Tuesday, November 8, 2022

Show HN: Slashbase – open-source collaborative IDE for databases in browser https://ift.tt/Y3oF1Hc

Show HN: Slashbase – open-source collaborative IDE for databases in browser https://ift.tt/t5J746e November 8, 2022 at 12:15PM
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Five Years of Data Show: Our Street Safety Projects are Making a Difference

Five Years of Data Show: Our Street Safety Projects are Making a Difference
By Julia Malmo

Ever wonder about the effectiveness of projects after they go into the ground? Us, too!  

That’s why, in 2017, we launched the Safe Streets Evaluation Program to help project teams understand whether a transportation safety project’s design is effective, and where there might be opportunities to adjust the design if not. Project evaluation data can also be combined across projects to help the SFMTA track the effectiveness of a certain type of safety improvement, which can in turn streamline the design of future projects. The Safe Streets Evaluation Program helps us work towards achieving Vision Zero, an initiative to prioritize street safety and eliminate traffic deaths in San Francisco.  

This week, we’re looking back on five years of data gathered through our Safe Streets Evaluation Program with the “2022 Safe Streets Evaluation Summary,” an interactive website summarizing the results of 18 bicycle, pedestrian and traffic safety projects implemented since 2017. 

Each of the 18 projects evaluated in the summary report added significant safety upgrades to the streets. Some introduced vehicle travel lane removals (road diets), separated bikeways, separated bike signals, or left-turn traffic calming devices. Others brought general improvements for pedestrians at intersections including pedestrian signal improvements, daylighting (red zones at intersections) and upgraded crosswalks. For every project, the SFMTA drew on a wide range of data—from project-specific observations, to police reports, to speed data—to create a comprehensive view into its’ effectiveness. 

An infographic displaying the project names, key design element, and results. In the lefthand column, under “Inventory” the following projects are listed: 7th Street, 8th Street, Folsom Streetscape, Golden Gate Avenue, Leavenworth Street, Turk Street, Central Embarcadero, Valencia Street, 6th Street, Safer Taylor Street, Indiana Street, California Street, Page Street, Fell Street, Polk Street, Second Street, Masonic Avenue, Left-Turn Safety. In the center column, under “toolbox,” the following elements are listed: Road lane reductions, separated bikeways, bike signals, pedestrian upgrades, left-turn traffic safety. In the righthand column, under “results”, the following findings are listed: Collisions decreased by 18%; 85th percentile speeds decreased by 3%; bicycle volumes increased up to 75%; vehicle-bike interactions at signals decreased by 93%; vehicles blocking the bike lane decreased by 90%; pedestrian-vehicle close calls decreased by 38%; vehicle travel time increased an average of 50 seconds for 7.3 miles of road lane reductions; left turn vehicle speeds decreased by 17%

The evaluation report indicates that the SFMTA’s safety tools are working together to create safer environments for all modes of transportation on city streets. 

You can dig into the whole set of outcomes on the website, but here are some highlights:  

  • Within the projects evaluated, annual collision rates decreased by 18%  

  • Bicycle-related collision rates decreased by 33% and pedestrian-related collision rates decreased by 32%   

  • Bicycle volumes on streets that received bicycle improvements increased up to 75% in the morning peak (8 AM to 10 AM) commute times, with similar growth in the afternoon/evening peak (4 PM to 6 PM) commute times.   

  • Thanks to protected bikeways, the rate of incidents of vehicles blocking the bike lane decreased by 90%.   

  • Close calls or near misses between pedestrians and drivers decreased across evaluated projects by 38%.  

  • Several projects in under-served communities such as the Bayview and Tenderloin are helping to address historic inequities and under-investment in these neighborhoods.   A table titled “Aggregate Project Findings Across Evaluated Projects”. At the top of the table, a blue bar lists out the column titles: Measure; Metric; Overall Findings; Capital Findings; Quick-Build Findings. In the Collisions section, the following sub-categories are evaluated. For Annual Collision Rate: Overall findings show an 18% reduction; Capital Findings show a 19% reduction; Quick-Build Findings show a 17% reduction. For Annual Bike-related collision rate: Overall findings show a 33% reduction; Capital findings show a 5% reduction; Quick-Build findings show a 42% reduction. For Annual Pedestrian Related Collision Rates: Overall findings show a 32% reduction; Capital findings show a 50% reduction; Quick-Build findings show a 26% reduction. In the Vehicle Speed section, the following sub-categories are evaluated. For 85th Percentile Speed: Overall findings show a 3% reduction; Capital findings show a 5% reduction, and Quick-Build Findings show a 3% reduction. For Max Speed Change Observed: Overall findings show a 20% reduction, and no data is present for either Capital Findings or Quick-Build Findings. For Vehicle Travel Time/Vehicle Travel Time in Seconds: Overall findings show 50.00; Capital Findings show 221.00; Quick-Build Findings show 21.50. For the Bike Volume section, the following sub-categories are evaluated. For AM Bike Volumes: Overall Findings show a 75% increase; Capital Findings show a 187% increase; Quick-Build findings show a 41% increase. For PM Bike Volumes: Overall Findings show a 72% increase; Capital Findings show a 107% increase; Quick-Build Findings show a 62% increase. In the Bike Signal Interactions and Close Calls section, the following sub-categories are evaluated. For Bike-Vehicle interactions: Overall findings show a 93% reduction; Capital findings show no data; Quick-Build findings show a 93% reduction. For Close Calls (near misses): Overall findings show a 62% reduction; Capital findings show no data; Quick-Build findings show a 62% reduction. For Average Daily Interactions Post-Implementation: Overall Findings show 2.2; Capital Findings show 0.3; Quick-Build Findings show 3.1. For Bike Compliance w/ Bike Signal: Overall findings show 87% compliance; Capital Findings show 86%; Quick-Build Findings show 88% compliance. For Vehicle Compliance w/ No Turn On Red: Overall findings show 90% compliance; Capital Findings show 86% compliance; Quick-Build findings show 92% compliance. In the Blocking the Bikeway section, Rates of Incidents was evaluated. Overall Findings show a 90% reduction; Capital Findings show a 19% reduction; Quick-Build Findings show a 90% reduction. In the Vehicle-Pedestrian Close Calls Section, Close Calls (near misses) were evaluatd. Overall Findings show a 38% reduction; capital findings show a 0% reduction; quick-build findings show a 34% reduction

The Safe Streets Evaluation team will continue to think creatively about how to build on the evaluation work and use it to inform our current and future projects, programs, and practices through developing a publicly accessible database of all Safe Streets Evaluation data and finding better ways to evaluate project outreach efforts. 

View the 2022 Safe Streets Evaluation Summary 

Learn more about the SFMTA Safe Streets Evaluation Program: SFMTA.com\safestreetsevaluation 

 



Published November 08, 2022 at 04:26AM
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Monday, November 7, 2022

Show HN: Multilingual Salary Calculator for Germany https://ift.tt/JM8n5sf

Show HN: Multilingual Salary Calculator for Germany Many people from a migrant background have difficulty understanding the German income tax system. Therefore, as a side project, I have created a multilingual gross-net-salary calculator for Germany. With this side project, my main goals are: 1. to create an intuitive method for non-German speakers to easily understand how and what is deducted from their gross salary in Germany, 2. to translate the calculator into Eastern and Central European languages (as they represent one of the largest groups of migrant workers in Germany) to make it easier for them to understand the German income tax system, 3. make it mobile-friendly so that anyone with only a mobile phone can easily use it. I am currently working on translations into Russian and Farsi (based on the requests from several organisations who are working with refugees in Germany), with other languages to follow. The website is already available in the following languages: Arabic, English, German, Hungarian, Romanian, Ukrainian. What do you think about design? Is it intuitive or does it need some improvement? All feedback is welcome. https://ift.tt/NWxgBeI November 7, 2022 at 05:06PM
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Show HN: A free keyword research tool using Search Suggestions and Autocomplete https://ift.tt/HOZ42lr

Show HN: A free keyword research tool using Search Suggestions and Autocomplete https://keywordsea.com November 7, 2022 at 02:26PM
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Show HN: RxJS Insights – a toolset for RxJS visualization https://ift.tt/H83CRp0

Show HN: RxJS Insights – a toolset for RxJS visualization Hi HN! I'd like to present you the thing that I'm building - the RxJS Insights. It was primarily created as something that allows for debugging complex RxJS streams, but after showing it to a few people it turned out that it might be more helpful in learning RxJS since it can visualize the observables execution step by step. The setup is somewhat similar to the Redux Devtools' one in a sense that it requires an application level component (the proper @rxjs-insights/* packages) to instrument the RxJS and to connect with the devtools. I invite you to try it out in your app (the setup is rather easy) or on StackBlitz (all links are in the readme). Thanks! https://ift.tt/jVeS719 November 7, 2022 at 02:32PM
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Show HN: Hacker News for Events https://ift.tt/g1nQK6Z

Show HN: Hacker News for Events NextEvent.dev is a Hacker News for events. Share tech and around tech events here. Who's hosting an event in your area this upcoming week? Share it here, let the community know. https://ift.tt/PlZRwAH November 7, 2022 at 11:22AM
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Show HN: textshader.com https://ift.tt/qb7Jm68

Show HN: textshader.com TLDR: Click the link to see some cool visualizations, refresh a few times for random examples, and try editing some of the code yourself. This is a small weekend project of mine inspired by shadertoy.com It's a static single-page site hosted on GitHub pages so the website design is pretty barebones. I'm not a web developer so I mainly wanted to create a Cool Thing with my free time and not have to learn tons of front-end to do it. I'd love to hear what people think about it and please post or send me any shaders you make! You can use the link button in the bottom right to share. I hope that this gets more people interested in shaders because GPU programming is a pretty different paradigm and learning it has made me a better software engineer even though my current job doesn't touch it. That said, textshader only runs normal Javascript on the CPU so think of it like a simplified sandbox and not the real thing. I'd highly recommend checking out shadertoy to level up to the real deal too! https://textshader.com November 7, 2022 at 10:02AM
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Show HN: Do You Know RGB? https://ift.tt/t8kUpbO

Show HN: Do You Know RGB? https://ift.tt/OWhvmMT June 24, 2025 at 01:49PM