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Microsimulation Modelling Report

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									                                Microsimulation Modelling Report
Index QLD-5
 Location / route / area                   Gympie Road: Stafford Road to Edinburgh Castle Road
 Project description                       Aimsun Analysis
 Purpose of modelling                      To see the effect of upgrading an unsignalised intersection to a
                                           signalised intersection. The route choice model was used to
                                           determine the proportion of vehicles that change routes when the
                                           intersection is upgraded.
 Model developed by                        Sandra Lennie
 Microsimulation software used             Aimsun NG
 Author of report                          Sandra Lennie
 Date of report                            16/03/06

Model scope description
 Geographical extent                       Gympie Road: Stafford Road to Edinburgh Castle Road
 Years modelled                            2006
 Time periods modelled                     6:45-7:45 and 4:30-5:30
 Time periodic variations (profiles) in:   No variation in flow demand
  Traffic demand
  Links
  Junction control
 Number of zones                           n/a
 Number of links                           40
 Number of nodes                           9
 Number of junctions                       9
 Number of traffic signals:                5 (all fixed time)
  Fixed time
  Vehicle-actuated
  Area traffic control system

Networks
 Base Network                              Built up over scaled aerial photos
 Basic geometry                            As per existing road layout
 Intersection layouts                      As per aerial photos and intersection layouts
 Traffic signal controls                   As per STREAMS
 Other network features, e.g.                  1. Roundabout
  Signposting                                  2. Giveway and stop signage at unsignalised intsections
  Ramp metering                                3. Bus stops
  Adjacent lane interaction
  Lane restrictions
 Time dependent features
 Carparks

 Future networks                           Options were developed by signalising one intersection. A Sidra
                                           analysis was used to determine suitable timings.
 Basic geometry                            n/a
 Intersection layouts                      n/a
 Traffic signal controls                   Sidra analysis used (as above)
 Other variations from base network

Vehicle and driver data
 Data type
 Default vehicle data used             Queensland vehicles as supplied by David Stewart
 Additional or non-standard vehicles   Vehicles used include cars, heavy vehicles, semi-trailers and buses
 used?
 Vehicle proportions                   Cars 95%, heavy vehicles 4% and semi-trailers 1%
 Headway
 Reaction time                         0.75
 Driver behavioural parameters, e.g.   Reaction time at stop 1.35 sec
  Familiarity
  Aggression
  Awareness

Base travel demand
 Source of raw data                    Manual traffic counts supplemented by STREAMS data
 Automatic vehicle counts              No
 Manual vehicle counts                 No
 Classified counts                     No
 Manual turning counts                 Yes
 Counts from signal control systems    Yes
 Counts from freeway management        No
 systems
 Number plate survey                   Partial
 Roadside interviews                   No
 Mail-back questionnaire               No
 Home interview                        No
 Commercial vehicle survey             No
 Other sources                         No

Base trip table estimation
 Method                                Traffic turning counts were input and then an API was used to extract
                                       an approximate matrix. Further finessing was undertaken
 Counts only                           Base travel demand data from above was used
 Synthesised from counts:              Yes
  Observed
  Modelled
  Other
 Details of time dependent demand      Peak hours were used in the model
 profiles used

Future trip table estimation
 Method                                No change was required
 Growth factors
 Modelled
 Other
 Adequately defined in the brief?
 Work complies with the brief?
 Work adequately documented?

Assignment details
 Algorithm                             c-Logit Route choice model was used, because it reduces the amount
                                       of “flip flopping” between time periods
 Cost coefficients                     Capacity was lowered on some routes and extra delay was forced
                                       onto back streets. This encouraged vehicles to use the main Arterial
                                       road (Gympie Road)
 Incidents                                No
 Signposting                              No
 Strategic Routes                         No

Calibration
 Calibrated To                            The saturation flow rate at signalised intersections was calibrated and
                                          the turning counts were also calibrated
 Trip length distribution
 Observed volumes                         Calibrated according to GEH statistic. 85% of turning counts must
                                          have a GEH of less than 5 and the total number of vehicles to enter
                                          the network must have a GEH of less than 4.
 Maximum flows                            Saturation flow rate was calculated and compared with an average for
                                          the region. No Saturation surveys were undertaken.
 Queue lengths
 Travel times
 Other (specify)

Validation
 Has the calibrated model been            No
 validated against data not used for
 calibration?
 Validated against
 Observed volumes
 Maximum flows
 Queue lengths
 Travel times
 Other (specify)

Model application
Please discuss below in one to two pages the following issues:
     results of investigating different scenarios
     sensitivity tests undertaken
     extent of the variation from default parameters
     difficulties encountered and ways to overcome modelling issues
     comments on the general robustness of model outputs

A stakeholder of the Kedron State School was concerned about the amount of traffic travelling outside the
local primary school on Leckie Rd. They suggested that this traffic be encouraged to divert away from
Leckie Rd to other parallel roads. It was thought that signalising Gympie/Edinburgh Castle Rd would
reduce the overall trip delay for motorists travelling from Edinburgh Castle Roundabout to Gympie Rd
(SB), because vehicles would be able to enter Gympie Rd more easily.

A micro simulation model was created in Aimsun NG to determine the effect of upgrading
Gympie/Edinburgh Castle Rd. The route choice model was used to identify the change in driver route
choice due to the upgrade.

The simulation model showed that upgrading Gympie/Edinburgh Castle Rd produced the opposite result
from what was expected. When Gympie/Edinburgh Castle Rd was signalised, more traffic was
encouraged to travel past the school along Leckie Road, because of extra delay at Gympie/Edinburgh
Castle Rd. Traffic entering and exiting from Edinburgh Castle Rd was now forced to wait for a green
signal, where previously they would have been able to select their own gaps in the Gympie Rd traffic.

In turn this extra delay experienced at Edinburgh Castle Rd is detected by the route choice model and as a
result other routes (such as Sadlier Street) become more attractive (because they now have less delay). In
fact, after the Gympie/Edinburgh Castle Rd intersection was signalised, there was a 19% shift in the AM
and 13% shift in the PM of traffic away from this intersection towards Leckie St and Sadlier St.

Public transport along Gympie Rd was not significantly affected.

Extent of variation from default parameters
Variation from the default parameters was only in the route choice model.
     The „Capacity Weight‟ factor which penalises low volume routes was significantly experimented
         with to achieve the correct balance between volumes on the back streets and the main arterial
         road.
     The „Scale‟ factor which determines the proportion of vehicles that use the shortest route was
         also experimented with.
     The „Beta‟ and „Gamma‟ factors were changed to reduce the amount of flip flopping between
         route choice time periods.
     Individual link volume and delay parameters were changed to attract or deter vehicles from
         using individual links
The basic method was to calibrate the global parameters first and then finesse the route choice model was
making small changes to the individual link parameters.

Difficulties encountered
Significant difficulties were encountered with the route choice model and the public transport
    1. Route choice model and the flip flop effect: The route choice model directs vehicles along the
         path with the least delay in each time period. This is best illustrated with an example. In the
         example, there are two alternative routes. The route choice model must direct vehicles to the
         destination using one of the alternative routes and the route choice model directs the majority of
         vehicles to the route with the least amount of travel time. In time period 1, the route choice
         model calculates that route 1 has the least amount of travel time and therefore in time period 2
         vehicles are attracted to route 1. However, now route 1 experiences a lot of delay and in time
         period 3, vehicles are directed to route 2. This process repeats and the volumes of vehicles on
         route 1 and route 2 flip flops at each change in time period.
         This problem was moderated stabilised by calibrating the Beta and Gamma factors.
    2. Public Transport: In the process of modelling this project a number of bugs were found with the
         public transport section of the Aimsun NG model. Particularly buses were finding it difficult to
         enter bus stops. These were reported to TSS and have now been fixed.

 Robustness of the model
Robustness of the model is unknown, so the modelling report given to the client highlighted the changes
between the different options rather than the absolute results.

								
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