Helping private and commercial drivers utilize Smart Loading Zones in a harmonious manner
Smart Loading Zones (SLZs) are a pilot program conducted by Automotus and the City of Pittsburgh to efficiently manage curbside parking in commercial areas, with the aim to decrease congestion and emissions. Our team was tasked to identify the source of existing SLZ problems, and design a solution that aligned with Automotus' vision while respecting user privacy and enhancing user experience.
Smart Loading Zones (SLZs) promised a curbside parking revolution, but instead delivered large-scale user confusion. Unclear signage, overly-complex onboarding processes, and general misunderstanding of SLZ goals and motivations led to widespread disapproval from primary users and the public.
We took a user-centric approach, delving deep into use cases, user types, and unmet needs when designing our solution.
The focus of our project was helping private and commercial drivers utilize Smart Loading Zones in an efficient yet harmonious manner by designing a dynamic sign, redesigning the payment process, and creating a reservation feature to increase the incentives for both commercial freight drivers and private drivers to utilize Smart Loading Zones.
We began by looking at information readily available to us. We conducted in-depth analysis on user data provided by Automotus, heuristic analysis on their app - CurbPass, and walked the wall to synthesize insights across methods.
We sorted and visualized data given to us by Automotus, and looked at registered users, park events, and vehicle types occupying SLZs.
We noticed massive discrepancies in registered user accounts compared to park events, possibly suggesting there to be poor signage and registration processes that lead to users parking without registering.
Despite being named Smart Loading Zones, private cars occupy these zones more often and longer, than commercial vehicles or freight trucks.
Next, we conducted a Heuristic Evaluation on CurbPass - the app portal to register and pay for Smart Loading Zones.
We used Nielsen's Usability Heuristics, and discovered most issues were regarding payment security, onboarding complexity, and unclear pricing. The onboarding process was simply complex and redundant
Drawing from our preliminary research, we decided to focus our research on the lack of information communication and the low ratio of commercial vehicles using SLZs.
We had gathered valuable insights from background information, and moved our research efforts to on-site observation and in-depth interviews.
We conducted intercept interviews with 18 participants near Smart Loading Zones across Pittsburgh, and sought out a balanced variety of commercial freight drivers, ride-share drivers, and private drivers. We synthesized our interview notes using affinity clustering and developed the following insights:
Lack of clear information makes users are unable to understand SLZ use cases and goals
Conflict of use case between private and commercial drivers
Inconsistent enforcement create misconceptions, reducing user incentives
Mismatch of mental models: The mental models users have with conventional parking does not match how SLZs charge and enforce their zones
By looking at our insights, interview notes, and on-site interviews, we began to envision our users. Thus, we developed two user personas and created two customer journey maps for the different use cases we outlined:
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Before beginning to explore solutions for our users, we looked back and consolidated our preliminary research with our intercept interviews into insights, questions, and design ideas. This process allowed us to isolate specific needs and match them with design ideas.
We proceeded to isolate specific user needs that derived from the above consolidation, and began storyboarding.
We created a total of 36 storyboards, each focusing on a user need. Each storyboard also contained a leading question, follow-up discussion questions, and a varying risk level. We wanted to create solutions of varying risk levels, to probe and assess the willingness of our users to try each solution. The storyboards focused on needs such as data gathering, pricing transparency, social pressure, and street-sign design.
We then presented these storyboards to 4 interviewees, and gathered the following insights:
Information transparency is crucial. Interviewees pointed out that displaying parking rates on the physical sign makes it easily digestible, and they know what to expect.
Reservation system saves users' time. Most participants expressed interest in the idea of reserving SLZs.
Users want to receive reminders through their phones. Participants expressed a strong interest in the idea of receiving reminders about their remaining parking time on their phones.
These findings further solidified our project direction. We decided to begin prototyping, with three artifacts in mind:
Our Lo-Fi prototype included a cardboard sign, and two paper app screens. We used a Wizard-of-Oz method to test the discoverability and usability of our prototypes. We found that all participants found it easy to understand and use. We also found that many participants were comfortable having their parking total displayed on the sign, as opposed to having to do mental math to calculate it.
Being the most in-depth research project I've completed, I learned a great deal about different research methods and applying them in a real context, with a real client. Although not my favorite topic, I still learned how to work in a team setting over a semester-long project. I learned about industry-specific research methods and really going out into the world and interviewing people. The use of intercept interviews brought me out of my comfort zone, and I also learned to always have backup questions and understand the variability of people when conducting interviews. This project was a great learning experience, and exposed me to extensive, industry-standard research methods.
