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PORT EVERGLADES AREA CUBEAVENUE MESOSCOPIC MODEL
presented by
Akbar Bakhshi Zanjani, Citilabs
Date
Dec 8-10, 2015
Scope of the Project
• Obtain Regional macro model (SERPM 7ABM)
• Mesoscopic model development and simulation using CubeAvenue
• Validate Avenue model to reflect the data collected usingdynamic O-D estimation (Cube Analyst Drive)
• Forecasting of horizon year Avenue model
• Sensitivity testing
Port Everglades DTA Model
Microscopic Model
Travel Demand ModelSERPM
Mesoscopic ModelAvenues
Multi Resolution Drill DownAbility to Scenario Plan at an OperationalLevel.
Procedure for Port Simulation
Run Regional Model
TDM Subarea Analysis forProject Area
O-D Trips by Time Segment
Dynamic O-D Estimationby Time Segments using
Cube Analyst Drive
Divide Peak PeriodTrips by 12
Calibration & Validation
Static O-D Estimation forModel Period using Cube
Analyst Drive
Subarea Network
Peak Period ApproachCounts
Using All Counts
*Subarea Analysis is performed for both base (2010) and future years (2040) in SERPM 7 ABM
Cube Model (Subarea Analysis)
• 4,584 zones with two scenario years (2010,2040)• 5 time periods:
– Early: 10pm - 6am– AM Peak: 6am – 9am– Midday: 9am - 3pm– PM Peak: 3pm – 7pm– Evening: 7pm – 10pm
• Subarea : 300 zones (230 internals)• Storage is calculated using following formulas:
– Freeways:• Link distance * number of lanes * 220
– Other roadways:• Link distance * number of lanes * 330
Information of SERPM 7.0 ABM
• Capacity values for each link are re-calculated based onfacility/area type and according to lookup tables in theSERPM 7 ABM
Capacity Values Used in SERPM Network
• AM and PM peak period trip tables from SERPM 7 ABM
• Subarea trip table is extracted in the “subarea analysis”application
– Similar highway assignment scripts as in SERPM 7 ABM areused to generate subarea trip tables
– Other inputs to this highway program, such as turn penalties,are also obtained from SERPM 7.
• Three major vehicle classes:
– Auto trips
– Long-Haul truck trips
– Short-haul truck trips
Trip Tables
• SERPM 7 network isused as the basenetwork for the studyarea (5 miles radius ofPort Everglades)
Updating Highway Network
• Modifying/updating the highway network to reflectthe existing highway facilities
– ramp distances,
– addition of roundabouts on Hollywood Blvd,
– number of lanes,
– capacities,
– True shape Display
Updating Highway Network
• SERPM network does not reflect the actual network ata few locations
Examples of Network Modifications
• Highway network validation (as explained previously)
• Development of Prototype Cube Avenue Model– A mesoscopic simulation to simulate the O-D trips into the highway
network for 12 time segments for each model period as AM peak and PMpeak.
– Uses the similar modeling components and settings as specified in theSERPM regional model.
– Outputs of the model Run: loaded highway network
packet log files
– These output results can be used to identify the heavily congestedlocations with displaying bandwidths of queues and link flows as well asanimating each individual vehicle simulation.
– The estimation results are summarized for VMT (Vehicle-Miles Traveled)and VHT (Vehicle-Hours Traveled) per each facility type in a post-process.
Validation
Validation
• Observed traffic count data– Traffic count data for 181 locations are constructed for every 15-min
interval for AM peak and PM peak, respectively.– These count data are used as part of model validation to replicate the
traffic pattern in the project area.
• Static O-D Matrix Estimation (ODME) using Cube Analyst Drive– the original O-D trips are based on the base year (2010) of the SERPM
ABM– Observed traffic count data provided for year 2014– Adjust the trip table based on peak period traffic counts
• Dynamic O-D Matrix Estimation (ODME) using Cube AnalystDrive– To replicate the traffic patterns in the project area for Year 2014– It modifies the O-D trips to provide the valid estimation corresponding
to the observed traffic counts
Count Locations (181 locations)
Facility Code Facility TypeNO. ofCount
Locations
41Higher Speed
Interrupted Facility134
61Lower Speed Facility &
Collector27
71 Freeway on-Ramp 3
73 Freeway off-Ramp 17
Total 181
Observed Traffic Count Data
Traffic Mode Type FHWA Vehicle Classification
Auto 1) Motorcycle2) Passenger cars3) Pickups, panel, vans4) Buses
Short-haul trucks 5) Single unit 2-axle trucks6) Single unit 3-axle trucks7) Single unit 4 or more-axle trucks
Long-haul trucks 8) Single trailer 3- or 4-axle trucks9) Single trailer 5-axle trucks10) Single trailer 6 or more-axle trucks11) Multi-trailer 5 or more-axle trucks12) Multi-trailer 6-axle trucks13) Multi-trailer 7 or more-axle trucks
• RMSE values for link approach counts by differentfacility types:
• AM Peak:
FacilityCode
Facility TypeNO. of Count
Locations
RMSE%
Auto SH Truck LH Truck
41Higher Speed Interrupted
Facility134 18.2 30.4 29.1
61Lower Speed Facility &
Collector27 23.4 21.4 28.5
71 Freeway on-Ramp 3 4.5 15.2 14.9
73 Freeway off-Ramp 17 22.7 25.2 28.4
Total 181 19.2 31.1 30.0
Results
• PM Peak:
Results
FacilityCode
Facility TypeNO. of Count
Locations
RMSE%
Auto SH Truck LH Truck
41Higher Speed Interrupted
Facility134 20.7 25.6 26.0
61Lower Speed Facility &
Collector27 14.4 38.7 37.0
71 Freeway on-Ramp 3 6.7 21.3 24.9
73 Freeway off-Ramp 17 16.3 22.7 18.3
Total 181 20.7 26.7 26.9
• Developing the future-year Cube Avenue model to estimate thetraffic volumes and to find out the bottleneck locations in thefuture year.
• The future year scenario is tentatively selected as Year 2025, butCitilabs may implement another future year analysis if requestedby the FDOT staffs.
• Peak Period OD trip table– interpolating the O-D trips between two scenario years as 2010 and 2040
• OD Trip table by 12 time segments for each peak period– Using the OD pair proportions in the estimated trip tables for the base
year (2014) to reflect the consistent traffic patterns
Future Year Scenario
Future Year Scenario
Performance Measures(AM peak period)
Base year 2014 Future Year 2025
VMT 3,026 M 3,292 M
VHT 223 M vehicle-hours 314 M vehicle-hours
Average delay 265 hours 537 hours
Performance Measures(PM peak period)
Base year 2014 Future Year 2025
VMT 3,137 M 3,374 M
VHT 250 M vehicle-hours 365 M vehicle-hours
Average delay 343 hours 685 hours
• 4 different scenarios are defined for sensitivityanalysis to estimate the effect of the changes by theVMT, VHT and average delay:– ±5% change in the trip ends for Port Everglades
– ±10% change in the trip ends for Port Everglades
Sensitivity Analysis
Future Year Scenario
Performance Measures(AM peak period)
Base year 2014 10% increase in the tripproductions from PortEverglades
VMT 3,026 M 3,054 M
VHT 223 M vehicle-hours 230 M vehicle-hours
Average delay 265 hours 287 hours
• SERPM 7 ABM network was modified to reflect the actualroadway system in the area of study
• A model was developed in Cube platform to simulatetraffic at a mesoscopic level
• Input files were obtained from regional model as well asother sub-consultant firms
• Model validation was performed using observed counts
• Forecasting and sensitivity testing of the horizon yearAvenue model
• Final Model Delivery and Documentation
Summary