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Truck Rental Price Comparison Search (US Only for Now)

Hacker News

Truck Rental Price Comparison Search (US Only for Now)

Most of the major truck rental providers like U-Haul, Penske, Hertz, and the others, are notorious for black box pricing. The daily rates and cost per miles fluctuate tremendously based on supply and demand, seasonality, day-of-week, location, and other variables. TruckRentalPrices.com aims to provide a window of transparency into these black boxes, to help renters better estimate out-of-pocket costs when budgeting their move. For this initial MVP, we ran tens-of-thousands of sample trip queries to the various providers quoting systems using Selenium and other libraries. We used random dates and locations as inputs. Our goal was to identify crucial metrics such as minimums, average, and maximum costs per mile and daily rates, and comparing weekend vs weekday rates. For example, our data shows that a 26' Budget Box Truck in the US has the following rates: Average Daily Base Rate: $105.30 Average Weekend Base Rate: $149.99 Average Cost Per Mile: $0.91 Minimum Cost Per Mile: $0.57 Maximum Cost Per Mile: $2.49 We also break down costs by state. We display this data along side the top 6 providers in the US: U-haul, Home Depot, Hertz, Penske, Budget, and Enterprise - on a single page. More providers and data will be added in the next phase. We will include a calculator that will provide cost estimates from point A to point B, and eventually opening up to more regions outside the US. This site was built using the following technologies: Next.js on Vercel, Cloudfront, Lambda@Edge, S3, Route53, and DynamoDB on AWS. Looking forward to your feedback, feature requests, or anything else that would be helpful. And thanks in advance for being kind in the comments :)

Visit product View on Hacker NewsSep 12, 2022

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nativeThis product was originally launched on this platform.

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43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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