![]() hour of day, day of year) Īfter cleaning, the dataset is just over 1 billion rows of historical traffic data, which I then uploaded to OmniSci using pymapd. Additional useful columns were added (e.g.Since we weren’t interested in each lane’s speed, but rather the average speed at the station, the lane specific columns were dropped. The method used for cleaning depended on the data element. Once the traffic history data was downloaded, there were still some data quality issues to take care of, including: To get an understanding of how commuter traffic has changed in previous years, we’ve decided to analyze traffic from 2015 to 2019. The data is recorded by stations located throughout the freeways and the stations collect a variety of data including speed (in miles/hr) and occupancy (percent that the lane is full). To test out OmniSci Immerse’s capability of automatically resampling data for clearer visualizations, we chose to work with the 5 minute samples. Multiple telemetry data series are available, ranging from speed to incident traffic information. Caltrans PeMS provides an abundance of traffic historical data for all of California, dividing the state into 12 districts San Francisco is in district 4. The data was obtained from California’s Department of Transportation’s (Caltrans) Performance Measurement System (PeMS) Data Clearinghouse and is publicly available. Obtaining Historical Traffic Data Caltrans Traffic Data By using OmniSci’s traffic flow analysis tools, we can visualize and analyze a billion rows of 5-minute traffic data from San Francisco in real-time. Even though sitting in traffic is one of my least favorite things (I can only listen to so many podcasts), seeing how Bay Area traffic patterns have changed is pretty exciting. Since OmniSci’s headquarters is in San Francisco, we wanted to see just how vehicular traffic flow has been changing in our region. With live monitoring of traffic, authorities can identify where there could be potential bottlenecks, dangerous intersections, and where to build more lanes. Nowadays, we can pull up current traffic flow maps in the city and pick the route with the least amount of congestion and headache.Īside from daily commuters, cities and municipalities are collecting massive amounts of traffic flow data each day and using it to make some pretty important data-driven decisions. Just finding the shortest distance route doesn’t cut it anymore, we’re now interested in which route will save us the most time. With that being said, the definition of the ‘optimal route’ has also changed with more technology and historical traffic data. I can’t remember the last time I drove without using some navigation tool to check the optimal route. Luckily, as tech savvy commuters, we’ve become experts in using tools like Google Maps and Waze to make driving a bit more bearable. This causes problems for residents and even bigger ones for city planners. Especially now that most jobs are located in these urban areas, daily commuters are pushing highway systems to the limits. Whether you live in San Francisco, Munich or Beijing, traffic is always a headache. ![]()
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