D.C. and continuous stop and go, we realized it was necessary to switch to electric bicycles. Needless to say we learned a valuable lesson that day! (Overall, we were proud to support an effort that resulted in a 15% reduction in time spent looking for a spot during all time periods on weekdays and weekends. You can read more about it here.)
Case Study: parkDC Neighborhoods
After the success of the parkDC: Penn Quarter/Chinatown pilot, Quality Counts partnered with Kittelson and Gorove/Slade to support DDOT in parkDC: Neighborhoods. To help DDOT respond to parking-related neighborhood concerns, we surveyed 110,000 vehicles in 2,300 block faces in five District neighborhoods.
We collected two types of data: occupancy data (parking occupancy by block), and vehicle data (type of vehicle, license plate state and number, and the display of residential/visitor parking permits) for every vehicle parked on the street. Essentially, this data helped DDOT either confirm or disprove hunches about who was taking up the most parking space. DDOT had a fact-based response to questions and issues raised from the public, and the data helped inform parking policies, including the location of loading zones and accessible spaces.
A crucial technology with that project was the ability to use a GIS-enabled map and cell phones to collect the data and have it automatically populate the database. Additional coordination was conducted to ensure counts were completed while school and Congress were in session, and the U.S. Secret Service, Capital Police, Metropolitan Police, and other government agencies in D.C. were informed in advance of the counts to minimize disruptions.
We faced our fair share of storms during this process. We did collection in the rain and snow, and had to cancel a day as we were collecting during a tornado warning. We used plastic bags and heat pads to protect our employees and equipment, but some days it was about knowing when the weather was just too much. In total, however, we were able to collect vehicle type data on 360% more blockfaces than we were originally scoped to collect because of our customized interface.
Using lessons learned from the data collection efforts, Kittelson helped DDOT develop guidance for future data collection efforts, and DDOT now has a mechanism for collecting updated vehicle data using in-house staff for additional neighborhoods in Washington, D.C.
Build in Time for Data Collection
Simply put: good data collection takes time. Technology makes us more efficient, but the manual elements of the process can be cumbersome. We need time to come up with customized strategies for our clients and implement them thoroughly, not rushing through the process and missing key data points. One of the best things project managers can do is understand the time-intensive nature of curbside data collection, and plan for it.
If you have questions about timing, approach, or anything else related to a specific data collection project, don’t hesitate to reach out at email@example.com to discuss this topic further!
About Quality Counts
Quality Counts (QC) is a nationwide full-service transportation data collection firm with more than 170 employees in 10 offices across the United States. Our company was founded in 2003 in Tigard, OR. Over the past 16 years, QC has leveraged its specialized commitment to exceptional customer service, quality products, and innovative technology to become one of the most respected, industry-leading transportation data collection firms in the country. Our processes are rooted in a customer-first, transparent business model that maximizes project efficiency and reporting accuracy. As demands for safety and information about transportation becomes increasingly necessary, QC aims to become the most trusted and adaptable data provider through our unmatched level of industry knowledge and experience. You can learn more about us at qualitycounts.net.