Newberg-Dundee Transportation Improvement Project. Appendix D: Traffic and Revenue Forecasts SENSITIVE BUSINESS RECORD

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1 C/O Macquarie North America Ltd. Suite 2664, 1055 Dunsmuir Street Vancouver, BC, V7X 1K8 Phone: (604) Fax: (604) Newberg-Dundee Transportation Improvement Project Appendix D: Traffic and Revenue Forecasts SENSITIVE BUSINESS RECORD

2 TABLE OF CONTENTS 1. STATED PREFERENCE Stated Preference Design Trip Information Bypass Description Choice of Preferences Socio Economic Characteristics SP Data Collection SP Survey Results 7 2. BASE MODEL DEVELOPMENT Base Network EMME Model SATURN Base Year Model Base Matrices Assignment method Calibration Validation of assigned link flows Travel Time Validation MODEL GROWTH ASSUMPTIONS TRAFFIC AND REVENUE FORECATS 31 Appendix D: Traffic and Revenue Forecasts PAGE I

3 1. STATED PREFERENCE 1.1 Stated Preference Design The SP survey was developed using the specialized software Askia, which adapts automatically the preferences in the survey exercise based on the characteristics of the interviewee. The survey form was structured in four sections: Trip information Bypass description Choice of preferences Socio economics characteristics These are described in more detail in the following sections Trip Information This section contained questions related to the type of vehicle used during the trip, vehicle occupancy, origin and destination, frequency, trip purpose and duration of the trip Bypass Description After describing the bypass, the interviewee was asked about the type of trip they were making and the likely route they would take, where the alternatives were: Newberg or Dundee (short distance); Newberg and Dundee (long distance); None of above (not in scope). The overall trip frequency, considering all trips undertaken on the 99W, was also asked in this section Choice of Preferences In this section, every interviewee was confronted with a set of choices (Stated Preferences), depending on the type of trip as described in the previous section and their declared travel time for the whole trip where the segmentation was: less than 30 minutes, between 30 and 60 minutes, more than 60 minutes. The interviewee had nine sets of binary choices between two alternatives A or B. The selection was based on the following semantics scale: Definitely A Probably A Don t know Definitely B Probably B The figure below shows the introductory screen to the SP exercise. Appendix D: Traffic and Revenue Forecasts PAGE 1

4 Introduction to Stated Preference Exercise The binary choices between trip alternatives were then presented (see the next figure). On the left the characteristics of the current trip on the 99W were presented and the corresponding travel time. On the right the trip characteristics using the bypass were presented, with the corresponding travel time and non-stop toll road price. Appendix D: Traffic and Revenue Forecasts PAGE 2

5 Example of SP Choice Exercise Socio Economic Characteristics In this last section, the different user characteristics were investigated, including age, occupation, gender, income, size of the home and number of vehicles in household. These questions were asked all interviewees. Further questions were made if: The interviewee was a student - study place was asked The interviewee was making a non-working job trip - the location of work place was asked. Finally, a question was asked about their opinion that best represented their vision of the Project (tolled Bypass). The idea was to obtain some element of political bias on the answers, especially from those completely in favour or against the application of a toll. The alternatives in this case were: It is a good solution to the congestion on the 99W and I will use it even if I have to pay a toll to use it I think it is a good solution and I would use it as long as people will not be charged for using it, or for its construction It will help to reduce pollution and congestion but it doesn t either benefit me or my family Appendix D: Traffic and Revenue Forecasts PAGE 3

6 I will not benefit directly from the proposed bypass, but it will result in making this part of Yamhill County more attractive to live and work I don t care if it reduces travel times. I am more concerned about its impact on the environment. 1.2 SP Data Collection A pilot survey was undertaken between May 30th and June 2nd 2006, gathering 66 interviews. This survey was undertaken face to face in different gas stations along the corridor. After the pilot survey, it was necessary to make some modifications to the SP exercise to obtain an improved and more realistic set of travel time and toll value combinations in the model. Also, the questionnaire text was slightly changed so as to improve interviewee understanding. The actual survey was undertaken online between June 6th and July 11th 2006, gathering some 220 valid interviews. The recruitment of potential interviewees was carried out at the same time as the origin destination surveys were taking place. This ensured that the interviewees were actual users of the corridor and were aware of local traffic conditions. After responding to the origin-destination survey, drivers were given a flyer with information about the survey and inviting them to complete an online Stated Preference survey in two websites, including the bypass project web site. A contact phone number was also provided in the flyer, where drivers could make comments or ask questions related to the survey. A copy of the flyer is shown below for reference. Appendix D: Traffic and Revenue Forecasts PAGE 4

7 SP Survey Recruitment Flyer Appendix D: Traffic and Revenue Forecasts PAGE 5

8 Appendix D: Traffic and Revenue Forecasts PAGE 6

9 1.3 SP Survey Results The discrete choice modeling process was undertaken with the completed surveys and several models with different segmentation types were tested, with and without segmentation, based on income level and trip purpose. The results are presented in the next table. The final model applied was developed from 1,280 observations, corresponding to 150 surveys. According to this segmentation, five coefficients were obtained, all of them statistically significant 1. The sign of the coefficients is negative, which is consistent as with more time and cost there is less utility for the users. Results of the SP Model Coefficients Value t-ratio Travel Time (work purpose) Travel Time (other purpose) Cost (low income) Cost (medium income) Cost (high income) The value of time is calculated as the quotient between travel time and the cost coefficients. The results for the different combinations are presented in the next table. Values of Time Obtained Segment (User class) Value ($/hr) (Cents/min) Work low income VOT Work medium income VOT Work high income VOT Other purpose low income VOT Other purpose medium income VOT Other purpose high income VOT BASE MODEL DEVELOPMENT 2.1 Base Network The road network was coded into a SATURN platform based on the existing EMME model built for the EIS work. The EMME model has a very detailed network coding of the Newberg and Dundee areas. It consists of 139 zones, 420 regular nodes and 1335 links (see figure below). 1 A test t (t-ratio) greater than 1.96 is necessary for to be significant statistically. Appendix D: Traffic and Revenue Forecasts PAGE 7

10 2.1.1 EMME Model For the purpose of this analysis, selection of the links of the EMME model were used to build the SATURN model, which is consistent with the traffic and Origin-Destination information collected in June. Worth pointing out that the purposes of the two models are quite different, with the original EMME model used to forecast operational conditions in the PM peak whilst the focus of our work is the forecast of traffic and revenue forecasts for a number of toll scenarios with the Bypass in place SATURN Base Year Model The base year model coded in SATURN has 25 zones, 128 regular nodes and 233 links. The figure below shows the extent of the model. Appendix D: Traffic and Revenue Forecasts PAGE 8

11 The link parameters coded were speed, length and traffic capacity based on the number of lanes. Each link has a flow-delay function associated with it estimated from the travel time survey carried out in June Link length was based on the EMME model and validated using geographic information from MAPTITUDE maps Base Matrices Base Year Matrices were built using the O/D survey information and the classified traffic counts collected in June 2006 as discussed previously Light vehicles matrices The O/D information was coded into the 25 network zones previously defined and shown. Appendix D: Traffic and Revenue Forecasts PAGE 9

12 Zone definition Zone General Location Zone General Location Portland/Vancouver 14 Dayton 2 Portland NE from I Pacific coast 3 Sherwood 16 Salem & south 4, 5, 8, 9, 18, 20 Newberg South E of I North 19 Wilsonville E of I North 21 Seattle & International 10 Dundee 22 South Portland 11 West of Dundee 23 Wilsonville E of I-5 using Lafayette/MacMinnville 24 SW of I5 (coming through 99W Dayton) 13 Willamina 25 NW coming through Dayton to NE A detailed checking process was undertaken with the data from each survey point and period to avoid unusual O/D patterns and prevent any distortions in the matrices. Therefore incomplete or inconsistent interviews were discarded from the database. This allowed the matrices for each site and direction to be built and then expanded using the traffic count data. For each station s, a sampling adjustment factor for the total of traffic, for each hour i and vehicle type j, is calculated as follows: where, Xp ( s,i, j ) = M( s,i, j ) Q( s,i, j ) M(s,i,j) = the number of vehicles of type j that were counted in station s during hour i Q(i,j,s) = the number of interviews taken from the vehicles of type j during hour i at station s The application of the factor Xp per station provides the 1-hour period average traffic volume of the station. This process allowed for the assignment of each matrix to obtain flows on each link. Next figure below shows the assignment of the survey point 1 matrix to the network. Please note there are no westbound trips at this stage of the O/D validation process as the survey data was collected in the eastbound direction only. This was due to space constraints to accommodate the survey station and the operational difficulties with closing two lanes of traffic. However, interviews were carried out over a 12 hour period to obtain daily trip patterns which would allow estimating the traffic patterns in the unobserved direction. Appendix D: Traffic and Revenue Forecasts PAGE 10

13 Assignment of Survey Point 1 Matrix After these partial assignments, the link flows were compared with the traffic counts to provide the expansion factor on the one-way survey flow. A process was then developed to determine the unobserved trips. This was based on the assumption that trips interviewed in one direction in a certain time period were symmetrical i.e. they would be returning in the opposite direction in another time period. Matrices were transposed to have the return trips structure (from the destination to the origin), and factorized by the missing part of the traffic counts. The figure below presents an example of the development of the matrix for site 1. After each site matrix is generated, all the site matrices are added to form the final matrix. Because site 5 has no O/D data, the missing trips were estimated using the distribution coming from the EMME model. Appendix D: Traffic and Revenue Forecasts PAGE 11

14 Example of Development of Matrix for Site 1 SITE 1 Matrix Site 1 WB AM + Matrix Site 1 EB AM = Matrix Site 1 AM (Both directions) Transpose Scale Factor Transpose Scale Factor Matrix Site 1 EB PM + Matrix Site1 WB PM = Matrix Site1 PM (Both directions) Matrix Site 1 WB OP Matrix Site1 EB OP + = Matrix Site1 OP (Both directions) Transpose Scale Factor Matrix Site 1 WB Wk Matrix Site1 EB Wk + = Matrix Site1 Wk (Both directions) Transpose Scale Factor Based on the procedure explained above, separate matrices for each model period were built (AM, OP, PM and WK). The matrices were also segmented by trip purpose (Work and Others) and resident status (Resident and Non Resident). After the assignment of these matrices, there were some trips missing on site 5 (difference between the observed and assigned traffic), where no O/D survey was carried out. This traffic corresponds to local traffic i.e. vehicles that did not cross any of the other surveys stations. The process to estimate these trips was based on the number of modeled trips missing and applying the EMME2 model OD distribution Heavy vehicles matrices During the data collection, O/D interviews were also carried out at site 4 in the westbound direction only. A partial matrix was built using the same process as the one described for the light vehicles. In this case the matrix was developed using an algorithm implemented in the module SATME2 of SATURN. This algorithm is able to built trip matrices from traffic count data based on the principle of Maximum Entropy. The starting point for this process was a prior matrix based on a combination of O/D survey data on site 4 and the patterns from the light vehicles matrices. Appendix D: Traffic and Revenue Forecasts PAGE 12

15 In the case of the weekend period, the prior matrix is based on the off peak prior matrix, using a scale factor to reduce total number of trips to weekend volumes Assignment method The assignment method used is Stochastic User Equilibrium Assignment with different user classes for each of which a generalized cost function is defined by applying the following behavioral parameters: Light vehicles: GC = Time VOT + LV Toll LV Heavy vehicles: GC = Time + Length * VOC + HV Toll HV where: GCLV and GCHV are the generalized cost of Light Vehicles and Heavy Vehicles respectively. VOT is the Value of Time VOC is the value of operating costs No tolls in base case, so toll is set to zero. For the purpose of the calibration, the model was segmented into 5 user classes as following: User classes User number Vehicle type Purpose Resident 1 Light vehicles Work Resident 2 Light vehicles Work Non Resident 3 Light vehicles Others Resident 4 Light vehicles Others Non Resident 5 Heavy vehicles 2.2 Calibration To give a measure of the goodness of fit of the modeled to the observed flows two types of validation were carried out: Validation of assigned link flows Validation of travel time Validation of assigned link flows The matrices of each period were assigned to the network and the modeled volumes of traffic at key points were compared with the observed volumes (traffic counts). Appendix D: Traffic and Revenue Forecasts PAGE 13

16 The GEH statistic was used in the validation of links counts. This statistic is useful in comparing two different values of flow on a link, V1 and V2. It is defined by: GEH = 2 ( V2 V1 ).5( V + ) 0 V 1 2 The reason for introducing such a statistic is the inability of either the absolute difference or the relative difference to cope with a wide range of flows. Links with a GEH parameter of 5.0 or less indicate a good fit. Tables overleaf present the results of the comparison between observed and modeled flows for all the traffic counts and period. AM Peak Period Validation Light Vehicles Heavy Vehicles Site Dir Count [veh/hr] Model [veh/hr] GEH Count [veh/hr] Model [veh/hr] GEH 1 99W East of Newberg WB W East of Newberg EB W West of Dundee WB W West of Dundee SB South of Newberg NB South of Newberg EB W West of Newberg WB W West of Newberg EB Appendix D: Traffic and Revenue Forecasts PAGE 14

17 Off Peak (OP) Period Validation Light Vehicles Heavy Vehicles Site Dir Count [veh/hr] Model [veh/hr] GEH Count [veh/hr] Model [veh/hr] GEH 1 99W East of Newberg WB W East of Newberg EB W West of Dundee WB W West of Dundee SB South of Newberg NB South of Newberg EB W West of Newberg WB W West of Newberg EB PM Peak Period Validation Light Vehicles Heavy Vehicles Site Dir Count [veh/hr] Model [veh/hr] GEH Count [veh/hr] Model [veh/hr] GEH 1 99W East of Newberg WB W East of Newberg EB W West of Dundee WB W West of Dundee SB South of Newberg NB South of Newberg EB W West of Newberg WB W West of Newberg EB Appendix D: Traffic and Revenue Forecasts PAGE 15

18 Weekend (WK) Period Validation Light Vehicles Heavy Vehicles Site Dir Count [veh/hr] Model [veh/hr] GEH Count [veh/hr] Model [veh/hr] GEH 1 99W East of Newberg WB W East of Newberg EB W West of Dundee WB W West of Dundee SB South of Newberg NB South of Newberg EB W West of Newberg WB W West of Newberg EB The tables show a very close correlation between modeled and observed flows. This is to be expected as the lack of route choice in the base model means that modeled flows match the observed Travel Time Validation Travel time data was collected for each of the 4 modeled periods. This information is used to update the parameters of the flow-delay function of homogenous sections of the road in order to represent current travel times. The following tables display the average speed and compare the observed time of each section of 99W and 219 to the modeled time. Appendix D: Traffic and Revenue Forecasts PAGE 16

19 AM Peak Travel Time Validation Westbound Eastbound Section Average Speed Obs. Time Mod. Time Diff Average Speed Obs. Time Mod. Time Diff [miles/hr] [min] [min] [min] [miles/hr] [min] [min] [min] 99W NE Quarry Rd and BrutschSt W Brutscher St and S Springbrook St W S Springbrook St and Villa Rd W Villa Rd and N College St W N College St and N Main St W N Main St and NE Fox Farm Rd W NE Fox Farm Rd and SW Niederberger Rd/SE Parks Dr W SW Niederberger Rd/SE Parks Dr and Trunk Rd W Trunk Rd and Archery Summit Rd W Archery Summit Rd and McDougall Rd Northbound Southbound NE Hash Rd&S Springbrook St/Wilsonville Rd S Springbrook St/Wilsonville Rd&E 2nd St E 2nd St&99W/Villa Rd Appendix D: Traffic and Revenue Forecasts PAGE 17

20 Off Peak (OP) Period Travel Time Validation Westbound Eastbound Section Average Speed Obs. Time Mod. Time Diff Average Speed Obs. Time Mod. Time Diff [miles/hr] [min] [min] [min] [miles/hr] [min] [min] [min] 99W NE Quarry Rd and Brutscher St W Brutscher St and S Springbrook St W S Springbrook St and Villa Rd W Villa Rd and N College St W N College St and N Main St W N Main St and NE Fox Farm Rd W NE Fox Farm Rd and SW Niederberger Rd/SE Parks Dr W SW Niederberger Rd/SE Parks Dr and Trunk Rd W Trunk Rd and Archery Summit Rd W Archery Summit Rd and McDougall Rd Northbound Southbound NE Hash Rd&S Springbrook St/Wilsonville Rd S Springbrook St/Wilsonville Rd&E 2nd St E 2nd St&99W/Villa Rd Appendix D: Traffic and Revenue Forecasts PAGE 18

21 PM Peak Period Travel Time Validation Westbound Eastbound Section Average Speed Obs. Time Mod. Time Diff Average Speed Obs. Time Mod. Time Diff [miles/hr] [min] [min] [min] [miles/hr] [min] [min] [min] 99W NE Quarry Rd and Brutscher St W Brutscher St and S Springbrook St W S Springbrook St and Villa Rd W Villa Rd and N College St W N College St and N Main St W N Main St and NE Fox Farm Rd W NE Fox Farm Rd and SW Niederberger Rd/SE Parks Dr W SW Niederberger Rd/SE Parks Dr and Trunk Rd W Trunk Rd and Archery Summit Rd W Archery Summit Rd and McDougall Rd Northbound Southbound NE Hash Rd&S Springbrook St/Wilsonville Rd S Springbrook St/Wilsonville Rd&E 2nd St E 2nd St&99W/Villa Rd Appendix D: Traffic and Revenue Forecasts PAGE 19

22 Weekend (WK) Period Travel Time Validation Westbound Eastbound Section Average Speed Obs. Time Mod. Time Diff Average Speed Obs. Time Mod. Time Diff [miles/hr] [min] [min] [min] [miles/hr] [min] [min] [min] 99W NE Quarry Rd and Brutscher St W Brutscher St and S Springbrook St W S Springbrook St and Villa Rd W Villa Rd and N College St W N College St and N Main St W N Main St and NE Fox Farm Rd W NE Fox Farm Rd and SW Niederberger Rd/SE Parks Dr W SW Niederberger Rd/SE Parks Dr and Trunk Rd W Trunk Rd and Archery Summit Rd W Archery Summit Rd and McDougall Rd Northbound Southbound NE Hash Rd&S Springbrook St/Wilsonville Rd S Springbrook St/Wilsonville Rd&E 2nd St E 2nd St&99W/Villa Rd The following figures summarize the information contained in the tables and compare modeled travel time to the average, minimum and maximum values observed. The figures show how the modeled travel times are close to observed average travel times on the corridor. It is worth emphasizing that there is considerable variability of travel times and the model has been calibrated to observed average travel times. Appendix D: Traffic and Revenue Forecasts PAGE 20

23 99W Westbound Travel Time Validation AM Period Time (min 99W Westbound Time Validation AM Period Length (miles) Min Time Max Time Average Time Modeled time 99W Westbound Travel Time Validation OP Period Time (min 99W Westbound Time Validation OP Period Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 21

24 99W Westbound Travel Time Validation PM Period W Westbound Time Validation PM Period Time (min Length (miles) Min Time Max Time Average Time Modeled time 99W Westbound Travel Time Validation WK- Period Time (min 99W Westbound Time Validation WK Period Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 22

25 99W Eastbound Travel Time Validation AM Period Time (min 99W Eastbound Time Validation AM Period Length (miles) Min Time Max Time Average Time Modeled time 99W Eastbound Travel Time Validation OP Period Time (min 99W Eastbound Time Validation OP Period Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 23

26 99W Eastbound Travel Time Validation PM Period W Eastbound Time Validation PM Period Time (min Length (miles) Min Time Max Time Average Time Modeled time 99W Eastbound Travel Time Validation WK Period W EastboundTime Validation WK Period Time (min Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 24

27 Hwy 219 Northbound Travel Time Validation AM Period Time (min 219 Northbound Time Validation AM Period Length (miles) Min Time Max Time Average Time Modeled time Hwy 219 Northbound Travel Time Validation OP Period Northbound Time Validation OP Period 5.00 Time (min Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 25

28 Hwy 219 Northbound Travel Time Validation PM Period Time (min 219 Northbound Time Validation PM Period Length (miles) Min Time Max Time Average Time Modeled time Hwy 219 Northbound Travel Time Validation WK Period Northbound Time Validation WK Period 5.00 Time (min Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 26

29 Hwy 219 Southbound Travel Time Validation AM Period Southbound Time Validation AM Period Time (min Length (miles) Min Time Max Time Average Time Modeled time Hwy 219 Southbound Travel Time Validation OP Period Southbound Time Validation OP Period Time (min Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 27

30 Hwy 219 Southbound Travel Time Validation PM Period Southbound Time Validation PM Period Time (min Length (miles) Min Time Max Time Average Time Modeled time Hwy 219 Southbound Travel Time Validation WK Period Southbound Time Validation WK Period Time (min Length (miles) Min Time Max Time Average Time Modeled time Appendix D: Traffic and Revenue Forecasts PAGE 28

31 3. MODEL GROWTH ASSUMPTIONS The table on the next page presents the annual average growth rate for each O/D pair, according to SDG s 25 zones model. The zonal description of the sectors is shown in the following table (together with sector relationship). Zone description - SDG Model Zone Sector Name Zone Sector Name 1 East Portland/Vancouver 14 West Dayton 2 East Portland NE from I West Pacific coast 3 East Sherwood 16 South Salem & south 4 Newberg Newberg 17 South 219 South - East of I-5 5 Newberg Newberg 18 Newberg Newberg 6 North 219 North 19 East Wilsonville - East of I-5 7 North 240 North 20 Newberg Newberg 8 Newberg Newberg 21 East Seattle & International 9 Newberg Newberg 22 East South Portland 10 Dundee Dundee 23 South 11 Dundee West of Dundee 24 West 12 West Lafayette/McMinnville 25 West Wilsonville - East of I-5 using 219 SW of I5 (coming through 99W Dayton) NW coming through Dayton to NE 13 West Willamina As the table shows the annual growth ranges from 0.1% up to 8.3% p.a. for the different SDG O/D pairs. Note that in the table any negative forecast growth has been converted to null values and shown as a blank. Appendix D: Traffic and Revenue Forecasts PAGE 29

32 Average Light Vehicle Growth SDG Zoning system O\D Total general 1 6.5% 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% 2 6.5% 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% 3 6.5% 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% 4 6.5% 6.5% 6.5% 8.3% 4.2% 6.3% 5.3% 2.6% 2.9% 4.0% 4.0% 6.1% 6.1% 6.1% 6.1% 6.6% 6.6% 5.6% 6.5% 3.1% 6.5% 6.5% 6.6% 6.1% 6.1% 5.7% 5 1.6% 1.6% 1.6% 3.4% 1.1% 1.3% 0.4% 0.2% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.7% 1.7% 1.3% 1.6% 0.6% 1.6% 1.6% 1.7% 1.1% 1.1% 1.3% 6 3.0% 3.0% 3.0% 6.3% 1.3% 1.7% 0.2% 0.4% 4.4% 2.2% 2.2% 0.7% 0.7% 0.7% 0.7% 0.6% 0.6% 2.6% 3.0% 3.0% 3.0% 0.6% 0.7% 0.7% 1.8% 7 1.8% 1.8% 1.8% 5.3% 0.4% 0.2% 3.4% 1.2% 1.2% 1.6% 1.8% 1.8% 1.8% 1.0% 8 0.6% 0.6% 0.6% 2.6% 0.3% 0.4% 0.7% 0.7% 0.2% 0.2% 0.2% 0.2% 0.7% 0.7% 0.8% 0.6% 0.6% 0.6% 0.7% 0.2% 0.2% 0.5% 9 4.7% 4.7% 4.7% 3.0% 0.9% 4.4% 3.4% 0.1% 0.8% 2.1% 2.1% 4.2% 4.2% 4.2% 4.2% 4.8% 4.8% 1.9% 4.7% 0.5% 4.7% 4.7% 4.8% 4.2% 4.2% 3.5% % 2.4% 2.4% 3.5% 1.5% 2.2% 1.2% 0.6% 1.0% 3.6% 3.6% 1.9% 1.9% 1.9% 1.9% 2.6% 2.6% 2.1% 2.4% 0.9% 2.4% 2.4% 2.6% 1.9% 1.9% 2.1% % 2.4% 2.4% 3.5% 1.5% 2.2% 1.2% 0.6% 1.0% 3.6% 3.6% 1.9% 1.9% 1.9% 1.9% 2.6% 2.6% 2.1% 2.4% 0.9% 2.4% 2.4% 2.6% 1.9% 1.9% 2.1% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% % 2.8% 2.8% 6.6% 1.7% 0.6% 0.7% 4.8% 2.6% 2.6% 3.0% 2.8% 2.8% 2.8% 1.6% % 2.8% 2.8% 6.6% 1.7% 0.6% 0.7% 4.8% 2.6% 2.6% 3.0% 2.8% 2.8% 2.8% 1.6% % 3.0% 3.0% 6.0% 2.2% 2.6% 1.6% 0.9% 1.3% 2.7% 2.7% 2.3% 2.3% 2.3% 2.3% 3.0% 3.0% 3.4% 3.0% 1.2% 3.0% 3.0% 3.0% 2.3% 2.3% 2.6% % 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% % 0.4% 0.3% 0.3% 0.2% 0.1% % 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% % 1.6% 3.0% 1.7% 0.6% 4.7% 2.4% 2.4% 2.1% 2.1% 2.1% 2.1% 2.8% 2.8% 3.0% 2.8% 2.1% 2.1% 1.9% % 2.8% 2.8% 6.6% 1.7% 0.6% 0.7% 4.8% 2.6% 2.6% 3.0% 2.8% 2.8% 2.8% 1.6% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% % 2.1% 2.1% 6.1% 1.1% 0.7% 0.2% 4.2% 1.9% 1.9% 2.3% 2.1% 2.1% 2.1% 1.2% Total 1.9% 1.9% 1.9% 5.6% 1.4% 1.8% 1.0% 0.5% 3.3% 2.2% 2.2% 1.2% 1.2% 1.2% 1.2% 1.6% 1.6% 2.5% 1.9% 0.3% 1.9% 1.9% 1.6% 1.2% 1.2% 1.8% Appendix D: Traffic and Revenue Forecasts PAGE 30

33 4. TRAFFIC AND REVENUE FORECASTS The following table is the Traffic and Revenue Forecasts for the Newberg Dundee Transportation Improvement Project: Appendix D: Traffic and Revenue Forecasts PAGE 31

34 AVERAGE DAILY TRAFFIC - THROUGH TOLLING OPTIONS AVERAGE DAILY TRAFFIC - THROUGH TOLLING OPTIONS 99W Tolling Flat Toll Year Concession Year ADT '000s Resident 6,728 6,921 7,119 7,324 7,534 7,750 7,972 8,201 8,436 8,678 8,943 9,216 9,498 9,788 10,087 10,395 10,713 11,040 11,377 11,725 11,842 11,960 12,080 12,201 12,323 12,951 13,614 14,310 Non Resident 12,124 12,403 12,688 12,980 13,279 13,585 13,897 14,217 14,544 14,879 15,219 15,568 15,924 16,288 16,661 17,042 17,432 17,831 18,239 18,656 18,843 19,031 19,221 19,413 19,608 20,608 21,662 22,769 Exclusion 11,153 11,472 11,800 12,138 12,485 12,842 13,210 13,588 13,976 14,376 14,815 15,268 15,735 16,215 16,711 17,222 17,748 18,290 18,849 19,425 19,619 19,816 20,014 20,214 20,416 21,457 22,555 23,708 Truck 1* ,009 1,061 Truck 2** 1,190 1,202 1,215 1,227 1,240 1,253 1,265 1,278 1,292 1,305 1,318 1,332 1,346 1,360 1,374 1,388 1,402 1,417 1,431 1,446 1,460 1,475 1,490 1,505 1,520 1,597 1,679 1,765 Total 31,910 32,721 33,553 34,407 35,283 36,182 37,105 38,052 39,025 40,023 41,089 42,185 43,311 44,468 45,658 46,881 48,137 49,429 50,756 52,121 52,642 53,168 53,700 54,237 54,779 57,574 60,518 63,613 99W Tolling Variable Toll Year Concession Year ADT '000s Resident 5,056 5,215 5,380 5,550 5,726 5,907 6,093 6,286 6,485 6,690 6,914 7,145 7,385 7,632 7,888 8,152 8,425 8,707 8,999 9,300 9,393 9,487 9,582 9,677 9,774 10,273 10,798 11,350 Non Resident 9,806 10,042 10,284 10,531 10,785 11,044 11,310 11,582 11,860 12,145 12,435 12,732 13,036 13,347 13,666 13,992 14,326 14,668 15,018 15,377 15,530 15,686 15,842 16,001 16,161 16,985 17,854 18,767 Exclusion 8,697 8,956 9,224 9,499 9,783 10,075 10,376 10,686 11,005 11,333 11,693 12,064 12,447 12,843 13,250 13,671 14,105 14,553 15,015 15,491 15,646 15,803 15,961 16,120 16,281 17,112 17,987 18,907 Truck Truck ,007 1,017 1,028 1,039 1,049 1,060 1,071 1,082 1,093 1,104 1,116 1,127 1,138 1,150 1,161 1,173 1,233 1,296 1,362 Total 25,031 25,702 26,391 27,099 27,828 28,576 29,345 30,136 30,948 31,783 32,674 33,590 34,533 35,504 36,503 37,532 38,591 39,680 40,802 41,957 42,376 42,800 43,228 43,660 44,097 46,346 48,717 51,208 Pass Through Tolling - Local Residents Excluded Flat Toll Year Concession Year ADT '000s Resident Non Resident 11,769 12,047 12,331 12,622 12,920 13,224 13,536 13,855 14,182 14,517 14,856 15,203 15,559 15,922 16,294 16,675 17,065 17,464 17,872 18,289 18,472 18,657 18,844 19,032 19,222 20,203 21,236 22,322 Exclusion 10,544 10,857 11,180 11,512 11,854 12,207 12,569 12,943 13,328 13,724 14,156 14,602 15,062 15,536 16,026 16,530 17,051 17,588 18,142 18,713 18,901 19,090 19,280 19,473 19,668 20,671 21,728 22,840 Truck ,000 1,051 Truck 2 1,177 1,189 1,202 1,214 1,227 1,239 1,252 1,265 1,278 1,292 1,305 1,319 1,332 1,346 1,360 1,374 1,389 1,403 1,418 1,432 1,447 1,461 1,476 1,490 1,505 1,582 1,663 1,748 Total 24,197 24,808 25,435 26,078 26,738 27,415 28,110 28,824 29,557 30,309 31,102 31,916 32,753 33,614 34,497 35,406 36,339 37,298 38,283 39,600 39,996 40,396 40,800 41,208 41,620 43,743 45,980 48,332 Pass Through Tolling - Local Residents Excluded Variable Toll Year Concession Year ADT '000s Resident Non Resident 9,277 9,511 9,750 9,996 10,248 10,506 10,770 11,042 11,320 11,605 11,893 12,189 12,492 12,803 13,121 13,447 13,781 14,124 14,475 14,835 14,984 15,133 15,285 15,438 15,592 16,387 17,225 18,106 Exclusion 7,933 8,185 8,445 8,713 8,990 9,275 9,570 9,874 10,188 10,511 10,863 11,226 11,601 11,989 12,390 12,804 13,232 13,675 14,132 14,604 14,750 14,898 15,047 15,197 15,349 16,132 16,957 17,824 Truck Truck ,006 1,017 1,027 1,038 1,049 1,060 1,071 1,082 1,093 1,104 1,115 1,127 1,138 1,149 1,161 1,220 1,282 1,348 Total 18,665 19,165 19,680 20,209 20,753 21,313 21,888 22,479 23,087 23,712 24,369 25,045 25,740 26,456 27,192 27,950 28,730 29,533 30,360 31,496 31,811 32,129 32,450 32,775 33,102 34,791 36,570 38,440 Truck 1 refers to 2 axle trucks Truck 2 refers to 3+ axle trucks

35 AVERAGE DAILY TRAFFIC - THROUGH TOLLING OPTIONS Pass Through Tolling - Local Residents and Visitors Excluded Flat Toll Year Concession Year ADT '000s Resident Non Resident 15,445 15,855 16,276 16,707 17,150 17,605 18,072 18,552 19,044 19,549 20,073 20,611 21,164 21,731 22,313 22,911 23,525 24,156 24,803 25,468 25,723 25,980 26,240 26,502 26,767 28,132 29,571 31,083 Exclusion Truck ,036 Truck 2 1,157 1,169 1,182 1,194 1,207 1,219 1,232 1,245 1,258 1,271 1,285 1,298 1,312 1,326 1,340 1,354 1,368 1,383 1,397 1,412 1,426 1,440 1,455 1,469 1,484 1,560 1,639 1,723 Total 17,298 17,727 18,167 18,619 19,082 19,558 20,045 20,545 21,058 21,586 22,132 22,693 23,270 23,864 24,477 25,112 25,776 26,478 27,237 28,086 28,366 28,650 28,937 29,226 29,518 31,024 32,610 34,278 Pass Through Tolling - Local Residents and Visitors Excluded Variable Toll Year Concession Year ADT '000s Resident Non Resident 11,497 11,831 12,176 12,530 12,895 13,270 13,657 14,054 14,463 14,884 15,319 15,766 16,227 16,701 17,189 17,691 18,208 18,739 19,287 19,850 20,049 20,249 20,452 20,656 20,863 21,927 23,048 24,227 Exclusion Truck Truck ,010 1,021 1,032 1,042 1,053 1,065 1,076 1,087 1,098 1,109 1,120 1,131 1,143 1,201 1,262 1,327 Total 12,924 13,274 13,634 14,003 14,384 14,775 15,177 15,590 16,016 16,455 16,908 17,375 17,857 18,354 18,871 19,408 19,972 20,570 21,215 21,926 22,145 22,366 22,590 22,816 23,044 24,220 25,458 26,760 Pass Through Tolling - Additional Local Communities and Visitors Excluded Flat Toll Year Concession Year ADT '000s Resident ,000 1,345 1,809 1,827 1,845 1,864 1,882 1,901 1,998 2,100 2,208 Non Resident 12,885 13,239 13,602 13,975 14,359 14,753 15,158 15,574 16,001 16,440 16,896 17,364 17,845 18,340 18,848 19,371 19,908 20,459 21,027 21,609 21,825 22,044 22,264 22,487 22,712 23,870 25,091 26,374 Exclusion Truck ,028 Truck 2 1,146 1,158 1,170 1,183 1,195 1,208 1,221 1,234 1,247 1,260 1,274 1,287 1,301 1,315 1,329 1,343 1,357 1,371 1,386 1,401 1,415 1,429 1,443 1,458 1,472 1,547 1,626 1,710 Total 14,720 15,093 15,476 15,869 16,273 16,687 17,113 17,549 17,998 18,551 19,061 19,594 20,156 20,751 21,387 22,074 22,824 23,655 24,591 25,662 25,919 26,178 26,440 26,704 26,971 28,347 29,797 31,320 Truck 1 refers to 2 axle trucks Truck 2 refers to 3+ axle trucks

36 REVENUE FORECASTS, OPERATING COSTS AND AVERAGE DAILY TRAFFIC - BYPASS TOLLING OPTIONS REVENUE FORECASTS, OPERATING COSTS AND AVERAGE DAILY TRAFFIC - BYPASS TOLLING OPTIONS Type: DISTANCE Scenario: CENTRAL Year Concession Year REVENUES $ '000s Resident ,010 1,077 1,148 1,224 1,236 1,248 1,261 1,274 1,286 1,352 1,421 1,494 Non Resident 2,928 2,609 2,399 2,596 2,809 3,039 3,288 3,558 3,849 4,165 4,438 4,728 5,038 5,367 5,719 6,093 6,492 6,916 7,369 7,852 7,930 8,009 8,089 8,170 8,252 8,673 9,116 9,583 Truck Truck Total Revenues 4,248 3,763 3,442 3,704 3,988 4,295 4,627 4,985 5,373 5,793 6,141 6,511 6,904 7,322 7,766 8,238 8,739 9,273 9,839 10,442 10,546 10,652 10,758 10,866 10,975 11,534 12,124 12,744 OPERATING COSTS $ '000s Total Costs 6,532 6,032 5,832 5,582 5,582 5,582 5,582 5,582 5,582 5,582 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 ADT Resident 1,405 1,445 1,486 1,529 1,572 1,617 1,663 1,711 1,760 1,810 1,896 1,986 2,081 2,180 2,284 2,392 2,506 2,625 2,750 2,881 2,910 2,939 2,968 2,998 3,028 3,182 3,345 3,516 Non Resident 7,814 8,270 8,754 9,265 9,806 10,379 10,985 11,627 12,306 13,025 13,593 14,185 14,804 15,449 16,123 16,826 17,560 18,325 19,124 19,958 20,158 20,359 20,563 20,768 20,976 22,046 23,174 24,359 Truck Truck Total Revenues 9,706 10,225 10,774 11,354 11,967 12,615 13,300 14,025 14,791 10,926 16,263 16,954 17,676 18,429 19,216 20,036 20,893 21,787 22,720 16,595 23,931 24,170 24,412 24,656 24,903 26,173 27,511 28,918 Type: POINT Scenario: CENTRAL Year Concession Year REVENUES $ '000s Resident ,004 1,014 1,024 1,076 1,131 1,189 Non Resident 2,536 2,259 2,077 2,247 2,431 2,629 2,844 3,077 3,328 3,600 3,882 4,186 4,513 4,867 5,247 5,658 6,101 6,578 7,093 7,648 7,724 7,801 7,879 7,958 8,038 8,448 8,880 9,334 Truck Truck ,013 1,065 1,119 Total Revenues 3,732 3,311 3,034 3,271 3,530 3,810 4,115 4,447 4,807 5,199 5,561 5,951 6,369 6,818 7,300 7,819 8,376 8,976 9,620 10,313 10,416 10,520 10,625 10,731 10,839 11,392 11,974 12,586 OPERATING COSTS $ '000s Total Costs 6,532 6,032 5,832 5,582 5,582 5,582 5,582 5,582 5,582 5,582 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 5,069 ADT Resident ,031 1,097 1,167 1,241 1,320 1,404 1,493 1,588 1,689 1,796 1,814 1,832 1,851 1,869 1,888 1,984 2,086 2,192 Non Resident 4,768 5,057 5,363 5,688 6,032 6,397 6,784 7,195 7,630 8,092 8,554 9,043 9,559 10,105 10,682 11,292 11,936 12,618 13,339 14,100 14,241 14,384 14,527 14,673 14,819 15,575 16,372 17,209 Truck Truck Total Revenues 5,620 5,974 6,353 6,757 7,188 7,650 8,143 8,669 9,233 9,836 10,372 10,938 11,537 12,169 12,838 13,545 14,293 15,083 15,918 16,801 16,969 17,139 17,310 17,483 17,658 18,559 19,508 20,505 Truck 1 refers to 2 axle trucks Truck 2 refers to 3+ axle trucks

37 BYPASS DISTANCE TOLLING - REVENUE PROFILE Revenue profile necessary to bridge the funding gap as compared to the funding gap and the operating costs Bypass Distance Tolling Revenue Profile 200, ,000 $ , , , ,000 80,000 60,000 40,000 20, Distance Toll Revenue Target Revenue Operating Costs

38 BYPASS POINT TOLLING - REVENUE PROFILE Revenue profile necessary to bridge the funding gap as compared to the funding gap and the operating costs Bypass Point Tolling Revenue Profile 200,000 $ , , , , ,000 80,000 60,000 40,000 20, Point Toll Revenue Target Revenue Operating Costs