Ariana Davies

Drivers hesitate identifying terminal entrances and park for convenience at curb spaces

Most drivers find trouble in identifying the correct places to go, hesitating on turns and crossing over quickly to lanes. This creates a snowball effect that backs up cars and forces traffic. 

Parking in the first spots that drivers see stems from close range identification of availability and convenience. Crowding cars to park in the first section of Curbside can block up to all lanes of traffic, also affecting outer lanes of travel.

Drivers NEED convenient system guidance

Drivers need easy access to terminal entrance points and curb access to improve the efficiency of the system. Integrating trackers into the structural entrances and syncing with vehicle GPS tracking allows the user to know how far they are from the nearest tracker, greying it out when a sensor is passed.

“No one knows how to move forward, everyone just stops.”

Upon my second visit, I overheard a ground traffic control worker laughing with one of her co-workers. She looked at a group of us in passing, saying these words, and we all laughed in unison. That was when the lightbulb hit, and I ran to speak with her again.

Workers and drivers need a system that supports their actions and guides them

On three seperate days, I drove to SFO Airport Departures level and observed, majorly Terminal 3 and partially Terminal 1 and 2, for a couple hours at a time. One of my interviews with a very kind ground traffic control worker was on the second observational day. The remaining interviews with passengers and drivers were on various days, some on-site and some outside of the area. For convenience, I’ve highlighted the major quotes and where they’ve come from corresponding to 3 out of 5 total insights I gained that contributed to the final UI solutions. For further insights and details, feel free to view the slide decks below.

Interactions snowball into traffic backup, safety hazards, and system delays

Themes of emotional behaviors impacting driver actions, worker versus driver moderation, structural control and pain points, convenience versus system efficiency, rideshare service interference, and traffic causals all reside within the system. Each portion of data interacts with one another under multiple levels of themes. Questions are posed for each section and similarity to help understand how drivers feels and how it correlates to the observations I made during my ethnography and field studies.

Perspectives show pain points

Visualizing three work models helped me grasp the perspective from: Passengers, Drivers, and Workers. A physical model laid the architectural system in front of me, mapping out each bus stop and shuttle location that could impact the flow, and visualization, of the system. A sequence model gave me insight into breakdowns when passengers need information to continue their route smoothly, especially in rapid succession. An identity model utilized quotes from my interview and ethnography study with a ground traffic control worker, breaking into how the system runs, how knowledgeable drivers are of the laws, and how the emotional direction of drivers all impacts their duties.

If you’d to see full detailed reasoning and insights for each model, please see this Work Models PDF.

System helps the workers that help the drivers

Synthesizing data from interviews and observations helped formulate a user empathy map for both a driver and a worker. To understand what they hear, feel, think, say, do, and see impacts their goals for a system and the pain points that they currently face. This narrows down the problems we need to solve to account for both sides of the system.

I broke down the driver’s journey into six parts of a series, utilizing quotes and concepts from each interview to better understand their actions, emotions, pain points, touch points, and opportunities for improvement in accordance to each part. This gives me a better layout to learn the process of the user in full, in accordance with work models, to map out a solution that achieves all aspects of the full experience. I chose to use the perspective of the driver for this user journey map as they interact the most with the system as a whole.

How might we create a navigational system that supports drivers in finding the right terminals and alerts drivers of available curb space through minimal UI and geofencing technology?

Covering three major groups of insights, I forged a system diagram of a potential solution system that would solve for multiple problems, including a seperate zone for rideshare services to not conflict with personal drivers. It creates decision points for the system to determine if a vehicle is near a terminal entrance point and if curb space is available. The system input and output operations change based on real-time data and I’ve added the potential involvement of ground traffic control workers in this new system.

Translating the system diagram into a conceptual model helps layout a more specific method to achieve this new system. Entrance sensors and existing vehicle GPS technology help match distance away on map software. Curb sensors are built overhead to track if a car is currently within the space, sending information to the driver once they’ve driven into a terminal to forge a zone that is available and guide them with time estimation.

Availability can also be estimated and this will give information to the driver that a second lane drop-off may be the most viable. A timer system is also implemented within the map software and car dashboard UI to grant five minutes of drop-off time. If time is exceeded, the ground traffic control workers and drivers are both notified, warning the driver and prompting them to exit the SFO Airport.

How can SFO Airport solve this?