![]() Next, we use randomTrips.py located in the tools folder within the SUMO home directory ( sumo -> tools), to generate random trips for a certain number of vehicles (200 vehicles in the example below). In SUMO, we setup a 5x5 grid with each road of length 200m, and 3 lanes, as below: netgenerate - grid - grid.number=5 -L=3 - grid.length=200 - output-file= In urban planning, grid road networks are pretty common. That’s it! Now let’s get to creating your first simulation of traffic flow on a network! Simulating traffic in grid networks There are multiple ways to install SUMO, but I prefer the pip install way, that installs SUMO as well as the python libraries to interface with SUMO. However, SUMO is open access and is fairly easy to get started in. For example, Anylogic, VISSIM, and Aimsun are companies that offer traffic and mobility modeling solutions. Traffic simulations seem to belong to a niche community of traffic flow researchers, or engineering contracting companies. In the near future, it’s important to simulate the effects of proposed connected vehicle and intelligent transportation technological innovations, to best realize their potential in streamlining traffic flows. or public buildings like airports and hospitals into the picture. The situation gets more complicated when integrating major events such as concerts, sporting events, etc. it is important to have realistic simulations of traffic flows so that the proposed projects have a best chance of succeeding in alleviating traffic. In another article I show how “Braess’s Paradox” leads to the unusual result, that increasing the number of roads in an urban network, could make traffic worse!īefore engaging in intensive infrastructure projects such as building new roads, adding lanes, traffic lights, etc. But what happens when you have multiple roads, as in typical urban road networks? Interestingly, just increasing capacity through more lanes or longer roads might not work as well as you thing, in road networks. Recent research suggests that streamlined interactions between autonomous vehicles could potentially reduce traffic jams for artificial scenarios like vehicles driving in a circle. In a previous article, I discuss the first paper that conclusively showed how traffic “phantom” shock-waves emerge from nothing, except driver interactions.Ĭars in a circle | Tadaki et al 2013 New J. Future directions in simulating realistic traffic.Analysis of key traffic performance metrics.This article is going to do this exact thing, using a case-study of traffic on grid networks. But apart from the SUMO documentation, a few Stack Overflow posts, and some YouTube videos, there aren’t many tutorials I’ve come across that teach you how to create a complex traffic simulation from start to finish. ![]() ![]() The Simulation of Urban Mobility (SUMO) platform is an open source platform that enables simulation of traffic flows in complex environments. Even understanding the emergence of traffic congestion in the most simple case - a single lane road, is challenging. Understanding, predicting, and ultimately - reducing traffic congestion in urban networks is a complex problem. Cars in a city | Image by Pexels from Pixabay
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