Successful implementation of the Lower Don Lands is also critical to opening up the rest of the vast vicinity to revitalization. The revitalized ‘Lower Don Lands’ will be a new urban district of mixed-use communities focused on the re-naturalized Don River and the historic Keating Channel. It will have a population of between 20,000 and 24,000 residents in approximately 12,000 housing units, and a range of employment space. Streets and trails would be extended through the precinct including Queen’s Quay, a re-aligned Lake Shore Boulevard, Cherry Street, Martin Goodman Trail, Don River Trail, the Lake Shore Boulevard trail and the water’s edge Waterfront Promenade. The new ‘south boulevard’ design for Queens Quay in East Bayfront will continue to Cherry Street, beyond which Queens Quay will become a local street connecting to Lake Shore Boulevard.

A major initiative in the Keating precinct is to improve north/south connections at three underpasses of the rail corridor. A new pedestrian underpass to the Distillery District at Trinity Street would be created. The existing Parliament and Cherry Street underpasses would be enlarged to provide more space for pedestrians and cyclists. The Cherry underpass would also accommodate street car service.

Challenges: The analysis was performed using a microsimulation model. Microsimulation models are more sophisticated than standard traffic analysis tools and allow users to test the impacts of congestion on multiple modes of transportation on a larger network. Microsimulation models also provide better representation of queues and their interaction with adjacent intersections, a function not found in other types of modelling platforms. A microsimulation model was selected for this project for many reasons, but primarily to model the interaction of multiple modes of transport such as autos, streetcars and pedestrians.Additionally, there is significant development proposed along the waterfront, and the collective impact of this development has not yet been tested on a larger network.

Finally, there were various levels of development that were proposed for the site and this is easily tested in a microsimulation environment. In terms of microsimulation analyses, there are many platforms that can be used. The two most commonly used platforms in North America are Paramics and Vissim. Both tools have their strengths and limitations. Vissim is best suited for corridor modelling where modes may share lanes. Paramics is better suited for modelling larger networks, with a high degree of route-choice.

Due to the size and complexity of the network, and the many possible routing patterns around the new developments, Paramics was selected as the preferred microsimulation modeling tool. The Paramics model was developed using the City of Toronto’s regional model to represent travel demand and transit information.

The network study area is bounded by, and includes, Jarvis Street to the west, Queen Street to the north, Leslie Street to the east and the waterfront to the south.

Conclusion: To quantitatively assess the mobility needs, the design team developed a microsimulation model using Paramics and a corresponding set of performance measures. This analysis compares the development scenarios by evaluating various performance measures.

These performance measures were developed with the broader sustainable mission in mind and include multi-modal measures such as person delay which factors in the delay felt by each person within a vehicle, thus ensuring that high occupancy vehicles such as buses and streetcars are prioritized over lower occupancy vehicles.

The performance measures include multi-modal measures such as person delay, transit delay, pedestrian crossing times, pedestrian waiting times, transit travel times and vehicle travel times. Through an evaluation of the transportation alternative solutions, a preferred solution was identified and carried forward as the Transportation Master Plan.

The Transportation Master Plan was developed to balance the needs of the various uses that would be served by the transportation network, while recognizing urban design and pedestrian environment considerations. The Transportation Master Plan consists of the individual pedestrian, transit, bicycle and road networks as well as the individual streets connections.

Together, these address the Study’s Problem Statement and meet the needs and transportation objectives of the study, which are:

• Shift towards non-auto modes

• Prioritize transit

• Increase and improve the pedestrian network

• Increase and improve the bicycle network

•Rationalize parking

• Improve the public realm Infrastructure

Improvements recommended from this study will be further developed during detail design. Future design work will include confirmation of details such as road excavation and transit requirements, construction staging, as well as pipe sizes and specific locations for water, wastewater and stormwater treatment facilities.

Eddie Lam.  City of Tronto / Arup

As a transportation or pedestrian analysis professional we know your primary goal is to deliver a high quality project on time and in budget – using our Build Better Models (BBM) ethos, Paramics is designed from the ground up to help you do just that, for example our core product range is up to 20 x faster than the nearest competitor under independent tests; this time you gain can be spent on improving model quality and helps you stick to challenging budgets.

At Paramics we focus on productivity, powerful analytics tools and helping you spend the right amount of time on the right task – spend less time building networks, more time using and improving them so you can deliver a superior service to your clients and project stakeholders.

Join us on 5th June to find out more about our product range, customer success stories, and current public and private sector offers.

Today, traffic and pedestrian simulation are mature technologies but not all applications are the same; now you can use your existing skillset, data and models while upgrading your software capabilities to an industry leading platform for traffic, pedestrian and hybrid modelling.

If you have any questions or would like to set up a meeting with our team in advance please contact kevin.malone@pb.com

http://landor.co.uk/modellingworld/2014/home2014.php

Challenges: The model that was used by the AERIS team for the Eco-Signal Operations microsimulation phase of the project was already built and calibrated before the project began in early 2013, so there was no need to use the powerful tools Paramics offers, such as the Estimator program. That being said, the number one reason that Paramics was chosen for this exercise was the Programmer functionality suite application programming interface (API) that can be used to modify or extend the existing capabilities of the program. Since the majority of the technology of the AERIS program does not exist or has been re-imagined, this was absolutely vital to the success of the program. There were two main components that were developed in the Programmer API interface: the individual application components and the environmental monitoring component. While Paramics does provide a useful pollution monitoring plugin, the AERIS goals required a custom made real-time environmental plugin, which would simulate connected vehicle information being communicated at at least once a second to the infrastructure. This presented a problem in that the majority of environmental modeling suites available are all “offline,” meaning that they were database driven, “after the fact” programs. The team made use of the Programmer’s ability to store and access variables in a vehicle’s memory at any requested moment, in real-time. This, combined with other commands in the API, allowed the team to build a novel, real-time version of the US governmental emissions model, called MOVES. The individual AERIS applications also made use of the limitless commands and override commands in the Programmer interface to mimic the needed connected vehicle features. This was helpful, because vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies are not expressly available in most modeling environments. Since Paramics was used in the signal operations applications phase of the project, one of the key pluses of the Programmer API suite was the interface with the signal system. Commands allow the querying, analysing, and override of the signal system in real-time. This feature was absolutely vital to the success of the modeling. Additionally, this information can also be customized on a vehicle-by-vehicle basis and stored in each vehicle’s memory. One last feature of connected vehicles that is better than conventional ITS technologies is that the real-time location of each vehicle is available to every other vehicle, as well as to the infrastructure. Paramics API features allowed us to design novel systems to mimic this technology in simulation very accurately and easily. In addition, one unique application that involves signal timing optimization utilized the command line operation of the Processor application of the suite. The optimization was using a specially built C++ interface, developed in house, that continually called and implemented the command line simulation in a loop to optimize the signal timings and test each of the options in practice. Without this special command line feature, the speed and accuracy of the process would not have been possible.

Conclusion: The model was used to communicate with partners, stakeholders, and the practicing community in a number of ways. The number one way was with the generation of data from the modeling runs. As previously discussed, the Programmer suite MOVES API that we built gave a detailed list of environmental impacts from the real-time simulation, which we built in to actionable reporting, such as cost benefit analyses, charts, and individual person-cost savings statistics. These were the results ultimately desired by the stakeholders, to better understand if there is a benefit in the technology worth pursuing. This data was combined with the mobility results, such as travel time, delay, etc., which Paramics easily produces natively. Some applications of the model for the signal operations phase looked very good in the Modeller suite and could be shown to clients in meetings and made in to videos for public presentations. These included showing the signal timing changing in real-time with priority requests, as well as modifying acceleration and deceleration profiles in real-time for the environmental optimization of approach trajectories. One other feature we took advantage of was the ability to export detailed shapefiles and information to ESRI standards, so that we can import in to ArcGIS. This allowed the team to build heat maps and other visualizations as needed from the simulation results. The AERIS project is ongoing and applications continue to be updated and improved as the team learns more from modeling sensitivity analyses. The results of the modeling are also helping the team to suggest future research to stakeholders and other members of the connected vehicle community which will be implemented in future phases of AERIS research.

Name: Sean Fitzgerel
Location: United States of America (Various)
Company: Booz Allen Hamilton

We will go through the tools available and how it could be used to mimic drivers anticipating the time taken to safely make a transfer.

The webinar will last for 30 minutes followed by a Q&A session.

Please register for your appropriate region using the links below:

Thanks and we look forward to meeting you online on then.

The Paramics Team

Region: Asia
Date: Monday, 7 April 2014
Time: 12:00 WST (Perth)
Register:  https://www1.gotomeeting.com/register/272433057

Region: Europe
Date: Monday, 7 April 2014

Time: 12:00 CEST (Paris)
Register:  https://www1.gotomeeting.com/register/441678160

Region: Americas Central
Date: Monday, 7 April 2014
Time: 12:00 CDT (Wisconsin)
Register:  https://www1.gotomeeting.com/register/692433177

Every few months we are giving you the chance to win a brand new Apple iPad and at the same time publicise your Paramics work to other members of the Paramics community and prospective clients worldwide. Our iPad competition is open to any Paramics user and you can enter as many times as you like. To enter just take 10 mins to tell us how you use Paramics to solve traffic and pedestrian modelling problems by completing the form found on:  http://www.paramics-online.com/competition.php

The current competition runs 1st March to 1st May 2014, the draw takes place on May 7th 2014 and the winner will be notified by email, please review the T&C’s for further information. Good Luck!

Join us at ITE Miami 9th-12th March 2014 to learn how Quadstone Paramics can save you time and money on your next traffic, pedestrian or hybrid modelling project in today’s tough economic climate.

Visit our stand #216; we’d love to tell you more about the great public sector license offers we have giving 60% discounts, free training and project support – perfect for public sector users reviewing models or those building models in house.

As a transportation or pedestrian analysis professional we know your primary goal is to deliver a high quality project on time and in budget – using our Build Better Models (BBM) ethos, Paramics is designed from the ground up to help you do just that, for example our core product range is up to 20 x faster than the nearest competitor under independent tests; this time you gain can be spent on improving model quality and helps you stick to challenging budgets.

At Paramics we focus on productivity, powerful analytics tools and helping you spend the right amount of time on the right task – spend less time building networks, more time using and improving them so you can deliver a superior service to your clients and project stakeholders.

Join us on 9th -12th March to find out more about our product range, customer success stories and Version 7 release including new editor and navigation, our unique shared spaces capabilities, the next stage of our signal optimisation, and mesoscopic integration.

Today, traffic and pedestrian simulation are mature technologies but not all applications are the same; now you can use your existing skillset, data and models while upgrading your software capabilities to an industry leading platform for traffic, pedestrian and hybrid modelling.

To find out more visit – http://www.paramics-online.com/usa

If you have any questions or would like to set up a meeting with our team in advance please contact Lenny.winsel@pb.com

Visit our stand #1114; we’d love to tell you more about the great public sector license offers we have giving 60% discounts, free training and project support – perfect for public sector users reviewing models or those building models in house.

As a transportation or pedestrian analysis professional we know your primary goal is to deliver a high quality project on time and in budget – using our Build Better Models (BBM) ethos, Paramics is designed from the ground up to help you do just that, for example our core product range is up to 20 x faster than the nearest competitor under independent tests; this time you gain can be spent on improving model quality and helps you stick to challenging budgets.

At Paramics we focus on productivity, powerful analytics tools and helping you spend the right amount of time on the right task – spend less time building networks, more time using and improving them so you can deliver a superior service to your clients and project stakeholders.

Join us on 12th-14th January to find out more about our product range, customer success stories and Version 7 release including new editor and navigation, our unique shared spaces capabilities, the next stage of our signal optimisation, and mesoscopic integration.

Today, traffic and pedestrian simulation are mature technologies but not all applications are the same; now you can use your existing skillset, data and models while upgrading your software capabilities to an industry leading platform for traffic, pedestrian and hybrid modelling.

To find out more visit – http://www.paramics-online.com/usa

If you have any questions or would like to set up a meeting with our team in advance please contact Lenny.winsel@pb.com

http://www.paramics-online.com/webinars.php

http://www.paramics-online.com/webinars.php

1. Assess existing conditions,
2. Estimate queue length and propagation,
3. Estimate conflicts (read end, crossing, and lane change), and
4. Test alternative mitigation strategies.

Challenges: This project is challenging due to the proximity of a major intersection to the plaza entrance.

Paramics was used to model the base case scenario by harnessing the following enabling features of Paramics:

1. Ability to model microscopic movements of vehicles including lane changes, merging and crossing,
2. Ability to model queue propagation and queue build up from one intersection to another,
3. Ability to estimate conflicts using the vehicle trajectory data of individual vehicles,
4. Visualization capabilities to present the findings of this study to the client and stakeholders.

The standard procedure of developing a microsimulation model was followed:

1. Import map and/or image-based data, such as engineering CAD drawings, GIS maps, and aerial photographs. These were used as a reference for network coding, so that the dimensions of the microsimulation network correspond to reality. Accurate geometry data are essential for the extendability of the simulation model.
2. Define roadway types or categories, which have some common set of characteristics such as number of lanes, design speed, lane width, etc.
3. Define vehicle composition and characteristics. Factors such as vehicle length, width, acceleration/deceleration rates, and maximum or desired speeds were specified.
4. Build up the network by coding intersections (nodes) as well as roadway segments (straight or curved links), with reference to the background geographic data as defined in step 1. Refine the intersections geometry (including ramp merge points) to assure realistic vehicles turns and takeovers.
5. Define priority rules for unsignalized intersections such as yield signs, stop signs, etc.
6. Define the traffic signal control logic for all signalized intersections, including information such as signal type (e.g. fixed-time, semi-actuated, fully actuated, demand-responsive, etc.), signal phasing, required detectors, and related algorithms (transit signal priority, offset transitioning, etc).
7. Define zones and vehicle generators areas such as parking garaged and lots, and define the appropriate links for releasing and attracting demand from these zones.
8. Input the dynamic (time-dependent) origin-destination (O-D) demand matrices. OD matrices are responsible for distributing the trips from/to the zones defined in step 7. Each O-D matrix will be specific to a time period and vehicle-type, and in some cases, a profile can be defined to further control vehicle release within a time period.

Conclusion: The simulation model was calibrated against turning movement counts, mid-block counts, and travel time prob-vehicle survey during the survey. Paramics was used to report to network wide performance measures including overall network speed, travel time, and number of conflicts, and time to collisions.

The vehicle trajectory files were extracted from Paramics and used as an input to SSAM (safety Surrogate Assessment Measures) by FHWA. As shown from the figure the SSAM was able to estimate rear-end, crossing, and lane change conflicts; which reflect to a reasonable extent our field observation, especially for the crossing conflicts.

Paramics was then used to test the following alternatives:

• Right Out and U-Turn At Downstream Intersection
• Right In & Right Out (Banning left Turn)
• Fully Actuated Signal Control At the Plaza Entrance

Most of the introduced mitigation strategics increased the travel delay for traffic along the main arterial and also resulted in more conflicts due to more lane changes, and stopping at the proposed intersection given its proximity to the main intersection.

• Number of Trips and Period of Analysis : 4,000 Vehicular Trips, Saturday Morning Shopping Time
• Network Size: No of Nodes: 36 No of Links: 70 No of Zones: 9 Length of Roads: 12 km
• Paramics Suites: Modeller, Estimator, Processor, Programmer
• Plugins Developed: Performance Measures Processing

Credits: CIMA, Hossam Abdelgawad & Brian Malone

The main objective of this project is to design efficient and robust ATSC using a multi-agent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction.Applying MARL approaches to ATSC problem was associated with a few challenges as agents typically react to changes in the environment at the individual level but the overall behaviour of all agents may not be optimal. This project concerns the development and evaluation of a novel system of Multi-Agent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC).

MARLIN-ATSC offers two possible modes:

(1) Independent mode, i.e. each intersection controller is independently working of other agents; and (2) integrated mode, where each controller coordinates the signal control actions with the neighbouring intersections.

Paramics enabled the design of MARLIN in the simulation environment by addressing the limitations of existing ATSC.

In this project MARLIN offers the following features and characteristics:

1) Decentralized design and operation- typically less expensive compared to the centralized system.

2) Scalable to accommodate any network size.

3) Robust – with no single point of failure.

4) Model-free – does not require a model of the traffic system that is challenging to obtain.

5) Self-learning – reduces human intervention in the operation phase after deployment (the most costly component of operating existing ATSCs).

6) Coordinated – by implementing mode 2 (integrated mode), which coordinates the operation of intersections in two-dimensional road networks (e.g. grid network), a new feature that is unprecedented in ATSC state-of-the-art and practice.

Challenges: The platform consists of two main layers; the first layer is an input configuration layer that is responsible for configuring and providing the necessary input to the second layer. The configuration layer has two main roles;

1) Configures the simulation-based learning environment (model) such that the simulated environment closely matches the real-world environment.

2) Configures the RL-design parameters.

The second layer is a control layer that includes three interacting components:

A. Agent: The Agent component implements the control algorithm; the agent is the learner and the decision-maker that interacts with the environment by first receiving the system’s state and the reward and then selecting an action accordingly. A Generic agent model is developed using Java Programming Language such that different levels of coordination, learning methods, state representations, phasing sequence, reward definition, and action selection strategies can be tested for any control task.

B. Simulation Environment: The simulation environment component models the traffic environment. Paramics models stochastic vehicle flow by employing speed regulations, car-following, gap acceptance, and overtaking rules. Paramics provides three methods of traffic assignment that could be employed at different levels: “all-or-nothing” assignment, stochastic assignment, and dynamic feedback assignment.

In this application a dynamic stochastic traffic assignment was used where: 1) a random noise was added to the travel cost to account for the heterogeneity among drivers’ perception of travel cost, 2) a dynamic feedback interval was used to update route travel times for familiar drivers in the simulation.

Paramics API functions were used to construct the state, execute the action, and calculate the reward for each signalized intersection. Some of the main challenges in deigning any RL system are the design of the state definition, action definition, and reward definition.

C. State Definition: Queue length. The agent’s state is represented by a vector of 2+P components, where P is the number of phases.

The first two components are: 1) index of the current green phase, and 2) elapsed time of the current phase.

The remaining P components are the maximum queue lengths associated with each phase.

• Action Definition: Variable Phasing Sequence The agent is designed to account for variable phasing sequence in which the control action is either to extend the current phase or to switch to any other phase according to the fluctuations in traffic, possibly skipping unnecessary phases. Therefore, this algorithm is an acyclic timing scheme with variable phasing sequence in which not only the cycle length is variable but also the phasing sequence is not predetermined. Hence, the action is the phase that should be in effect next.

• Reward Definition: the reduction in the Total Cumulative Delay The immediate reward for certain agent is defined as the reduction (saving) in the total cumulative delay associated with that agent, i.e., the difference between the total cumulative delays of two successive decision points. The total cumulative delay at time k is the summation of the cumulative delay, up to time k, of all the vehicles that are currently in the intersections’ upstreams. If the reward has a positive value, this means that the delay is reduced by this value after executing the selected action. However, a negative reward value indicates that the action results in an increase in the total cumulative delay.

Conclusion: MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto for the morning rush hour. Paramics enabled us to produce a rich performance measures and emission modelling results. The results are reported for BC control systems (existing conditions), MARL-I (represents MARLIN-ATSC Independent Mode with no communication between agents), and MARLIN (represents MARLIN-ATSC Integrated Mode with coordination between agents).

The performance of each control system is evaluated based on the following measures of effectiveness:

- Average Delay Per Vehicle (sec/veh)

- Average Max. Queue Length Per Intersection (veh)

- Average Standard Deviation of Queue Lengths Across Approaches (veh) – Number of Completed trips

- Average CO2 emissions factors (gm/km)

- Average Travel Time For Selected Routes (min) The Table compares the performance of the BC against the MARLIN-ATSC system with and without communication among agents, i.e., MARLIN and MARL-I, respectively.

The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level; and travel time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in downtown Toronto. Also Paramics enabled us to extract route travel times results using Analyzer. The route travel times were found really essential to judge on the performance of MARLIN for coordination along arterials. These route travel times were reported for a major freeway (Gardiner Expressway) and a major arterial (Lake Shore Blvd) in downtown Toronto as shown in the attached figures. Also the disaggregate level of the results produced by Analyzer enabled us to observe the variability in travel time along different routes, an important component in estimating the cost of congestion. It was also interesting to find that the Gardiner Expressway EB traffic (inbound) travel time improves by 19% in the MARLIN scenario. Alleviating the congestion on the arterial streets and consequently freeway off-ramps contributes the most to these savings on the freeway. This clearly shows the effect of the downstream capacity on the freeway performance. For the Gardiner WB direction traffic was not as congested as the EB but MARLIN still attains 4% improvement in average route travel times.

Figure showing the platform components and their interaction

Figure showing the configuration parameters and input parameters and the interface of MARLIN-ATSC

Figure Showing the Study area boundaries and street names

Table showing detailed performance measures enabled from Paramics

Figures showing routes analyzed using Paramics Analyser

Figure showing route travel times produced by Paramics Analyser

Samah El-Tantawy (University of Toronto), Baher Abdulhai (University of Toronto)

Challenges: A representative set of data was collected during the course of the project. Required data can be summarized as follows:

• Satellite images

• AutoCAD overlay

• Road Geometry Attributes

• Traffic Parameters

• Traffic Signals

• Turning Restrictions and Movements

• Roadway Counts

• Cordon Counts

• Traffic Generation Areas

Vehicular demand was estimated using a regional EMME demand model for the Greater Toronto Area (GTA).

Screenline counts had to be defined at the model boundaries and then an OD estimation process was applied using a Genetic Algorithm optimization software developed at the ITS Centre. The new OD matrix was then assigned to Paramics Modeller to estimate traffic flow and compare it to observed mainline counts and turning movement counts. – Traffic signals at the area varied from fixed time, to semi-actuated, to fully actuated, to adaptive signal control (e.g. SCOOT) and transit signal priority along the Queens Quay street car line. A rigorous programming effort was conducted to code all the signal operations types and their operation were compared against field performance measures (travel time and delay studies). Paramics programmer enabled us to code any type of signalized intersections, especially before the inception of the newly released ASC in Paramics.

The development process went through the following steps:

1. Import map and/or image-based data, such as engineering CAD drawings, GIS maps, and aerial photographs. These were used as a reference for network coding, so that the dimensions of the microsimulation network correspond to reality. Accurate geometry data are essential for the extendibility of the simulation model.

2. Define roadway types or categories, which have some common set of characteristics such as number of lanes, design speed, lane width, etc.

3. Define vehicle composition and characteristics. Factors such as vehicle length, width, acceleration/deceleration rates, and maximum or desired speeds were specified.

4. Build up the network by coding intersections (nodes) as well as roadway segments (straight or curved links), with reference to the background geographic data as defined in step 1. Refine the intersections geometry (including ramp merge points) to assure realistic vehicles turns and takeovers.

5. Define priority rules for unsignalized intersections such as yield signs, stop signs, etc.

6. Define the traffic signal control logic for all signalized intersections, including information such as signal type (e.g. fixed-time, semi-actuated, fully actuated, demand-responsive, etc.), signal phasing, required detectors, and related algorithms (transit signal priority, offset transitioning, etc).

7. Define zones and vehicle generators areas such as parking garaged and lots, and define the appropriate links for releasing and attracting demand from these zones.

8. Input the dynamic (time-dependent) origin-destination (O-D) demand matrices. OD matrices are responsible for distributing the trips from/to the zones defined in step 7. Each O-D matrix will be specific to a time period and vehicle-type, and in some cases, a profile can be defined to further control vehicle release within a time period.

Conclusion: In this network, the Gardiner Expressway is a major corridor running across the East/West ends of the study area. The Gardiner Expressway carries a large number of trips that are destined at to the downtown core through the Spadina St., York St. /Yonge St., and Jarivs St. off-ramps. These locations show congestion accumulation to a level that may block the off-ramp of the Expressway; an example is the Spadina St. off-ramp as shown in the Figure. This demand pattern creates a constant traffic flow being fed to the downstream intersections at the off-ramp locations, resulting in queue spill backs and queue propagation as shown in the figure. The redesign of the Queens Quay corridor resulted in 1 % overall savings in network travel times for year 2007; however it resulted in 2.9% increase in network travel times for year 2021. So the final recommendation of this project was to keep the current design of the corridor.

Number of Trips and Period of Analysis: 23,000 Trips , AM Peak Period

Network Size:

No of Nodes: 561

No of Links: 1100

No of Zones: 53

Length of Roads: 293 km

Paramics Suites: Modeller, Processor, Programmer, Converter.

Plugins Developed: Actuated Traffic Control, Adaptive Signal Control, and Performance Measures Processing

Publications / Reports – Report: Abdulhai B , Roorda M., Wahba, M., Abdelgawad, H., and Georgi, A. “Development Of Comprehensive Microsimulation Models For The Toronto Queens Quay Corridor” Model Development” Submitted to Arup, Water Front Revitalization Plan, 2007

Particularly in urban areas with high rates of population and employment growth, such as the City of Calgary, the Greater Golden Horseshoe (GGH) and the Vancouver Lower Mainland, transportation networks are experiencing unprecedented demands from both the goods movement and the passenger travel sectors.  On the positive side, high demands on the roadway infrastructure are a sign of increased prosperity, long term growth in the economy (notwithstanding the recent economic downturn) and increasingly active and mobile communities.  On the other hand, however, increasing congestion threatens the future of both mobility of urban residents and the economic competitiveness of firms that rely on fast and reliable goods transportation.  Furthermore the traffic safety, emissions, and noise impacts of transportation infrastructure use are significant. This is a direct and imminent threat to not only our lifestyle, but also our economic prosperity and the quality of the environment that we live in.

One potential way to improve both mobility and safety along congested urban corridors that carry significant commercial vehicles is to segregate truck traffic from passenger traffic by implementing exclusive truck facilities in key economic corridors.

This project assesses the impact of exclusive truck facilities on urban freeway performance in the Greater Toronto Area (GTA). A large-scale regional microscopic traffic simulation model was developed and used to model two alternative truckway configurations in the GTA, including a truck only highway and a truck lane conversion on Highway 401. The effect of infrastructure changes on travel distances, travel times, exclusive truck lane usage, and travel speeds was assessed.

Challenges: This project went through the following development stages:

Develop a regional EMME/2 Model to estimation OD demand matrices to the micros-simulation model

Estimate induce demand due to the introduction of a new infrastructure (such as truck only lane or new exclusive truckway)

Develop a microsimulation Paramics model that captures the routing option for trucks moving through the study area (see complex geometry of coding exclusive truckway facility, to the right).

Develop a Truck-Only Plugin to simulation the Truck-Only along Highway 401 through the utilization of the Application-Programmer-Interface (API) tool; the developed plug-in therefore can be used in combination with the other general Paramics functionalists. TOL plug-in is designed to address behaviour associated with Truck Only Lanes, the user is responsible for providing the physical network structure under which the plug-in can influence driver behaviour (see customized input parameters in the figure to the left).

Conclusion: Paramics Programmer capabilities, and specially the HOV plugin, enabled us to model the TOL plugin and capture the behavioural component of trucks while choosing between General Purpose Lanes (GPL) vs the TOL. Addition of a 4-lane truck only highway results in greater travel time improvements for trucks, and sometimes results in shifting of traffic bottlenecks on Highway 401 to downstream locations (see AM peak travel times reported on Paramics network below). Conversion of a freeway lane on Highway 401 results in increased congestion for passenger cars, but improved travel speeds for trucks. Both scenarios show truck facility usage ranges from 100 to 800 trucks per hour per direction.

Number of Trips and Period of Analysis – 260,000 Trips , AM Peak PM Peak Period – Network Size – No of Nodes: 4,307 No of Links: 4,929 No of Zones: 673 Length of Roads: 1,300 KM

Paramics Suites – Modeller, Processor, Programmer

Plugins Developed – Performance Measures Processing, Truck –Only Plugin (Developed based on HOV plugin), Conflict Analysis Plugin

Funding Agency: Infrastructure Canada, University of Toronto
PIs: Matthew Roorda (University of Toronto), Baher Abdulhai (University of Toronto),Clarence Woudsma (University of Waterloo)

Simulation of traffic in the Indianapolis Central Business District.  Testing of impacts of 8-mile multi-use trail throughout the downtown.

Objective:

Provide a tool for the City of Indianapolis for use in future studies.

Jacobs Edwards and Kelcey and Stump-Hausman worked together in the creation of a model of downtown Indianapolis, IN. The model was bounded by the White River and by Interstates 65 and 70, covering an approximate area of 11km2 (4mi2).

The City of Indianapolis requested creation of the model to test the traffic impacts of the Indianapolis Cultural Trail, a world-class multi-use bike and pedestrian trail currently under construction. (The initial trail segment subsequently opened in late 2007.) The model was geared towards this use, but was also built to be used by the City in other future studies.

The model was also used by the same study team for the Indianapolis Northeast Transit Alternatives, investigating alternatives for bringing new transit uses (LRT, BRT, AGT) through downtown Indianapolis from the northern suburban areas.

The model contains all roadways in the study area, including local streets and the interstate highway system. Traffic is fed into the model via 252 zones, with each zone generally representing a city block. The model includes the 30 current bus routes throughout the downtown area stopping at 266 coded bus stops.

Using a plug-in to the Paramics software, the 15-block core of the downtown area also included an independent pedestrian network, representing counted pedestrian flows. The pedestrians provided additional friction to vehicular flow through the downtown streets.

For presentation purposes, wide use was made of the 3D PMX models, including custom-made buildings representing Indianapolis landmarks and brickwork to represent the Cultural Trail alignment.

Highlights:

• Integrated Transit/Roadway Microsimulation
• All Roadways within 11km2 (4mi2) downtown area
• Presentation-Quality 3DVisualization