Home » Posts tagged 'traffic flow theory'
Tag Archives: traffic flow theory
I’m sharing this article on phantom traffic jams:
Seibold, B. (2019) Traffic Ghost Hunting: When the biggest problem with traffic is nothing at all, Nautilus, https://medium.com/@NautilusMag/traffic-ghost-hunting-ac071197695d [Last accessed: 4/9/2019]
Have you wondered why the road or path is congested only to find out there seems to be nothing causing it? This is the phantom or ghost traffic jams caused by simple behaviours of travellers whether on motor vehicles, cycles or people like slowing down their movement or changing lanes. These disruptions cause a “ripple effect” on the traffic stream much as like waves are generated by a stimulus on calm waters.
There is an excellent article on the efficiency of transportation systems:
Gleave, J. (2019) Space/Time and Transport Planning, Transport Futures, https://transportfutures.co/space-time-and-transport-planning-1aae891194e5 [Last accessed: February 25, 2019].
It is highly recommended not just for academics (including students) but also for anyone interested in transportation and traffic. It’s like a crash course in transportation engineering with a lot of basic concepts in traffic engineering and traffic flow theory being presented for easy understanding by anyone. Enjoy!
We teach our students about car-following theory and applications in our transportation engineering courses at the undergraduate, graduate and post-graduate levels. This is an important part of traffic flow theory and essential in understanding and modelling traffic flow/ behaviour. Among its applications is in the development of simulation models such as those that are now commercially available like VISSIM and CUBE-DynaSim and the older but still very useful TRAF-NetSim. Car-following theory allows for a more accurate simulation of real-world traffic behaviour considering the many parameters describing traffic flow as well as the assumptions that need to be in place for various scenarios.
Here’s an interesting article that’s basically about car-following, specifically mentioning the proper (ideal?) spacing between vehicles and how such discipline can lead to less congestion along our roads.
Simon, M. (2017) Math says you’re driving wrong and it’s slowing us all down, Wired, https://www.wired.com/story/math-says-youre-driving-wrong-and-its-slowing-us-all-down/?CNDID=37243643&mbid=nl_121517_daily_list3_p4 (Last accessed: 12/17/2017).
I noticed that a previous post on common causes of congestion is popular among those who read this blog. The causes mentioned there focused on the lack of discipline among pedestrians and motorists. Their behaviour (e.g., commuters standing in the middle of the road to get a jeepney or UV Express ride, jeepneys and UV Express stopping in the middle o the road to load/unload passengers, etc.) often lead to congestion as they effectively reduce road capacities.
Another major cause of congestion are road crashes. They don’t have to be fatal crashes as the road capacity reduction caused by stopped vehicles is enough to cause congestion along the road including traffic along the opposite direction. The latter phenomenon, if we can categorise it as such, is due to the inquisitive nature (i.e., uses) of people. Traffic approaching the crash site typically slows down as drivers check what happened. Following are some photos I took as we approached a crash site atop the Marcelo Fernan Bridge connecting the Islands of Cebu and Mactan. Note the build-up of vehicles behind the crash involving 3 vehicles.
Road crash involving 3 vehicles in the middle of the Marcelo Fernan Bridge in Cebu
The resulting congestion behind the crash site
Such events are typical causes of congestion and can actually be analysed using traffic flow theory involving waves in traffic. A generally normal flow of traffic (before the incident) is disturbed by the crash, which is assumed to be an isolated event. This results in a change in the traffic characteristics (flow, speed and concentration of vehicles) triggering a shockwave. The shockwave moves backwards and manifests through the chain reaction of drivers hitting their brakes in succession to slow down due to the incident. As such, it is possible to determine how long the build-up of vehicles would be upstream of the incident location.