Effect of Time-of-day Pricing Initiative at NY/NJ Port Authority Facilities on Transit Ridership

Transcription

1 Effect of Time-of-day Pricing Initiative at NY/NJ Port Authority Facilities on Transit Ridership Kaan Ozbay, Ph.D., P.E. Associate Professor, Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, 623 Bowser Road Piscataway, NJ USA, Tel: (732) Fax: (732) Mustafa Anil Yazici, M.Sc. (Corresponding Author) Graduate Research Assistant, Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, 623 Bowser Road Piscataway, NJ USA, Tel: (732) Fax: (732) 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 Phone: Fax: Word count: Figures + 7 Tables = 7367 Abstract: 158 Submission Date: August 1, 2005 Paper Submitted for Presentation and Publication at the Transportation Research Board s 85 th Annual Meeting, Washington, D.C., 2006

2 ABSTRACT In this study, the effects of the Port Authority of New York and New Jersey s (PANYNJ) time-of-day pricing initiative on transit ridership were investigated. The program was introduced in March 25, 2001 and effective in two tunnels and four bridges that connect New Jersey with New York City. The transit ridership data between New Jersey and New York, which were composed of New Jersey Transit and PATH ridership volumes, were analyzed through statistical tests. Main concerns in the analysis were the effect economic decline and September 11, which happened simultaneously with the time-of-day pricing program and could also affect transit ridership. Results show that even though there was a decrease in transit ridership starting from 2001, the reason was found to be mainly the economic decline, and partly the inconvenience and increased transit travel time due to the infrastructure loss after September 11. There was no indicator of a negative effect of time-of-day pricing on transit ridership. 2

3 INTRODUCTION The Port Authority of New York and New Jersey s (PANYNJ) time-of-day pricing program was introduced at the two tunnels and four bridges that connect New Jersey (NJ) with New York City (NYC) on March 25, The program generated incremental revenue to support an intermodal capital investment program, and also produced traffic management benefits to address congestion (1). This study focuses on the impacts of the pricing program on transit ridership between NJ and NYC, which can be found in detail at the Evaluation Study of PANYNJ s Time-of-day Pricing Initiative final report (2). The toll change for cars introduced with time-of-day pricing initiative was 2.00$ (4.00$6.00$) for cash users between peak and off-peak period. Again among car riders, E-Zpass users paid 1.40$ more (3.60$5.00$) during peak hours and 0.40$ more (3.60$4.00$) for off-peak period. Trucks paid the same difference of 2$ (4.00$6.00$) for cash use, however, truck E-Zpass users paid 2.40$ (3.60$6.00$) more for peak and 1.40$ (3.60$5.00$) more for off-peak hours. The transit facilities of interest (New Jersey Transit, PATH, New York Waterway Ferry) had no fare changes during the implementation period of time-of-day pricing. Three distinct data sets are used for the analysis. The first one was provided by New Jersey Transit, and consists of monthly, and route based bus and railway ridership data from 1986 to The second data set contains ferry ridership data consisting of monthly route based ridership data from 1999 to The third data set is comprised of PATH ridership data, which is more aggregate, available yearly, without any route specific information for a time period between 1987 and 2003, and weekday station based averages for years 2000 through Statistical tests are performed for the analysis, in after filtering the datasets to isolate the possible coupling effects of September 11 terrorist attacks on transit ridership in NYC. Description of the Transit System: Infrastructure and Ridership New Jersey Transit s commuter rail and Interstate bus services linking NJ and NYC and the Port Authority s Trans Hudson (PATH) system are the systems that can be used as an alternative to the PANYNJ facilities. Private bus carriers and several ferry routes also serve as a potential substitute for private automobile use. NJ Transit serves almost all the major population centers in NJ, with an access to New York Penn Station, which is at 32 nd street, between 7 th and 8 th avenues in Manhattan, NYC. NJ Transit lines are heavily used by workers living in NJ for commuting to NYC. Although PATH serves relatively a smaller area compared to NJ Transit, many riders use PATH because of its direct access to downtown Manhattan. That is why, riders going to downtown Manhattan may even prefer using PATH lines, changing from NJ Transit to PATH at Newark or Secaucus junction. The preference of New York bound transit riders for PATH is also clearly observed after 9/11. Due to the lack of direct access to downtown Manhattan, PATH Newark ridership faced the highest percentage of decrease after the destruction of World Trade Center (WTC) station and closing down of the Exchange Place station serving to downtown (7). However, ferry lines experienced a sudden increase after 9/11 since ferries acted as a substitute to PATH line crossing Hudson River. 3

4 PAST WORK There are not many studies about the impacts of road pricing on transit ridership. NYC, in particular, exhibits a unique character with its wide metropolitan area, with many daily commuters from suburbs and even neighboring states. Research results for United States indicate that road pricing tend to impact mostly departure times and route choice with a modest impact on high occupancy vehicle (HOV) usage rather than a transit impact. Results from Europe and Asia similarly indicate shifts in travel time and routes, shifts to HOVs, and, more relevant for this study, a small shift to transit. This is likely due to more widespread usage of transit systems in Europe and Asia. San Diego's value pricing project (FasTrak Express Lanes) on Interstate 15, a three-year demonstration that allows single-occupant vehicles (SOVs) to use the existing high occupancy vehicle (HOV) lanes on I-15 for a fee, has experienced a relative success in promoting bus usage, through new express bus service being supported with revenue raised from the ExpressPass/FasTrak program. The new route has provided expedited service for existing transit users, but has not attracted peak direction travelers from the I- 15 main lanes (FHWA, 2000). In San Diego, improving bus service with revenues provided an alternative travel option to I-15 travelers, which makes it different from the New York City case (in which no new transit alternatives were created). Although using FasTrak revenue to improve transit service along the I-15 corridor was one of the San Diego demonstration project s objectives, only a very small percentage of respondents to a telephone survey of 1500 commuters supported funding transit. One year later, in the Fall of 1998, the survey was re-conducted and 5% of respondents indicated that FasTrak revenue should be used to fund the Inland Breeze express bus or other transit service in the I-15 corridor (5). No transit ridership effects have been noted for the Lee County (Florida) LeeWay variable pricing program or Houston Texas QuickRide program. Similarly, the conclusion of the SR-91 study is that the Express Lanes have had no perceptible effect on either commuter bus or commuter rail traffic in the corridor (6). As far as the European experience is concerned, following the implementation of the Trondheim (Norway) toll ring in 1991, there was a 10% decrease in traffic passing the ring both in the peak and off-peak charging hours. The traffic increase in the evenings and weekends (with no fees) was slightly above 8%. Travel surveys indicated a slight increase in the use of public transport and cycling. (The toll ring effects are difficult to single out because of parallel improvements in public transport and in the bicycle road network.) (4). DATA ANALYSIS The analyses are based on three distinct data sets mentioned below. New Jersey Transit Ridership Data: Data were obtained from New Jersey Transit, including monthly and quarterly ridership counts from 1986 to 2004 for services including bus and commuter rail services from NJ to NYC. Ferry Ridership Data: Ferry ridership data were obtained from PANYNJ, including monthly ridership counts from 1999 to 2004 for services including the ferry operation services from NJ to NYC as well as inter-state ferry routes for NYC. The data are available in route-based format including information for weekdays and weekends 4

5 separately. For the analyses, the routes not serving NJ to NYC were not considered in order to solely focus on the specific trips between NYC and NJ. PATH Ridership data: PATH (aggregate) ridership data were in two different formats: 1.Annual total passenger traffic for an 11 even year time period between 1992 and Weekday annual average of passenger traffic between 2000 and Monthly PATH data were not available, thus PATH had to be excluded from monthly tables, figures and analysis throughout the study. It is important to highlight two main issues related to the use of these data sources. The first issue arises because the PATH data contain trips within NJ, as well as NYC bound trips. Unfortunately, since the data is not route based; it is not possible to accurately separate NYC bound trips starting and ending at NJ. Moreover, the main PATH stations in NJ also serve as connecting hubs between ferry and bus services (8,9) for riders that are traveling to NYC. Thus, these trips constitute a portion of trips from NJ to NYC (and vice versa for the opposite direction). It is thus reasonable to assume that NYC bound trips are the major part of the PATH ridership. In summary, since there are no data that could be used to extract trips starting and ending at NJ from the PATH data set, raw data without attempting to make assumptions about the percentages of these two types of trips namely, NJ to NYC and NJ to NJ, were used. However, due to the reasons described above and the close interaction between PATH and other transit modes serving NYC, using total PATH ridership data can be considered to be the most straightforward approach that does not employ any assumptions that cannot be easily supported in the absence of reliable origin-destination data for PATH trips. Descriptive Analysis Two different types of descriptive analyses were conducted to assess the impacts of the time-of-day pricing initiative on transit ridership: 1.Analysis of Annual Transit Ridership: The purpose of this approach is to compare annual ridership data and identify the trends in transit ridership on an annual basis. 2.Analysis of Monthly Transit Ridership: Raw monthly data is considered in order to take a closer look at the changes in transit ridership on a monthly basis for the months between 2000 and Analysis of Annual Ridership In the financial year defined between July 1, 2000 and June 30, 2001, NJ Transit ridership reached an all-time high for NYC bound commuter rail service- an average of 90,000 daily riders, including the transfers from Newark and Hoboken to NYC (7). Total annual ridership on the PATH system peaked at 74 million trips, was its highest level since PATH took over the operations of the Hudson and Manhattan Railroad in These were the peak points of increasing demand for trans-hudson traffic, which followed the same pattern with the robust growth of NYC employment until 1990 s. Ridership in 2001 was expected to surpass the level reached in However, September 11 did not allow to test this expectation and ridership changed 0.8%, 6.6%, - 2.9% in 2001, 2002, and 2003 respectively, after 5.5% increase in (See TABLE 1) 5

6 Monthly data showed that ridership at all transit modes, except physically damaged PATH lines, continued to increase until September 11. Transit ridership peaked in July 2001 (4 months after the time-of-day pricing initiative); though its rate of growth was lower than the ones in previous years. TABLE 1 Transit Annual Ridership (Thousands) 1 ROUTE New Jersey TRANSIT RAIL* Rail Year-to-year Change % 5.50% 1.60% 4.80% 6.00% 6.20% 6.40% 8.00% 2.80% -4.20% -1.30% 3.10% New Jersey TRANSIT BUS Bus Year-to-year Change % 4.80% 0.60% 2.60% 2.80% 2.20% 1.50% 2.50% 1.70% -3.10% -1.90% 0.60% New York HARBOR FERRY FerryYear-to-year Change % 27.60% 54.10% -2.49% % PATH (2) PATH Year-to-year Change 1.41% 5.13% 0.14% 2.25% 2.57% 4.47% 3.60% 10.03% -5.80% % -7.78% %17 OVERALL TRANSIT RIDERSHIP Overall Year-to-year Change % 4.99% 0.67% 2.95% 3.39% 3.56% 6.03% 5.50% 0.79% -6.63% -2.88% 1.54% 1 Includes in-new Jersey Traffic 2 Estimated Value TABLE 1 exhibits that the Ferry ridership shows a sudden increase after 2001 accompanying a sharp decrease in PATH ridership. However, ferry ridership experiences a sudden decrease in This can be interpreted by the full recovery of the PATH facilities by the end of 2003, since ferries acted as a substitute of PATH, when the PATH line passing through NJ to NYC was not functional due to physical damage caused by in WTC collapse. So the full recovery to pre-9/11 conditions may have caused the shift of PATH passengers from ferry back to PATH. One thing to note is that the 2004 PATH ridership data obtained from NJ Transit annual report were only available for the fiscal year, namely until June The ridership data for the rest of the year were estimated using the monthly percentage trends. Information published by PATH in its news releases state that the PATH ridership has increased in 2004 and even exceeded the projected values after the re-opening of WTC and Exchange Place stations and PATH recovered to its pre-9/11 ridership (10). NJ Transit Quarterly Report also states that PATH ridership increased 17% between fiscal years 2003 and Thus, it is clear that the changes in 1 Since all the analysis in the report is performed for the calendar year instead of fiscal year, an assumption made to get the 2004 calendar year ridership. Same increase between the fiscal years was applied for calendar year ridership increase to estimate the 2004 PATH ridership. This assumption is reasonable and on the safe side assuming that the positive trend in PATH ridership will continue for the rest of the months in

7 transit trend is more related to economic and other external factors such as the events of 9/11 and loss of some critical infrastructure than to the time-of-day pricing. The 2001 economic recession, which was a continuation of the economic slowdown that started in the summer of 2000, also marks the start of the changing trend from increase to decrease in NJ Transit ridership. The same decreasing trend after 2001 is also observed in the PATH ridership as shown in TABLE 1. As shown in TABLE 1, PATH shows a significant decrease in However by September 2001, a total of 52 million PATH trips is recorded well ahead of the level reached at the equivalent time in 2000 (Travel Trends, Winter 2002). Thus, even a very simple analysis of the data states that the time-of-day pricing which was introduced in March 2001 did not seem to have a noticeable negative impact on transit ridership. Moreover, the weekday averages in the pre 9/11 period are higher than the corresponding 2000 values. The transit service restrictions to NYC and the reduction in economic activities, e.g. destruction of facilities and relocation of businesses (11) following September 11 can be said to reduce the total PATH ridership. In aggregate level, it is hard to draw a conclusion about the effect of time-of-day pricing initiative starting in March 2001 since the beginning of economic slowdown resulting in decrease in central business employment, which is a key factor determining transit demand. Time-of-day pricing initiative also coincides with 9/11, which undoubtedly affected the transit demand both as a result of the physical destruction of transit facilities and as a result of short term psychological impact on public transport usage. Analysis of Monthly Transit Ridership Monthly changes in transit ridership are shown in FIGURE 1. It can be observed that transit ridership exhibited an increasing trend until August 2001, and no immediate effect of time of the day pricing after March 2001 can be seen. Monthly trends demonstrate that time of the day pricing did not have a significant effect during the first 4-5 months after the beginning of the time-day pricing initiative. FIGURE 1 clearly shows the existence of a seasonal trend in terms of transit ridership. The demand reaches its minimum in winter, then by March ridership shows an increase and then stays at a relatively lower level until June but then starts an increasing trend in the summer until fall. Ridership reaches its highest peak in the fall. Until September 2001, 6 months after the beginning of the time-of-day pricing, the monthly ridership data continue to show the same seasonal trend observed in previous years. However, although this same trend cannot be observed in 2001, this usual pattern is observed again by Thus, this abnormal deviation from the usual pattern in 2001 can be explained by September 11 attacks. While investigating the effects of time of the day pricing, this fact was always taken into account. 7

8 22000 Overall Ridership Total Transit Ridership March 2001 Jan-00 Feb-00 Apr-00 May-00 Jul-00 Sep-00 Oct-00 Dec-00 Feb-01 Mar-01 May-01 September 2001 Jul-01 Months Aug-01 Oct-01 Dec-01 Jan-02 Mar-02 Apr-02 Jun-02 Aug-02 Sep-02 Nov-02 FIGURE 1 Monthly Overall Transit Ridership Trends Before and After Time-ofday Pricing (Excluding PATH data) Before performing more formal statistical analysis, effect of time-of-day pricing is studied by looking at the distribution of annual ridership over the months. This way, monthly patterns can be also investigated without the effect of the yearly change in total ridership, since the percent distribution for each month was calculated as the ratio of the total ridership for the time period considered for the specific year and the individual ridership for each month. TABLE 2 shows monthly percentages of monthly s between years 1998 and Note that 8-months period (January-August) for each year was employed in the calculations to eliminate the bias that will be introduced due to the unusual fluctuations in ridership for the time period after the events of 9/11. TABLE 2 Changes of Monthly Percentages of Transit Ridership for 1998 to 2001 Months / Years January 12.1% 12.3% 12.6% 14.1% February 11.6% 12.3% 13.2% 13.2% March 12.8% 14.0% 14.8% 14.5% April 12.8% 13.7% 13.6% 14.5% May 12.5% 13.4% 14.6% 15.1% June 12.8% 14.0% 14.6% 14.9% July 12.9% 13.7% 13.9% 14.7% August 12.6% 13.7% 14.7% 15.1% As seen in TABLE 2, 2001 does not show a different pattern than previous year and the percentage shares in 2001 for all the months after March until August with the exception of April, are either higher than or equal to the percentages of corresponding months in previous years. This means that the share of these months increased in

9 compared to This also supports the conclusion that the time-of-day pricing did not cause a significant reduction in demand for the months after its initiation. The events of 9/11, as shown in FIGURE 1, had a drastic effect on total transit ridership peaked and then dropped again. Both of these observations are mainly due to the fact that the 9/11 attacks destroyed main PATH facilities serving NYC, and subsequently PATH passengers shifted to NJ Transit and ferry services. That s why the analysis of monthly ferry ridership provides a complementary perspective. Ferry ridership increased after 9/11 and declined significantly after the reopening of PATH facilities in the second half of In 2002, overall monthly ridership shows a decrease as much as -6.63%, (TABLE 1). Thus, there is an overall decreasing trend in 2002 transit ridership that can be associated with the overall economic and physical conditions aftereffects of 9/11. Statistical Analysis of Transit Ridership Besides the descriptive analysis of ridership data, statistical tests are conducted to check the validity of the conclusions based on the descriptive analysis. For an unbiased analysis, it is necessary to differentiate fluctuations resulting from seasonal changes in ridership from the changes resulting from external effects such as economy, events of 9/11 or time-of-day pricing. To achieve this goal, the following scenarios are tested: 1. Case-1: Testing the changes in seasonal transit ridership: Significance of differences in transit ridership for different seasons, such as summer, winter, autumn, and spring, were tested 2. Case-2: Effect of economy and other changes that might affect demands on an annual basis was investigated 3. Case-3: Effect of time-of-day pricing: Differences in transit ridership before and after time of the day pricing. Analysis of Variance (ANOVA), or also called F-test, which is related to the widely used t-test, was employed for the analysis of Case-1 and Case-3 stated above. Paired t-test was employed for Case-2. With ANOVA differences between means of two or more groups can be tested, where in t-test only two groups can be tested. ANOVA gives a more reliable result with less probability of rejecting the null hypothesis that is true thus committing a type-i error, compared to multiple t-tests. Like t-test, ANOVA test results also give a statistical value found for the tested sample and a theoretical critical value according to the desired confidence level, above which a variation between the tested samples can be said to exist. For testing the variance among and between the seasons, two-way ANOVA test is employed. While conducting two-way ANOVA test, the data points are divided into relatively homogenous subgroups and significance tests are implemented within and across the each homogenous group. The aim is to determine the effect of each subgroup to the total variation. Percent shares of each data are used for the analysis and calculated using equation below. M i y i = *100 (1) M where: i i 9

10 i: Month index y : Percent share of month i i M : Average traffic during month i i Case-1: Testing the Changes in Seasonal Transit Ridership In Case-1, the seasonal variation of the ridership is investigated when no major external factors (i.e., the economic recession, 9/11, time-of-day pricing) are present. Two-way ANOVA test is employed by using a two-factor full factorial design without replications using the data sets shown in TABLE 3. A year is divided into calendar seasons, where the data sets for each season consist of percentage of monthly ridership for the whole year, calculated as stated in Equation-1 above. As stated before, using monthly shares instead of absolute values of riderships themselves, avoids biased results resulting due to annual ridership changes. The null hypothesis is chosen to be as no variation in transit share between seasons, and test is conducted for years 1998, 1999, 2000 and 2003, but 2001 and 2002 data are excluded from the analysis to isolate the analysis from effects of both time-of-day pricing in 2001 and 9 / 11. TABLE 3 An Illustration of Two-way ANOVA Structure of Case 1, an Example for 1998 December January February March April May June 8 July August September October November Changes among the seasons Changes within the seasons 95% confidence level is selected for seasonal variation ANOVA test. The ANOVA tests for all the other years, except 2000, show the existence of a seasonal variation, which is resented with test statistics in TABLE data are more representative of recent conditions in the sense that transit system recovered to its old condition by 2003 and economy started to pick up data can also be seen as an exception because it marks the beginning of the economic recession. Nevertheless, year 2000 statistics are found to be very close to critical statistical value at 95% confidence level, which shows that seasonality will still be observed at a slightly lower but still acceptable confidence level. For 1998, 1999 and 2003 where the overall system is quite stable, seasonal variations are observed in a consistent manner. Overall, in the absence of a major external factor between 1998 and 2003, the seasons are concluded to have similar trends for various years. So a conclusion of a seasonal variation can be drawn. 10

11 TABLE 4 Summary of ANOVA Test for Seasonal Variation Year Type of Variation F value F critical value Significance within seasons No 1998 among seasons Yes random error within seasons No 1999 among seasons Yes random error within seasons No 2000 among seasons No random error 2001 Excluded from the analysis 2002 Excluded from the analysis within seasons No 2003 among seasons Yes random error Case-2: Effect of Economy on Annual Transit Ridership For this case annual riderships are analyzed to understand if increase for two consecutive years before time of the day pricing initiative is experienced. Contrary to Case-1, riderships are taken as their original numbers rather than the percentages, since the aim of the test is to detect any absolute demand change. The tested hypothesis is that there is no statistically significant ridership difference between two consecutive years. To investigate this hypothesis, a time period of 1998 to 2001 are analyzed so that the combined effects of economy and 9/11 and time-of-day pricing are isolated. Paired t-tests are employed for consecutive years to identify the existence of a statistically significant annual change in transit ridership. 12-month period is tested for and , but first 9-month period is tested for analyses to exclude the September 11 effect. Therefore, the critical t-statistic and the average ridership number for this period are not consistent with and periods. The test results are presented in TABLE 5. TABLE 5 Results of Paired t-tests for Annual Ridership Interval Average Ridership t-statistic Critical t-statistic Significance Yes Yes Yes For all the year pairs that are analyzed, test statistics exceed the critical value. This finding points out a significant difference in annual ridership, so the hypothesis of equal ridership level can be rejected. This conclusion supports the statement that the 11

12 transit ridership continued to increase until 9/11. It should also be noted that economic recession can also be detected from this analysis, where period shows less growth then the previous years since sample test statistic is closer to the critical value. Studies in transit demand area points out a relationship between Central Business District (CBD) employment and transit demand (13,14,15,16). FIGURE 2 and FIGURE 3 show that between 1992 and 2005, both transit and employment continue to increase until 2000 and experience stagnation, coinciding with the economic recession in year 2000 and decrease until year 2004 when it enters an increasing trend again. Least squares regression analysis between the annual employment and total annual ridership produces an acceptable R 2 of Thus, it can be concluded that the employment-transit ridership relationship described in the literature exists in the study area and employment levels can be used to explain the transit demand changes. FIGURE 2 New York City Monthly Employment Over Years (Source: New York State Department of Labor) 12

13 FIGURE 3 Total Transit Ridership Case-3: Impacts of Time-of-day Pricing In this case, the months before and after the time-of-day pricing for years between 1998 and 2001 are considered and null hypothesis is chosen as there is no statistically significant change in transit ridership during the time period just after the implementation of time-of-day pricing. Similar to Case-1, monthly shares are used for the analysis to exclude the annual ridership change which is found to exist in Case-2. Like Case-1, an ANOVA test, structured as shown in TABLE 6 is performed. Also, taking the seasonal factors into account, pre-time-of-day pricing months are chosen to be December-January- February and by the same consideration, post-months are chosen to be March-April-May. Summer period, June-July-August for the same years is also analyzed to see if any further effect is experienced. The same scheme presented in TABLE 6 is applied for analysis of post-time of the day pricing. No further test could be performed for following months since 9/11 impacted the transit system considerably. TABLE 6 Two-way ANOVA for Pre-Time-of-day Pricing December 1997 January 1998 February 1998 December 1998 January 1999 February 1999 December 1999 January 2000 February 2000 December 2000 January 2001 February 2001 Changes among years Changes within months 13

14 TABLE 7 Summary of ANOVA Test for Time-of-day Pricing Effect Period Pre-Time-of-day pricing Post- Time-of-day pricing (March-April-May) Post- Time-of-day pricing (June-July-August) Type of Variation F value F critical value Significance within seasons No among seasons No random error within seasons No among seasons No random error within seasons No among seasons No random error The ANOVA results in TABLE 7 show that both periods before and after the time-of-day pricing (December to February 2001, and March to May 2001) are statistically comparable to the same periods of previous years, i.e., 1998 thru Likewise, test for further effect that might have happened in summer, also has not yielded a significant test statistic (TABLE 7). So, the statistical analyses indicate that, with a 95% confidence, there is no statistically significant change in transit ridership during the time period just after the implementation of time-of-day pricing. CONCLUSIONS AND DISCUSSIONS The factors that were mentioned to have an impact on ridership are: (1) Physical Effects of 9/11 and Related Statistics, (2) Effect of Economic Decline and CBD Employment, and (3) Time-of-day Pricing. The analysis of ridership data shows that the physical destruction of major transit facilities in NYC has caused a decline in ridership as expected. Those non-functioning transit lines also have increased travel times and decreased the level of access to downtown Manhattan resulting in lower ridership numbers. However, in this case physical recovery of the infrastructure is a relatively short process in terms of transit demand, so increased travel time and reduced accessibility can be mainly used to explain short-term fluctuations in transit demand. This observation can be also verified with recent ridership data that the demand level reached its pre-9/11 values after the physical recovery. Longer-term changes in transit ridership can be mainly attributed to economic slow-down and resulting reduction in CBD employment, which is cited in literature as a major factor affecting the transit demand (13,14,15,16). Aside from economic recession, 9/11 attacks has also caused a reduction in the number of employees working in NYC. Employment and transit ridership follow the same pattern, as shown in FIGURE 2 and FIGURE 3, namely a slow-down by and then a decrease by and an increasing trend in year Thus, the overall decline in economy and employment, combined with business re-location can explain the decrease in transit demand after On the other hand, the improving economic situation with an increasing employment rate in NYC in

15 (FIGURE 2), and the decrease in transit travel times due to the restoration of transportation facilities to their pre-9/11 status (resulting in overall lower travel times and better accessibility) can all be seen as the other contributors of the increasing trend in transit ridership after 2003 (See TABLE 1, FIGURE 3). The NJ Transit and PATH statistics that show high values before September 2001 clearly demonstrate that a significant ridership decrease has not been observed prior to July 2001 while the time-ofday pricing program is already active. The statistical analyses investigating the transit patterns before/after period of time of the day pricing (Case-3) also indicate that the timeof-day pricing initiative did not seem to have a noticeable impact on transit ridership, which was verified at 95% confidence level using the ANOVA test (TABLE 7). The discussion presented above leads us to conclude that the transit ridership slowdown at year 2000 and decrease after 2001 are best explained as a result of economic decline that started before time-of-day pricing and accelerated after 9/11 instead of time-of-day pricing initiative. To further analyze possible impacts of the time-of-day pricing program on transit ridership, a disaggregate database containing hourly ridership data is necessary. Without a more disaggregate transit ridership database showing the breakdown of peak and offpeak period ridership separately, it is not possible to further analyze and pinpoint the time-dependent changes in transit ridership, if any, after the time-of-day pricing initiative. ACKNOWLEDGEMENTS This project was sponsored by a grant from the Federal Highway Administration s Value Pricing Program, Special thanks are due to Mark Muriello and Danny Jiji (PANYNJ) for their continuing support and assistance throughout this investigation. 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. Muriello, M.F., and D., Jiji. The Value Pricing Toll Program At The Port Authority Of New York & New Jersey: Revenue For Transportation Investment And Incentives For Traffic Management, Presented at the 83rd Annual Meeting of the Transportation Research Board, Holguín-Veras J., K.,Ozbay and A., De Cerreno, Evaluation Study of Port Authority of New York and New Jersey s Time-of-day Pricing Initiative, Final Report,2005, Report No: FHWA/NJ Federal Highway Administration (FHWA). Report on the Value Pricing Pilot Program. July Accessed 01/ Norwegian Public Roads Administration (NPRA) (2003), Accessed 01/ San Diego Association of Governments (SDAG. Report to the California legislature: San Diego s Interstate 15 Congestion Pricing and Transit Development Demonstration Program. December report.pdf, Accessed 01/ University of California at Berkeley. ITS decision: congestion pricing., _pricing_report.htm, Accessed 01/

16 7. Edward J. Bloustein School of Planning and Public Policy. Travel Trends: Summer Alan M. Voorhees Transportation Center, Rutgers University, Vol.2, No.2, Edward J. Bloustein School of Planning and Public Policy. Travel Trends: Winter Alan M. Voorhees Transportation Center, Rutgers University, Vol.1, No.1, PANYNJ Downtown Restoration Program Report, /ppt/env/deis/v1/08a_path.pdf 10. PATH Media Release, Tenantwise, Special Report: WTC Tragedy, April Accessed 01/ New York State Department of Labor, Accessed 14 February, C. Hendrickson. A Note on the Trends in Transit Commuting in the US, Relating to Employment in the CBD. Transportation Research Part A: General, 20A(1), 1986, pp Kain, J., Impacts of Congestion Pricing on Transit and Carpool Demand and Supply. In National Research Council, Curbing Gridlock: Peak-Period Fees to Relieve Traffic Congestion, Transportation Research Board, Washington, DC, Special Report242, 1994, pp Taylor, B.D., and C.N.Y. Fink, Factors Influencing Transit Ridership:A Review and Analysis of the Ridership Literature, UCLA Department of Urban Planning, Working Paper 16. Fielding, G.J. Congestion Pricing and the Future of Transit. Journal of Transport Geography, Vol. 3(4), 1995, pp