Milan Zlatkovic, Assistant Professor, Department of Civil and Architectural Engineering, University of Wyoming

Better, faster, safer – is improved street connectivity the solution you were looking for?

 

 

 

Good street connectivity is supposed to have many benefits: increased network capacity, a better distribution of traffic flow, reduced congestion, improved accessibility, a higher level of safety, better livability and more options for non-motorized traffic modes. Just to name a few. But which of these benefits can really be witnessed in a real-world scenario? This is what my team and I wanted to find out.
Testing the theory in the real world
Simply put, street connectivity is a measure of density of network connections and directness of paths. Good street connectivity has many short links, numerous intersections that connect all joining roadways, and preferably no culs-de-sac. Because of the perceived positive effects of enhanced street connectivity, several transportation and planning agencies together with governmental representatives initiated the Utah Street Connectivity Study to come up with recommendations applicable to Utah conditions.
The main objective of our research project, which was performed as a part of the Utah Street Connectivity Study, was to quantify benefits and impacts of good street connectivity on different real-world network types and scales. In addition to that, we wanted to provide guidelines for measures that would make the street connectivity implementation more effective.

“Conducting case studies from three cities in Utah, USA, we were able to show that better street connectivity is the key – to less travel time and delays, and increased accessibility and safety.”

Combining microscopic and mesoscopic modelling
The project was performed using PTV Visum and PTV Vissim traffic simulation. Traffic modelling of street connectivity benefits consisted of two types of models, mesoscopic models of three community-scale networks (urban, suburban and rural), and three microscopic models of selected neighbourhood networks (campus-type, urban and rural) in Utah, USA. The two types of models were integrated by using the outputs of the mesoscopic models as inputs for microscopic models. The models recorded different levels of traffic performance and measures of effectiveness, such as volumes, vehicle miles travelled, speeds, delays, distances travelled and stops per vehicle.
The result: Several benefits, but also negative effects
We were able to witness several benefits and positive developments: In urban and suburban community-scale networks, enhanced connectivity resulted in a significant reduction in network travel times and delays. VMT on higher-rank streets were reduced as a result of a more balanced distribution of traffic flows within the network. Travel times and delays in the tested rural network were increased, but the traffic volumes and VMT were reduced along higher-rank roads. This is a consequence of different characteristics of a rural network, which generally has higher speed limits and fewer intersections, so introducing new intersections results in increased delays. However, the benefits of a more balanced traffic distribution, as well as shorter travel distances are evident in all community-scale networks.
In the campus-type neighbourhood network we came across more negative effects on traffic operational performance. Here better street connectivity was shown to attract more traversing traffic. However, this does not have to be the rule, since in most cases this will depend on the location of the network and the proximity of high-capacity and high-speed highway facilities, as well as connections to those facilities. Improving connectivity in urban and rural neighbourhoods does not seem to attract more traversing traffic, but it can provide a safer and better environment for non-motorized traffic modes. These benefits are much higher in an urban network because of the overall lower speeds and more intersection with traffic control devices.
The next steps
We found that the integration of the two models from Visum and Vissim is very beneficial in creating and manipulating transportation networks. We have already used the same approach in another project and will continue to do so.
More about Milan:
Research is Milan’s main game. After his Bachelor studies in Transportation and Highway Engineering in Belgrade, Serbia, he came to the US to pursue his Masters and PhD in Civil Engineering. For over two years now, he has been an Assistant Professor at the University of Wyoming with a research and teaching focus on traffic signals, transportation systems and traffic flow. The challenges and possibilities of the new-age technology advancements, especially regarding traffic modelling and simulation, are his main motivation for research.
You want to get in touch with Milan? Visit his university page or connect with him on LinkedIn.
Milan’s above described paper on Street Connectivity is available via the website of the Transportation Research Board.
Get to know the PTV Expert:
Sebastian Sielemann, Product Manager for PTV Visum at PTV Group
Milan’s research shows, that micro- and macrosimulation complement each other well. Combining the strengths of both allows for more complex and realistic modelling. That is why we at PTV Group see PTV Vissim and Visum not a s two separate tools but as partners. Consequently, we have been working on connecting them more closely and making exchange and integration easy. Watch our YouTube tutorial on how to export a Visum model to Vissim to learn more. And if you are already an expert in Vissim but also want to know your way around Visum, you best check out this video on how to get started.
Our LinkedIn User Forums for Vissim and Visum are a great place to get to know traffic modelling experts and practitioners from all over the world and to exchange knowledge. Looking forward to meeting you there!

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