PeopleMaps

• The Elevator Pitch
• A Little Deeper into the Theory
• The Motivation (a high school senior's frustrations with GPS)
• Application Architecture and Flow
• Path Clustering Algorithm
• MapReduce Algorithm
• Eventual Direction

The Elevator Pitch

In its essence, the PeopleMaps project is an alternative to traditional route suggestion platforms (such as Google Maps and Apple Maps) which relies solely on users' behavior to suggest routes between locations.

It differs from Waze and similar feature-based crowdsourced algorithms in that it completely rejects the idea of quantifying why a particular user may have chosen a route (traffic congestion, accident on the road, etc). Instead, it uses the facts that:

1. the majority of paths taken in a region are taken by locals

2. locals have an intimate, holistic, and immediate understanding of their environment

to advocate for determining best paths based on user behavior.

A Little Deeper into the Theory

Humans can digest a vast number of nuanced factors that are difficult to measure, quantify, or weight in aggregate, for example:

• the safety of a particular neighborhood
• the status of an ongoing construction site
• the presence of children on a particular street
• a route typically congested at peak hours

Though many of these are measurable by a centralized institution, determining a holistic best path from them is a difficult problem. Also, it's sometimes wasteful to quantify every possible factor for every environment--often only 2 or 3 factors are relevant in any particular case.

Therefore, PeopleMaps treats users as distributed, decentralized network of smart sensors who report their "opinion" of various paths to the central system in the form of "votes": each time a user takes a path P between point A and B, he or she is casting a vote for that path over all other possible paths. Then when giving path suggestions, PeopleMaps returns the highest-voted path between the requested endpoints.

By further weighting the most recent votes most highly, PeopleMaps aims to achieve a real-time best-route suggestion scheme at minimal infrastructural cost. Where other route suggestion schemes must wait for data (from sensors, reporters, etc.) to be digested before updating their suggestions, PeopleMaps takes the most up-to-date information directly into account.

The Motivation (a high school senior's frustrations with GPS)

When I lived outside D.C. as a high schooler four years ago, I would often accompany my dad on various errands around the greater-D.C. region. We relied on a GPS for directions, often holding an iPhone and typical car GPS next to each other and trying to reconcile our own knowledge with the GPS suggestions to find the best--aka smoothest, fastest, most pleasant--routes. To our frustration and sometimes humor, the GPS suggestions would lack basic knowledge that any local in the area had, largely because the GPS offered us only binary options: toll or no toll? Use or avoid highways?

The reality was that a relatively small sequence of binary options generally did not yield the best holistic path. The paths we knew from experience were a nuanced, weighted combination of factors. If someone asked us for the best paths personally, we would have different answers than any GPS we used.

And so the idea for PeopleMaps was born: why shouldn't people like us be consulted for best path suggestions? We lived and drove around the area for years. We knew it better, more intuitively, than any centralized system. We were the most powerful sensors, most nuanced sources of information on our local environment.

Four years later, PeopleMaps is readily being developed, with a functioning iOS application prototype already in existence and hosted on Amazon Cloud.

Application Architecture and Flow

PeopleMaps is currently an iOS application that is hosted in the cloud using Amazon Beanstalk for auto-scale. It uses MongoDB in the backend for data storage and as a MapReduce compute engine.

The iOS Core Location framework is used to monitor users’ movements in the background. Though for now the Standard Location Service (SLS) is used, which constantly records the user’s location at a high level of accuracy (but consumes a lot of energy), a simple switch to the Significant Change Service, which detects when the user starts moving, can be used to greatly cut down on the energy consumption.

An algorithm developed in-house is used to determine when a user has completed a path, at which point the path is pushed to a NodeJS/Express server over a RESTful API, and the server batch-inserts paths into MongoDB. Each path is stored as document with start, end, and set-of-coordinates fields.

When a user requests requests a path, the highest-voted path as defined above is returned (see below for algorithm details).

Path Clustering Algorithm

At the highest level, paths between the same endpoints (or close to same, as defined by MongoDB's geospatial $near operator) are compared to each other, and if they are deemed "similar," are bucketed into the same cluster. When a new path is introduced, it is compared to a representative path from each bucket in order to be classified.

Paths are represented using a "bag of points" model, where each point is a geospatial coordinate. The Jacard index, defined as the intersection of two sets divided by their union, is used to compute the similarity of two bags of points. A pair of paths with a high Jacard index are very similar. Different cut-off values for what constitutes "similar" when speaking about sets of coordinates were experimented with and can further be refined with a larger set of data.

Points are represented as latitude-longitude coordinates. In order to determine if two points are same (or close to same), the geometric distance between them is calculated and if it falls within a threshold, they are considered the same. Again, the threshold can be refined as more data comes in.

The MapReduce Algorithm

In order to generate clusters, MongoDB's native MapReduce engine is used. The map function groups documents based on the concatenation of their start and end points, and the reduce function applies the path clustering algorithm above. MapReduce is used to parallelize the path clustering for different endpoints, of which there are obviously a massive number.

Eventual Direction

My best recommendation for the future of the PeopleMaps project is for it to merge with an existing, common route suggestion platform (such as Google Maps) as an alternative route that can be requested. While the benefits of user-based suggestions are clear, it wouldn't hurt to also utilize monitored information-- or leave it up to the user to decide which mode they prefer, at least until the PeopleMaps theory is more thoroughly tested.