Ozbay, Yanmaz -Tuzel , Holguin -Veras

The Impact s of Time -of -day Pricing Initiative at NY/NJ Port Authority Facilities Car and Truck Movements

Kaan Ozbay, Ph. D. , P.E. Associate Professor, Department of Civil and Environmental Engineering, Rutgers, The State University of , 623 B owser Rd. Piscataway, NJ 08854 USA, Tel: (732) 445 -2792 Fax: (732) 445 -0577 e-mail: [email protected]

Ozlem Yanmaz -Tuzel, MSc. (Corresponding Author) Graduate Research Assistant, Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, 623 Bowser Rd. Piscataway, NJ 08854 USA, Tel: (732) 445 -0576 Fax: (732) 445 -0577 e-mail: [email protected]

Jose Holguin -Veras, Ph.D., P.E. Associate Professor Department of Civil and Environmental Engineering 4030 Jonsson Engineering Center Rensselaer Polytechnic Institute 110 Eighth Street Troy, NY 12180 -3590 Phone: 518 -276 -6221 Fax: 518 -276 -4833 Email: [email protected]

Word count: 4 909 (text) + 3 Figures + 6 Tables = 7159 Abstract: 249

Res ubmission Date: November 15, 2005

Paper Submitted for Presentation and Publication at the Transportation Research Board’s 85 th Annual Meeting , Washington, D.C., 20 06

TRB 2006 Annual Meeting CD-ROM Paper revised from original submittal. Ozbay, Yanmaz -Tuzel , Holguin -Veras 1

ABSTRACT In this paper the impacts of the time -of -day pricing initiated in March 25, 2001, on traffic at the Port Authority of and New Jersey (PANYNJ) facilities are analyzed . The analyses are based on the traffic data routinely collected at a ll toll lanes by PANYNJ. Since terrorist attacks at World Trade Center disrup ted the transportation network, and made it impossible to isolate and analyze the reaction to the PANYNJ toll prices ( 4), time period before the September 11, 2001 event was considered for the impact analysis . The research has confirmed statistically significant shift towards pre -peak s both in the mornings (5 -6AM) and afternoons (3 - 4PM) in car traffic percent share after the time -of -day pricing . Also, weekday truck traffic percent share showed statistically significant shift to morning pre -peak (5 -6AM) and afternoon post -peak hours (7 -8PM). However, weekend car and truck traf fic percent share did not have statistically significant change in peak -shoulder hours (11AM -12PM and 8 -9PM). In addition, weekday and weekend peak -period car percent share experienced statistical ly significant decrease only at George (lower and upper levels). Unlike car traffic, truck traffic decreas ed for all peak -periods on both weekdays and weekends at all crossings after th e time -of -day pricing , though the decrease in peak traffic was statistically significant only on weekdays . These findings indicate d that PANYNJ time -of -day pricing initiative wa s successful to spread weekday peak - period traffic to the hours just before or after the peak toll rates are in effect, for both cars and trucks.

TRB 2006 Annual Meeting CD-ROM Paper revised from original submittal. Ozbay, Yanmaz -Tuzel , Holguin -Veras 2

INTRODUCTION The Port Authority of New York and New Jersey (PANYNJ ) controls some of the most important tran sportation facilities in (NYC) , including the city’s airports, port facilities and the Hudson River Crossings (FIGURE 1). The PANYNJ operates the following river crossings: (upper level, lower level, and Palisades Int erstate Parkway (PIP)), Lincoln and Holland Tunnels, , and Outerbridge Crossing. In essence, PANYNJ facilities link all the vehicular traffic between New Jersey and N YC, carrying an average daily eastbound traffic of 352,000 v ehicles, or more than 126 million eastbound vehicles in 2004 ( 1).

FIGURE 1 PANYNJ's port district and key facilities

In March 25, 2001, new pricing structure was initiated with tolls varying according to time - of -day and payment technology (cash, Electronic Toll Collection using E -ZPass ). PANYNJ saw the plan as a means for reducing congestion, facilitating commercial traffic management, and increasing the use of mass transit and E -ZPass. Tolls are collected in the eastbound (N YC bound) direction only. PANYNJ time -of -day pricing program e stablishes a high cash toll at all times of day, wi th discounted E -ZPass tolls set at higher levels during peak hours and at lower levels during the off -peak hour s. Peak hour toll s are effective on weekdays from 6 -9AM and 4 -7PM , as well as on weekends and holidays from 12 -8PM. Overnight h ours are effective from 12AM -6AM only for trucks using E -ZPass (2). The toll schedule before and after the time of pricing for car s and trucks is shown in TABLE 1 .

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TABLE 1 Tolls for Cars and Trucks ( 2) Type of Payment Before Time -of -day Pricing After Time -of -day Pricing Cars Cash Peak $4.00 $6.00 Cash Off -Peak $4.00 $6.00 E-ZPass Peak $3.60 $5.00 E-ZPass Off -Peak $3.60 $4.00 Trucks (per axle) Cash Peak $4.00 $6.00 Cash Off -Peak $4.00 $6.00 E-ZPass Peak $3.60 $6.00 E-ZPass Off -Peak $3.60 $5.00 E-ZPass Overnight $3.60 $3.50

In a previous paper by ( 3), elasticities for passenger and commercial vehicles were calculated using traffic data. T he purpose of this paper is to assess the traffic impacts of the PANYNJ time -of -day pricing using the traffic data routinely collected by the PANYNJ at the corres ponding toll lanes. The main objective is to answer some important questions highlighted below: 1. Were there any changes in peak/peak -shoulder period E -ZPass usage after time -of -day pricing initiative? 2. Were there any significant changes in overall traffic a fter time -of -day pricing initiative? 3. Were there any significant changes in the hourly distribution of traffic volumes during the peak and peak -shoulder s after time -of -day pricing initiative?

The terrorist attacks at the World Trade Center caused a sustain ed period of disruptio n to the transportation network, including redistribution of traffic on the roadway network, temporal modal shifts, and overall reduced demand reflecting the economic displacement from Lower Manhattan. These changes made an assessment of the reaction to the PANYNJ toll prices impo ssible to isolate and analyze (4). Thus, to focus solely on the traffic impacts of time -of -day pricing initiative, time period from January 2000 to August 2001 is analyzed, excluding the time period after the September 11, 2001 event.

DATA SOURCES The database used in this study was obtained from PANYNJ ( 5). It includes hourly, daily, weekly, and monthly eastbound (from New Jersey to New York ) traffic counts during weekdays (Monday, Wednesday, and Friday ) and weekends ( Saturday and Sunday ) from January 2000 to August 2001 . The database contains a variety of information including vehicle type (Bus, Truck, and Car) and payment type (E -ZPass, Cash) of each crossing. METHODOLOGY The main purpose of this study is to investigate the facility -spec ific traffic patterns and impacts of the time -of -day pricing initiative on the traffic flow of PANYNJ facilities for the time period January 2000 -August 2001 where the normal traffic patterns were not disrupted due to facility closures, and operational res trictions /regulations, following 9/11/2001. The steps followed throughout the analysis are summarized below. 1) Analyses of seasonal variations at individual facilities .

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2) Analysis of cash vs. E -ZPass usage before and after the time -of -day pricing initiative. 3) Before -after analysis, to determine the change in travelers’ behavior during peak and peak -shoulder periods using hourly traffic data before and after the time -of -day pricing initiative. 4) Application of statistical significance tests to determine the signif icance level of the changes in traffic after the time -of -day pricing initiative. SEASONAL FACTOR ANAL YSIS While investigating the travel patterns at PANYNJ facilities, it is important to differentiate the changes in traffic due to time -of -day pricing from the facility specific seasonal changes when no external factor (i.e. new toll schedule) is imposed to the system. In this part of the analysis, the role of seasonal variations in traffic is investigated through a simple statistical model. This model does n ot attempt to analyze the role of underlying economic conditions on variations in traffic, but treats these factors as part of the random error related to external factors. This analysis identifies three sets of factors using the data set from January 2000 to December 2000: 1. Factor_1: Temporal variations due to fluctuations on different periods of the day, days of the week, and months of the year. 2. Factor_2: Fluctuations in traffic among years for a specific time period of a day 3. Other random errors: Fluctu ations due to external factors difficult to capture such as, economic growth, and sampling errors. The statistical model representing the traffic flow can be given by ( 6):

yij = m +αi + β j +εij (1) where;

yij : Observed precent share of traffic at level i, j

m : Mean of all observatio ns yij

α i : Effect of Factor_1 at level i

β j : Effect of Factor_2 at level j

ε ij : Random error term To eliminate the fluctuations depending on peak/ off -peak periods , AM/PM and peak/off - peak traffic are investigated separately. To eliminate variations in demand due to toll changes, years with fixed tolls and typical work days are selected. Moreover, to reduce external factors traffic is represented in terms of percent share with respect to total daily traffic. To fully determine the effects of these factor s on traffic, two -way ANOVA test is employed by constructing a two - factor full factorial design without replications. During the seasonal variation analysis two different cases are cons idered for each time -of - day (AM/PM peak and off -peak) and each crossin g. In Case -1, monthly data between January - February 2000 and January -February 2001, both of which are before the time -of -day pricing initiative are compared. This case is developed to understand the impacts of economic changes between year 2000 and 2001 on the traffic flows when everything else in the system remained unchanged. In Case -1, a limited sample size of 12 weekdays and eight weekend days is used, and no formal consideration of weather -related impacts of snowfall is explicitly included in the analy sis In Case -2, monthly variation among and within the seasons of year 2000, before the time - of -day pricing is investigated. This case provides information regarding seasonal variation among months/seasons of year 2000, when everything else in the system is fixed. The analysis results for the crossings experiencing a statistically significant variation are shown in TABLE 2. The complete results of the ANOVA tests are provided in ( 7). The statistical

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significance level for each type of variation is determined by comparing F -value (calculated statistic from ANOVA which represents the ratio of standard deviations of two samples) and F - critical value (extracted from the f -distribution in statistical tables based on d egrees of freedom of the samples). If the f -value is greater than the F -critical value, the null hypothesis is rejected at 95% Confidence Level; i.e. there is a seasonal variation for that specific crossing. The ANOVA test results state that i n 2000 there is a statistically significant variation among seasons for Bayonne Bridge, Goethals Bridge and traffic during PM peak hours. In addition, the seasonal variation in off -peak hours is statistically significant only at Goethals Bridge. For all other periods and cases, the fluctuation among months and years are statistically insignificant. These findings indicate that each bridge/tunnel has its own specific seasonal variation trend. Therefore the traffic impact analysis should be done for each cr ossing and time period separately, considering the seasonal variations. TABLE 2 Seasonal Variation Analysis Results Time Type of F critical Crossing Case F value Significance Period Variation value within seasons 0.1244 5.1432 No Bayonne P.M. peak Case -2 among seasons 7.1217 4.7571 Bridge Yes random error within seasons 0.2524 5.1432 No P.M. peak Case -2 among seasons 6.6618 4.7571 Yes Goethals random error Bridge within seasons 0.7111 5.143 2 No Off -peak Case -2 among seasons 5.5969 4.7571 Yes random error within seasons 0.1009 5.1432 No Lincoln P.M. peak Case -2 among seasons 6.9363 4.7571 Tunnel Yes random error

E-ZPASS USAGE AS A F UNCTION OF TIME -OF -DAY The trend in E -ZPass ownersh ip by time -of -day is another critical issue to be considered, which can help to understand whether the changes in E -ZPass usage after the time -of -day pricing initiative are due to the new toll schedule, as opposed to being part of the natural trend. In ord er to see the cash vs. E -ZPass usage trends from January 2000 to August 2001, percent share of cash and E -ZPass users for each crossing are calculated considering all weekdays and all vehicle types. Since toll increase mostly affects the behavior of users traveling at peak and peak -shoulder s, periods between 5 -10A M and 3 -8PM are considered. Pre -peak hours refer to time periods between 5-6AM and 3 -4P M; post -peak hours refer to time periods between 9 -10AM and 7 -8PM . Si milarly, peak hours refer to time periods between 6 -9AM and 4 -7PM . Percent share of traffic at each specific period is calculated as the ratio of traffic during that time period to the traffic observed during the whole time period . Since each crossing exhibits a similar behavior in terms of E -ZP ass usage ( 7), for illustration purposes only the average percent share of cash and E -ZPass users traveling at all crossings and the analysis results for crossings experiencing the highest change among from Janu ary 2001 to August 2001 are presented here. The detailed analysis of each crossing can be found in ( 7). These findings regarding E -ZPass usage as a function of time -of -day are consistent with the results discus sed by ( 8).

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The trends in E -ZPass and cash usage during peak and peak -shoulders are shown in FIGURE 2. Two distinct time periods are observed with considerable change in percent share of E - ZPass and cash user t raffic. First of these time periods begins in October 2000, and the second begins in April 2001. Both periods are related to the introduction of E -ZPass discounts. In October 2000, news of PANYNJ’s toll increase plans and public stakeholder briefings began to be publicized by the media. The news of planned higher cash toll rates ($7.00 in the initial public plan) and deep E -ZPass discounts may have spurred many cash -paying PANYNJ customers to switch to E -ZPass at this time. In addition, in September 2000, time -of -day pricing and E -ZPass technology were initiated at the New Jersey (NJ) Turnpike. E -ZPass users traveling on the NJ Turnpike started to pay discounted tolls during off -peak hours. Therefore, commuters using both NJ Turnpike and PANYNJ facilities m ay have started to get E -ZPass tag to take advantage of lower toll levels and lower delays at both facilities. To focus solely on the impacts of PANYNJ time -of -day pricing initiative, TABLE 3 shows the absolute changes in E -ZPass vs. cash usage only after the PANYNJ time -of -day pricing initiative, and the total changes from January 2000 to August 2001, for all crossings and for the crossings which experienced the highest changes during this time period, Holland and Lincoln Tunnels. Detailed analysis of the impacts of NJ Turnpike time -of -day pricing can be found in ( 7). As shown in FIGURE 2 and TABLE 3, cash users share the low est portion of traffic both at peak and peak -shoulders compared to E -ZPass users. The gen eral trend of cash users in all periods is a decreasing trend. From January 2000 to August 2001, percent share of cash users at peak and peak -shoulder s reduced by 27.85% and 18.93%, respectively. Moreover, after PANYNJ time -of - day pricing initiative, perce nt share of cash users traveling at peak and peak -shoulder s reduced by almost 30% and 13%, respectively from the time period October 2000 -March 2001 to April - August 2001. These findings indicate that the discounted toll levels and the expedited crossing at the toll booths for E -ZPass users may have attracted cash commuters to use E -ZPass. Analysis results of E -ZPass users traveling at peak and peak -shoulders are shown in FIGURE 2 and TABLE 3. E -ZPass users share the highest portion of the traffic compared to cash users during all periods. In addition, unlike the cash users, percent share of E -ZPass users continuously increased from January 2000 to August 2001. The level of increase at peak and peak - shoulder percent of E -ZPass users is 13% and 27%, respectiv ely from January 2000 to August 2001. Similarly, a fter the time -of -day pricing initiative, percent share of E -ZPass users traveling at peak and peak -shoulder periods increased by 5.4 % and 20.8%, respectively . These trends indicate that after the time -of -da y pricing initiative peak -shoulder E -ZPass usage increased at a higher rate compared to peak -period E -ZPass usage demonstrating the success of time -of -day pricing to encourage commuters to shift to off -peak from peak periods. As shown in TABLE 3, the highe st change in percent share of cash and E -ZPass users traveling at peak and peak -shoulders is observed at Holland and Lincoln Tunnel s. Reduction in cash user percent share at peak and peak -shoulder periods are almost 33% and 25%, respectively from January 2 000 to August 2001. Moreover, with the introduction of E -ZPass discounts and expedited crossing at the toll booths, the highest reduction in cash usage is observed at Lincoln Tunnel, with a reduction of 18.34 % from October 2000 -March 2001 to April -August 2001. The observed increases in E -ZPass use at all crossings suggest that prior to the toll increase , peak -period drivers realized little benefit of better traffic flow with E -ZPass due to the heavy conges tion experienced in peak hours. After the toll cha nge, the incentive to use E -ZPass at the crossings has been greatly enhanced by virtue of the deeper discount for electronic

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transactions , and the expedited crossing at toll booths , which resulted in overall higher usage of E - ZPass and declined traffic dur ing peak periods.

pre-peak-cash All Crossings post-peak-cash pre-peak-E-ZPass post-peak-E-ZPass 50 peak-cash peak-E-ZPass 45

40

35 March 2001 Time of Day 30 Pricing - PANYNJ

25

20 percentusage(%) 15

10

5

0

9 9 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 -9 -9 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 v c b r r n l g t v c n r r y l g p o e e a p u Ju u c o e a a p a Ju u e N D F M A J A O N D J M A M A S month FIGURE 2 Cash versus E -ZPass usage over time – all crossings, peak and peak -shoulder s

TABLE 3 Trends in E -ZPass and Cash Usage between January 2000 and August 2001 % share of % share of % share of % share of Payment -time % % demand demand demand demand period Change Change (Jan -00 ) (Aug - 00 ) (Oct 00 -Mar 01 ) (Apr -Aug 01 ) All Crossings Cash pre -peak 6. 8 5.78 -15.00 5.52 4.81 -12.86 Cash post -peak 8.44 6.51 -22.87 6.55 5.66 -13.59 E-ZPass pre -peak 8.19 10.85 32.48 9.02 10.24 13.53 E-ZPass post -peak 10.61 12.91 21.68 9.31 11.94 28.25 Cash -Peak 25.71 18.55 -27.85 24.9 17.2 -30.92 E-ZPass peak 40.18 45.39 12.97 43.25 45.6 5.43 Lincoln Tunnel Cash pre -peak 6.56 4.96 -24.39 5.91 5. 19 -12.18 Cash post -peak 9.92 7.09 -28.53 8.17 6.77 -17.14 E-ZPass pre -peak 6.65 9.79 47.22 8.45 10.14 20.00 E-ZPass post -peak 10.70 13.8 28.97 12.61 13.74 8.96 Cash -Peak 28.23 18.39 -34.86 21.81 17.81 -18.34 E-ZPass peak 37.78 45.96 21.65 42.96 46.52 8.29 Cash pre -peak 8.46 6.56 -22.46 7.54 6.45 -14.46 Cash post -peak 10.47 7.67 -26.74 8.79 7.46 -15.13 E-ZPass pre -peak 7.26 10.81 48.90 9.36 11.14 19.02 E-ZPass post -peak 9.39 12.32 31.20 11.01 12.35 12.17 Cash -Peak 32.19 22.08 -31.4 1 26.43 21.71 -17.86 E-ZPass peak 32.2 40.55 25.93 36.85 40.91 11.02

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ANALYSIS OF THE CHAN GES AFTER THE TIME -OF -DAY PRICING INITIATIVE In this section, changes in traffic patterns after the time -of -day pricing initiative are investigated. Seasonal variati on shows differences depending on the crossing, time -of -day , and most crossings have their own seasonal patterns depending on time -of -day after the time -of -day pricing initiative. Therefore percent shares of peak/off peak period traffic are compared separa tely for each facility, for the same months of years 2000 and 2001. The methodology can be summarized as follows. 1. Analysis of total daily traffic trends among weekdays and weekends. 2. Analysis of changes in abso lute and percent shares of AM/PM peak and peak -shoulder traffic after the time -of -day pricing initiatives for weekdays/weekends and cars/trucks separately. 3. Application of statistical tests to determine the significance level of these changes Changes in Total Daily Traffic Distribution In the first pa rt of the before and after analysis, changes in total daily traffic is investigated. FIGURE 3 shows the average weekday traffic trends, from January 2000 to August 2001. The daily traffic distribution during weekdays is stable most of the time until Septem ber 2001. The highest traffic at PANYNJ facilities is observed at the George Washington Bridge (GWB) upper level with a volume of 80,000 veh/ day, and the lowest traffic is observed at Bayonne Bridge with a volume of 10,000 veh/ day. The second highest traff ic is observed at GWB lower level followed by Lincoln Tunnel, Goethals Bridge, Holland Tunnel, Outerbridge Crossing and GWB_ PIP.

Total Daily Volume for each bridge/tunnel

80000 GWB upper level 70000 Lincoln 60000 GWB lower level

50000 Holland Outerbridge 40000 Goethals volume 30000 GWB_PIP

20000

10000 Bayonne

0

0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 - r- - - - t------l- - n a y n g c v n b pr n u p Ja a u u o Ja e u J e M M J A O N F A J S month

FIGURE 3 Average weekday traffic on PANYNJ c rossings

Changes in Peak and Peak -Shoulder Traffic In order to determine whether or n ot the changes in percent share of peak and peak - shoulder traffic at each crossing is statistically significant, 1 -tailed paired two -sample t -tests are

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conducted at 95% confidence level. The analysis is performed for each cr ossing, each vehicle type (cars, and trucks), each day type (weekdays, weekends) separately. Since the main objective is to assess whether or not time -of -day pricing is able to shift traffic from peak to peak -shoulder , alternate hypothesis is that percent share of peak -shoulder traffic after the time -of -day pricing initiative is larger than the percent share of peak -shoulder traffic before the time -of -day pricing. This hypothesis can be tested by conducting 1 - tailed t -test. For the analysis of the PANYNJ time -of -day pricing initiat ed in March 25, 2001, time period April 2000 -August 2000 is compared with the time period April -August 2001. This pre - September 11, 2001 data set helps the research team to isolate the impacts of September 11 tragedy from the im pacts of time -of -day pricing initiative. For each data set, 15 data points are considered (5 months with 3 weekdays in each) for weekday analysis. In the case of weekends, number of data points is 10. In order to decrease seasonal variations, same months a re compared and percent share of periods are used, instead of absolute traffic flows. For weekday analysis, pre -peak, post -peak and peak hours during both AM and PM periods are analyzed separately for each vehicle type at each crossing. For each peak -shou lder period a one hour time period considered. For the “AM pre -peak” is taken as 5 -6AM, “AM post -peak” is taken as 9 -10AM, “PM pre -peak” is taken as 3 -4PM, and “PM post -peak” is taken as 7 -8PM. For the weekend study, time period 11AM -12PM is taken as pre -peak and 8 -9PM is taken as post -peak. Changes in Peak and Peak -Shoulder Car Traffic TABLE 4 summarizes t-tests results (p -values) for each crossing for weekday and weekend car traffic after the PANYNJ time -of -day pricing initiative. Shaded cells represent the facilities experiencing statistically significant changes at 95% Confidence Level. A nalysis results for passenger cars indicate that increase in percent share of car traffic during AM pre -peak is statistically significant for all crossings except Outer bridge Crossing, whereas during AM post -peak period the increase in percent share of car traffic is statistically significant for GWB lower level, Holland Tunnel and Lin coln Tunnel. During weekday PM period, percent share of pre -peak traffic increased stat istically significant ly for Bayonne Bridge, Goethals Bridge, Holland and Lincoln Tunnel s, whereas percent share of post -peak traffic increased for Goethals Bridge, GWB_PIP and Outerbridge Crossing. These findings demonstrate that there is a statistically s ignificant increase in peak -shoulder traffic, and this increase is generally towards pre -peak periods. Unlike weekday, weekend traffic analysis indicates that the peak -shoulder car traffic did not change statistically significantly for any of the crossings , except GWB_PIP after the time -of -day pricing initiative . As shown in TABLE 4, for some of the crossings and time periods , percent share of peak - period traffic increased after the time -of -day pricing. To investigate in detail this different trend in peak -period car traffic, first the facilities experiencing reductions i n peak -period car traffic are investigated followed with the analyses of facilities that experienced increased peak -period percent share of car traffic after the time -of -day pricing initiat ive. Facilities Experiencing Decrease in the Peak -Period Car Traffic: As shown in TABLE 4, percent share of peak -period car traffic decreased for all crossings except Bayonne Bridge (PM peak , weekend ), Holland Tunnel (AM and PM peak , weekday ) and Lincoln T unnel (AM and PM peak , weekday ). However, only the reduction in GWB lower and upper level s is statistically significant on both weekdays and weekends . Th ese results indicate that even though time -of -day pricing had an impact on reducing peak -period traffic , this reduction is not statistically significant for most of the crossings.

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TABLE 4 P -Values for P eak /Peak -should er Car Traffic Change after Time -of -day Pricing Initiative at PANYNJ Weekday Weekend Facility 5-6A.M. 6-9A.M. 9-10 A.M. 3-4 P.M. 4-7 P.M. 7- 8 P.M. 11 -12 P.M 12 - 8 P.M. 8-9 P.M. Bayonne 0.017* 0.0007** 0.1555* 0.0034* 0.0072* 0.150* 0.468* 0.0065* 0.494* Bridge (S) (S) (NS) (S) (S) (NS) (NS) (S) (NS) Goethals 0.021* 0.0998** 0.4045* 0.029* 0.2323** 0.011* 0. 096* 0.0767** 0.219* Bridge (S) (NS) (NS) (S) (NS) (S) (NS) (NS) (NS) GWB Lower 0.0028* 0.4930** 0.0295* 0.0762* 0.0444** 0.138* 0.089* 0.0223** 0.4563* Level (S) (NS) (S) (NS) (S) (NS) (NS) (S) (NS) 0.0057* 0.0645** 0.2995* 0.0986* 0.4274** 0.029* 0.0226* 0.2279** 0.111* GWB_ PIP (S) (NS) (NS) (NS) (NS) (S) (S) (NS) (NS) GWB Upper 0.0001* 0.1328** 0.2256* 0.3101* 0.0029** 0.0768* 0.077* 0.0322** 0.151* Level (S) (NS) (NS) (NS) (S) (NS) (NS) (S) (NS) Outerbridge 0.0871* 0.3814** 0.3766* 0.4523* 0.0649** 0.0107* 0.080* 0.1619** 0.2799* Crossing (NS) (NS) (NS) (NS) (NS) (S) (NS) (NS) (NS) Holland 0.002* 0.0879* 0.0495* 0.0144* 0.0160* 0.115* 0.054* 0.0696* 0.1464* Tunnel (S) (NS) (S) (S) (S) (NS) (NS) (NS) (NS) Lincoln 0.0004* 0.4700* 0.0064* 0.0008* 0.3153* 0.102* 0.285* 0.3458* 0.199* Tunnel (S) (NS) (S) (S) (NS) (NS) (NS) (NS) (NS) Note: (1) GWB: George Washington Bridge, PIP: Palisades Interstate Parkway (2) *: increase in percent share of traffic, **: decrease in percent shar e of traffic (3) Shaded cells refer to the facilities experiencing statistically significant changes at 95% confidence level Facilities Experiencing an Increase in the Peak Period Car Traffic : As shown in TABLE 4, peak -period percent share o f car traffic incre ased for Bayonne Bridge (PM pe ak, weekend), Holland Tunnel (AM/PM peak, weekend), and Lincoln Tunnel (AM/PM peak, weekend). Among these crossings only the change in Bayonne Bridge (PM peak, w eekend) and Holland Tunnel (PM peak) are stati stically significant. At first glance, these results are counterintuitive based on the main incentive of time -of -day pricing which aims to decrease peak -period traffic. To interpret these results in the context of time -of -day pricing , increase in peak -shou lder traffic should be analyzed, as well. If the increase in peak -shoulder percent share of car traffic at these crossings are higher compared to the increase in the peak period percent share of car traffic, it can be claimed that the percent share of thes e specific crossings are increasing irrespective of the time -of -day pricing initiative. In TABLE 5, comparison o f the increase in peak and peak -shoulder periods’ percent share of car traffic on weekdays and weekends is shown. The peak -shoulder percent sha re of car traffic is calculated as the sum of percent share of traffic at pre -peak hours and post -peak hours, since the users shifting from peak period can either prefer pre -peak hours or post peak hours. The analysis results indicate that on weekdays and weekends for all crossings, the increase in peak -shoulder percent share of car traffic is much higher compared with the increase in the peak period percent share of car traffic, supporting the hypothesis that the peak -shoulder percent share of traffic is increasing at a higher rate compared to peak period percent share of traffic, and the time -of -day pricing is effective in spreading the traffic demand to peak -shoulder s. However, increase in weekend percent share of peak and peak -shoulder car traffic is muc h lower compared to the weekday percent share of traffic. This result supports the hypothesis that the peak -shoulder percent share of traffic is increasing at a higher rate compared to peak period percent share of traffic, and time -of -day pricing is effect ive in spreading the traffic to peak -shoulder s. However the increase in both peak and peak -shoulder percent share of traffic is statistically insignificant on weekends.

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TABLE 5 Compar ison of Weekday & Weekend, P eak and Peak -shoulder Car Traffic after the Time -of -day Pricing Initiative at P ANYNJ Percent Share of Demand Percent Share of Demand Percent Share of Demand Weekday Morning Weekday Afternoon Weekend peak peak -sh peak pe ak -sh peak peak -sh percent percent % Change percent percent % Change percent percent % Change share share share share share share peak peak peak Bef. Aft. Bef. Aft. Peak Bef. Aft. Bef. Aft. Peak Bef. Aft. Bef. Aft. Peak sh. sh. sh. Bayonne ------32.79 33.97 7.36 7.73 3.60 5.03 49.48 50.04 10.4 8 10.6 6 1.1 4 1.71 Holland 16.67 16.78 8.73 9.30 0.66 6.53 15.89 16.38 10.11 10.56 3.08 4.45 42.83 43.2 10.7 2 10.92 6 0.86 1.98 Lincoln 23.53 23.61 9.85 10.55 0.34 7.11 11.86 11.90 9.85 10.55 0.34 7.11 46.81 46.95 11.11 11.20 6 0.29 0.80

Changes in Peak and Peak -shoulder Truck Traffic The statistical significance test results for pre -peak, peak, post -peak and overnight truck traffic are shown in TABLE 6 . The increase in truck traffic is statistically significant during AM pre -peak, and PM post -peak periods fo r most of the crossings. During overnight hours and peak - periods, on the other hand, some crossings experience increase while others experience decrease in the percent share of truck traffic. These results indicate that even though time -of -day pricing give truckers an incentive to shift their travel periods, it might not be the only factor affecting the truckers’ travel patterns. D eclining truck traffic may have also been a precursor to the economic recession that began in the New York -New Jersey region in 2001. Moreover, from commercial vehicle surveys ( 7) it is observed that their travel decisions are, in essence, determined by customer needs and various operational constraints. As a result, they will switch to off -peak periods only if the receivers are willing to accept off -peak deliveries. In addition, since truck dispatchers pass the price increases on to the customer, they are expected to be less concerned about the toll levels ( 7). Unlike weekday traffic, weekend peak -shoulder truck traffic did not change in a statistically significant manner for any of the crossings, except Goethals Bridge after the time -of -day pricing initiative. However, decrease in peak -period truck traffic is found to be statistically insignificant on weekends. TABLE 6 P-Values for P eak /Peak -should er Truck Traffic Change after Time -of -day Pricing Initiative at PANYNJ Weekday Weekend Facility 0-5 A M 5-6 A M 6-9A M 9-10 AM 3-4 P M 4-7 PM 7- 8 PM 11 -12 P M 12 - 8 PM 8-9 PM Bayonne 0.012 ** 0.0354* 0.0204* 0.0503** 0.3697* 0.0477** 0.0165* 0.4281* 0.292** 0.0569* Bridge (S) (S) (S) (NS) (NS) (S) (S) (NS) (NS) (NS) Goethals 0.22 * 0.1746* 0.0492** 0.0065** 0.0130* 0.0015** 0.0026* 0.0215* 0.1822** 0.0714* Bridge (NS) (NS) (S) (S) (S) (S) (S) (S) (NS) (NS) GWB Lower 0. 01** 0.0494* 0.0444* 0.3261** 0.3969* 0.3384** 0.0606* 0.0661* 0.1273** 0.2046* Level (S) (S) (S) (NS) (NS) (NS) (NS) (NS) (NS) (NS) GWB Upper 0.0004 * 0.0011* 0.04 80** 0.0829** 0.3510* 0.3494** 0.0429* 0.538* 0.0487** 0.2408* Level (S) (S) (S) (NS) (NS) (NS) (S) (NS) (S) (NS) Outerbridge 0.079 * 0.3368* 0.0248** 0.1821** 0.0164* 0.0076** 0.0057* 0.1853* 0.1898** 0.0679* Crossing (N S) (NS) (S) (NS) (S) (S) (S) (NS) (NS ) (NS) Holland 0.01 * 0.0074* 0.0020** 0.1662** 0.2423* 0.0649** 0.0006* 0.3318* 0.0919** 0.4247* Tunnel (S) (S) (S) (NS) (NS) (NS) (S) (NS) (NS) (NS) Lincoln 0.0 2* 0.0018* 0.0347** 0.0010** 0.0294* 0.1297** 0.1871* 0.1414* 0.0686** 0.3086* Tunnel (S) (S) (S) (S) (S) (NS) (NS) (NS) (NS) (NS) Note: (1) GWB: George Washington Bridge, PIP: Palisades Interstate Parkway

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(2) *: increase in percent share of traffic, **: decrease in percent share of traffic (3) Shaded cells rep resent the facilities experiencing statistically significant changes at 95% CL

CONCLUSIONS The traffic analyses discussed here have attempted to gain insight into the behavior of users as response to time -of -day pricing. While investigating the travel pat terns at PANYNJ facilities, the research team tried to differentiate seasonal traffic changes taking place at each crossing (when no external factors like a new toll schedule imposed to the system), from changes in travel patterns due to the time -of -day pr icing initiative. Second, the trend in E -ZPass us age by time -of -day was investigated to understand whether the changes in travel patterns were due to the difference of toll discounts between cash and E -ZPass users, and more importantly, expedited crossing at toll booths. Then, based on the findings obtained from facilit y-specific properties, a before -after analysis is performed for daily average traffic and hourly traffic for each PANYNJ facility. The summary of the findings are presented below. 1. Seasonal f actor analyses show that before time -of -day pricing, there is a statistically significant seasonal variation in the Bayonne Bridge, Goethals Bridge and Lincoln Tunnel PM peak traffic . For all other facilities, seasonal variation is statist ically insignific ant at all periods. 2. Before 9/11 , time -of -day pricing initiative caused a reduction in the percent share of cash users and an increase in the percent share of E -ZPass users. For both peak and peak -shoulder s, E - ZPass users share a higher percent than cash u sers. During peak -shoulder s for each crossing, cash user percent share ranges between 6% and 8% with a decreasing trend; whereas E -ZPass user percent share ranges between 10% and 15% with an increasing trend. A similar trend is observed in the peak period E-ZPass share as well. For every crossing analyzed in this study, E -ZPass user percent share ranges between 44% and 55%, and cash user percent share ranges between 18% and 30%. Like peak -shoulder s, there is an increasing trend in E -ZPass user share and a d ecreasing trend in cash user share after the introduction of time -of -day pricing. 3. The findings obtained from the before -after analysis using statistical tests indicate that the time -of -day pricing initia ted in March 25, 2001 resulted in an increase of th e percent share of peak -shoulder traffic for both trucks and cars during weekdays. Specifically, percent share of pre - peak period (A M and P M) car traffic increased statistically significantly after the time -of -day pricing. Moreover, truck traffic percent s hare experienced statistically significant changes during AM pre -peak, and PM post -peak periods during weekdays. T his observation is suppor ted by the findings of Muriello et al. ( 8), which indicate that from 2000 to 2001, 6 -7AM traffic declined by 5.7%. However, during weekends, time -of -day pricing did not have a statistically significant impact on the peak -shoulder car/truck traffic percent shares for any of the crossings with the exception of GWB_PIP and Goethal s Bridge, respectively . 4. Peak period car traffic analysis shows a similar trend on weekday and weekends. Among the crossings experiencing a decrease in percent share of peak -period car traffic, only the decrease in GWB lower and upper levels is statistica lly significant. In addition, peak -period percent share of car traffic increased at Bayonne Bridge, Holland and Lincoln Tunnel s. A closer look at travel patterns for these crossings show that rate of increase of percent share of “ peak -shoulder ” is much hig her compared to the rate of increase of percent share of “peak” period car traffic. These results support that time -of -day pricing had a statistically significant impact on spreading peak traffic to peak -shoulders on weekdays . This impact is statistically insignificant on weekends. 5. The analysis results for weekday peak -period truck traffic show that truck traffic decreased statistically significant ly in morning peak period for some of the crossings after the

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time -of -day pricing initiative. The decrease in weekend peak traffic is found to be statis tically insignificant . Traffic impact analysis for passenger cars suggest that PANYNJ time -of -day pricing program has been successful in shifting car travel demand to earlier hours in both morning and afternoon pea k-periods on weekdays. This major finding suggests that $1.00 discount during off - peak periods does not provide an incentive to shift to later hours for travelers who have neither flexibility nor desire to travel later. In add ition, traveler survey results state that majority of passengers have higher flexibility to arrive early (19.6 minutes on average) than to arrive late (12.2 minutes on average) ( 7) supporting the traffic impact analysis results. Weekend traffic, on the other hand, does not hold any statistically significant difference by time -of -day during peak and peak -shoulder periods, stating that the travel pattern on weekends is inelastic to $1.00 toll differential. These m ajor findings suggest that late evening and weekend car traffic into NYC may be very inelastic to the current toll differential. Traffic impact analysis for truck traffic; on the other hand, indicate that even though PANYNJ time -of -day pricing give trucke rs an incentive to shift their travel periods, it is not the only factor affecting the truckers’ travel pattern. D eclining truck traffic on peak -shoulders may have also been a precursor to the economic recession that began in the New York -New Jersey region in 2001. Moreover, as obtained commercial vehicle surveys, ( 7), for truck dispatchers, other than saving on tolls by shifting to off -peak hours, there are other concerns like on -time delivery, customer needs and various operational constraints that determine their decisions. As a result, they would switch to off -peak periods only if the receivers are willing to accept off -peak deliveries. The overall traffic impact analysis of PANYNJ time -of -day pricing program state that travelers’ value of travel time (willingness to pay), their price elasticity, and concerns o ther than saving money on tolls are among the other important factors that will affect the success of pricing policies in this and similar other facilities.

ACKNOWLEDEMENTS This project was sponsored by a grant from the Federal Highway Administration’s V alue Pricing Program. Additional support was pr ovided by USDOT UTC program and the Port Authority of New York and the New Jersey Department of Transportation. Special thanks are due to Mark Muriello and Danny Jiji (PANYNJ) for their continuing support and assistance throughout this project. The opinions and conclusions presented are the sole responsibility of the authors and do not reflect the views of sponsors and other participating agencies.

REFERENCES 1. PANYNJ, 2003 Annual Airport Traffic Report www.panynj.gov/aviation/ewrcoverfram. .HTM , Accessed 3/16/05. 2. Port Authority of NY & NJ Toll Rates. http://www.panynj.gov/tbt/ TOLL_RATES. pdf . Accessed 2/25/05. 3. Ozbay, K., D. Ozmen -Ertekin, O. Yanmaz -Tuzel, J. Holguin -Veras "Analysis of the Value Pricing Impacts at NY/NJ Port Authority Facilities” Accepted for publication in Transportation Research Record: Journal of the Transportation Rese arch Board, TRB National Research Council, Washington, D.C., 2005.

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4. Edward J. Bloustein Schoo l of Planning and Public Policy. New York –New Jersey Vehicular Crossings One Year after September 11, 2001 : A Vehicular Tran sportation System in Transition. Rutger s, University Travel Trends Newsletter , Vol.2, Spring 2003 . 5. Port Authority of New York New Jersey, 2003 6. R.E. Walpole, R. H. Myers, and S.L. Myers. Probability and Statistics for Engineers and Scientists . Prentice Hall International, Inc, Sixth Edition,1998. 7. Holguín -Veras, J., K. Ozbay and A. De Cerreño “Evaluation Study of Port Authority of New York and New Jersey’s Time -of -day Pricing Initiative” FHWA -NJ -2005 -005. FHWA, U.S. Department of Transportation, 2005. 8. M.F. Muriello, and D. Jiji. The time -of -day Pri cing Toll Program at the Port A utho rity of New York & New Jersey: Revenue for Transportation Investment and Incentives for Traffic Management. CD -ROM. Transportation Research Board, National Research Council, Washington, D.C., 2004 .

TRB 2006 Annual Meeting CD-ROM Paper revised from original submittal.