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Incorporating Traffic Operations into Demand Forecasting Model Daniel Ghile, Stephen Gardner 22 nd international EMME Users’ Conference, Portland September 15 -16, 2011. Presentation Outline. Background Conventional (regional) Model Sub-area Model Operational Elements
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Incorporating Traffic Operations into Demand Forecasting ModelDaniel Ghile, Stephen Gardner 22nd international EMME Users’ Conference, PortlandSeptember 15 -16, 2011
Presentation Outline • Background • Conventional (regional) Model • Sub-area Model • Operational Elements • Model Result Comparison • Model of Off-Street Parking Lots • Volume Calibration and validation • Applications
Background • Location: Kelowna, West Canada • Downtown Plan:
Background • Objective: Assess implications of transportation and land use changes on traffic operations in the downtown core; • Approaches applied to assess the detailed traffic operational analysis • Sub-area demand model using EMME • A micro simulations model focused on network impacts • Intersection Capacity analysis using synchro • The focus is on the Sub-area travel demand model
Regional Model • Study area: covers City of Kelowna, West Kelowna, Lake Country and Peachland • Calibration: 2008 fall/spring traffic conditions • Link-based capacity approach • Drawbacks/limitations • Coarse Zone system and network in the downtown area • Calibration limited to link volumes at screen lines • Off-street parking lots not treated as ‘special zones’
Sub-area Model • Uses the regional model as a base • Detailed zone system and network in the downtown area • Transition area: provides a transition between the detailed zone system in downtown and the coarse regional zone system • Turn capacity based on Highway Capacity Manual (HCM) operational methodology • Traffic assigned to off-street parking lots • Detail Intersection coding
Operational Elements –Model attributes • Model Procedures: • Regional model approach applied to trip generation, distribution and mode split calibration • Traffic assignment uses fixed demand generalized cost multi-class assignment with class specific volumes • Travel time is key input to trip distribution and assignment and consists of : • Link travel time • Delay
Operational Elements-Travel time • Link Travel time: • Modified Bureau of Public Roads (BPR) equation applied in both Regional and Sub-area models • Adjustment factor applied to capture difference in link capacity for various road classes (0.8 to 1.1) • Intersection Delay: • Key component of travel time in congested network • Volume and capacity are key inputs to delay • Proper representation of turn capacity is required to realistically capture delay at intersections
Operational Elements- Capacity • Regional Model Capacity: • Link based capacity • Drawbacks • Approach capacity is insensitive to conflicting volumes • All stop controlled approaches have fixed capacity (400 veh/h) • Exclusive left turns have fixed capacity (200 veh/h) • Right and left turns are assumed to have the same capacity • Signal phasing and timing are not taken into account • Sub-area Model Capacity: • HCM operational methodology approach applied to estimate capacities at signalized and unsignalized intersections • Dynamic adjustment of capacities based on projected volumes
Operational Elements- Signalized inter. • Signalized intersection Capacity: • Approaches disaggregated to lane groups • Saturation flow rate estimated as per HCM guideline • Assumptions made on lane width, grade, percentage of heavy vehicles, bus, parking and pedestrian activities • Right turn and left turn adjustment factors estimated based on HCM equations • Left turn adjustment factor is very complicated • Five cases considered to estimate left turn adjustment factor
Operational Elements- left turn factor • Left turn adjustment factor for permitted phase: • HCM approach applied • Adjustment facto for the lane from which permitted left turn are made is estimated by: --Eq.1 • Adjustment facto for the lane group from which permitted left turn are made is estimated by: --Eq2 PL =proportion of left turn traffic in shared lane, EL1= through car- equivalent for permitted left turn, N =number of lanes • Parameters used in Eq. 1 and 2
Operational Elements- Sample Input and outputs, Capacity comparison • Turn Capacity Equations • EMME turn attributes • Operational input and output attributes for a sample signalized intersection • Capacity Comparison – Signalized intersection
Operational Elements- Unsignalized int. • Unsignalized intersection Capacity: • Opposing Volumes, critical gap, follow-up time ,base capacity estimated as per HCM approach • Capacity adjusted by impedance factor • Operational input and output attributes for a sample unsignalized intersection • Capacity Comparison – Unsignalized intersection
Off-street Parking lot Modeling • Parking lots defined as special zones • Base year volume established based on parking spaces, observed parking occupancy, and parking duration • A procedure was developed that assigns auto trips to parking lots • Volume deducted from regular zones to account for the volume allocated to parking lots • Volume deduction is proportional to the trip generation of the zones and inversely proportional to the travel impedance between the parking lot and the adjacent zone
Link Volume Deviation • Up to 20 screen lines defined within downtown core • Up to 92% of the link volumes fall within the acceptable deviations • Key link volumes all within the acceptable limits
Goodness of fit test • Coefficient of determination (R2)- 0.91 • Slope between model and observed volumes – almost 1.0 • The coefficients show good relationship between the model and observed volumes
Intersection Turn Volumes • Turn volumes compared at key intersections along Highway 97 • Most major turn movements replicate actual observed volumes
Model Application • Produce O-D input to micro simulation model • Test alternative packages of road/transit network improvements; • Evaluate the transportation impact of various land use scenarios; • Evaluate alternative transportation demand management (TDM) strategies
Summary of Findings • Demonstrated capability to replicate HCM turn capacities; • Potential applications include • Sub-area model, • Local intersection improvements • Projection of turn capacities at major intersections • Limitations include • Intensive coding and error prone • Application is limited to short term operations • Detail input required for new or changed traffic control • All vehicles are served irrespective of capacity