How might we design public transport navigation for travellers on a short visit?
A friend of mine recently moved to New York to pursue higher education. While having a casual conversation with him, I learned about how troublesome and inaccessible the subway system is in New York.
He talked about how switching between trains was a problem, making mistakes when selecting between uptown and downtown and lastly route changes and train delays.
In January of 2018, just 58.1 percent of all weekday trains arriving at stations on time. If you add the overhead of learning an already complex public transport to the delay, you have a very frustrated traveller on your hands.
Having experienced a simpler navigation system in Delhi, India (Where we have both stayed for a bit), I set out to find out the details of why NY Subway system was difficult to use.
To understand the problem at hand, We conducted desk research, collected survey results, interviewed New Yorkers and evaluated navigation applications used in New York.
From our research we uncovered that a lot of factors play into making navigation difficult for a new settler.
Understanding a new public transport system becomes difficult when you have an existing mental model of how it should work.
Travelers feel apprehensive to take public transport in NY as they fear they might experience terrifying encounters with the homeless population
Locals still had problems figuring out uptown or downtown, symbols and they often got locked out during payment.
People don't plot out their Public Transport route in advance. When the encounter route changes, delays and rush hours, they find their trips being restructured entirely.
Bilal moves to a New York when he gets a new job at a startup. He has always lived in a small town in Europe.
When he moved to the new city, he finds himself overwhelmed, confused and apprehensive. He needs to figure out how to use the public transport to meet his date at a restaurant.
To Form our guiding principles, we used idea mash up where we formed crazy solutions to solve for very real problems. Although these solutions were far from implementable, we formed solid principles we would like our final solution to use
After we conducted research we realized that maybe we were focusing on the lesser issues. We were trying to make delays communication better, advocating for aggregation of services and make cultural information available as a travel guide.
We realized that these solutions were either already implemented or won't be impactful. After Affinity mapping and concept generation we revisited the problem and revised our design goals.
How might we improve the navigation by drawing more from user behavior?
How might we normalize cultural differences for tourists/new settlers for a short visit?
How might we provide onboarding support for people with limited knowledge about the city?
We finalized on contextual navigation with just in time information that was only possible through augmented reality and location anchors.
Furthermore, using AR based cards opened up possibilities to improve navigation and culture normalization through personalized directions and photo sharing respectively.
Once we had our initial sketches, we used Balsamiq to make our low fidelity prototypes.
We went through 3 rounds of rapid iterations based on user feedback.
AR features for subway
AR Location Sharing
Basic AR navigation
When we settled on AR, we wanted to implement a lot of user problems we discovered during our research. To implement that, we thought it would be best to let user define their specific problems. However, we could soon see why this wouldn't work.
The problems with this were plenty:
We went through 3 rounds of rapid iterations based on user feedback and iterated our concept further.
A hyper local, contextual information-focused solution that adds value for new travelers and daily commuters alike.
Finding the right entrance is not as easy as it looks. From our research, experienced New Yorkers faced this issue too. If you take the incorrect gate, you not only end up losing money but also get locked out of the payment system for 18 mins. That's a loss of both time and money!
With our solution, we aim to bring uptown and downtown signs into focus. Commuters and travelers can now prevent making costly mistakes.
Lines, numbers, and colors mean different things to people used to a different transport system. With subway micro navigation, a user needs to only worry about his next steps. You can now onboard faster without being delayed by deciphering signboards.
The filter chips are an addition that can add delight to the user's journey. Users in AR mode can set filters to see relevant content. For example, travelers can discover and learn about the culture through the AR culture filter.
By reimagining the crowd-sourced data Google collects, a traveler can now take a data-driven decision to optimize his travel experience.
Using the crowdsourced information, users can wait next to predestined location to ensure that they beat the crowd and have a comfortable travel experience.
Crowdsourced data can also help users select subway cars best suited for their needs.
As the subway pulls up, users can take onboarding decisions using the sanitation, crowd, and temperature information.
Building upon the idea of publicly available AR anchor cards, we found personalized directions to be another fruitful use case. Areas with complicated layouts and vertical traversal can benefit from personalized guided stickers. These stickers are shared privately between one Google Map user trying to reach the location shared by another Google Map User.
New settlers like Bilal can navigate to a common meeting place by requesting AR anchor powered directions from a friend or an acquaintance.
To ensure the discoverability of this feature, the interaction pattern is kept the same as sharing live location.
A friend or an acquaintance can help someone like Bilal who's unfamiliar with the location by providing them with user-generated anchor points. This feature is based on user behavior of navigating based on landmarks or places of significance.
Instead of being on the call and guiding in real time, the helpful friend can set the route with AR anchor points once and share the directions.
After receiving the location from the friend, Bilal can now anticipate the journey and be prepared for any twists and turns before they show up.
Through AR polaroid, we aim to normalize cultural difference by making Bilal acquainted with the culture, the people and location. Bilal would be able to see past events, public photos and short form videos so that Bilal knows what to expect when he visits the place.
Taking the concept further, AR polaroid could also be used as a private photo map feature. Bilal could record his experiences of visiting a new place on the map. When he later visits the place again, he can relive the experience by comparing old photos with the real location.
This gives Bilal a chance to reminisce, share experience that feel lived in and form a stronger tie with Google products.
How might we provide onboarding support for people with limited knowledge about the city?
Based on user testing, we designed cards with different levels of information that would be helpful based on their location.
The initial card design lacked visual hierarchy and the information was not grouped in a way to give users information at one glance. The revised cards har better hierarchy, were easy to follow can consisted of subway car suggestion as well based on crowdsourced data.
The bottom bar was designed to let users filter information most relevant to them in the AR mode. Apart from setting the filters, the user would be presented most relevant information such as route delays, landmark suggestions. If the user wants, a simple swipe up will give them all the information they need without having to leave the AR view.
How might we provide onboarding support for people with limited knowledge about the city?
The personalized AR Anchor Point's interface was based on the Google Pixel Playground(AR stickers.) This would ensure consistency with the ecosystem.
The initial card design did not give enough information about the system status. The revised card allowed users to view a snapshot of the location of the sender. By making this interaction, user would be assured that he would be travelling to the right location.
How might we normalize cultural differences for tourists/new settlers for a short visit?
Through AR polaroid, we aim to normalize cultural difference by making Bilal acquainted with the culture, the people and location. Bilal would be able to see past events, public photos and short form videos so that Bilal knows what to expect when he visits the place.
This is one of my favorite projects! My colleague and I started this project with the intent of making a smartwatch app. However, by putting faith in the research process and working to solve the problem, we ended up with a different solution altogether. It was a satisfying to have the final concepts connect back to the research while being solution agnostic.
While exploring the concept of micro navigation, we were able to expand the concept to build upon the features already offered by Google Maps. Micro navigation for instance could be merged with personal location sharing and Local guides. AR Polaroids shared publicly to reduce cultural shock can also be used to create memory markers that could be visited at a later date.
Google Maps being a mature and complex application for navigation, we suggested features for it while being aware that adding complexity brings a decrease in quality of experience. While the functionality we added has value, the implementation will only be simplified once AR navigation is widely adopted.
The second thing that I feel could have been better is feature discovery. Knowing that Google Maps is complex and has different moving parts, we had to map the entry point for our features to that of the underlying feature supporting it. For example AR cards had a dependency on the often undiscovered Live AR view. If I had more time I would have loved to redesign a version of Google Maps that had a bigger focus on AR.
.webp)
.webp)
