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City of Edmonton Develops Citywide Traffic Simulation and Modeling for Fast-growing City

Using DYNAMEQ, City Planners Developed a Roadmap to Model Edmonton’s Roadways for Smarter Future Planning

Meeting Travel Demand in a Fast-growing City

City of Edmonton's DYNAMEQ city-wide traffic simulation model
City of Edmonton's DYNAMEQ city-wide traffic simulation model (Image Courtesy of City of Edmonton)

Edmonton is the capital city of the Canadian province of Alberta. With a population increase of 25% during the previous ten years, it is also the Canadian city with the fastest growth. The necessity for a new transportation master plan, stressing greater multi-modality and bringing with it significant infrastructure and development projects, was recognized by city planners as necessary to satisfy demand.

One billion Canadian dollars will be invested in freeway conversion projects. To support these investments, the city needs to efficiently make wise operational decisions for projects spanning all phases of planning, design, and construction.

Advancing City Modeling

City employees realized that more modeling software was needed to meet their objectives. First, in addition to more localized project consequences, the new model must account for the implications of wide area route choice, such as traffic diverted to other corridors. Second, to complement the city's current EMME strategic travel demand model and provide a consistent platform for operational traffic planning and forecasting, the new model would need to be implemented throughout the city.

For an undertaking of this scope, conventional modeling techniques would be ineffective. To construct citywide traffic simulations and a dynamic traffic assignment (DTA) model for the entire city, the City of Edmonton looked for software.

Implementing a Phased Approach

City of Edmonton path analysis which illustrated network wide route choice
City of Edmonton path analysis which illustrated network wide route choice (Image Courtesy of City of Edmonton)

The city planners selected DYNAMEQ as the preferable option to address this need because they already utilized EMME to model travel demand in the city. It gave the team the operational information they needed to assess various transportation scenarios, like queuing and delays, at the size essential to comprehend the effects of traffic on route selection and trip detours.

To simulate individual automobiles with car-following, lane-changing, gap acceptance, and signal control, the City of Edmonton deployed DYNAMEQ. Using a simulation-based DTA technique, it also created trustworthy route selections on expansive networks and in situations of extreme congestion, offering constant traffic simulation at any size for a project- and regional-level applications.

The city started its phased implementation of DTA and traffic simulation using DYNAMEQ in keeping with continuing project timetables. Given a condensed project schedule, they needed a traffic operations model that could effectively assess the effects of suggested alternatives on both in-corridor and off-corridor trips.

They created a DYNAMEQ model to address this need successfully. Furthermore, the quick schedule for bridge reconstruction offered a chance to increase model confidence by evaluating outcomes against data on volumes and traffic times gathered during construction.

To assess the traffic implications of corridor construction work, lane reductions, turn prohibitions, and parking limitations, they created a larger model for the second phase and used it for many projects. The city used this model to help plan, design, and assess the effects of several overlapping and concurrent initiatives. Compared to traditional project-specific microsimulation tools, it offered significant timeline and cost savings benefits.

The city launched the third phase after the first two were successfully implemented. They conducted a transit study using DYNAMEQ. The Valley Line light rail transit (LRT) model, which includes signal control operations and transit signal priority, was created and deployed to study route deviations and the traffic impact of the LRT system. The projected LRT system's DYNAMEQ simulations successfully simulated the effects of traffic on major thoroughfares, including detours up to two miles away from LRT crossings.

A Roadmap for Citywide Traffic Simulation and DTA

(Image Courtesy of City of Edmonton)

A citywide DYNAMEQ model is now available to planners. It has been used for various capital planning initiatives, such as road upgrades, bike lanes, neighborhood redevelopment, and transit planning studies. The model is used for operational forecasting throughout the city and has additionally been used in partnership with the city's traffic engineering team to examine more advantageous project alternatives at a tenth of the expense of traditional microsimulation tools.

Per the EMME strategic travel model horizons for the city, the DYNAMEQ model is routinely modified and calibrated for three-hour busy periods- the morning and the evening—across the 2030 and 2050 prediction years. It was discovered that the DYNAMEQ simulation model is appropriate for medium- and long-range forecasting in terms of dependable route selection through DTA convergence and the capacity to create probable future-term signal control strategies from simulated traffic patterns.

The city's DYNAMEQ experience serves as a guide for effective model adoption. The city created model credibility and confidence with little outlay thanks to a small pilot study. The town then expanded crucial decision support by scaling up new initiatives with capital planning projects. Arun Bhowmick, the general supervisor of the City of Edmonton, explained that a small team initially started the DYNAMEQ project. However, the project has expanded since then and is currently managed as a citywide endeavor.

Nowadays, operational traffic planning projects throughout the city rely on the citywide DYNAMEQ traffic simulation and dynamic traffic assignment model, enabling relatively long planning research and incorporation with the regional travel framework.

Author: Jo Evans, Product Marketing Manager, Mobility Simulation team


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