Traffic jams are sometimes caused by too many people trying to drive on road that's too small. However some traffic jams happen even when their is plenty of road space. This post explores why this happens with today's cars and how these traffic jams will be fixed with fast self driving cars.
To demonstrate the concepts I'll use a simple web based traffic model. You can play with this model to see what else causes traffic jams.
First, the simulation shows traffic on a circular road. At first the cars drive without issue, but after some time you can see speed perturbations (acceleration & deceleration) form because each driver can slow down faster then they can speed up. Eventually a jam forms where cars are nearly stopped which causes the cars behind them to stop.
How can we fix this? There is plenty of room on the road.
You can completely eliminate these types of traffic jams simply by increasing the max acceleration rate of the vehicles to match their comfortable deceleration rate. This essentially helps pull cars out of traffic jams so that they don't slow down the cars behind them.
This is really hard in practice on public highways because no sane regulator will advocate for people to drive faster to reduce traffic jams, even if on paper it will be safer. Also, some vehicles like trucks simply can't accelerate fast enough to avoid causing traffic jams. There's no hope to solve this problem on public roads where there is a wide range of vehicle performance.
However, on roads where the specifications of all cars can be defined (like the simulation) these traffic trams could be engineered away by only allowing cars to accelerate as quickly as they decelerate. Who knows how much energy this would save, but its a lot. This is one of many reason why I'm advocating to create a purely self driving right of way on tracks BART tracks.
As a candidate that is not raising money and without a public profile, endorsements are a helpful way to fight campaign obscurity. I’ve posted all the organizations who have reached out to me and my answers here.
It surprised me to learn that many very powerful organizations pick who they’ll endorse without considering all the candidates. While this is probably standard practice in politics, to me it suggests cronyism is alive and well. This practice is probably effective at scaring people into not running for the office. However, if the intent of the organization is to find the best candidate they should make their endorsement decisions after they evaluate all the candidates.
We’ve got to hold our leaders to higher standards.
Here are organizations that made endorsements for the District 7 BART Director race without considering all candidates. Endorsements were either given before the candidate filing period was over or didn’t engage all the candidates.
This list will be updated as information becomes available are known.
To get a better understanding of the issues BART (Bay Area Rapid Transit) faces, I wrote a script to visualize BARTS ridership data for the average weekday in 2015. The result is a visual that shows when and where people travel on BART
The regions I used are also shown below. I had to group stations into regions to avoid the graphs looking like magic Eyes.
Disclaimer: Its unclear that if the time BART states for each ride entry is when the rider started or ended the ride. All these graphs assume the ride time is the station exit time. All times would be shifted forward the length of their ride, if this time is the entry time.
Stations fit into 1 of 4 profiles.
All BART station graphs.
Here's the data source on BART's site: http://18.104.22.168/origin-destination/
Here's the ipython notebook used to create the graphs.
AutoBART is based on the assumption that autos can safely navigate the route quickly, drive close together and pull out of the right of way to pick up passengers. Here are a couple ways to test these assumptions.
There are many 2d game library's that could be used to test the control models of autos. This would be helpful to learn the dynamics of platooning and the tolerances of staying within lanes.
Researchers are already using 3d game engines like Unity to simulate environments for self driving car software. To simulate driving in BART, CivilMaps could make a 3D map by mounting a LIDAR sensor to the front of an existing BART car. Comma.ai released an app (chffr) to help learn from driving conditions. Maybe this could be used to show how hard/easy it would be to drive in BART (instead of on the road).
1/10th scale cars on a setup track
The self driving team at UC Berkeley has made an opensource self driving car (BARC) out of a 1/10th size remote control car. The parts cost $500 dollars looks like an involved setup. These could be tested or raced on a track in a warehouse.
Here is a video of one of the BARC projects.
What you'll find.
This blog is a raw dump of information about how to create the worlds best mass transit system in the Bay Area.