Incorporating Traffic Operations into Demand Forecasting Model Daniel Ghile, Stephen Gardner 22 nd international EMME Users’ Conference, Portland September , 2011

Presentation Outline 1.Background 2.Conventional (regional) Model 3.Sub-area Model 4.Operational Elements 5.Model Result Comparison 6.Model of Off-Street Parking Lots 7.Volume Calibration and validation 8.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, E L1 = 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-Capacity Comparison

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

Operational Elements- Unsignalized int.

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 (R 2 )  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