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Personalized, Multi-modal, Navigation App. Using Behavioral Economics and Machine learning create personalized multimodal journeys.

Our technology understands the user and plans routes based on their preferences, in real time, using all available mobility options.

Photo of Motti Sigel
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RouteValet are solving urban congestion. Though over $110 Billion have been spent on mobility technology since 2010, traffic continues to get worse. Our cities are coming to a standstill. Traffic is frequently the number one factor of quality of life in a city.

We have created a navigation app which creates seamless routes across all modes of transportation, based on unique user preferences. The user enters their desired destination, and route valet collects real time information of all mobility options in the city. We then create seamless routes, optimized for the user’s preferences, including car, public transit, ridehail, rideshare, walking and cycling. These routes are shown to the user in the format of a navigation app.

Traffic is a severe economic burden in of terms fuel, maintenance and parking. It also severely limits the GDP of a city by reducing productivity. Arriving at work after sitting in traffic for an hour, decreases employee effectiveness.

At RouteValet, we solve traffic by focusing on people, rather than vehicles. We use Behavioral Economics and Machine Learning to understand what drivers are really looking for. Then, we create personalized routes, combining cars with public transit, delivering the best of both worlds.

Consider the simple case of driving a car to a parking lot and then riding a train to your final destination. The key is combining public transit options with private vehicle options such as car and ridehail. Now, users whose usual mode of transit is a private vehicle are enabled to reduce their private vehicle usage by advantageously combining public transit. In this way, we increase user delight, satisfaction with their city and simultaneously reduce traffic.

RouteValet is unique for two distinct reasons. Firstly, we are the only routing system we know of, which is creating personalized routes, based on behavioral economics and machine learning.

Secondly, we have succeeded in combining private vehicles with public transportation in integrated, seamless routes.

Public transit apps are based on ‘Raptor’ algorithms. Developed by Microsoft engineers in the early 2000s, they are now open source. Raptor creates routes by synchronizing the schedules of vehicles with both fixed routes and timetables. These algorithms are suited for fixed route and schedule vehicles only, and not private cars.

Car navigation apps are based on ‘Shortest Path’ algorithms. The first of these was invented by Dijkstra in the 1970s. Since then, other improved versions have been developed, such as A Star. These algorithms are optimized for a single, dynamic vehicle, and cannot integrate with a fixed route vehicle, such as a train or bus.

In contrast, leveraging Behavioral Economics and Machine Learning, RouteValet has succeeded in creating algorithms which integrate and optimize routing across both private vehicle and public transit mobility options. Further, these routes are made considering the full user profile. RouteValet are the first to create personalized, integrated routes.

Describe who will use your solution (1,000 characters)

Navigation apps are used by us all, every day. RouteValet goes one step further and personalizes the process. For example, if you live in a suburb, where you don't have access to public transit, but don't want to face downtown traffic, RouteValet will give show you where to drive to make the best connection to The Metro. If you need to get across town, RouteValet compares the costs and time of car, ridehail and BRT. In the modern city, frequently one mode of travel is not the best choice. RouteValet gathers all the real time data (weather, traffic, public transit schedules etc.) and creates personalized, seamless routes.

Describe your solution's stage of development

  • Pilot - you have implemented your solution in a real-world scenario

Tell us about your team or organization (500 characters) RouteValet was founded by Motti Sigel and Ilan Friedson in February 2017. Motti has a decade of experience working with governments and large corporations. Ilan has a decade of experience in data science. They are both graduates of Israel's leading Technology University - Technion. RouteValet is currently in pilot in the cities of Tokyo and Osaka, in conjunction with local Industry partners as well as in Jerusalem and Tel Aviv.

Size of your team or organization

  • 2-10

Funding Request

  • $50,000

Describe how you would pilot your idea (1000 characters)

To pilot our technology, we partner with cities and transit agencies. First, we select the region for the pilot. For example, the Miami-Dade metropolitan area. Next, we select mobility options to include. For example, cars, uber, lyft, The Metro and other local bike share, ride hail and other mobility options. Then, we will integrate all of the required API's into the RouteValet platform. The app is then launched to the public, either as a stand-alone app or a white label app. The launch can be done via usual marketing channels, such as social media and local organizations. We earn revenue from the cities and agencies based on a SaaS model. For example, many public transit agencies pay routing companies for the routing technology used in their websites or apps. From mobility companies there is a revenue share. For example, Uber and Lyft pay Google Maps for sourcing rides to them. This revenue would of course be shared with the city.

Describe how you would measure the success of your pilot (1000 characters)

We would gauge the success using two key performance indicators. The first would be the usage statistics of the app. The second is the data gathered on the usage of the app itself. How many people selected car only routes? How many selected multi-modal routes? This data is then compared to the baseline data from Miami-Dade to study, to evaluate whether we have succeeded in increasing the use of modes of transport other than the car.

Attachments (1)

Operators Intro to RouteValet - August 2018.pdf

In this image, you can see five personalized, multimodal routes. For each route you see the vehicles, cost and time. Even when compared with car only routes, multimodal routes can be both faster and cheaper. For example, using BRT to avoid traffic or save parking fees. By giving users access to all of the mobility options in their city, we enable them to make smart choices, based on their personal preferences.


Join the conversation:

Photo of Matteo Cappelleti

Hey Motti Sigel ! Great Concept, I wanted to ask you, where do you get all the info from to feed the algorithm to calculate route times?

Photo of Motti Sigel

Hey Matteo Cappelleti thanks for the question! So the info comes from multiple external APIs, like Google Maps and Mapbox as well as the ride-hail and ride-share companies as well, like Uber and Via. We take that data and then plug that into our machine learning data base to create personalized routes. For example, we won't suggest buses to someone who prefers trains or we'll recommend a walk on a nice day.
By the way, we're live in Jerusalem, Tel Aviv and Manhattan. We'll be live in Tokyo and Osaka by the end of November. I'm hoping that Miami-Dade will be next!