Methodology for Evaluating Managed Toll Lanes within …docs.trb.org/prp/16-2889.pdf · Methodology for Evaluating Managed Toll Lanes within an Existing Tolled Corridor By Jack Klodzinski,

Methodology for Evaluating Managed Toll Lanes within an Existing Tolled Corridor

By

Jack Klodzinski, Ph.D.* Traffic Forecast Manager AECOM at Florida’s Turnpike, PO Box 613069, Ocoee, Florida 34761 Office 407-532-3999 x 3819 E-mail: [email protected]

and Tom Adler, Ph.D. Resource Systems Group Inc. 55 Railroad Row, White River Junction, Vermont 05001 Office: (802) 295-4999 Fax: (802) 295-1006 E-mail: [email protected] Submitted TRB 2016 Word Count: 6,736 with 4,486 words and 9 figures & tables * Corresponding Author July 31, 2015

mailto:[email protected]

ABSTRACT

Florida’s Turnpike Enterprise (FTE), part of Florida Department of Transportation (FDOT),

initiated the Integrated Congestion Pricing Plan (ICPP) Study with the Federal Highway

Administration (FHWA). This included developing a forecasting methodology to evaluate

managed toll lanes in an existing tolled corridor. This approach is unique because travelers in

these corridors can choose between two competing tolled alternatives; one with fixed toll levels

and the other with higher dynamically-priced tolls. An Express Lanes Time of Day (ELToD)

forecasting tool previously developed for FDOT was re-designed to expand its forecast

capability for this application. Its mode choice sub-model depends on driver behavior data

including Value of Travel Time Savings (VTTS). Model parameters were redefined based on a

State Preference (SP) Survey specifically done with information collected from users of tolled

corridors.

Forecasts developed for the Tampa, FL region’s Veterans/SR-589 express lane facility using

ELToD showed 5 to 6% express lanes shares in year 2020 growing to 8% in 2040. The express

lanes toll forecast was at the minimum in year 2020 ($0.25 higher than general toll lanes base

cost) and in 2040 was about $0.50 above the base toll during the peak. This methodology was

also applied to the HEFT/SR-821 express lanes project in southeast Florida. The results

produced by ELToD appeared reasonable given the unique application of facilities with dynamic

pricing side-by-side with conventional fixed tolling. As expected, more traffic was forecasted to

use the managed lanes in southeast Florida where the traffic density and VTTS was higher.

3 Adler & Klodzinski, ELToD-TRB 2016

INTRODUCTION 1

One of the primary purposes of evaluating congestion management strategies on Florida’s 2

Turnpike System is to identify options that will help preserve and prolong the ability of the 3

system to serve growing travel demand even after the facilities can no longer be widened. One 4

of these options is Express Lanes. Florida’s Express Lanes are dynamically priced toll lanes to 5

manage congestion (1). Specifically for Florida’s Turnpike facilities, the express lanes are co-6

located in an existing tolled corridor. 7

In partnership with the Federal Highway Administration (FHWA), Florida’s Turnpike Enterprise 8

(FTE) initiated the Integrated Congestion Pricing Plan (ICPP) Study in February 2011 to 9

evaluate the potential for implementing congestion pricing strategies on the Turnpike System (2, 10

6). A comprehensive overview of the ICPP study is provided in another paper by Shbaklo et al. 11

As part of the ICPP Study, two project traffic and revenue estimates were completed for the 12

Veterans Expressway/SR-589 in Hillsborough County (the Tampa region) and the Turnpike/SR-13

821 in Miami-Dade County. This paper focuses specifically on the SR-589 project traffic and 14

toll forecasting approach for the express lanes. 15

Project Details 16

SR-589 is currently a four-lane, limited-access toll facility that extends 15 miles from near 17

Courtney Campbell Causeway west of the Tampa International Airport to Dale Mabry 18

Highway/SR-597 in northern Hillsborough County, FL. It is a major facility serving commuter 19

travel in the Tampa Bay Area. According to INRIX, a traffic research group, the Tampa Bay 20

Area had the largest increase in the nation in commuter hours spent in traffic in 2012. 21

The project widens the facility from four to eight lanes, which includes adding one general toll 22

lane and one express lane per direction between Memorial Highway and Hutchison Road for a 23

length of approximately 9 miles. The widening extends approximately two miles further to Van 24

Dyke Road. Figure 1 shows a map of this project. Also, conversion of SR-589 to all-electronic 25

tolling (AET) coincides with the implementation of express lanes as part of the corridor 26

construction. 27


28

Figure 1 Veterans/SR-589 Toll Road in the Tampa, FL. area 29

Tolls in the express lanes will be collected electronically using mainline toll gantries at the 30

Anderson and Sugarwood Toll Plazas, and are set to initially be $0.25 higher than the general toll 31

lanes during the off-peak hours. During the peak hours, tolls are dynamically adjusted to reflect 32

actual traffic conditions in the express lanes. Dynamic toll rates are set to maintain a desired 33

LOS, such as D or better during peak hours (for example, vehicle speeds of 45 mph or greater for 34

95% of the time as on the I-95 express lanes in Broward County). The overall criteria for the 35

corridor coincide with a design speed of 60 miles per hour (mph). In addition to the beginning 36

and ending points of the express lane segments there will be intermediate access to, and egress 37

from the express lanes. The toll plan for SR-589 is shown in Figure 2. The plan shows that toll 38


collection in the express lanes will take place at the existing Anderson and Sugarwood mainline 39

toll plaza gantries. 40

41

Figure 2 Veterans/SR-589 Express Lanes Access Plan 42

43


LITERATURE REVIEW 44

At some point, funding limitations and/or corridor build out is reached, constraining the ability to 45

provide adequate supply for the future traffic demand and thus necessitating other options for a 46

congested corridor. US DOT has suggested that pricing options be considered for new roadway 47

capacity and FHWA has developed a guide for priced managed lanes (24). 48

In recent years, traffic operational data suggests that tolling does not have a negative impact on 49

drivers. On the contrary, managed toll roads have had steady increases in traffic volume in spite 50

of toll charges (7). Congestion management is now a viable approach for existing 51

conventionally-tolled corridors and a potentially more attractive option than the traditional toll 52

road projects (9,10). Managed lanes have even been perceived as potential customer service ITS 53

related projects as highlighted by Eden (18). 54

Starting with SR-91 as the first managed toll lane in the country in 1995, there has been a 55

considerable amount of managed toll lanes either put in use, under construction, or under study 56

around the country (3,5,11,12,24). Thus, significant research and project work has been done 57

regarding traffic and revenue forecasting of managed toll lanes to mitigate congestion in 58

currently non-tolled corridors (16,17). Oryani et al, Seegmuller et al, and Kringer et al are just 59

some of examples of the extensive work done to date on how to forecast managed lanes and what 60

model parameters should be considered for studying these new tolling options (13,15,25). The 61

approach for modeling a managed toll lane includes a toll choice model and a Value of Travel 62

Time Savings (VTTS) (14,30,31,32). Even Value of Reliability (VOR) is being considered as 63

another variable of measure as a standard input parameter as evidenced by such authors as 64

Vovsha et al and L. Kellis (19,27,28). Klodzinski et al has developed a model for Florida’s 65

Turnpike Enterprise in line with this general approach for managed toll lanes (8). 66

However, little research or data has been collected regarding management of congestion in 67

existing tolled corridors with fixed toll rates predefined (7). In modeling perspective, there is no 68

previous or current research on how to model a managed toll lane on an existing tolled corridor. 69

Despite this lack of research, there is a need for a modeling methodology to be developed since 70

projects were under study and now being constructed (SR-589 & SR-821) for Florida’s 71

Turnpike. 72


FORECAST APPROACH 73

The traffic estimates for this study were accomplished through a two-step process. With the need 74

to provide traffic and toll rate forecasts by hour and by direction, the forecasting process utilized 75

two modeling tools; a Travel Demand Model (TDM) and ELToD, a time-of-day custom 76

managed toll lanes model that is the focus for this paper. The TDM is required for producing an 77

input network and trip matrix for the ELToD. It establishes the base corridor traffic demand. 78

The ELToD procedure uses four primary sets of inputs: 79

1. Total daily traffic estimated for the corridor (in a matrix layout) 80

2. Hourly distribution of total traffic within the corridor (by direction) 81

3. Geometric configuration of the facility: section length, free flow speed, lane capacity, 82

passenger car equivalent factor (PCE), numbers of general use and managed toll lanes 83

(the network corresponding to the traffic matrix) and 84

4. Toll costs: dynamic toll policy curve in the form of an equation including toll rate limits. 85

ELToD estimates the split that will occur between general toll and express lanes using the 86

volumes from an origin-destination (O-D) trip matrix (easily produced from a subarea extraction 87

of a travel demand forecast model). It estimates the split by solving for the supply/demand 88

equilibrium using both toll level and travel times from each hour. Potential project corridor 89

diversion is assumed to be considered by the demand model with the express lanes project 90

modeled at the express lanes base toll rate ($0.25 higher than the general toll lanes). All required 91

input data is necessary to run the model but some have been derived from actual field studies of 92

the I-95 Express in Florida such as Reliability and Equilibration settings. Figure 3 provides a 93

flow chart showing the general data requirements for running the ELToD model. 94


95

Figure 3 ELToD Modeling Flowchart 96

The hourly distribution is based on observed traffic data and held constant (i.e., does not reflect 97

peak shifting in this application). A separate peak-spreading procedure can be used to determine 98

the extent of peak spreading that would occur given projected hourly traffic volumes, toll rates 99

and lane capacities. Diversion to alternative routes in this application is considered to be 100

adequately represented with the travel demand model. The O-D trip matrix from the travel 101

demand model utilized as the input to the time of day application is assumed to be traffic that 102

would want to use the corridor and not divert out. However, a computation based on the 103

specified LOS volume to capacity limit input value provides separate output of hourly volume 104

that exceeds the limit. 105

Supply Side 106

The supply side relationship between traffic volume and travel times is represented by Akcelik 107

curves that estimate the section travel times separately for the general use and managed toll lanes 108

in each direction (23). These curves were developed based on queuing theory to more accurately 109


represent congestion levels in over-capacity conditions. The Akcelik curves are calculated in the 110

model by: 111

Ratio of congested time to free flow time = 112

𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒 𝑀𝑢𝑙𝑡113

= (1

𝑆 + (𝑔𝑝𝑏 × 𝑔𝑇 × ((𝑉𝑜𝐶 + 𝑔𝐴𝑘𝑐𝑒𝑙𝑖𝑘𝑂𝑓𝑓𝑠𝑒𝑡 − 1) 114

+ ((𝑉𝑜𝐶 + 𝑔𝐴𝑘𝑐𝑒𝑙𝑖𝑘𝑂𝑓𝑓𝑠𝑒𝑡 − 1)2 + (𝑔𝑝𝑎 × 𝑔𝑃115

× (𝑉𝑜𝐶 + 𝑔𝐴𝑘𝑐𝑒𝑙𝑖𝑘𝑂𝑓𝑓𝑠𝑒𝑡

𝑐 × 𝑔𝑇)))0.5))) /(

1

𝑆) 116

𝑈𝑛𝑑𝑎𝑚𝑝𝑒𝑑 𝑇𝑇 = 𝐹𝑟𝑒𝑒 𝐹𝑙𝑜𝑤 𝑇𝑖𝑚𝑒 × 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒 𝑀𝑢𝑙𝑡 117

𝑈𝑛𝑑𝑎𝑚𝑝𝑒𝑑 𝑇𝑇 is the travel time. 118

Where: 119

T is the length of the time period in hours (default = 1) 120

S is the free flow travel speed for the facility (mph) 121

𝑔𝑃 is a facility-specific parameter (J in Figure 4) 122

c is the facility capacity (veh/hr) 123

VoC is the one-hour volume to capacity ratio 124

Default parameters for the Akcelik curves are shown in Figure 4. 125

126

Figure 4 Akcelik Parameters 127

Facility Type FSUTMS Free Flow (S) Facility-Specific Time Length (T)

(FT) FT Number (mph) Parameter (J) (hours)

Freeway 10-19, 80-89 65 0.1 1.0

Toll Facility 90-99 65 0.1 1.0

Multi-Lane Highway 20-29 50 0.2 1.0

One-Way Street 60-69 50 0.2 1.0

Major Arterial 30-34, 70-74 50 0.4 1.0

Minor Arterial 35-39, 75-79 40 0.8 1.0

Collector 40-59 30 1.6 1.0

1Akçelik , Rahmi. Travel Time Functions for Transport Planning Purposes: Davidson's Function, its Time-Dependent

Form and an Alternative Travel Time Function. In Australian Road Research , 21(3), September 1991, pp 49-59.

Table 1

Parameters for the Akcelik Volume-Delay Functions1 - Default Values


The result of these curves is that travel times equal the section length divided by the free flow 128

speed for low volumes and increase dramatically as volumes approach or exceed the hourly 129

capacity. Figure 5 illustrates the Akcelik curve along with other widely used curves. 130

131

Figure 5 Volume/Capacity Curves 132

Toll rates are computed for each hour and direction based on the express lane’s volume to 133

capacity ratio using power curves (Power curves rather than splines or other piecewise linear 134

forms are used to avoid discontinuities that could prevent convergence of the equilibration 135

process). As illustrated later in this paper, power curves can be used to represent a wide range of 136

tolling policies, from those that increase tolls gradually as traffic increases throughout the range, 137

to those that are intended to “protect” a certain level of service and thus increase rapidly as the 138

limits of that level of service are approached. The rates are set so they fall within a specified 139

minimum to maximum toll range, with a shape determined by a specified power curve exponent. 140

These rates can be used as computed, manually adjusted or a special optimization procedure can 141

be used to determine the toll rate that garners the most revenue, express lane volume or any 142

objective function subject to the min/max rate limits and level-of-service conditions. Figure 6 143

displays a sample of toll policy curves set with a LOS E limit. The I-95 Express (95 Exp. Phase 144

1) minimum and maximum curves as applied in the field are step functions shown for 145

comparative purposes. 146


147

Figure 6 Toll Policy Curve Examples 148

Demand Side 149

The demand side is presented by a binary logit-based toll route choice model. The general form 150

of this model is shown below. 151

)1/(1)())()(( TOLLTTTT ELGUeELP

152

Where: EL and GU represent the managed toll lanes and general use lanes, respectively 153

is a scale parameter 154

is an estimated travel time coefficient 155

is an estimated toll coefficient 156

The model determines the hourly toll (express lane) share based on the difference in travel times 157

between the general purpose and express lanes and the toll amount. 158

Coefficients for the logit equation were taken from a 2011 joint stated & revealed preference 159

survey effort conducted in Southeast/Miami, FL area as part of a full traffic and revenue study on 160

I-75 (21, 26). The revealed preference component of the effort was performed for existing 95 161

Express customers to better understand traveler behavior and develop a basis for incorporating 162

Revenue Potential Increases

Traffic Volume Increases

LOS A LOS B LOS C LOS D LOS E


reliability into a discrete toll choice modeling equation. The intent was that the newly formulated 163

choice model equation could serve as the basis for estimating traffic and revenue on other 164

express lane projects around the state. From over two thousand completed surveys, the time and 165

cost coefficients from that study reflected a VTTS of $11.16/hour for I-95, with the lower bound 166

at just over eight dollars and the upper bound just above fourteen dollars per hour. This is an 167

important parameter for tolled project evaluations. Tseng & Verhoef, Macke et al, and Kruesi 168

are just a few examples of the extensive work done to etimate VTTS (30, 31,32). 169

The logit model scale () was adjusted so that it replicated the observed 2011 time-of-day 170

distribution on the I-95 Express facility in southeastern Florida. Survey data was also collected to 171

aid in establishing the correct parameters and constants for the full toll choice model executed in 172

ELToD to reflect Florida drivers’ behavior when choosing to use express lanes (26). The scale 173

of a logit model affects the steepness of the logit curve which in turn reflects variance of the 174

utility function’s error term. By convention, the logit model’s variance parameter is set 175

arbitrarily to 1.0 but in practice the variance depends on the degree to which travelers’ behaviors 176

are affected by random factors other than those that are explicitly included in the utility function. 177

For the choice between general purpose and express lanes, the variance is likely quite low 178

because travel time differences can be easily discerned and there is little else other than time and 179

cost that distinguishes the two types of lanes. A higher scale parameter implies lower variance 180

and results in higher shares being allocated to the alternative with the highest utility. 181

Another value established to calibrate the choice model was travel time Entropy or level of 182

unreliability in the general purpose lanes. This is the measure of uncertainty in a distribution and 183

was determined to be a function of the mean and standard deviation of the travel time 184

distribution reported by the preference survey respondents. This value was added which helps to 185

explain the choice to use of express toll lanes when a time savings is not realized for a trip. 186

The actual formulas implemented in the ELToD Model are: 187

Utility for Express Lanes Alternative 188

𝑈𝐸𝐿 = 𝛽𝑡𝑖𝑚𝑒 × 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒𝐸𝐿 + 𝛽𝑐𝑜𝑠𝑡 × 𝑇𝑜𝑙𝑙𝐶𝑜𝑠𝑡𝐸𝐿 + 𝛽_𝑇𝑜𝑙𝑙𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 189

Utility for General Purpose Lanes Alternative 190

𝑈𝐺𝑃 = 𝛽𝑡𝑖𝑚𝑒 × 𝑇𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒𝐺𝑃 + 𝛽_𝐸𝑛𝑡𝑟𝑜𝑝𝑦 × 𝐸𝑛𝑡𝑟𝑜𝑝𝑦_𝐺𝑃 × 𝐷 191


The definitions for the utility equations are as follows: 192

U_EL = Utility for the express lanes alternative 193

U_GP = Utility for the general purpose lanes alternative 194

β_time = Travel time coefficient 195

TravelTime = travel time in express lanes (EL) or General Purpose (GP) lanes 196

TollCost EL= express lanes toll cost 197

β_Entropy = Calibrated entropy coefficient 198

Entropy GP = General purpose lanes entropy per mile 199

D = Distance of adjacent general purpose link in miles 200

β_TollConstant = Calibrated toll constant 201

The choice model was calibrated/validated using the following I-95 express lanes corridor data 202

from years 2011 & 2012: 203

Traffic volumes on the 95 express lanes and general purpose lanes 204

Percentage of total vehicles eligible to use the express lanes estimated from a Bluetooth 205 origin-destination data 206

Average express lanes toll cost 207

Average express lanes time savings (compared to the general purpose lanes) 208

Average observed entropy (reliability) of the general purpose lanes 209

Average daily collected revenue 210

The calibration and validation resulted in a 6.9% difference between 47,404 observed average 211

weekday transactions and 50,668 model produced transactions. Also, there was only a 0.5% 212

difference between the average weekday revenue (observed was $67,314 Vs. Model $67,624). 213

Figure 7 displays the comparison of hourly traffic volumes and toll rates between the model and 214

observed data by direction. Because the supply and demand functions are both highly non-215

linear, the simultaneous solution of these functions is most conveniently found using an iterative 216

method. 217


218

Figure 7 Comparison of 95 Express Volumes and Tolls 219

220


ELToD IMPLEMENTATION & APPLICATION 221

The traffic level of service in the Express Lanes of the Veterans/SR-589 would be maintained 222

through variable pricing, with the tolls rising as congestion levels increase (speed degrades). The 223

Express Lanes Time of Day (ELToD) toll model provides the means to forecast traffic by hour 224

and direction in the Express Lanes via the supply and demand equilibrium processes as 225

described. The ELToD model was coded in Cube Voyager Script to coincide with the Florida 226

Standard Urban Transportation Model Structure (FSUTMS). This delivered a more seamless 227

transition from the demand model subarea corridor extraction for ELToD input. 228

Travel Demand Model 229

Matrix Estimation was employed to simplify the model development process while vastly 230

improving model accuracy in terms of traffic volumes. During this process, model updates 231

included socioeconomic data and the network database. After a good corridor level validation 232

was completed and regional updates were done for future years, the model was used to produce a 233

project corridor extraction with the associated trip table. 234

Data Collection & Development 235

ELToD incorporates multiple input data fields to this assignment application. A 13x13 origin-236

destination trip matrix and corresponding network link-node diagram were used as model input 237

from the regional model. Facility characteristics were updated according to project design for 238

the ELToD model. This network link data included: 239

• from/to nodes, 240

• number of lanes, 241

• capacity, 242

• speed, 243

• direction of travel, 244

• length of segment, 245

• facility classification, 246

• project segment number, 247

• link/location to identify the output data for each managed toll lanes project segment, and 248

• min/max tolls (an override option for each managed toll lanes project segment). 249


Network attributes were revised with ArcMap GIS software. A snapshot of the network and 250

node data is provided in Figure 8. Besides the O-D trip matrix & network, an hourly directional 251

traffic distribution for the corridor is required. Other required model input are the parameters 252

related to the volume-delay curve, toll policy, and toll choice model as well as toll values for 253

pricing the facility. 254

255

Figure 8 Corridor Subarea Network and Surrounding Tampa Area 256

All of the input coefficients and parameters can be adjusted to represent or test a specific express 257

lane project alternative, toll rate, or dynamic pricing policy. The following is a description of the 258

main parameters and their importance. 259

Toll, Time: These are coefficients derived from the logit models that fix the value of 260

time. They are region-specific and should be adjusted based on data from the region in 261

which the model is applied. 262


Reliability: Reliability is a value to capture the disutility associated with travel time 263

variability. Entropy is utilized as a representation of unreliability and set to 0.119 based 264

on the survey data. 265

Scale: This value determines the steepness of the logit route choice curve. A higher 266

value results in a more deterministic switching more akin to the traditional route 267

assignment – i.e. switch to XL lanes only when the time cost trade-off is favorable. This 268

has been calibrated to reflect general time-of-day patterns on 95 Express in south Florida. 269

Akcelik offset: This shifts the volume/delay curve to the left by the given number of V/C 270

units, resulting in travel time increases beginning at lower volumes. 271

Damping factor: This is a setting that affects the speed and stability of the 272

equilibration and can be modified if there are convergence problems. 273

PCE: Passenger car equivalency factor. A value of 1.0 implies all cars; higher values 274

reflect increasing truck fractions. 275

The remaining settable parameters are used to adjust the toll rate policy, which is determined by 276

the truncated power curve as seen in Figure 6 and described by the following equation: 277

𝑇𝑜𝑙𝑙 𝑅𝑎𝑡𝑒 = 𝑀𝑖𝑛((𝑀𝑖𝑛 𝑇𝑜𝑙𝑙 + (𝑀𝑎𝑥 𝑇𝑜𝑙𝑙 − 𝑀𝑖𝑛 𝑇𝑜𝑙𝑙)278

× (𝑋𝐿 𝑣 𝑐⁄ + 𝑣 𝑐⁄ 𝑡𝑜𝑙𝑙 𝑜𝑓𝑓𝑠𝑒𝑡)𝑇𝑜𝑙𝑙𝐸𝑥𝑝, 𝑀𝑎𝑥 𝑇𝑜𝑙𝑙) 279

Toll parameters: 280

Min toll: The minimum allowable toll rate ($/mi). 281

Max toll: The maximum allowable toll rate ($/mi). 282

V/C toll offset: The amount below capacity at which toll rates ascend sharply. 283

TollExp: Exponent of the toll rate curve – a high value (e.g. 20) 284

approximates threshold pricing where rates increase only above a LOS as set by the V/C 285

toll offset. 286

The choice model coefficients represented in the version presented here were developed 287

specifically for the Tampa Bay Florida region of toll roads based on the stated preference survey. 288

The time and cost coefficients from that study reflect a $7.80/hour average value-of-time. An 289

interesting observation is that the VTTS for the ICPP corridors are lower than surveys conducted 290

for 95 Express in Florida. Previous studies have focused on choices between toll routes and toll-291


free routes. However, it appears that the incremental willingness to pay for a premium toll 292

facility versus a standard toll facility is lower than the willingness to pay for a toll facility versus 293

a toll free facility. This finding in Florida is consistent with another research effort that RSG (the 294

consultant who administered both surveys) conducted in 2012 for managed toll lanes on an 295

existing toll road in suburban Chicago. 296

The lane capacity was assumed at an LOS E hourly limit. The damping factor was set to ensure 297

model iterations do not cause cycling between extreme values as the Akcelik curves hit their 298

steeper sections. Also, based on the survey responses, the toll constants for peak & off-peak 299

were adjusted to reflect this Express Lanes project (two-lane vs. four-lane) thus providing some 300

disincentive due to the inability to pass a slower moving vehicle. 301

The parameters in the Toll Rate equation above can be adjusted to approximate any general toll 302

policy with a passive to aggressive pricing curve. For this project, a more passive pricing curve 303

was used to service more traffic at lower V/C values. 304

Other forecast assumptions were truck traffic would not be permitted to use managed toll lanes 305

and there are no HOV discounts (though the model has the ability to separate these into three 306

different modes). The demand model network has the entire project corridor tolled with the 307

additional minimum toll cost applied to the express lanes. Then, the ELToD model estimates the 308

split based on the toll differential with dynamic pricing implemented through the choice model 309

and defined toll policy. 310

311

312


FORECAST RESULTS 313

Detailed output for all network links are produced by hour, direction and project segment for: 314

General Toll Lanes volume 315

Express Lanes volume 316

Express Lanes share 317

Per mile toll rate 318

Volume to capacity ratios 319

Speed 320

Revenue estimate (based on toll rate, volume, & distance if applicable) 321

The Express Lanes traffic for the Veterans Expressway/SR 589 is summarized in Figure 9 for the 322

Sugarwood and Anderson toll plazas, respectively. The split between managed toll lanes and 323

general toll lanes from a daily traffic perspective showed the managed toll lanes carry five 324

percent of the total AADT in 2020. The Express Lanes shares grow over time, and by 2040, they 325

account for eight percent of the total AADT. The general toll lanes have an annual compounded 326

growth rate of 2.7 to 3.0 percent over the 20-year period, while the Express Lanes growth rate 327

ranges from 6.1 to 6.7 percent, depending on the location. 328

The peak direction in the morning is southbound, and conversely, it is northbound in the 329

afternoon. The opening year peak hour northbound volume is the greatest, at 570 vehicles. By 330

2040, the northbound peak hour volumes are the highest, at 1,450. A benefit from the ELToD 331

model is having both disaggregated and aggregated traffic and toll information available in a 332

consistent, cohesive form. The hourly traffic forecasts are easily summarized into daily traffic 333

volumes, by direction, and by Express Lanes segment, with the corresponding toll amount for 334

project analyses. 335

The model reflects the toll policy, a minimum toll amount to travel the Express Lanes will be 336

$0.25 greater than the adjacent general toll lane at every tolling location. If the tolling location 337

has no adjacent general toll, then the minimum express lane toll is simply $0.25. On the 338

Veterans Expressway/SR-589, the peak period express lane toll amount in 2020 is not expected 339

to be higher than the minimum $0.25 toll. By 2040, the average peak period toll amount will be 340

up to $0.25 greater than the minimum toll. Figure 9 is a summary of the toll forecasts in the 341

managed lanes. The displayed values are the incremental tolls above the base tolls in the general 342

toll lanes on the Veterans/SR-589. 343


A review of potential work commute trips in terms of total peak hour costs show reasonable 344

results. For the morning commuter traveling from the Suncoast Parkway/SR 589 to I-275, the 345

peak period cost to use the Express Lanes ranges from approximately $0.50 in 2020 to $0.75 in 346

2040. Conversely, a return trip in the evening from I-275 to the Suncoast Parkway/SR 589 has 347

additional toll that ranges from $0.50 in 2020 to $0.85 in 2040. 348

349

350

Figure 9 Mainline Managed Lanes Hourly Volume & average toll 351

352

SB NB SB NB SB NB SB NB

Sugarwood Mainline 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$

Anderson Mainline 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$

Sugarwood Mainline 0.40$ 0.25$ 0.25$ 0.50$ 0.30$ 0.30$ 0.35$ 0.45$

Anderson Mainline 0.35$ 0.25$ 0.25$ 0.35$ 0.30$ 0.25$ 0.30$ 0.35$

Sugarwood Mainline 2.4% 0.0% 0.0% 3.5% 0.9% 0.9% 1.7% 3.0%

Anderson Mainline 1.7% 0.0% 0.0% 1.7% 0.9% 0.0% 0.9% 1.7%

Daily

2020

2040

Annual Growth Rate

20 to 40

Year SegmentAM Peak (7-10) PM Peak (4-7) OP


CONCLUSIONS AND RECOMMENDATIONS 353

For this joint FHWA/FTE Integrated Congestion Pricing Plan (ICPP) project, several traffic and 354

revenue studies were conducted throughout the state. This included developing a forecasting 355

methodology to evaluate express lanes in an existing tolled corridor. This approach is unique 356

because travelers in these corridors can choose between two competing tolled alternatives; one 357

with fixed toll levels and the other with higher dynamically-priced tolls. This design included a 358

specialized express lanes time of day model, or ELToD. The approach applied a regional 359

demand model to provide base input (corridor network and trip matrix) to the ELToD Model. 360

The ELToD model employs a choice model to determine the split of traffic between the general 361

toll and express lanes with a dynamic pricing component. The choice model was previously 362

validated to observed I-95 Express traffic and revenue data. For establishing choice model 363

parameters to forecast express lanes traffic and toll rates for FTE facilities (existing tolled 364

corridors), focus groups and stated preference surveys were conducted (ref ICPP 2 report). The 365

Veterans/SR-589 in the Tampa Bay, Florida region was selected to highlight the successful 366

application of the forecast approach. 367

The Veterans/SR-589 forecast results concluded express lanes shares of 5-6% in year 2020 368

growing to 8% in design year 2040. For the toll in the express lanes, it is not forecasted to be 369

higher than the minimum (which is $0.25 higher than the general toll lanes base cost) and in the 370

design year was up to $0.50 above the base toll during the peak period. 371

This methodology was also applied to the HEFT/SR-821 managed lanes project in southeast 372

Florida. Based on the project and survey data collected and analyzed, the input parameters were 373

redefined for this project. Both projects are currently under construction. When comparing the 374

two projects and evaluating the reasonableness of the results for a planning level traffic study, 375

the methodology was deemed successful. More traffic was forecasted to use the Express Lanes 376

in south Florida where the traffic density and value of time was higher. 377

Application to other projects may consider additional evaluation and model alternative tests. For 378

example, if high v/c ratios are realized in the general toll lanes during the peak periods, 379

additional analyses is recommended for the project. The trip table could be refined to reflect 380

additional potential diversion out of the corridor, an adjacent surface street facility if available 381

could be added, or peak spreading could be employed to refine the hourly distribution to consider 382


shifting of driver departure times. The ELToD Model has been designed to allow flexibility in 383

application to a project based on the characteristics of the project. Should it be desired to employ 384

a VTTS distribution, test different toll policies, or additional diversion considerations, the user 385

can update the model using the Cube scripting language. It is also recommended that additional 386

forecasting work be completed for investment grade studies such as applying a 387

mesoscopic/dynamic traffic assignment (DTA) model, micro simulation model, or other 388

microscopic level operational modeling tool for more detailed analysis such as presented by 389

Velasquez et al (20). 390


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