「The Road Less Traveled」
Black Ice
1. Social Background & Motivation
COVID-19 is the ongoing pandemic that is infecting millions around the world. Families have been
separated and workers have been stranded in danger. In such devastating occurrences, the subway
and bus stations in Japan are still in frequent use. 1Approximately 6.4 million people ride the Tokyo
metro each day. Furthermore, the city of Tokyo has many rush hours ranging throughout eachday which creates great concentrations of people, a highly vulnerable situation that can leadto mass infection. 2The social distancing policy also has heavy impacts on human mental health, with47% of US adults reporting feelings of depression. In response to this large scale pandemic
we’ve established our product, “The Road Less Travelled” (RLT).
2. Description of Business
RLT is a mobile application that serves two purposes: determining optimal travel routes for commutersand suggesting optimal gathering locations with less population concentration. Both of these
purposes stem from our machine learning algorithm, which utilizes security camera footage from local
restaurants, malls, and other public locations to provide an estimate of population data in a given
area, all of which is done with and through close cooperation with the local government. In the modernworld, there exists an expansive network of security cameras providing constant surveillance aroundcities worldwide, whose data hold high utility potential. For example, 3Tokyo plans on installing an
additional 22,000 security cameras on metros in 2020. Using this footage, RLT is able to generate an
estimate of real-time population concentration in public areas through object detection algorithms,
namely 4Convolutional Neural Network (CNN), which ensures speed by only requiring one
forward propagation pass through the network to make a prediction, making it optimal for real-time
detection. The object detection algorithm is perfected through deep machine learning throughstimulation using sample data imagery.
When logging onto our app, users will be able to find a list of recommended gathering
locations, determined based on the user’s current location and the population
concentration levels in nearby gathering areas through the use of our machinelearning algorithm. This allows families and friends to find suitable spots for social
gatherings without heavily exposing themselves to the riskof contracting COVID-19, addressing the issue of loneliness due to the lack of social
interaction by highly reducing risk. Currently, many people are opting to lockthemselves at home due to hysteria regarding the danger of contracting the virus.
5While this does keep them safe from COVID-19, it leads them to sacrifice social
interaction with their family and friends, which is crucial towards wellbeing and mental
health. Our app makes users feel more comfortable and safe going outside,
allowing them to achieve a more optimal lifestyle balance by improving mental
RLT’s User Interface
health. Secondly, commuters are able to input their desired destination, and ouralgorithm will generate the optimal travel route for them that limits social interaction
and prevents crowding. The navigation system will borrow the Google Maps
pathfinding algorithms with an additional Deep Reinforcement Learning model. The machine learningmodel will have higher reward values for paths with less people and lower reward values for paths thatare crowded. The algorithm incorporates this to achieve the best balance between efficiency and safety,
aiming to bring the user to their destination as fast as possible, and as safe as possible. RLT aids thecombat against the fears that COVID-19 has shed upon our society and gifts users with a sense ofreassurance through reunification with their loved ones, in addition to an added level of physical safety.
RLT earns revenue in a variety of different ways. Since we are a social organization that aims to benefit
society, we would likely receive government subsidies in order to fund our operation. 6To add on, since
90% of iOS Apps are free, we believe that RLT should also be free to download in order to increaseusage. However, we would still be able to earn revenue by running advertisements within the
1https://www.tripsavvy.com/the-worlds-busiest-subway-systems-4146031
2

https://www.kff.org/coronavirus-covid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substance-use/
https://securitytoday.com/articles/2019/03/12/tokyo-to-install-22000-security-cameras-on-metro-in-advance-of-2020olympics.aspx?admgarea=ht.networkcentric&m=1
4
https://medium.com/@akash29/real-time-object-detection-for-a-specific-objects-on-tensorflow-apps-7a60fd39e1e5
https://www.nytimes.com/2017/06/12/well/live/having-friends-is-good-for-you.html
3

app. Our large potential user base would mean that advertisements on our app would receive
high viewership; this would lead to us being able to charge higher prices and thus would increase ourrevenue.
Sustainability and Social Development:
RLT benefits local small restaurant or shop owners who typically do not receive much foot traffic; thesesmall business owners are the ones who are being harmed most by COVID-19. Our app is able torecommend these lesser known meetup locations over popular locations such as Starbucks andMcDonalds, granting these smaller businesses more publicity and promotion during this time ofcrisis. This would drastically improve local economies and the wellbeing of disadvantaged small
business owners and employees. In terms of sustainability, the collection of the data that RLT
analyzes does not require the construction of any additional infrastructure, as it is already being
collected by preexisting security cameras. Furthermore, RLT makes public transportation safer by
preventing crowding, which encourages the use of public transportation and reduces carbon emissions.
All in all, RLT makes cities cleaner, greener, and healthier.
3. Marketing:During the COVID-19 pandemic, our target customers are commuters who require the use of public
transportation, such as workers and students and also that are separated from their families andfriends since they cannot find a safe space to meet up. RLT will attract its first level of consumers byapproaching the Japanese government in order to gain access to security cameras in public areas. At
the same time, it will attend restaurant/plaza industry conferences in order to spread awareness of
RLT’s existence and also initiate potential partnerships. After the cornerstones of the business areestablished, RLT will make their product known to the public by running social media advertisements.
Apps and websites such as Instagram, Facebook, and YouTube are known for their appeal to a varietyof generations. 7These social media advertisements will be especially eﬀective during timesof pandemics as the average time spent on social media has greatly increased. Our competitors,navigation applications such as Google Maps and Baidu Maps operate in a way that is similar to RLTin that they process traﬃc data in real time and suggest potential paths to a given destination.
However, RLT’s unique selling point is that RLT oﬀers optimal paths based on real time human traﬃcrather than car traﬃc a groundbreaking service that competitors have failed to dissect. RLT can also
recommend gathering locations through machine learning, while competitors are solely limited tonavigation.
4. Financial Plan:
As a social organization, RLT will rely on the government for financial support, especially in thefirst two years when the company is still in debt. The reliance on the government will root from eitherof two forms: from government subsidies or from forming a PPP (private public partnership) with thegovernment. The reason for the negative profits in the early years is due to the high amounts of R&D, alarge part of which is market research. Brand awareness is also very critical for an app like RLT’s, andtherefore, RLT will require high amounts of promotion for the first years. Profit will start to materialize inYear 3, due to the exponential increase in sales as the app becomes more popular and begins togenerate more revenue from selling advertisement spots on the app.

6

https://techcrunch.com/2013/07/18/paid-apps-on-the-decline-90-of-ios-apps-are-free-up-from-80-84-during-2010-2012-saysflurry/
7
https://www.nytimes.com/interactive/2020/04/07/technology/coronavirus-internet-use.html