Downtown Kelowna Traffic Model

Daniel Ghile, Stephen Gardner

ABSTRACT This paper describes a detailed traffic model for the downtown Kelowna area recently developed by EBA. The model integrates a travel demand model, micro-simulation and capacity analysis models. The objective of the project is to evaluate the impacts of alternate transportation strategies and land use scenarios on traffic operations in the downtown area, particularly along Highway 97.

EBA previously developed a regional transport model for the Regional District of Central Okanagan. The Sub-Area Demand Model applies the standard ‘four-step’ modeling process and uses the regional model as a base. The model contains a more detailed road network and zone structure within the downtown area. The sub-area demand model applied non-traditional approaches which cover:

Traffic assignments that incorporate turn capacities based on the operational methodology of the Highway Capacity Manual

Treatment of off-street parking lots as “special zones”

The model calibration replicates the 2008 fall or spring travel conditions. The calibration process covers all four stages of the model process. Origin-destination trips extracted from the demand model provided input to the micro simulation model.

The micro-simulation provides visualization of the road performance and traffic flow through the intersections along Highway 97 and is used for dynamic traffic assignment. The capacity analysis model provides detail traffic operational elements.

Application of the traffic model so far includes:

Assessment of traffic performance for the 2008 and 2030 base year,

Evaluation of conversion of one-way road system to two-way system,

Assessment of implications of closure of local road (Mill Street).

1.0 INTRODUCTION

In 2008, EBA developed a regional EMME/3 transportation model for the Regional District of Central Okanagan (RDCO). It covers the City of Kelowna, West Kelowna, Lake Country and Peachland. This regional model has been applied to test various land use scenarios, and to determine and evaluate transportation requirements in the context of the regional network.

In 2011, the City of Kelowna requested us to develop a detail traffic operation model that will assist them to analyze and assess traffic operations within the downtown area, particularly along Highway 97. The City also wanted to use the model to assess and evaluate the implications of alternate transportation strategies and land use changes on traffic operations. The RDCO regional model is appropriate for transportation planning purposes, but it is not suitable for detail traffic operations analysis.

The limitations of the RDCO regional model in respect to detailed traffic operations analysis include:

The lack of detailed zone system and network within the downtown area;

Few screenlines in the downtown core;

Calibration limited to link volume level, not to turn volume level;

We addressed these limitations by developing a new traffic model tool that integrates detailed demand forecast, micro simulation and capacity analysis models. The traffic model applies the following three-step approach:

Sub-area Demand Model: This covers development of a multi-modal sub-area transportation model using the EMME platform.

A micro simulation model (VISSIM): This uses the VISSIM platform to undertake dynamic traffic assignment and to present simulation of travel conditions.

Intersection Capacity Analysis: This assesses performance of the key intersections using the Highway Capacity Manual operational methodology.

The focus of this paper is on the demand forecasting and the intersection capacity analysis components.

2.0 SUB-AREA DEMAND MODEL

2.1 Model Development

The sub-area travel demand model uses the regional transportation model as a base. It incorporates refinements to the regional model structure in the downtown core by including a detailed zone system and road structure within the downtown area. It also incorporates off-street parking lots as independent special zones.

We have also considered a transition area around the downtown core that provides a buffer between the detailed zone system in the downtown area and the coarse regional zone system. The transition buffer also includes zone split and additional links/roads.

2.1.1 Zone System

The RDCO regional model comprises a total of 277 active traffic zones (or centroids). The sub-area model increases the number of active zones to 357. The increase only occurred within the downtown area and the transitional area.

The sub-area model considers every block within the downtown area as a zone. The number of zones within the downtown area increased to 49 from 11 in the regional model. Within the transition area, the number of zones Increased twofold (from 41 to 83). The sub-area model also treats off street parking lots within the downtown area as ‘special zones’. Figure 1 illustrates the downtown, the transitional area and the overall study area.

Figure 1: Study Area

2.1.2 Network Elements

The network component comprises mode of travel, road network, and transit network. The mode of travel covers High Occupancy Vehicle (HOV), Single Occupancy Vehicle (SOV), Bus and walk/bike. The sub-area model incorporates all road types including local roads within the downtown area, while outside the downtown area the model primarily covers provincial highways, arterial and collector roads.

The EMME model within the downtown area incorporates the following road attributes in order to properly capture turn capacities as per the HCM operational methodology:

Direction of approach legs at intersection (east, west, north, south) ;

Turn types (U-turn, though, left turn, right turn) ;

Traffic control types(signal, unsignalized) ;

Laning configurations ranging from a single lane approach to multi-lane approaches. Figure 2 illustrates the laning configurations considered in the model.

Existing signal phasing and timing including advance left turn green time, through green time, cycle length etc;

Left turn lane types (dedicated or shared), and left turn phasing (protected, permitted or protected and permitted);

Downtown

Transition Area

Regional Area

Right turn lane types (dedicated or shared).

The attributes listed above were represented by numerical values in the turn interface.

Figure 2: Laning Configuration

2.1.3 Cost parameters

The traffic assignment procedure uses generalized cost as travel impedance input. The generalized costs comprise travel costs and travel times. The travel costs considered in the model include vehicle operating cost, transit fares and parking costs. All monetary costs were converted to value of time before using them in the assignment process.

2.2 Model Procedures/Approaches

2.2.1 Overall Approach

The Regional as well as the Sub-area Demand Models use the standard ‘four-step’ modeling process. The traffic assignment procedure in the RDCO regional model uses link-based capacities. Each link contains link specific ‘volume-delay function’ parameter (code). The first digit of the code represents capacity, which is related to traffic control and number of turn lanes (for signalized intersections), while the second digit represents the posted speed. The regional model traffic assignment procedures are appropriate from a regional context; however, its application in detail traffic volume projection at intersection level is limited.

The traffic assignment procedure in the Sub-area demand model applies non-traditional approaches. The travel time (delay) estimation takes into account turn capacities at intersections. It applies the operational methodology of the Highway Capacity Manual (HCM) that takes into account intersection laning configuration, type of traffic control, approach and opposing traffic volumes and other operational elements. For signalized intersections, the inputs also include signal phasing, cycle time, and green time.

We developed an algorithm that allows dynamic adjustment of capacities, which takes into account the EMME volume projections in each iteration. This specifically captures the impact of conflicting volumes on capacities.

There are similarities as well as differences in the procedures used in the RDCO regional model and the downtown subarea models. The similarities between the two models include:

The procedures applied to estimate the trip generation, trip distribution and mode split for both models are similar;

The traffic assignment procedure of both models uses a fixed origin-destination trip tables and link based generalized cost to produce class specific volumes;

In areas outside the downtown, both models use link-based capacity approaches to estimate travel times;

The two models use the modified Bureau of Public Roads (BPR) equation to estimate travel times on links (outside intersections);

The key difference between the two models is the traffic assignment procedures at intersections. The differences include:

The sub-area model uses turn capacities at intersections, while the regional model uses link capacities.

The sub-area model takes into account signal phasing and timing in capacity estimation at signalized intersections. The regional model however does not include signal phasing and timings as input.

The sub-area model estimates capacities by movement type and lane groups. The regional model however uses the same capacity for all movements on the same approach unless a turn penalty is applied at turns.

The sub-area model considers conflicting volumes, signal timing, availability of dedicated turn lane etc. to estimate capacities of turn movements. The regional model assumes an additional fixed capacity of 200 veh/ h for all dedicated turn lanes. A turn penalty can be included to constrain turn capacity.

At unsignalized intersections, the sub-area model takes into account approach volumes, conflicting volumes and intersection laning configuration to estimate the capacity. The regional model however assumes a fixed capacity for all stop-controlled approaches and does not consider approach or conflicting volumes as input.

Another difference between the two models is treatment of off street parking lots. The sub area model treats the off street parking lots as special independent zones and assigns trips to the parking lots. In the regional model, no consideration was given to off street parking lots.

The procedures used in the sub-area model to estimate turn capacities at intersections are described below.

2.2.2 Intersection Capacity Estimation Signalized Intersections

The methodology applied to estimate turn capacities at signalized intersections are:

Approach disaggregation into a number of lane groups as per HCM guidelines;

Estimation of saturation flow rate for each lane group by taking into account the ideal saturation flow rate and applying various adjustment factors which include adjustments for left-turns, right-turn, road width and grade, bus, pedestrian, and parking activities etc. Assumptions were made on some inputs.

Capacity estimation for each lane group based on the computed saturation flow rate and the existing signal timing inputs. The capacity estimation takes into account phasing, green time and cycle length. The capacity estimation assumes a fixed green time.

We developed procedures to estimate right turn and left turn adjustment factors as per HCM guidelines. The right turn adjustment factors cover exclusive right turn lane, shared lane and single lanes. The left turn adjustment factor covers the following five cases:

Exclusive lane with protected phasing;

Exclusive lane with permitted plus protected phase;

Exclusive lane with permitted phase;

Shared lane with protected plus permitted phase;

Shared lane with permitted phase;

Effect of parking, and pedestrian activities were accounted by applying a gross adjustment factor based on road classes.

Figure 3 illustrates a sample of user defined turn attributes for a signalized intersection.

Figure 3: Sample of EMME turn attributes at Signalized intersection Unsignalized Intersections

Capacity estimation at unsignalized intersections was also based on the HCM guidelines. The approaches used to estimate the capacity at unsignalized intersections include:

Identification of conflicting movements for each movement;

Calculation of Conflicting volumes for each movement ;

Estimation of critical gap and follow-up time for each movement

Preparation of base capacity for each movement using the gap acceptance approach. This takes into account conflicting volumes, critical gap and follow-up time;

Establishment of capacity by adjusting the base capacity

Figure 4 illustrates a sample of user defined turn attributes for unsignalized intersection.

2.2.3 Off Street Parking Lots

Standard demand models typically assume vehicles park in the same zone. This procedure is acceptable from a regional context. The sub-area model however treats off street parking lots as special zones. The purpose is to properly replicate volumes on roads and at intersections by assigning trips to the off-street parking lots

For regular zones, the trip generation uses land use data as input. For the off-street parking lots, there is no associated land use data. The auto trips to/from the parking lots are however related to the trips of the nearby regular zones. An algorithm was developed within the assignment procedures to account for this. The procedures adopted to estimate traffic to and from the off street parking lots are:

Attribute DescriptionDirections (1 =east, 2=w est3=North, 4=south)movement type ( 1= thru2=left, 3 =right

lanes Number of lanes@green green time ,existing @cycle cycle length ,existing@volop Opposing volumes@flt left turn adjustment factor@frt Right turn adjustment factor@caplt left turn capacity@caprt right turn capacity@capth through capacity

@dir

@mov

Establish a ‘base year’ inbound and outbound traffic at the accesses of each off-street parking lots

Estimate origin-destination (O-D) auto trips from and to the parking lots;

Deduct the auto trips ending at the parking lots from the auto trips of the adjacent regular zones

Super impose O-D trips from/to the parking lots on to the overall auto O_D trips

Assign traffic to road network including to the centroid connectors of the parking lots.

Figure 4: Sample of EMME turn attributes at unsignalized intersection

2.3 Model Calibration The model calibration/validation covers each stage of the model development process in order to ensure that the model results reasonably replicate the observed travel characteristics. The model calibration replicates the 2008 fall or spring travel conditions. Data used for calibration include: household travel survey, travel time survey data, traffic count data; and transit ridership data.

2.3.1 Trip Generation The initial trip generation calibration covers matching trip generation results with the household trip generation values. The total auto volumes computed using the trip diary survey data were then compared with the total auto volumes based on the count data along the screenlines. The comparison indicated that volumes based on the trip diary survey were lower than the volumes based on the counts. The trip generation component was then re-adjusted globally to match the volumes based on the count data. The calibration covered iterative adjustments to the trip rate coefficients until the difference between the model and observed values are acceptable.

Attribute DescriptionDirections (1 =east, 2=w est3=North, 4=south)movement type ( 1= thru2=left, 3 =right

lanes Number of lanespvolau turning volumes@vlopu Opposing volume @tcrt critical gap@tflow follow -up time

@poj vehicle impedance adjustment factor

@cap capacity

@dir

@mov

2.3.2 Trip Distribution

The trip-distribution calibration process included

Matching model origin-destination (O-D) trips with the corresponding household O-D trips at superzone level. Superzones are zone groupings, and the model includes 15 superzones.

Comparing model trip length frequency distribution with the household survey trip length frequency distribution by trip purpose;

Average trip length comparison between model and observed values

The overall comparisons indicate that there is good fit between model and observed results.

2.3.3 Mode Split

The mode split calibration covers adjustment of the coefficient of the logit functions until the model mode split results replicate split from the household survey results at acceptable level. The mode split was calibrated at the superzone level.

2.3.4 Trip Assignment

One of the key measures to validate a transportation model is its ability to replicate the observed volumes. In the calibration process, adjustments were applied to centroid connectors, volume delay functions, and minor adjustments to other network attributes as necessary.

The trip assignment calibration covers:

Link volume comparison between model and observed values along screenlines,

Goodness of fit test,

Travel time comparison along selected routes,

Comparison of turn volumes between model and observed values at key intersections.

Link Volume Comparison

Error tolerances between model and observed volumes were based on the guideline given in National Cooperative Highway Research Program (NCHRP 255) Report. Based on this approach, the permitted percentage errors are low on high volume roads and high on low volume roads. Figure 5 shows comparison of model volumes versus observed volumes.

Goodness of Fit Test

Coefficient of determination (R2) and the slope between model and observed volumes were used to test goodness of fit test between model and observed volumes. As shown in Figure 5, R2 is 0.93 and slope is almost 1.0. This indicates that there is a strong linear relationship and good fit between the model and the observed volumes.

Travel Time

The model calibration also includes comparison of the model travel time against the actual observed travel times along selected routes. The comparison shows that for majority of the routes, the model travel times is within 15% of the observed travel time.

Turn Volume Comparison

The downtown model calibration also covers comparison of model turn volumes with the observed turn volumes at key intersections. In general, the comparison indicates that there is good match between the model and observed turn volumes for the major turn movements.

Figure 5: Link Volume Comparison in Downtown Area

2.4 Application After calibration, future year sub-area models were developed for the 2030 horizon year. The future sub-area models were used to produce O-D trip inputs for the micro simulation model and volume input to the capacity analysis models. The model was also directly used to evaluate implications of conversion of one-way roads (Leon Avenue and

Lawrence Avenue) into two-way roads. In the future, the model could independently be used to evaluate:

Alternative packages of road/transit network improvements;

The transportation impact of various land use scenarios; and

Alternative transportation demand management strategies

3.0 TRAFFIC MICRO SIMULATION

As part of the project, EBA developed a traffic micro-simulation model for the City of Kelowna's downtown core. This model enables visualization of the overall network performance, traffic flow through the series of signalized intersections. The program uses the current signal timing plans. The model adopted the EMME zoning system, and the EMME centroid connectors. The connectors represent mid-block commercial or parking accesses in the model.

The origin-destination trip tables derived from EMME served as traffic input in VISSIM. The calibration and validation process measures operating speed and travel time on the key links, and comparing model and observed volumes at intersections. The model outputs include intersection volumes, queuing, delay, etc.

Another purpose of the micro-simulation is to dynamically assign traffic onto the road network. This takes into account available routes, real-time operational conditions, and driver behavior and route choice. Distance, speed and level of congestion determine the choice of alternative paths.

In micro-simulation models, the output volumes are service volumes. i.e., the discharge volumes at intersections are limited to the intersection capacity. Demand exceeding capacity is however reflected in operational elements such as longer queues, increased delays at intersections and peak period spreading. Figure 6 illustrates a 3D visualization of a part the downtown area.

Figure 6: ViSSIM Micro-Simulation Model for Downtown Kelowna - 3D Visualization

The micro-simulation applications include:

1. Assessment of 2008 Traffic conditions in the downtown area;

2. Evaluation of one-way to two-way conversion of Leon and Lawrence Avenues;

3. Review implication of closure of Mill Street; and

4. Assessment of reconfiguration of Bernard Avenue to a three-lane cross-section.

4.0 INTERSECTION OPERATION ANALYSIS

4.1 Intersection Capacity Analysis

The project also includes intersection performance analysis at the key intersections along Highway 97 for the 2008 and 2030 horizon years. The peak hour intersection performance analysis was based on the Highway Capacity Manual operational methodology as implemented in Synchro 8. The Synchro analysis incorporates current signal timings for the signalized intersections.

The volume inputs for the intersection analysis were extracted from the EMME models. The Synchro analysis results include volume to capacity (v/c) ratio, Level of Service (LoS) and 95th percentile queue length. Figure 7 present the Synchro analysis results for 2008 horizon year.

Figure 7: 2008 Intersection Performances in Downtown Kelowna

As shown in Figure 7, a number of movements operate close to a capacity and at a level of service D or worse in the 2008 traffic condition.

Figure 8 shows the Synchro analysis results for 2030 horizon year.

Figure 8: 2030 Intersection Performances in Downtown Kelowna

Under the 2030 p.m. peak hour conditions, many movements along Highway 97 are projected to operate with v/c ratio more than 1.0 and a Level of service D or worse. The eastbound movements on Highway 97 at Abbot Street and the westbound on Highway 97 at Water Street are predicted to operate with v/c ratio greater than 1.0 and a LOS “F”. The 95th percentile queue length of the eastbound movement on Highway 97 at

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Downtown Kelowna Traffic Model

Abbot Street is predicted more than 400 meters. This means traffic will spillover to upstream intersections. This could be better illustrated with a micro-simulation.

4.2 Sim Traffic Micro Simulation A micro-simulation analysis was also undertaken using Sim Traffic 8. This was primarily undertaken to visualize traffic operation especially vehicle queue projection for the 2030 horizon year. Figure 9 illustrates a Sim Traffic graphical illustration of a video screen capture for the 2030 traffic conditions. This indicates that the vehicle queue of the eastbound movement is projected to extend beyond Richter Street.

Figure 9: Sim Traffic Graphical Illustration for 2030 horizon year

5.0 SUMMARY AND CONCLUSIONS

The following summarizes the findings and applications:

Integration between demand and traffic operation models. The VISSIM model used the 2008 and 2030 EMME O-D trips as input for the dynamic traffic assignment and for traffic operation assessment. The capacity analysis model applied the 2030 projected EMME volumes to assess future traffic performance in the downtown area.

The micro simulation model was used to assess, evaluate and present 3D- visualization for the 2008 and 2030 traffic conditions and for alternate road improvement options;

With the existing laning configuration, many movements along Highway 97 in the downtown area are projected to operate very poorly under the 2030 traffic conditions. The traffic queue is projected to extend beyond Richter Street.

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Downtown Kelowna Traffic Model

Lessons learned include:

Replication of turn volume by a demand model

The sub-area model requires detail input data including land use, turn count data, etc

The model includes intensive coding of network data, and as a result it is error prone. This approach is therefore not recommended for uncongested networks or for large study area.

Future applications include:

Assessment of implications of future land use changes

Evaluation of future road network improvement options at intersection level;

Corridor wide traffic operations assessment for alternate road network improvement options;

Evaluation of alternative transportation demand management strategies

6.0 ACKNOWLEDGMENT

The authors would like to acknowledge the inputs and support provided by the Regional Services, Infrastructure Planning and Land Use Management departments of the City of Kelowna in particular Andrew Albiston and Ron Westlake in the development of the traffic model.

7.0 REFERENCES

Highway Capacity Manual 2000, TRB, National Research Council, Washington, D.C., 2000

Highway Traffic Data for Urbanized Area Project Planning and Design, NCHRP Report 255, Washington, D.C., 1982

Kelowna Downtown Parking Management Plan, City of Kelowna, by Boulevard Transportation Group Ltd. July 2010

AUTHORS’ INFORMATION Daniel B. Ghile, M.Sc., P.Eng. Traffic/Transportation Engineer EBA, A Tetra Tech Company Direct Line: 604.685.0017 Ext. 351 Email: dghile@eba.ca Stephen Gardner, M.Sc.

4B2_Ghile,D- Downtown Kelowna-paper.doc

Principal Specialist EBA, A Tetra Tech Company Direct Line: 604.685.0017 Ext. 337 Email: sgardner@eba.ca