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, we conducted desk research, collected survey results, interviewed New Yorkers, and evaluated existing navigation applications in the city. Our research revealed numerous factors that contribute to the difficulties faced by new settlers when navigating the city.
View Process DeckUnderstanding 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 New York after accepting a new job at a startup. Having always lived in a small town in Europe, he feels overwhelmed, confused, and apprehensive when he arrives in the city. He now needs to figure out how to use the public transportation system to meet his date at a restaurant.
To establish our guiding principles, we employed an "idea mash-up" approach, brainstorming unconventional solutions to address real problems. While these initial ideas were not immediately implementable, they helped us develop strong principles for our final solution.
After conducting research, we realized we might have been focusing on less impactful issues. Our initial ideas involved improving delay communication, advocating for service aggregation, and providing cultural information in a travel guide format. However, we determined that these solutions were either already in place or wouldn't yield significant results. Following 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 settled on contextual navigation with real-time information, made possible through augmented reality and location anchors. Additionally, AR-based cards provided opportunities to enhance navigation and cultural understanding through personalized directions and photo sharing.
After creating initial sketches, we used Balsamiq to develop low-fidelity prototypes and conducted three rounds of rapid iterations based on user feedback.
AR features for subway
AR Location Sharing
Basic AR navigation
After deciding on AR, we aimed to address many user problems identified in our research. To do this, we initially considered allowing users to define their specific issues. However, we quickly realized this approach wouldn't be effective.
The problems with this were plenty:
We went through 3 rounds of rapid iterations based on user feedback and iterated our concept further.
A hyperlocal, contextual information solution that benefits both new travelers and daily commuters.
Finding the right entrance isn't as straightforward as it seems. Our research revealed that even experienced New Yorkers encounter this issue. Taking the wrong gate results in not only financial loss but also a 18-minute lockout from the payment system, leading to a frustrating waste of time and money.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.
Expanding on the concept of publicly available AR anchor cards, we identified personalized directions as another valuable application. Areas with complex layouts and vertical navigation could greatly benefit from these customized, guided stickers, which are shared privately between two Google Maps users – one seeking directions to a location shared by the other.
New settlers, like Bilal, can navigate to a common meeting place by requesting AR anchor-powered directions from a friend or acquaintance. To ensure feature discoverability, the interaction pattern mirrors live location sharing.
A friend or acquaintance can assist someone unfamiliar with a location by providing them with user-generated anchor points. This feature leverages the common user behavior of navigating with landmarks and places of significance. Instead of real-time guidance, the helpful friend can set the route with AR anchor points once and share the directions.
After receiving the location from a friend, Bilal can anticipate the journey and be prepared for any unexpected changes.
To normalize cultural differences, AR Polaroids familiarize Bilal with the local culture, people, and place. He can view past events, public photos, and short-form videos to understand what to expect during his visit.
Taking the concept further, AR Polaroids could also function as a private photo map. Bilal could record his experiences of visiting a new place, and when he revisits, he can relive the experience by comparing old photos with the real location. This allows Bilal to reminisce, share lived-in experiences, and strengthen his connection to Google products.
How can we best support new arrivals and those unfamiliar with 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 can we best equip newcomers and those unfamiliar with 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 can we help tourists and new settlers quickly acclimate to and understand local cultures during their short visits?
Through AR Polaroids, we aim to normalize cultural differences by familiarizing Bilal with the local culture, people, and place. He can view past events, public photos, and short-form videos to understand what to expect during his visit.
This is one of my favorite projects! My colleague and I initially envisioned a smartwatch app, but by trusting the research process and focusing on solving the problem, we arrived at a completely different, yet satisfying, solution. It was great to see the final concepts directly stem from the research, without being tied to any specific solution.
While exploring micro navigation, we expanded the concept to build upon existing Google Maps features. For instance, micro navigation could be merged with personal location sharing and Local Guides. Publicly shared AR Polaroids, designed to reduce cultural shock, could also function as memory markers for future visits.
Google Maps is a mature and complex navigation application, so we were mindful that adding features could decrease the user experience. While the added functionality has value, its implementation will be simplified once AR navigation becomes more widespread.
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 instance, 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.
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