No question that designing and operating a transportation system is a difficult problem. Hopefully, by dissecting the problem into manageable pieces and synthesizing them brings clarity to the process.
The goal is to identify key factors that initiate a traffic breakdown event and cause delay. We build upon current knowledge using the “classical methods” of transportation analysis and the latest advances in computation and graphics.
Newtons Laws of dynamics, fundamentals of traffic flow theory (TFT), probabilistic modeling and statistics are used.
Drivers are defined to be self-optimizers and risk averse at the same time.
This is age of Big Data. Our mathematical models rely heavily on empirical evidence. The need to carefully collect data is discussed.
ITS is a burgeoning field. The cartools package is designed with this in mind.
Lessons Learned
The ultimate aim of using cartools package tools is to improve our chances of mitigating congestion and improving performance.
Much emphasis has been placed on “explaining traffic breakdown.” What have we learned about traffic performance using the cartools package?_ The most notable lesson learned is:
We hope that you agree.
Package Potential
The potential of the cartools package as a learning tool has been demonstrated. Hopefully, this platform can be adapted and expanded to help users identify the root cause or causes for other congestion problems.
My next task is add HTML widgets and animation to the package. Adding these features will allow the user to simply conduct sensitivity analyses on-line. For example, the effects of traffic density \(k\) on a bottleneck breakdown can be explored. A slider for \(k\) will be provided so a user change it at will and explore its impact.
This package is open to the public, https://github.com/PJOssenbruggen/Basic. In other words, we are inviting people to share their experiences with the package with us. See https://guides.github.com/activities/forking/.
GitHub promotes this activity. See https://github.com/explore.