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Reward-Based Change of Driver Behavior for Peak-Time Congestion Relief

Using real time analytics with a ML / Stochastic prediction platform to engage commuters and optimize peak time trip demand.

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During the Community Engagement Meetings, better public transportation/new transportation/anti-congestion was one of the top 10 trending responses regarding what participants wanted to create in Corktown.

We combine machine learning (ML) with our patented stochastic model dynamics, to offer real time analytics and predictions which improve the commute of people and cargo, traffic management, safety and quality of life. Our algorithms have shown high accuracies for predictions 2 hours into the future. In Gothenburg, Sweden we tested 99% prediction accuracy for traffic demand. Our findings are backed by scientific publications. Unlike Google Maps, Waze, INRIX, TomTom, we provide reliable ETAs that hold true even in dense traffic.

For the Indianapolis challenge we suggest an innovative way to address urban congestion, emissions and associated costs based on proven field solutions that work by shifting traffic demand to alternate times when demand is lower. The approach will use our existing patented technology for congestion prediction and traffic demand, including predicting best times to departure. It is a model that works opposite to the traditional toll road concept. We propose then, a small-scale pilot on a congested city corridor that will be directed towards peak time commuters to promote a change of driver behavior by attempting to shift their departure times to alternate time slots and away from peak hour traffic, leading to reduction of morning and afternoon peak commute. Apart from using this method to manage Monday-Friday traffic demand, these shifts can also be applied to manage demand during special events.

A parallel goal of this project is to convert more Car-Centric users, currently estimated at 45%, to Combiners, currently estimated at 37%. To that end, collaboration with the City, IndyGo and accessibility of the MyStop app platform by our API,  for enhanced functionalities, monitoring participation and promotion of the pilot incentives, will go a long way for this approach to be properly implemented.

 Other goals include the identification of challenges and solutions for a wider project implementation and the promotion of flexible work hour policies by employers.

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

Using incentive rewards to drive off-peak, will work in any congested corridor or region where some commuters may be able to change their driving times away from peak travel times. In Indianapolis, these end users are the Combiners and the Car-Centric mobility users, driving Monday through Friday, during morning and evening peak hour traffic. On the B2B side, strategic partner participants as the City and IndyGo may also choose to use the solution towards different ends such as addressing increasing congestion and emission problems due to inability of the current market options to effectively resolve these, or converting more Car-Centric mobility users to Combiners and increase the number of bus users.

Describe your solution's stage of development

  • Prototype - you have built a prototype and tested it with potential users
  • Pilot - you have implemented your solution in a real-world scenario

Insights from previous testing (500 characters)

For congestion, rewards for off-peak driving have shown the same impact as charging fees has, using incentives to change people’s driving decisions. Users are more likely to travel in adjacent hours if it is close to their typical departure time. Previously, the biggest barrier to participation was due to lack of program awareness by the non-respondents. Among those aware of the program, the top barrier was lack of schedule flexibility. This was more significant for lower income participants.

Tell us about your team or organization (500 characters)

Our company was founded in Sweden in 2014, delivering Intelligent Traffic solutions from a world class team with deep expertise in traffic flow, cultivating advanced competencies and know-how through disruptive technologies. Founder/CEO is assoc. prof. from Lund Univ., Berkeley, New York Univ., TAMU CSO is prof. at Univ. of Mass., uncertainty quantification expert CTO is mature entr., cloud, infrastructure expert Other members serve on bus. dev. /AI. More at: http://ximantis.com/about/

Size of your team or organization

  • 2-10

Funding Request

  • $50,000

Rough Budget (500 characters)

6 – Month Project: $30,000 - Group Project Team $15,000 - Direct and Indirect Expenses (travel related, consulting or other professional services, hosting, hardware, software, etc.) $ 5,000 - Ximantis License $50,000 Total

Describe how you would pilot your idea (1000 characters)

Initially, based on community and other participant input (IndyGo, City, etc,) we will act to better refine our work plan and the budget, accounting for specific requirements and system definitions. After the contract, the project will be executed over a period of 6 months, with activities planned in three phases. Phase-1 will last for 2 months, involving data collection, ML training and analysis. Phase-2 will run for another 2 months and involve simulations and on-site experimentation. Lastly, Phase-3, will also last 2 months and involve system deployment and validation, concluding with a project report analyzing the data and quantifying the results.

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

From the early stages, collaborating with IndyGo, the City and other strategic partners, we will define specific requirements and associate them with relevant KPIs. The targets will have to be measurable as: Decrease in trips taken during the morning rush hour, increase in trip taken during the subsequent less congested hours of the morning commute, similar trip reductions during the evening peak period and associated increase at the times before and after the evening peak congestion interval, Travel time reductions during peak times, observed changes to commuting modes as a result of incentives, etc. Also, apart from the data evidence, of great interest will be input in the form of surveys or comments from the riding public to give more clarity on the effectiveness of the incentives in Indianapolis and their matching to expectations - the end user’s perspective and the ways the new technology affects them and their daily routines.

Sustainability Plan (500 characters)

For effectiveness and full impact, the approach requires a long-term funding source, non-monetary incentives or both. Inviting partners like IndyGo, puts them in a position to reach out to Car-Centric users, to increase ridership & market share. Other mobility/car-share providers or merchants offering rewards may also apply for participation. Indy and its residents will benefit from the solution due to congestion/emissions reduction, leading to immediate cost savings and better quality of life.

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