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Introduction

Imagine living in a rural North Carolina county where your car breaks down unexpectedly. A ride-hailing trip to work costs nearly $50 each way, and borrowing a vehicle is not always an option. The nearest bus stop is several blocks away, with limited shelter, and the schedule is hard to interpret. A trip between nearby towns requires multiple transfers, long wait times, and careful planning just to arrive on time.

For many rural communities, daily mobility depends on a combination of intercity bus routes and county-level demand-responsive services. These services provide important connections, yet they often operate with limited coordination. Riders may need to switch between systems that use different schedules, booking processes, and payment methods. Small mismatches in timing or coverage can quickly turn a simple trip into a multi-hour journey.

Transportation providers operate within practical constraints as well. Travel demand varies by time of day and location, service areas are geographically spread out, and staffing and vehicle availability are limited. Planning decisions must balance coverage, reliability, and cost while responding to changing travel patterns.

The challenges facing rural transportation systems are therefore varied and interconnected. Addressing them requires thoughtful service design, careful use of data, and innovative approaches that reflect both community travel needs and operational realities.

This competition invites students to rethink rural mobility by designing AI-informed, data-driven concepts that connect intercity buses and microtransit into a coordinated, efficient, and user-friendly system. Student teams are encouraged to use real datasets and realistic assumptions to propose innovative yet feasible service designs for rural and suburban North Carolina in the Triad and the surrounding regions.

Problem Statement

Competition Goal

The goal of this competition is for student teams to identify and address a specific, well-defined mobility challenge within the intercity and microtransit systems serving rural areas of the North Carolina Triad region. Teams are expected to select a distinct pain point—such as service coverage gaps, transfer inefficiencies, demand uncertainty, operational constraints, or user experience challenges—and focus their analysis and design around that issue.

Students will develop AI-informed, data-driven service concepts that improve coordination between intercity bus routes and demand-responsive microtransit. Using real-world datasets and realistic assumptions about agency resources, teams will propose innovative yet feasible designs that enhance coverage, cost-efficiency, and overall system usability for rural communities.

Proposals should demonstrate how data, geospatial insight, and thoughtful service design can be combined to create more connected and adaptable rural transportation systems.

Study Area and Scope

This competition focuses on rural and lower-density transit contexts in the Piedmont and Central Piedmont regions of North Carolina, with particular emphasis on travel patterns in and between the counties that surround the Greensboro–Winston-Salem–High Point metropolitan area.

The geographic study area includes the following nine counties: (a) Guilford County (Greensboro, High Point), (b) Forsyth County (Winston-Salem), (c) Rockingham County, (d) Stokes County, (e) Davidson County, (f) Randolph County, (g) Alamance County, (h) Chatham County, and (i) Caswell County

These counties represent a mix of urban, suburban, and predominantly rural population centers where transit options vary significantly in coverage and frequency. Travel within this region is supported by a combination of:

• Intercity and regional bus services, including routes operated by the Piedmont Authority for Regional Transportation (PART) and the NCDOT intercity bus network, connecting major Triad cities and regional destinations.

• County-level services, including demand-responsive, deviated fixed-route, and microtransit-style operations that primarily serve lower-density areas.

Teams must select one primary transit service as the anchor for their analysis and design. The selected service should operate across multiple communities and/or counties, making coordination and integration a central challenge. As part of your competition work, you will ground your analysis and design in the realities of this region (its route networks, community needs, and agency capacities), while using data to support decisions that have relevance beyond a single county, and how it integrates with other transit options.

While each service is unique, many share recurring challenges across the region. Teams should identify one or more specific pain points, such as:

• First/last-mile gaps between homes, jobs, and regional bus stops

• Transfer inefficiencies, including long wait times or poorly coordinated schedules

• Demand uncertainty, making it difficult to decide when and where to deploy vehicles

• Limited service span, especially outside peak hours or on weekends

• Driver shortages and high operating costs strain rural systems.

• Operational constraints, such as driver availability and fleet size

• Low ridership and car dependence reduce service viability

• Limited real-time data and forecasting tools make planning difficult

• User experience friction, including fragmented booking, payment, or trip information

• Geospatial spread limits the reach and responsiveness of demand-based models.

Teams should clearly articulate which pain point(s) they are addressing using their solution and why those challenges matter for system performance and rider experience.

Problem Tasks

Based on the selected service and pain point, teams are expected to:

• Analyze existing service patterns using available datasets (e.g., GTFS, travel surveys, mobility traces).

• Propose an AI-informed, data-driven design that improves coordination between intercity and microtransit services.

• Describe how the proposed solution could be applied across multiple locations, even if illustrated through a specific corridor or service.

• Ensure that the design reflects realistic operational and resource constraints faced by transit agencies.

The goal is to demonstrate how data, geospatial insight, and thoughtful service design can improve the performance and usability of rural and regional transit systems more broadly.

Task 1 Service Design and Coverage

Aims to answer: What did you decide to design, and what does the service look like?

In this task, teams will develop a clear and realistic service design that addresses a specific pain point within a selected transit service operating in the Triad region. Your proposal should clearly explain what service is being improved, what problem is being addressed, and how the redesigned service would operate on a day-to-day basis.

A successful team will demonstrate the following items:

• Select one primary transit service as the anchor for your design (e.g., an intercity bus route, a county-level microtransit service, or a suburban transit). You may include transit in “urban” areas, though focus on connectivity in suburban or rural boundaries where the population density is sparse.

• Identify one or more specific service-related pain point(s)

• Propose a service design that addresses the pain point, which may include:

• Adjustments to routes, stops, or service zones

• New or improved transfer points or hubs

• Coordination strategies with nearby intercity or microtransit services

• Describe how your selected service interacts with other mobility options, including:

• How riders reach the service (first/last-mile access)

• How transfers are handled between services

• How schedules or service areas complement each other

• Clearly describe the operating model, including:

• Type and approximate number of vehicles involved

• Service span (hours of operation, days of week)

• Basic scheduling or coordination logic between services

• Use maps or diagrams to illustrate:

• Routes or corridors

• Service zones

• Transfer points or hubs

• Keep assumptions realistic:

• Do not assume unlimited vehicles, drivers, or funding

• Designs should adapt or enhance existing services rather than replace them entirely

Teams may illustrate their design using a specific corridor or community, but the emphasis should be on how the service model addresses the chosen pain point and how the concept could be applied in similar settings across the region.

Task 2 Artificial Intelligence and Data Integration

Aims to answer: How did data and AI help you identify the pain point and shape that design?

In this task, teams will explain how data and AI-informed methods were used to understand the selected service, identify its pain point(s), and inform the proposed service design described in Task 1. The emphasis is on demonstrating thoughtful, transparent use of data to support decisions.

Teams are encouraged to use available datasets to explore travel patterns, service coverage, demand variability, and transfer behavior. AI and data science tools may be used for pattern discovery, basic prediction, or decision support. Fully implemented software systems are not required, but explanations must be concrete and grounded in data.

What Is Expected

• Describe clearly all the datasets used and why they were relevant, such as:

• GTFS data to understand routes, stops, schedules, and transfers

• Travel survey or mobility trace data to explore demand patterns

• Census or county-level data to understand where people live relative to services

• Any other relevant dataset that supports your analysis

• Explain how data was used to identify or validate the pain point, for example:

• Mapping where stops or routes are far from likely trip origins

• Identifying long wait times or poorly aligned schedules

• Observing spatial or temporal demand mismatches

• Detecting underutilized routes or overburdened service windows

• Describe any AI or data-driven methods used, in clear and practical terms:

• Simple demand estimation or trend analysis

• Clustering locations or trips to identify service zones

• Rule-based or algorithmic logic for routing, batching, or dispatch

• Scenario comparison (e.g., “before vs. after” design concepts)

• Connect the analysis directly to your service design, by explaining:

• How data insights influenced route choices, service areas, or schedules

• Why AI-informed reasoning led to a different design than intuition alone

• What trade-offs were revealed through analysis

• Keep assumptions realistic and transparent:

• Clearly state what data limitations exist

• Explain any assumptions made where data was incomplete

• Optional (Bonus, Not Required):

• Simple prototypes, simulations, or dashboards

• Pseudocode or workflow diagrams explaining the logic

• References to open-source tools or known algorithms

Task 3 Incentives and User Engagement

Aims to answer: Why would people choose to use your service, and how does your design encourage consistent ridership over time?

In this task, teams will explain how their proposed service design encourages people to use it and supports long-term viability. Even well-designed routes and schedules will underperform if riders are unaware of the service, find it confusing to use, or see little value compared to other travel options. This task asks teams to think from the rider’s perspective and consider how pricing, incentives, technology, and outreach can shape travel choices.

The goal is to propose thoughtful strategies that align with how people actually decide to travel, particularly in areas where many trips are currently made by car.

What is expected

• Describe your fare and payment approach, such as:

• Flat fares, distance-based pricing, passes, or bundled options

• Whether and how fares are integrated across services

• Propose one or more incentive strategies that encourage trial or repeat use, such as:

• Loyalty rewards or referral programs

• Free or discounted introductory periods

• Employer or institutional partnerships that support transit use

• Explain how riders interact with the service, including:

• Booking and payment (e.g., a unified app or platform)

• Information clarity (real-time updates, transfer guidance)

• Optional engagement features (feedback, ratings, or suggestions)

• Articulate the value proposition for riders, especially those who have access to a car:

• Cost savings compared to driving

• Convenience or reliability

• Productive travel time (e.g., Wi-Fi, guaranteed seating)

• Outline how the service would be introduced and promoted, for example:

• Local outreach, digital campaigns, or demonstrations

• Partnerships with community organizations or employers

• Simple, clear messaging explaining how the service works

Teams should focus on why these strategies would work for the selected service and context, rather than listing many ideas. A small number of well-justified incentives is stronger than a long list without explanation.

Task 4 Stakeholder Needs and Constraints

Aims to answer: Can your proposed solution realistically be implemented given agency resources, costs, and operational realities?

In this task, teams will demonstrate an understanding of the real-world constraints faced by transit agencies and regional partners and explain how their proposed design can be implemented in practice. Even strong technical ideas must operate within limits related to staffing, fleet availability, budgets, and coordination across jurisdictions. This task asks teams to justify why their proposal is not only innovative, but also workable.

The focus is on reasoned judgment, not detailed accounting. Teams are not expected to produce full budgets or engineering designs, but they should show awareness of the trade-offs involved in their choices.

What Is Expected

• Discuss operational feasibility, including:

• Approximate number and type of vehicles required

• Driver or staff needs and whether they are reasonable for the service context

• Example:

• “The proposal relies on reallocating two existing vans rather than adding new vehicles.”

• Address cost considerations at a high level, such as:

• Why the proposed service model is cost-effective

• How design choices help control operating costs (e.g., smaller vehicles, targeted service hours)

• How ridership or incentives might support financial sustainability

• Explain how your design fits within agency constraints, including:

• Technology adoption and training requirements

• Dispatch, maintenance, or coordination needs

• Phased implementation (pilot first, then expand)

• Describe coordination with stakeholders, where relevant:

• How different services or agencies might align schedules, data, or operations

• Whether a regional operator or lead agency plays a coordinating role

• Acknowledge risks and trade-offs, such as:

• Demand uncertainty

• Driver recruitment challenges

• Technology integration issues

• and briefly explain how these risks could be managed or mitigated.

Teams should clearly justify why their proposal makes sense given limited resources, and why it represents a practical step forward rather than a purely idealized solution.

Submission Requirements

The competition will unfold in three stages: