1. Introduction. 1.1 Project Background. ANTONY JOHNSTONE Transport Associate Aurecon

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1 ANTONY JOHNSTONE Transport Associate Aurecon RAFAEL CARVAJAL Operational Modelling and Visualisation Coordinator Main Roads WA PERTH FREEWAY MODELLING In May 2015, Aurecon was engaged by the Main Roads Western Australia (MRWA) Network Operations Directorate to develop the largest transport simulation model ever built in Western Australia. The initial model was 31km in length, 170 km2 in area, and covered of Perth's Northern Freeway and surrounding suburbs. A second stage was then commenced which further extended the model area to also cover the majority of the Perth s Southern Freeway, resulting in a total modelled Freeway length of 82km. The model enabled the Main Roads Network Operations team to gain crucial insights into how the network s infrastructure will perform in future years, and to understand the impact of proposed upgrades or developments. At the time the project pushed Aimsun s pre-existing capabilities TSS has selected the Freeway Simulation Model as one of its flagship projects to present at its user group meetings around the world. 1. Introduction 1.1 Project Background In 2015, Main Roads WA commissioned Aurecon and TSS to develop a model of the Mitchell Freeway and its surrounding network for its Freeway Management Strategy. The network improvements are part of a larger suite of network upgrades being implemented through the Traffic Congestion Management Program.

2 Figure 1 Traffic Congestion Management Program summary

3 2. Model Form 2.1 Aimsun The modelling software used in this study was Aimsun with the Hybrid model experiment type. A Hybrid model operates as a microsimulation model within user defined cordon areas, simulating individual vehicles and their interaction with other vehicles and the surrounding road environment. Outside of this cordon, a mesoscopic model environment is used to replicate the flow, delay, and route choice of variables using more simplistic algorithms. 2.2 Model Build Summary The road network was extended from the 2015 Mitchell Freeway model which was initially established through the import of the MRWA Regional Operations Model (ROM24). Further detail was then coded within the study area as well as the establishment of the initial cordon matrices. Detailed coding of lane and junction descriptions were developed using latest aerial photographs of the region and knowledge of the network operation. During the calibration process, model parameters were adjusted to align the model to that of the existing on-street behaviour. These parameters included: lane changing cooperation aggressiveness brake intensity acceleration side lane cooperation distance merging distance look ahead distance reaction time factor jam density The Aimsun version used was version (R37672 x64). 2.3 Base Model Network Microsimulation Areas The mesoscopic model area of the Hybrid model is the red cordon outside the green dotted line and covers the wider area. The key area within the green dotted line was modelled using the more detailed micro-simulation cordon environment. The micro-simulation cordon area primarily consists of the Mitchell Freeway/Kwinana Freeway corridors between Safety Bay Road and Burns Beach Road, and the connecting interchanges. A micro-simulation cordon area was also included at the intersection of Reid Highway and Wanneroo Road.

4 Figure 2 Base model network The base model network was built using available digital aerial photography, with site visits to confirm the accuracy and operation of the modelled network. Based on the supplied data, the model was constructed to a 1:1 scale, ensuring correct vehicle operation and accurate reaction to the road geometry and other vehicles. 2.4 Site Visits Site visits were undertaken using a chartered helicopter to observe and record the on-site conditions. Secondary observations for the central corridor train services and localised site visits were also undertaken.

5 3. Model Form 3.1 Traffic Demand Estimation Methodology The figure below describes the traffic demand estimation methodology. The initial traffic demand was established from the ROM24 strategic model traffic demand in the study area. The traffic counts were balanced and used in matrix furnessing, static adjustment, departure adjustment and Dynamic User Equilibrium (DUE) assignment calibration. Figure 3 Traffic demand estimation methodology 3.2 Zone Structure The foremost component in defining the zoning structure for the hybrid model was established from undertaking the static traversal procedure. Using this procedure, the static traversal OD (Origin- Destination) matrix was created, with the zone structure based on the Main Roads higher tier ROM24. Disaggregation of these ROM24 zones was undertaken where considered applicable, based on the main areas of trip generation within the study area. Disaggregation was considered necessary where there was one ROM24 zone covering several external road links, or where one ROM24 zone covered multiple internal areas that would each load a relatively high number of trips to a certain area of the network. In total 384 zones were applied to the model.

6 3.3 Trip Balancing Surveyed count data, SCATS traffic data and freeway detector data were analysed and used to develop a traffic diagram. As the count data was collected from different sources and at different times, there are discrepancies between the in/out flows in some road sections. Therefore, the counts were carefully balanced based on the flowing criteria: Minimising the difference between in and out flow of a road section (within 10 % or 100 veh/h). If there are significant differences between the 2015 and 2016 data year sources. Ensuring the entry and exit trips of an intersection are consistent with the trips in the upstream and downstream intersections. The balanced trips were applied in matrix furnessing and creating the traffic demand profile. 3.4 Demand Matrices Refinement The ROM24 cordon matrix is considered coarse in the Mitchell Freeway model area, which is not surprising given the extent of the ROM24 model area. With this, the ROM24 matrices underwent a refinement process to better reflect the traffic generators within the model area Aimsun Matrix Refinement The methodology below describes the refinement of the demand matrices. 1. The matrices were adjusted to collected survey data using the Furness method. The Furness method of matrix updating is an iterative process to derive matrices that results in the best match to survey data. Trip end totals for each Aimsun zone were formed from external link survey data, internal link survey data, and other filler zones with values based on their survey differences, surrounding land use, and number of individual households. Within this, OD pairs were also fixed to known survey values or as established during the calibration process. 2. As part of the calibration process the matrices were adjusted using the Aimsun matrix adjustment process to refine the distributions created by the ROM24 zone disaggregation. These were then checked to make sure large changes had not been made and manual refinement or containment then took place where necessary. 3. The departure adjustment was then undertaken to provide 30 minute matrices based on the 30 minute survey data sets. This allows profiles to be generated for individual OD pairs, rather than globally for all zones. 4. Lastly manual adjustments were conducted on key sections to match the observed turn counts, travel times, speed flow curves and queue lengths. All adjustments were considered minor compared to the total volumes of the sections. In summary, the following chart shows the matrix development process. Figure 4 Matrix development process

7 4. Traffic Assignment Process 4.1 Path Assignment Within Aimsun, the initial static assignment route choice was calculated using cost equations by taking into consideration section capacity and section travel times. Following this, the mesoscopic DUE assignment takes place by running the model multiple times and adjusting the paths accordingly until convergence occurs to a relative gap of 3.00%. The section capacities and global route choice parameters, including the user defined costs and attractiveness weightings were modified to aid calibration. The path assignment in the hybrid model was based on these DUE paths. Figure 5 summarises the path assignment process. Figure 5 Path assignment process 4.2 Assignment Convergence The dynamic user equilibrium assignment convergence has been used to assess the variability between each iteration. The figures below illustrate the AM and PM peak period assignment convergence respectively, and demonstrate that the model is stable. Figure 6 AM peak period assignment convergence

8 Figure 7 PM peak period assignment convergence 5. Model Calibration and Validation 5.1 Calibration Criteria Model calibration is an adjustment process to assist in the development of an accurate simulation of on-street conditions. Model calibration for this model has been based on the following criteria: Vehicle Behaviour: Undertaking a visual check to confirm the observed on-street vehicle behaviour is consistent with that observed in the model. Link Counts: Comparing observed and modelled link counts for general traffic Turn Counts: Comparing observed and modelled turning movements for general traffic Root Mean Square Error (RMSE): RMSE compares the difference between observed and modelled data. The RMSE will equate to a percentage and the percentage shall not be greater than 20% for intersection movements. Screenlines: Comparing observed and modelled total link counts for general traffic across defined boundary lines over the modelled peak hour periods.

9 Table 2 AM peak link calibration results (Whole Model Area) between 7:30am and 8:30am XY Scatter Plots Criteria Modelled R 2 Value R 2 value for modelled versus observed volumes for all individual links > Line of best fit y=0.9x-1.1x y = 0.94x Links Criteria and Measures Criteria Observed Total Modelled Achieved Achieved GEH statistic < 5.0 for individual link volumes >75% of cases % GEH statistic < 7.5 for individual link volumes >80% of cases % GEH statistic < 10.0 for individual link volumes >90% of cases % <400vph within 50vph >75% of cases % vph within 12.5% >75% of cases % >2000vph within 250vph >75% of cases % Link Criteria and Measures for Volumes Greater Than 200vph Criteria Observed Total Modelled Achieved Achieved GEH statistic < 5.0 for individual link volumes >75% of cases % GEH statistic < 7.5 for individual link volumes >80% of cases % GEH statistic < 10.0 for individual link volumes >90% of cases %

10 Table 3 PM peak link calibration results (Whole Model Area) between 5:00pm and 6:00pm XY Scatter Plots Criteria Modelled R 2 Value R 2 value for modelled versus observed volumes for all individual Freeway links > Line of best fit y=0.9x-1.1x y = 0.97x Links Criteria and Measures Criteria Observed Total Modelled Achieved Achieved GEH statistic < 5.0 for individual link volumes >75% of cases % GEH statistic < 7.5 for individual link volumes >80% of cases % GEH statistic < 10.0 for individual link volumes >90% of cases % <400vph within 50vph >75% of cases % vph within 12.5% >75% of cases % >2000vph within 250vph >75% of cases % Link Criteria and Measures for Volumes Criteria Observed Modelled Achieved Greater Than 200vph Total Achieved GEH statistic < 5.0 for individual link volumes >75% of cases % GEH statistic < 7.5 for individual link volumes >80% of cases % GEH statistic < 10.0 for individual link volumes >90% of cases %

11 5.2 Validation Criteria Model validation is necessary to ensure that a model accurately represents an existing traffic situation and can be used with confidence to test alternatives. Model validation for this model has been based on the following criteria: Travel Times: Comparing observed and modelled journey travel times for general traffic over the modelled peak hour periods. Speed-Flow Data: Comparing the observed and modelling speed data at predetermined locations along the Freeway. Weaving Validation: Comparing the observed and modelling weaving volumes at predetermined locations along the Freeway. Queue Lengths: Undertaking a visual check to confirm the modelled queue operation is consistent with those observed on site. Heat Map: Comparing observed and modelled vehicle speeds along the Freeway over the peak periods. Speed-Flow Diagram: Comparing observed and modelled speed flow diagrams at predefined locations. The key aspect for the validation was the travel times as this was the main performance indicator for the option testing. The figures below show the travel times for the peak directions of travel for the peak periods. Note that with the CBD in the centre of the model, there are two peak tidal directions per period. Two sets of observed travel time data was also used for the comparisons. Figure 8 Mitchell Freeway southbound AM peak travel time

12 Figure 9 Kwinana Freeway northbound AM peak travel time Figure 10 Mitchell Freeway northbound PM peak travel time

13 Figure 11 Kwinana Freeway southbound PM peak travel time 5.3 Speed-Flow Data Validation The speed/flow validation diagrams are useful in comparing the flow breakdown at key merge points on the freeway. Finite model parameters were adjusted accordingly to get the throughput and speeds at these locations as close as possible to the observed conditions Hutton Street Southbound Figure 12 shows the Hutton Street southbound speed/flow results at this key Freeway location. Whilst the pattern is similar, the modelled data at the breakdown sits at a slightly higher speed than the observed data. Figure 12 Hutton Street Southbound Speed Flow Data Validation

14 5.3.2 Whitfords Avenue Southbound Figure 13 shows the Whitfords Avenue southbound speed/flow results. These results show a similar pattern between the modelled and observed. Figure 13 Whitfords Avenue Southbound Speed Flow Data Validation Berrigan Drive Northbound Figure 14 shows the Berrigan Drive northbound speed/flow results. These results show a similar pattern between the modelled and observed. Figure 14 Berrigan Drive Northbound Speed Flow Data Validation

15 5.3.4 Armadale Road Southbound Figure 15 shows the Armadale Road southbound speed/flow results. These results show a similar pattern between the modelled and observed. Figure 15 Armadale Road southbound Speed Flow Data Validation 6. Lessons Learnt Being such a large model, various aspects were a learning lesson. Some of these are detailed below. A huge amount of data was required and therefore a large amount of time was required to clean and process the data. Identifying the strategic model exported attributes was initially quite difficult. The impact of ghost strategic model attributes did also impact the calibration process during the static assignment stage until they were identified. While it was relatively easy to identify where in the network issues were occurring however sometimes it was difficult to identify the detailed reason for delay to occur in a meso location. The path assignment after the DUE looked more sensible with the feature disabled calculation of new paths checked. The model run time was faster than expected: 20 minutes for DUE iterations, 2 hours for Hybrid model.

16 Presenter s Bio Antony Johnstone Antony is a Chartered Transport Associate based in Perth, WA. He leads the WA Traffic and Transport Planning team and has worked on a wide range of private and public sector projects in Australasia. He has experience in a range of software with expertise in leading and managing modelling projects as well as running modelling training modules and presentations. His transport planning and traffic engineering skills have been applied to infrastructure planning, public transport design, and development assessments. Rafael Carvajal Rafael is a Civil Engineer with MBA qualifications and over 13 years experience in traffic and transport engineering. He is skilled in leading and managing modelling projects and in the development of robust models to support decision-making, solving traffic congestion issues, experienced in transport modelling, model development and skills transfer to junior team members. He is experienced and proficient in Traffic Signal Design & Operational Modelling, Traffic Management and Highways Design in urban areas. He has contributed to the evaluation of road network operations strategies, plans and operational services. Rafael has identified, recommended and implemented opportunities to improve the delivery of road network operations.