SmartCurb: Turning Trash into Cash — A Data-Driven Revolution in Urban Logistics

Introduction

Urban waste collection is a fundamental municipal service, yet it remains largely inefficient. In many cities, garbage trucks systematically service every street, even when a significant portion has no bulky waste present, leading to unnecessary fuel consumption, labor costs, and carbon emissions.

As cities face increasing financial and environmental pressures, optimizing these systems has become essential. Traditional collection models rely on fixed routes and schedules rather than real-time data, limiting their ability to adapt to actual conditions. This project explores how data-driven decision-making and modern technologies can transform this outdated approach into a more efficient and sustainable system.

The core concept behind SmartCurb is to shift from static collection to a dynamic, demand-based model. By integrating a resident interface, a pre-sweep validation phase, and the S.C.O.P.E (SmartCurb Optimization & Prediction Engine), the system ensures that only verified waste locations are serviced. This approach improves operational precision, reduces unnecessary routes, and allows for accurate resource allocation.

Several key objectives guided the development of this solution:

Maximize financial efficiency by reducing operational costs and ensuring rapid return on investment for municipalities

Eliminate unnecessary collection routes by relying on verified, real-time data

Improve operational efficiency through optimized routing and precise resource allocation

Reduce environmental impact by minimizing fuel consumption and emissions

Enhance service quality by increasing collection frequency and flexibility

This project aims to provide municipalities with a scalable, cost-effective solution that reduces waste, lowers emissions, and improves overall service delivery, contributing to more sustainable and efficient urban environments.

Framework

A multi-step development methodology was used to design, validate, and evaluate the SmartCurb system with a strong focus on financial and operational performance. Initial research relied on official municipal documents, public budgets, and environmental reports to understand real-world waste collection costs, fleet operations, and infrastructure constraints. The City of Dorval was selected as a case study, providing reliable benchmark data for both operational modeling and financial analysis.

This research was complemented by field observations, where multiple residential streets were analyzed to measure the actual presence of bulky waste. These observations revealed that a significant portion of streets were empty during collection periods, forming the empirical basis for the system’s optimization logic and later sensitivity analysis.

The solution was developed in three integrated components:

Resident Interface: A digital platform where residents upload images, descriptions, and locations of bulky waste, creating a real-time, demand-based dataset while also promoting reuse and upcycling within the community.

Pre-Sweep Validation System: A light municipal vehicle is deployed the day before collections to complete a full-city verification pass, confirming listed items and identifying unreported waste to ensure a fully accurate and 100% reliable dataset for municipalities.

S.C.O.P.E. (SmartCurb Optimization & Prediction Engine): Processes validated data using image analysis to estimate waste characteristics, determines required trucks and workforce, and generates optimized routes using mapping tools and real-time traffic data to minimize costs and inefficiencies.

Finally, a comprehensive financial model was constructed using municipal cost structures. This included CAPEX, OPEX, and a three-scenario sensitivity analysis (optimistic, conservative, pessimistic) based on varying levels of waste presence and resident participation. This methodology ensured both technical feasibility and strong financial rigour.

Financial Modeling and System Validation

A comprehensive financial model was developed to evaluate the feasibility and scalability of SmartCurb using the City of Dorval as a benchmark. Official municipal budgets and operational data were used to construct realistic cost structures, including labor, fuel, maintenance, and landfill fees. This allowed for a direct comparison between the current collection system and the SmartCurb model under identical conditions.

The system was evaluated across three sensitivity scenarios—optimistic (25% empty streets), conservative (20%), and pessimistic (15%)—based on field observations and varying levels of resident participation. Across all scenarios, SmartCurb consistently reduced annual operating costs from approximately $525,000 to a range of $290,000–$321,000, generating annual savings between $204,000 and $235,000. These results confirm that even under less favorable conditions, the system remains financially advantageous.

Capital Investment and Return on Investment

To transition from a working prototype to a fully deployable platform capable of supporting large-scale municipal use, a total investment of approximately $321,200 was estimated. This includes system development, backend infrastructure, APIs, dashboard interfaces, hosting, legal costs, and project management. While this initial cost may appear significant, financial projections demonstrate strong long-term viability.

The projected payback period ranges from 1.36 to 1.57 years, depending on the scenario. Over a five-year period, SmartCurb generates a return on investment between 217% and 266%, corresponding to net financial benefits of up to $856,000. This confirms that municipalities not only recover their investment quickly but also achieve substantial long-term savings.

System Performance and Operational Efficiency

The SmartCurb system integrates three core components: a resident interface, a pre-sweep validation phase, and the S.C.O.P.E (SmartCurb Optimization & Prediction Engine). Together, these components transform static collection into a fully data-driven operation. The resident interface provides initial inputs, the pre-sweep ensures complete data accuracy, and S.C.O.P.E processes this information to optimize resource allocation and routing.

This approach significantly reduces unnecessary routes, leading to lower fuel consumption and labor costs. Additionally, optimized logistics enable municipalities to increase service frequency from one to three collections per month, improving accessibility without increasing overall costs.

Environmental Impact

Operational improvements translate into measurable environmental gains. By eliminating unnecessary routes, SmartCurb reduces total distance traveled, resulting in fuel savings of roughly 15,000–25,000 liters per year for a municipality the size of Dorval. Using standard diesel emission factors, this corresponds to a reduction of about 6–8 tonnes of CO₂ annually. These reductions are equivalent to the carbon sequestration of approximately 200–350 mature trees per year. These results demonstrate that SmartCurb delivers financial savings and significant environmental impact.

Adoption and Real-World Validation

To validate real-world adoption, an external resident survey found that 85% of respondents believe change is needed, while 86% indicated they would support and use SmartCurb if it provided more frequent and reliable service. These results confirm that the system is not only financially viable, but also strongly aligned with resident demand.

Discussion and Conclusion

The results show that SmartCurb is not just an optimization, it is a paradigm shift in municipal service design. For decades, cities have accepted a trade-off: better service requires higher costs. SmartCurb breaks that assumption. It demonstrates that a city can cut over $200,000 in annual costs while tripling service frequency. In civil engineering terms, this is the equivalent of achieving maximum efficiency with maximum performance, something rarely realized in public infrastructure systems.

What makes this even more impactful is that the system goes beyond optimization. The resident interface introduces a community-driven reuse and upcycling platform, transforming waste into opportunity. Instead of sending items directly to landfills, residents can give them a second life, reducing disposal volumes and associated costs. This adds a new layer of value: SmartCurb doesn’t just collect waste more efficiently, it actively reduces the amount of waste that needs to be collected.

The integration of verified data through the pre-sweep and the precision of the S.C.O.P.E engine ensures these benefits are achievable in real-world conditions. Strong financial returns, rapid payback period, and resilience across all scenarios confirm that this is a solution municipalities can rely on with confidence.

Ultimately, SmartCurb proves that infrastructure can be smarter, cheaper, and better at the same time. It challenges the idea that efficiency and quality must compete, offering instead a model where cities save money, residents receive better service, and environmental impact is reduced, all at once.

Future Development and Next Steps

The next phase is to present SmartCurb to the City of Dorval to secure a Letter of Intent. This will support raising investment for a six-month pilot program to validate financial projections in real conditions. Following successful validation, the approximately $321K development cost would be adopted by the municipality, with long-term service incentives.

After deployment, expansion to additional cities within three years will be essential to ensure scalability and profitability. The long-term model includes a subscription-based service (≈$6,000–$10,000/month per municipality), adjusted for size and density, enabling sustainable growth and broader impact.

Acknowledgements

This project was supported by several individuals and sources who contributed valuable guidance and resources. I, Wolf, would like to thank my father for constantly questioning and challenging my ideas, he kept poking holes in my project until I made sure there were none left, strengthening the final outcome.

We would also like to thank an MBA graduate from McGill for providing insight on the financial structure and viability of the project.

We are grateful to our teachers for their support, feedback, and encouragement throughout the development process.

Additionally, we thank municipal employees and public works departments who provided access to official documents, operational data, and budgetary information essential for accurate analysis, as well as trusted government data sources and mapping technologies used in modelling.

Finally, we would like to thank Evan Michael Nadeau for conducting a resident interest survey that served as a valuable source for this project.

Government & Municipal Sources

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Project-Specific Sources (Primary Work)

Nadeau, E. M. (2024). Resident interest survey on bulky waste collection services [Unpublished survey data].

Wolf, W., & David, D. (2026). Comparison of municipal bulky waste collection system (Dorval) and SmartCurb system [Spreadsheet]. Retrieved from https://docs.google.com/spreadsheets/d/15s2BkJ-4xK3v9Jws4Pmk4q97k8-qhZe5yDT8gNldM64/edit

Wolf, W., & David, D. (2026). SmartCurb development cost analysis [Spreadsheet]. Retrieved from https://docs.google.com/spreadsheets/d/1dNLp8uxgoetuZXIWRz0ktQJ3oEZk92DgVxvzt4t5bkg/edit

Images

SmartCurb. (2026). [S.C.O.P.E algorithm showing non-optimized and optimized waste collection routes] [Screenshot]. SmartCurb Internal System.

SmartCurb. (2026). [S.C.O.P.E algorithm optimized route visualization with route data and truck breakdown] [Screenshot]. SmartCurb Internal System.

SmartCurb. (2026). [Resident interface showing item listing process for bulk pickup] [Screenshot]. SmartCurb Platform.

SmartCurb. (2026). [Resident interface displaying active listings for scheduled pickups] [Screenshot]. SmartCurb Platform.

Whip Around. (n.d.). [Original garbage truck photograph, edited to include the SmartCurb logo] [Edited photograph]. Whip Around. https://whiparound.com/blog/6-garbage-truck-manufacturers-for-fleet-owners-compared/

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SmartCurb. (2026). [SmartCurb logo] [Logo]. SmartCurb.