Transit competitiveness in polycentric metropolitan regions

Transportation Research Part A 41 (2007) 19–40 www.elsevier.com/locate/tra

Transit competitiveness in polycentric metropolitan regions Jeffrey M. Casello School of Planning and Department of Civil Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ont., Canada N2L 3G1

Abstract This paper analyzes the potential to, and impacts of, increasing transit modal split in a polycentric metropolitan area – the Philadelphia, Pennsylvania region. Potential transit riders are preselected as those travelers whose trips begin and end in areas with transit-supportive land uses, defined as ‘‘activity centers,’’ areas of high-density employment and trip attraction. A multimodal traffic assignment model is developed and solved to quantify the generalized cost of travel by transit services and private automobile under (user) equilibrium conditions. The model predicts transit modal split by identifying the origin–destination pairs for which transit offers lower generalized cost. For those origin–destination pairs for which transit does not offer the lowest generalized cost, I compute a transit competitiveness measure, the ratio of transit generalized cost to auto generalized cost. The model is first formulated and solved for existing transit service and regional pricing schemes. Next, various transit incentives (travel time or fare reductions, increased service) and auto disincentives (higher out of pocket expenses) are proposed and their impacts on individual travel choices and system performance are quantified. The results suggest that a coordinated policy of improved transit service and some auto disincentives is necessary to achieve greater modal split and improved system efficiency in the region. Further, the research finds that two levels of coordinated transit service, between and within activity centers, are necessary to realize the greatest improvements in system performance.  2006 Elsevier Ltd. All rights reserved. Keywords: Polycentric cities; Activity centers; Multimodal modeling; Traffic assignment; Modal split; Transit competitiveness; Mobility

1. Introduction In the last half of the 20th century, North American urban development patterns have become greatly dispersed. The trend began in the post war period with a heavy migration of residential activities outside of traditional urban cores. Later, in the 1970s and 1980s, employers followed seeking generally lower rents and access to more highly educated labor. While residential activities were generally low-density, commercial ventures tended to be more clustered, as a result of land use controls and forces off agglomeration.

E-mail address: [email protected] 0965-8564/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra.2006.05.002

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In the 1990s many of these commercial clusters have expanded to form ‘‘activity centers,’’ locations of concentrated employment outside of urban cores. Regions with several activity centers in addition to the traditional downtown are referred to in the literature as polycentric metropolitan areas. These areas present new challenges for transportation professionals to plan, design and operate efficient transportation systems. Briefly, polycentric cities compared to traditional monocentric urban areas tend to exhibit more distributed trip patterns, more reliance on private automobile and less use of public transportation and non-motorized modes. These characteristics can result in negative transportation system performance, including occurrences of regional congestion and mobility limitations for some part of the population. This paper analyzes the potential to improve overall system performance by increasing public transportation ridership in polycentric metropolitan areas, using the Philadelphia Pennsylvania region as a test case. The approach taken here is to preselect travelers whose origin and destination pairs are strong candidates for public transportation, and whose normal auto path consumes capacity on regionally congested links. The disutility of travel by transit and private auto is quantified using a multimodal traffic assignment model. After modeling and calibrating the base-case situation, alternative policies are explored to provide transit incentives, to implement auto disincentives, and to combine these policies. For each alternative, the model is solved and changes in transit ridership and system performance are quantified. The results demonstrate that an integrated, regional approach addressing transit competitiveness for trips between and within activity centers can reduce congestion and increase mobility in metropolitan areas. 2. Literature review The concept of polycentric metropolitan areas has been researched extensively in many fields. One common characteristic is the need to identify areas outside of traditional urban cores that represent ‘‘activity centers,’’ or nodes in the polycentric region. Quantitative definitions largely grew out of regional economic literature, and as such, are largely based on total employment and employment density. Briefly, there are two common approaches to quantifying activity centers. The first method, typically attributed to McMillen and McDonald (1997), uses sophisticated methods to identify areas that exhibit statistically significant deviations in employment from surrounding zones. The second approach, traditionally attributed to Giuliano and Small (1991), sets a priori threshold levels for both employment and employment density; geographical areas (typically TAZs) which exceed these values belong to activity centers. Casello and Smith (accepted for publication) have proposed a modification to the Giuliano and Small approach that includes a trip attraction weighting for the disaggregate employment types present in a potential activity center. The authors call these areas Transportation Activity Centers. That research identified 21 suburban activity centers in the Philadelphia metropolitan area, four of which serve as inputs to the model used here. A substantial body of research exists on transportation patterns associated with activity centers. NCHRP (1989) analyzed six suburban activity centers in the United States to develop a comprehensive database on travel characteristics: origins, destinations, trip purpose, length and mode. TRB (1990) measured suburban congestion, evaluated suburban trip generation and modal split, and enumerated policy needs for more efficient activity centers. Cervero (1989) identified 57 suburban activity center sites throughout the United States from which he developed tremendous aggregate data on the transportation infrastructure that support these SACs. Cervero and Wu (1998) have done targeted analysis on the San Francisco metropolitan area. Modarres (2003) presents an excellent literature review of activity center transportation analysis in his work analyzing public transportation access to disaggregate employment types within Los Angeles’ activity centers. Kuby et al. (2004) analyzed more than 200 Light Rail Transit (LRT) station to determine which land use or network characteristics were effective predictors of ridership. Their intention was to identify if LRT could attract sufficient ridership in polycentric metropolitan areas. Their conclusions include that ‘‘a station need not be downtown to generate substantial ridership,’’ that employment was a statistically significant variable in attracting riders, and similar factors explained ridership at CBD and non-CBD stations. Boile´ and Spasovic (2000) and Boile´ (2002) first proposed the use of multimodal traffic assignment models to evaluate the impacts of changes in pricing on modal split. The work presented here relies heavily on these formulations; the extension to their work is to recognize the positive correlation between land use density and

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transit ridership established by Pushkarev and Zupan (1982), TCRP (1996) and Nelson/Nygard (1995) in selecting origin and destination pairs. 3. Activity centers identification and analysis The goal of this research is to examine how public transportation may improve regional transportation system performance. The intent is to identify travelers whose origin and destination pairs are well served by public transportation, and whose trip path consumes critical regional highway infrastructure. As noted above, activity centers are areas of high density of development; prior research suggests that transit’s competitiveness increases as land use density increases. Thus, trips originating and destined for regional activity centers are strong candidates to increase transit modal split and to improve system performance. 3.1. Identifying critical activity centers Casello and Smith (accepted for publication) have identified 21 suburban activity centers, two secondary urban centers and three major urban centers in the Philadelphia metropolitan area. These centers represent areas of not only high-density employment, but also concentrated trip attraction within the region. While it is feasible to include all these centers in the subregional model developed here, a subset of these centers are selected based on the following observations. Improving transit modal split produces the most positive impacts for trips utilizing congested highway infrastructure. Furthermore, reducing congestion is most desirable on the highest-volume regional links, as delay reductions on these links result in the greatest absolute time savings and the greatest reduction in negative externalities. Thus, this model analyzes trips associated with a subset of activity centers which are adjacent to congested, high-volume regional roadways.

Fig. 1. Philadelphia metropolitan area and major highway sections.

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The Philadelphia region is served by the following major highways, shown in Fig. 1: 1. Interstate 95, running north–south adjacent to the Delaware River. 2. The Pennsylvania Turnpike, composed of routes I-276 and I-76, running east–west through the region. 3. Interstates 676 and 76 (known locally as the Schuylkill Expressway), from Camden, New Jersey through downtown Philadelphia westward, connecting to both Interstate 476 to the north and Interstate 276 to the west. 4. Interstate 476, connecting I-95 south of the city to I-76 and I-276 northwest of the city. 5. US 202 which acts as a suburban beltway along the western border of the metropolitan region. 6. US 422, the fastest growing corridor in the region, extending the Philadelphia suburbs to the northwest. Recurrent congestion occurs on several of these highways. SEPTA (2001) using 1997 Annual Average Daily Traffic (AADT) estimates notes that during peak periods I-76 eastbound and westbound operate at Level of Service (LOS) E or F along the corridor between center city Philadelphia and the interchange with I-276. The same study found that US 422 operates at LOS F both eastbound and westbound for approximately five miles from its intersection with US 202. The six mile segment of US 202 northbound and southbound between Norristown and King of Prussia is also operating under unstable or failure conditions. Based on this study’s findings, the Schuylkill Expressway, US 202 and US 422 appear to be primary corridors on which improving the highway performance may have the greatest regional impacts. The impacts of other regional roadways, such as I-476 and I-276, on these major regional routes’ performance should also be considered. These critical highways either pass through or are adjacent to eight of the activity centers identified in Casello and Smith (accepted for publication). These centers are shown as the shaded areas in Fig. 2.

Fig. 2. Philadelphia metropolitan area with the eight activity centers considered.

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3.2. Trips associated with the identified activity centers Table 1 shows the daily trips between the eight activity centers. The total number of trips is shown, as well as trips disaggregated into three categories: home based work (HBW), home based non-work (HBNW), non-home based (NHB) for the eight activity centers studied in the Philadelphia region. The data shown were provided by Walker (2003) and are the output of that agency’s regional travel model. DVRPC models a ‘‘combined peak’’ period including trips made between 7:00 a.m. and 9:00 a.m. and 3:00 p.m. and 6:00 p.m. This model disaggregates time of day sensitive parameters such as freeway capacity and transit service parameters to better reflect the peak period. The output trip table from this combined peak period for the eight activity centers is shown in Table 2. 3.3. Observations on trip volumes The trip volumes presented in Tables 1 and 2 offer some insight into the Philadelphia region’s travel patterns. For commuting trips, home based work trips during peak hours, Center City dominates as a destination,

Table 1 Daily trip table for the eight activity centers considered Camden

Center City

Fairmount

West Phila

City Line

Consh – PM

Norristown

King of Prussia

4656 360 96 58 45 20 16 9

726 90,500 11,949 11,787 6698 1567 1223 412

91 5760 4414 1555 825 135 102 44

90 10,540 2340 5441 1416 241 191 72

29 2258 547 609 7080 362 224 123

9 276 63 62 272 18,005 3682 819

6 114 30 29 106 2490 12,635 2151

15 278 73 62 264 2338 8575 38,864

Daily non-home based Camden 3627 Center City 200 Fairmount 35 West Phila 29 City Line 11 Consh – PM 6 Norristown 5 King of Prussia 6

204 50,221 3449 5859 1531 191 70 150

38 3519 1880 890 234 23 11 20

28 6050 845 3284 381 37 15 31

12 1540 228 378 3997 113 36 82

5 146 19 28 109 7848 994 555

4 69 7 13 41 992 4013 1528

5 127 16 24 73 562 1563 29,096

Daily home based work Camden 398 Center City 124 Fairmount 51 West Phila 23 City Line 25 Consh – PM 12 Norristown 11 King of Prussia 3

454 15,960 4511 2934 2987 1155 1027 234

45 997 745 269 286 91 83 19

56 2143 854 812 578 172 153 37

16 367 158 115 575 172 151 35

2 67 24 17 69 1339 907 110

1 30 16 12 35 525 2038 208

6 85 33 21 83 627 2078 1233

Daily home based non-work Camden 631 Center City 36 Fairmount 10 West Phila 6 City Line 9 Consh – PM 2 Norristown 0 King of Prussia 0

68 24,319 3989 2994 2180 221 126 28

8 1244 1789 396 305 21 8 5

6 2347 641 1345 457 32 23 4

1 351 161 116 2508 77 37 6

2 63 20 17 94 8818 1781 154

1 15 7 4 30 973 6584 415

4 66 24 17 108 1149 4934 8535

Total daily trips Camden Center City Fairmount West Phila City Line Consh – PM Norristown King of Prussia

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Table 2 Combined peak period trip table for the eight activity centers considered Center City

Fairmount

West Phila

City Line

Consh – PM

Norristown

King of Prussia

Total combined peak Camden 1454 Center City 134 Fairmount 32 West Phila 22 City Line 19 Consh – PM 8 Norristown 8 King of Prussia 2

Camden

319 31,069 4723 4112 2726 675 542 153

38 1898 1568 499 319 56 41 17

37 3423 938 1918 571 105 85 25

10 667 194 200 2543 129 91 37

3 80 22 19 82 6345 1259 222

2 36 11 8 36 813 4738 626

6 88 30 20 86 787 3229 11,776

Peak non-home based Camden 1017 Center City 56 Fairmount 8 West Phila 6 City Line 3 Consh – PM 2 Norristown 2 King of Prussia 1

52 14,142 937 1562 393 43 16 34

12 966 540 235 62 5 2 6

7 1589 222 987 105 10 3 8

4 378 64 101 1239 27 7 21

1 38 5 5 24 2343 248 128

1 20 1 2 9 251 1192 389

2 30 4 5 17 134 419 8185

Peak home based work Camden 222 Center City 65 Fairmount 23 West Phila 13 City Line 13 Consh – PM 6 Norristown 6 King of Prussia 1

245 8744 2463 1580 1621 576 494 112

23 539 410 145 157 45 38 9

29 1126 454 452 315 86 74 16

6 190 83 61 336 84 73 16

1 26 12 9 33 812 494 56

1 15 7 5 18 287 1218 115

3 38 18 10 39 331 1181 725

Peak home based non-work Camden 215 Center City 13 Fairmount 1 West Phila 3 City Line 3 Consh – PM 0 Norristown 0 King of Prussia 0

22 8183 1323 970 712 56 32 7

3 393 618 119 100 6 1 2

1 708 262 479 151 9 8 1

0 99 47 38 968 18 11 0

1 16 5 5 25 3190 517 38

0 1 3 1 9 275 2328 122

1 20 8 5 30 322 1629 2866

with nearly 60% of all peak period trips ending in that activity center. More broadly, the combination of West Philadelphia, Fairmount and Center City accounts for almost 75% of total trip destinations in the peak period. There exists, however, a strong commuting pattern among the suburban activity centers (Conshohocken/ Plymouth Meeting, Norristown, and King of Prussia). For example, from Norristown the number of trips destined for King of Prussia is nearly 2.5 times as high as the number of trips from Norristown destined for Center City. Similarly, an equal number of trips from Norristown are destined for Plymouth Meeting as are destined for Center City. These volumes suggest that commuting patterns are not simply radial from the suburbs into the central business district, but rather contain a strong inter-suburban component. Table 3 summarizes commuting patterns for these eight activity centers based on whether the origin and the destination are suburban (the three centers defined above plus City Line Avenue) or urban (Camden, Center City, Fairmount and West Philadelphia). The data in Table 3 shows that 22% of all commuting trips associated with these eight activity centers begin and end within a suburban activity center, while only 13.5% begin in a suburban center and end in an urban center. Table 3 also suggests that currently there is little evidence of a strong ‘‘reverse commute,’’ with only 1.8% of trips beginning in an urban activity center and ending in a suburban activity center.

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Table 3 Urban–suburban commuting patterns for the Philadelphia region Origins

Urban Suburban

Destinations Urban

Suburban

16,533 (62.6%) 3569 (13.5%)

485 (1.8%) 5818 (22.0%)

Considering non-home based trips (NHB), several additional observations are possible. First, the total number of NHB trips during the peak period is greater than the number of HBW trips. It is reasonable to assume that a significant portion of these peak period NHB trips occurs as an intermediate stop during the morning or afternoon commute – trip chaining. A second observation is that intra-center trips account for more than 77% of all NHB trips that occur in the peak period. This value is significantly higher than for HBW trips, where 49% of trips are intra-center. Similarly, less than 7% of NHB trips go between an urban activity center and a suburban activity center. If the assumption that most peak NHB trips are intermediate commuting stops is extended, the high number of intra-center trips implies that most trip chaining occurs near either the origin or the destination. This suggests that mixed use development that supports non-motorized modes (i.e. Transit Oriented Development, TOD) might be effective in accommodating this trip demand and reducing the number of vehicular trips in a region. Lastly, the King of Prussia activity center generates and attracts a disproportionate number of NHB trips. Intra-King of Prussia trips account for approximately 2.8% of the total commuting (HBW); intra-King of Prussia trips account for more than 21.3% of total NHB trips. This is partially explained by the presence of a regional shopping center in the area, the King of Prussia Plaza and Court which together have 2.9 million square feet of gross leasable area (GLA) (Philadelphia Business Journal, 2003). 4. Modeling transit competitiveness and system performance A multimodal traffic assignment model is used to quantify the disutility of travel at user equilibrium for all modes. At equilibrium, transit competitiveness can be quantified for the origin–destination pairs identified above. Impacts of policy and operational changes for both transit and auto travel on modal split and system performance can be directly computed from the output of the model. Components of this model include the following: 1. The trip table (trip volumes by origins and destinations – from Tables 1 and 2); 2. A representation of the transportation infrastructure in link – node format, including pertinent link variables such as: capacity, length, free-flow travel time and tolls; 3. A measure of traveler disutility for each mode; 4. A representation of ‘‘external’’ traffic, that is traffic that does not originate or is not destined for the subregional area, but consumes transportation infrastructure capacity; 5. A representation of transit service, including waiting time, in-vehicle travel time, and fare (out of pocket expense); 6. A traffic assignment algorithm that is known to converge to equilibrium; 7. Model calibration, to ensure that the model output accurately reflects the transportation network conditions. These model components are discussed in the following sections. 4.1. Peak hour trip table The trip table for this study is computed by converting the DVRPC peak period (5 h) trip volumes to a single peak hour volume. The Highway Capacity Manual (Transportation Research Board, 1985) gives several graphs of hourly variations on different classifications of roadways. The HCM examples suggest ranges from

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nearly uniform peak periods, with 20% of total volume occurring in the highest peak hour, to a sharply peaked condition where nearly 40% of peak period traffic occurs in the highest-volume hour. Given this range of values, and the intention of this modeling effort to evaluate congested freeway conditions, it is assumed that 40% of the peak period trips occur in the peak hour. 4.2. Network representation The network infrastructure representation is shown in Fig. 3. Freeway sections are shown as solid lines, while principal arterials are represented as lighter weight dashed lines. Nodes 1–56 are actual intersections of highways. Nodes 58, 60 and 61 are centroids of the Center City, Fairmount and Camden centers, respectively. Nodes 64–70 are TAZ centroids used to model the trips within the Norristown activity center. Similarly, nodes 71–76 are origins and destinations for trips internal to the Plymouth Meeting activity center; nodes 77 and 78 serve the same purpose for the King of Prussia Center. Nodes 79–82 are the centroids of the King of Prussia, Norristown, Plymouth Meeting and City Line Avenue centers, respectively. Node 84 serves as the centroid to the West Philadelphia center. The model also represents intra-zonal trips for those TAZs with intra-zonal flows greater than 500 trips per hour. For example, more than 10,000 trips originate and end within one TAZ (node 78) in the King of Prussia center during the combined peak period. To model these trips, three additional source/sink nodes (nodes 85–

Fig. 3. Model representation of the highway infrastructure.

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87) are created within the TAZ. A similar approach is taken for two additional TAZs within the Plymouth Meeting center. Note that no internal travel is modeled for the urban centers. It is assumed that the modal split for trips within these centers is sufficiently high, and that non-motorized modes serve these trips particularly well. Trips to and from the urban centers are included in the model to represent the volume these trips add to the suburban activity center traffic. The resulting network is one with 90 nodes and 192 links (384 directional) representing more than 191 (382) kilometers of highway. Of these links, 204 represent highways; 48 are imaginary, zero cost links connecting center or intra-center centroids to the transportation network. An additional 132 links represent transit connections within and between activity centers. The capacity of each link is taken from DVRPC (2000) models. The DVRPC has three capacity values (high, medium, low) for each highway class and for each Area Type through which the highway carries traffic. For this model, a single rounded mean capacity value is used for each highway functional class. Lengths of each link are calculated by hand using large-scale commercially available maps. Free-flow travel time is computed using the distances and speeds based on DVRPC (2000) recommended speed for each functional class. 4.3. Auto traveler disutility The total cost experienced by a driver is computed as a function of travel time, travel distance, and out of pocket expenses. Travel time is calculated using the Bureau of Public Roads formulation (Bureau of Public Roads, 1964). Estimating the value of travel time has been the subject of extensive research (see, for example, the Victoria Policy Institute, 2002; Villain and Bhandari, 2002; Cohen and Southworth, 1999; Small and Winston, 1999; USDOT, 1997). Estimates vary greatly depending on, primarily, locality. There are, however, several points upon which researchers agree. First, commuters have the highest value of time; second, the value of time increases with personal income; finally, commuter’s travel time should be estimated as a function of local wages and benefits. Given these criteria, the assumed value employed in this model formulation is $25 per hour. This value is within the limits of empirically derived values in the referenced studies and is consistent with the upper end of commuters’ salaries and benefits. The model uses this value of time for all trips occurring in the peak hour. The cost of travel per unit distance is assumed to be $0.15 per vehicle kilometer. This value is intended to represent only fuel cost and operation under congested conditions. Out of pocket expenses include tolls and parking. Tolls are only present in the submodel on two links representing the Pennsylvania Turnpike and on Route 676 connecting Camden to Philadelphia. The cost of trips along these links is incorporated in the model. Additional out of pocket expenses are included for parking for trips that are destined for nodes within the urban activity centers. 4.4. Model calibration – modeling external trips A major challenge in developing and calibrating the model is to represent trips external to the activity centers. These trips are necessary to ensure that links are operating with sufficient volume to match observed conditions. Further, changes in system performance, increases and decreases in travel times, are experienced not only by the travelers associated with the activity centers but also by external users. The methodology used here to represent the external trips is to assign iteratively an artificial, bidirectional travel demand for trips between each origin–destination pair that defines a link in the network proportional to the capacity of the connecting link. For example, the link defined as connecting nodes 1 and 2 has a capacity of 1200 vehicles per hour; external traffic is modeled as a demand between nodes 1 and 2 of 1080 trips, in this case 90% of capacity. This approach allows the artificial demand to be the primary calibration tool. Moreover, by adding artificial demand between nodes, the model solution actually assigns these trips to the lowest cost route. In other words, the external demand between link end-points may not be accommodated by only the link between them, but may also seek lower cost paths through the network. After several iterations, it was determined that an external travel demand equal to 90% of link capacity resulted in a sufficiently accurate representation performance. Several O–D pairs connected by links which

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are chronically congested were assumed to have slightly larger demands. For example, link 9–63 (and link 63– 9) is one of the most frequently congested roadway sections in the region; the demand for travel between nodes nine and 63 is assumed to be 95% of the capacity of link 9–63. A summary of the model formulation is shown in Table 4. 4.5. Modeling transit service Transit service between activity centers is typically modeled as artificial links between the centroids of zones (Sheffi, 1985); that approach is taken here. The total generalized cost of transit time is computed as a function of the access time to the transit service within a TAZ, the waiting time which is calculated based on the line’s headway, in-vehicle travel time, and the fare. Access time is calculated for each TAZ based on transit’s area coverage within the zone and the quality of vehicle and pedestrian access to the transit system. Waiting time is assumed to be one-half of the headway up to a maximum value of 10 min for single seat trips on rail systems. For bus systems, the waiting time is one-half the headway with a maximum of 10 min for headways up to

Table 4 Summary of network modeling approach #Nodes: 90 • • • •

62 roadway intersections 8 center centroids 15 TAZ centroids 5 intra-zonal sources

#Links: 384 • • • •

102 ‘‘real’’ (204 directional) 24 centroid connectors (48 directional) 56 inter-center transit connections 76 intra-center transit connections

Total distance modeled: • 382 km Travel time equation:  vb  • FFTT  1 þ 0:25 c

ð1Þ

• where FFTT is the free-flow travel time, v is volume, c is capacity and b is an estimated parameter Link parameters: free-flow speeds (kilometers per hour), capacities (vehicles per hour – lane), b (from (1)) • • • • •

Freeway: 100, 2000, 7 Expressway: 85, 1400, 7 Arterial: 50, 800, 7 Ramps: 60, 1140, 7 Centroid connectors: 100, 10,000, 1

Travel costs: • Value of IVTT: $25 per hour • Cost associated with distance: $0.15 per kilometer • Parking costs: – Center City $7 per trip – Camden $7 per trip – Fairmount $5 per trip – W. Phila. $3 per trip # Trips modeled: • 20,503 ‘‘real trips’’ in peak hour v • External trips added on links such that P 0:90 c

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30 min. For headways greater than 30 min, the waiting time is assumed to be 1/3 the headway with a maximum value of 15 min. These estimates have a significant shortcoming. Using this formula, increasing a headway from 30 min to 1 h only imposes a 5-min additional waiting time on transit service. This penalty underestimates the added inconvenience of delayed (or earlier) travel. This methodology, however, is the standard practice and is adopted here. A traveler experiences two waiting times on trips requiring a transfer. The model reflects this combined waiting cost by calculating the first waiting time as described above. The second waiting time, associated with the transfer, is computed as one-half the minimum of the two line’s headways. The in-vehicle travel time is computed based on published transit operator schedules including the Southeastern Pennsylvania Transit Authority (SEPTA) and the Delaware River Port Authority’s PATCO service between Center City Philadelphia and Camden, New Jersey. It is assumed that these published schedule times (PST) include typical transit passenger volumes. Higher transit volumes will likely cause only marginal increases in travel time. As such, the transit link travel times are computed using the BPR function, with very high capacity. Only in the case of very high volumes does the travel time significantly depart from published schedules. Transit service within the suburban activity centers is modeled based on existing service; as an example, Fig. 4 shows the existing transit service in the King of Prussia area and its model representation. Inter-center transit travel times, types and fares are shown in Table 5. A summary of the transit modeling approach is shown in Table 6. 4.6. Equilibrium traffic assignment solution methodology Many traffic assignment models exist that have been shown to reach equilibrium (Patriksson, 1994). The assignment algorithm used here was developed and coded by Bar-Gera (2002). The model is solved to

Fig. 4. King of Prussia activity center transit service.

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Table 5 Transit connection matrix showing in-vehicle travel time, service type, and fare Camden

Central business district

Fairmount

West Philadelphia

City Line

Plymouth Meeting Conshohocken

Norristown

King of Prussia

CAM

–

14 min Patco $1.15

22 min Patco – HR $2.20

19 min Patco – HR $2.20

50 min Patco – Bus 2.45

80 min Patco – Bus $3.56

57 min Patco – RR $4.03

77 min Patco – Bus $4.13

CBD

14 min Patco $1.15

–

8 min HR $1.30

5 min HR $1.30

34 min Bus $1.30

66 min 1 Bus $2.41

43 min RR $2.88

61 min 1 Bus $2.88

FM

22 min HR – Patco $2.20

8 min HR $1.30

–

13 min 2 HR $1.30

42 min HR – Bus $1.59

72 min HR – Bus $2.41

47 min Bus – RR $2.88

69 min HR – Bus $2.88

WP

19 min HR – Patco $2.20

5 min HR $1.30

13 min 2 HR $1.30

–

31 min HR – Bus $1.59

80 min HR – Bus $2.41

47 min HR – HSL $2.41

63 min HR – Bus $2.88

CLA

50 min Bus – Patco $3.56

34 min Bus $1.30

42 min Bus- HR $1.59

31 min Bus – HR $1.59

–

46 min 2 Bus $1.59

41 min Bus – RR $1.59

58 min 2 Bus $1.93

PM

80 min Bus – Patco $3.56

66 min 1 Bus $2.41

72 min Bus – HR $2.41

80 min Bus – HR $2.41

46 min 2 Bus $1.59

–

31 min Bus $1.30

60 min 2 Bus $1.93

NT

57 min RR – Patco $4.03

43 min RR $2.88

47 min RR – Bus $2.88

47 min HSL – HR $2.41

41 min RR – Bus $1.59

31 min Bus $1.30

–

33 min 1 Bus $1.30

KOP

77 min Bus – Patco $4.13

61 min 1 Bus $2.88

69 min Bus – HR $2.88

63 min Bus – HR $2.88

50 min 2 Bus $1.93

60 min 2 Bus $1.93

33 min 1 Bus $1.30

–

HR = heavy rail.

Table 6 Summary of transit modeling cost components and assumptions Total travel cost • Value of Travel time * (Access time + Waiting time + IVTT) + Fare Access time • Empirically derived based on: transit area coverage within a TAZ, pedestrian connections, and parking availability at stations Waiting time assumptions • Bus systems, single seat ride: WT = h/2, 610 min for h < 30 min; h/3 6 15 min for h > 60 min • Rail system, single seat ride: WT = h/2, 610 min " h • Either mode with a transfer: Total wait time = WT1 + WT2, where WT1 = Single seat rule; WT2 = 1/2 * min{h1, h2} In-vehicle travel time (IVTT): • Empirically derived from published schedules’ times (PST)    v • Mathematically modeled as : PST 1 þ 0:15 10; 000 • Represents transit’s insensitivity to volume Fares • ‘‘Per-trip cost’’ calculated as monthly pass cost divided by 44 commuting trips per month

ð2Þ

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equilibrium using an 800 mHz PC in less than 1 min. Unlike some large-scale models solved with the Franke– Wolfe algorithm, this model solution technique solves to sufficient accuracy (in terms of link flows and costs) to be certain that changes in the model outputs are a result of changes in the system representation. It is also important to note that because the solution reached is user equilibrium, the results can be described as stable: no individual user has motivation to change his route or mode because no better alternative exists. Naturally, the stability of the results is predicated upon the assumption that the model represents human behavior sufficiently well. The model which predicts mode choice based on lower generalized cost, including travel time and out of pocket expenses, makes two major assumptions. First, a cost-based modal split model ignores captive riders, those for whom auto travel is not possible, and therefore probably underestimates total transit ridership. In terms of variables included in the mode split model, the literature suggests that a series of input variables may be considered (Ben-Akiva and Morikawa, 2002) which may increase (environmental concerns) or decrease (comfort, safety, etc.) the likelihood of transit ridership. It is unclear if the simplification of mode choice to include only travel time and out of pocket expenses is likely to overestimate or underestimate transit ridership in the Philadelphia Region. 5. Measures of system performance This work posits that on a system-wide (macro) level, increasing transit’s modal split for a subset of regional trips can improve the regional transportation system performance; congestion can be reduced resulting in higher efficiency, lower cost travel. Therefore, to measure the effectiveness of proposed operational, policy or design changes, pertinent statistical measures include: 1. Total system cost – the disutility of completing all trips (real and external) throughout the network; with fixed demand calculating total system cost also computes average user cost. Mathematically, total system cost is given by XX GCod  T od where GCod ¼ minfGCTod ; GCA ð3Þ od g o

d

where GC is the generalized cost from origin (o) to destination (d) by transit (T) and auto (A); Tod is the travel volume from o to d. 2. The number of transit links used, as a measure of transit’s integration and ubiquity in the transportation network; 3. The total system delay in person-hours. In this case, delay is defined as the increase in link travel time versus the free-flow travel time. To calculate total delay, the increased travel time is multiplied by the flow on the link. Mathematically, the delay function is given as v b XX FFTTij  a  T ij ð4Þ c i j where FFTTij is the free-flow travel time from i to j; a, b are defined as in the BPR travel time equation; v is the volume on link i, j; c is the capacity of link i, j; Tij is the flow on link i, j. 4. The transit modal split. The model assigns trips to the lowest cost path for an origin–destination pair. The path may be made up of all auto links, a single transit link, or a combination of auto and transit links. The transit modal split can be computed as the number of trips assigned to transit links versus the total number of trips assigned. 5. Transit competitiveness, defined as the ratio of generalized cost of a trip between a given O–D pair by transit and automobile. A ratio greater than one suggests that auto has lower generalized cost. Mathematically, this ratio is given as Competitiveness ¼

GCTod TTTod  VOTT þ fareod ¼ A TTA GCA od  VOTT þ X od  0:15 þ Auto OOP od

ð5Þ

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where TTod is the travel time from the origin to the destination by transit (T) or auto (A) in minutes; VOTT is the value of travel time $ per minute; fare is the transit fare from origin to destination; X is the distance from origin to destination in kilometers; Auto_OOP is the auto out of pocket expense in $ per trip. This measure of transit competitiveness has many positive features. The ratio is very easy to compute from the model output. Further, because the statistic is a ratio of generalized cost, the assumption regarding value of time is somewhat less important. On a more macro-level, transit competitiveness can be thought of as a measure of mobility. Measuring the generalized cost ratios for classifications that correspond to the trip types gives insight into how well transit is serving regions of the metropolitan area. For example, transit competitiveness can be computed for all trips within an activity center, or for all trips between suburban centers and urban centers. As transit is more competitive for whole groups of trips, the region’s mobility (i.e. travel alternatives) can be said to increase. In this research, weighted transit competitiveness statistics are computed for: 1. 2. 3. 4.

King of Prussia intra-center trips; Norristown intra-center trips; Plymouth Meeting intra-center trips. Urban to suburban inter-center trips (trips beginning in an urban activity center and terminating in a suburban activity center); 5. Suburban to urban inter-center trips (trips beginning in a suburban activity center and terminating in an urban activity center); 6. Suburban to suburban inter-center trips (trips beginning in one suburban activity center and ending in a different suburban activity center). Mathematically, this weighted average can be expressed as 1 XX GCTod T od  T Sk o d GCA od

8o; d 2 S k

GCTod > GCA od

and

ð6Þ

where Sk represents the six subsets described above. These macro- and micro-level statistics are used to quantify existing conditions and to evaluate proposed measures to improve system performance. 6. Summary of base-case results The peak hour is modeled and calibrated and the base-case results are summarized in Table 7. The total system cost incurred by users of all modes in this peak hour is $631,499. Transit is the lowest cost alternative for five suburban to urban connections with one intermodal trip. For all other categories, no transit service offers lower disutility than automobile service. The total delay experienced in the system is 6019 person-hours. The transit competitiveness ratio for all trips modeled is 4.1. Further analysis of transit competitiveness for subsets of trips is computed. The maximum ratio for a single O–D pair, the minimum ratio for a single O–D pair and the weighted average for each of the six categories are presented in Table 8. The King of Prussia activity center is made up of two TAZs. One of those TAZs is further divided into four sub-TAZ level model analysis zones – Zone 78 is further subdivided into zones 78, 85, 86 and 87 in Fig. 4.

Table 7 Base-case results Case

Total system cost

Transit modal split (%)

Transit links useda

Total system delay (h)

Transit competitiveness

Base case

$631,499

7.2

5

6109

4.1

a

Modal split and transit links used is based only on suburban trips.

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Table 8 Base-case cost ratios for each of the six trip categories Category

Maximum

Minimum

Weighted

1. King of Prussia intra-center 2. Norristown intra-center 3. Plymouth Meeting intra-center 4. Urban to suburban inter-centers 5. Suburban to urban inter-centers 6. Suburban to suburban inter-centers System wide

16.7 21.6 19.0 2.9 1.6 4.2

1.7 3.9 1.1 1.2 <1 1.4

5.2 9.0 4.5 1.8 1.3 2.3 4.1

Thus, the King of Prussia activity center consists of five origins and destinations or 20 O–D pairs. The maximum observed generalized cost ratio for trips within the King of Prussia activity center is from origin 86 to destination 85 – where transit costs approximately 16.7 times more than auto. The minimum observed generalized cost ratio for King of Prussia trips is from zone 87 to zone 85, where transit costs only 1.7 times as much as auto. Overall, for trips within King of Prussia, transit currently offers relatively poor service, costing on average 5.2 times as much as automobile travel. The Plymouth Meeting center shows an even greater disparity in quality of transit service between O–D pairs, ranging from a maximum of 19.2 to a minimum of 1.3. This implies that for at least one O–D pair within the Plymouth Meeting center (76 to 75) transit is completely uncompetitive and for another O–D pair (79 to 84) transit cost exceeds auto cost by only 30%. Of the three suburban activity centers, Plymouth Meeting offers the most competitive transit service (has the lowest weighted average of cost ratios) for intra-center trips, while Norristown offers the least competitive. The third column of Table 8 shows the minimum cost ratio for each subset of trips. The lowest value is 1.1 for Plymouth Meeting intra-center trips; all subsets have a minimum value less than 1.8 except Norristown intra-center trips where the minimum is 3.9. In general, these low minimum values suggest that there is at least one origin–destination pair belonging to each trip category for which transit could become competitive with minor pricing or operational changes. 7. Transportation system improvements In the following sections the existing transportation conditions are modified in an attempt to improve system performance. The changes are broadly classified into two categories: auto disincentives and transit incentives. Auto disincentives are based primarily on increasing the cost of automobile trips; the motivation for these cost increases is to attribute to the driver a portion of the external costs associated with his trip. The transit incentives include lower cost service, faster service (through priority treatment, for example) and the introduction of new transit service in suburban areas. Finally, a combination of auto disincentives and transit incentives is explored. 7.1. Auto disincentives Three alternative auto disincentives are considered. First, to represent higher marginal cost of auto travel, the cost per unit distance is increased from the base-case $0.15 per kilometer to a $0.30 per kilometer. This change may represent a higher fuel tax, distance-based insurance charges, or perhaps greater use of tolls. The second proposal is to implement a cordon toll of $4.00 for trips entering the city limits. This type of toll charge had been successfully implemented in Singapore, prior to implementing a larger network of toll collection devices. More recently, London has imposed a cordon toll with very promising results. The last alternative implements a $2.00 fixed charge for all suburban destinations (both activity center destinations and intracenter trips). This charge represents parking fees. The model is revised to represent these changes and rerun. The macro-output statistics are shown in Table 9. Each of these three pricing changes achieves one of the primary goals, reducing system-wide delay. The increased distance cost policy is most effective, reducing delay by approximately 386 h or about 6.3%.

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Table 9 Macro-output statistics for the auto disincentive model runs Case

Total system cost

% Change in system cost

Transit modal splita (%)

Transit links useda

Total system delay (h)

% Change in delay

Transi competitiveness

Base case A1 – Increased distance cost A2 – Cordon toll A3 – Suburban fixed charge

$631,499 $742,202 $650,847 $650,584

N/A 17.5 3.1 3.0

7.2 9.1 7.8 7.8

5 9 6 9

6109 5723 5828 5983

N/A 6.3 4.6 2.0

4.1 3.3 3.9 2.0

a

Modal split and transit links used is based only on suburban trips.

Increasing distance charges also results in the greatest transit modal split for suburban trips (an increase of 1.9% over the base case) and the largest number of transit links used. Last, this policy reduces the system-wide generalized cost ratio on links for which auto is less expensive from 4.0 to 3.3, meaning that on the whole, transit is slightly more competitive with the auto for these trips. As could be expected, increasing auto travel costs increases the total system cost significantly, in this case by 17.5%. The cordon toll and suburban fixed charge policies also reduce the network delay by 4.6% and 2.0%, respectively. Each policy results in approximately the same transit modal split of 7.8%, a marginal increase over the base case. The cordon toll results in only one new transit link being used. With suburban fixed charges, four origin–destination pairs within the Plymouth Meeting activity center are served best by transit, resulting in a total of nine transit links being used for suburban trips. The suburban fixed charges is the most effective policy in improving the overall competitiveness of transit on auto-dominated routes, reducing the system-wide cost ratio from 4.0 to only 2.0. Auto disincentives are somewhat ineffective in producing substantive improvements in system performance. On the macro-level, no auto disincentive policy reduces system delay by more than 6.3%, and this gain causes a large increase in total system cost. Implementing a suburban parking charge is most effective in increasing transit’s overall competitiveness (lowers the overall cost ratio), but this policy does not increase transit’s modal split. This is largely a result of the high base-case cost ratios shown in Table 7. 7.2. Transit incentives As with auto disincentives, three transit incentives are explored and compared to the base case. The first measure is to eliminate fares on the transit system. Such a policy aims to match the low marginal cost of automobile travel and to test the elasticity of transit ridership to out of pocket expense. A second measure tested is to increase the operating speed of existing transit service. To this end, in-vehicle travel time is reduced by 10% and 15% for rail service and bus service, respectively. These reductions in travel time could be achieved by operational changes such as improved vehicle performance (acceleration or deceleration rates, maximum speeds) or priority signal treatments in mixed traffic. Last, the impacts of high-frequency, high-area coverage transit service for each suburban activity center are evaluated. Conceptually, this service could be minibuses operating on 10 min headways between high traffic generators within the activity centers. Each of these alternatives is coded and the model is resolved. The macro-output statistics are shown in Table 10. Table 10 Transit incentive macro-outputs Case

Total system cost

% Change in system cost

Transit modal splita (%)

Transit links useda

Total system delay (h)

% Change in delay

Transit competitiveness

Base case T1 – Free transit T2 – Increased operating speed T3– Suburban distribution

$631,499 $607,843 $623,519 $627,889

N/A 3.8 1.6 0.9

7.2 12.6 8.6 7.9

5 8 5 8

6109 5492 5717 5857

N/A 10.1 6.4 4.1

4.1 3.5 3.8 2.8

a

Modal split and transit links used is based only on suburban trips.

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As with the auto disincentive policies, each of the transit incentive policies increase the transit modal split and in turn reduce the system-wide delay. The most effective policy in both respects is the free transit alternative which increases transit modal split by 5.4%, makes transit the lower cost alternative on three additional routes and decreases system delay by 617 person-hours (10.1%). Logically, eliminating transit fare reduces the total system cost by 3.8% as transit riders do not pay a fare and auto users experience less congestion. Increasing transit’s operating speed marginally reduces the total system cost (1.6%), increases transit modal split (1.4%) and decreases system delay (6.4%). The introduction of suburban distribution service – high speed, high-frequency service within each of the suburban activity centers – produces very small benefits in terms of system cost and system delay reductions, approximately 1% and 4%, respectively. Transit modal split also increases only marginally, by less than 1%, as these circulator services are lowest cost only on several lightly traveled suburban O–D pairs. As expected, lowering the cost of transit relative to the automobile increases transit’s competitiveness in each case. The most effective policy for increasing transit competitiveness is introducing the suburban distribution service, followed by implementing free transit and lastly increasing operating speed. These findings suggest that in-vehicle travel time and fare are less of a deterrent to suburban transit use than insufficient service: poor area coverage and very long headways. To summarize, transit incentives fail to produce substantive changes in system performance. Substantial system-wide delays remain, and total system cost is reduced by only 4% with free transit. The most promising case for improving transit’s system-wide competitiveness is the suburban distributor service, as the weighted competitiveness index is reduced from 4.0 in the base case to 2.8. Given the marginal improvements in system performance realized by the auto disincentive and transit incentive policies independently, it is logical to test combinations of these policies. Two cases are considered. The first case is the combination of suburban fixed charges and improved transit service. Ideally, revenue raised through parking charges could finance the introduction of a competitive transit alternative. These changes are coded into the model, and the model is rerun. The results are shown in Table 11. This combined policy of dissuading suburban auto use and providing a suitable transit alternative causes 22 additional transit links to be used over the base case and increases the transit modal split by nearly 7%. Of these 22 new links, 18 serve Plymouth Meeting intra-center trips and four serve King of Prussia intra-center trips. Transit serves over 17% of both centers’ intra-center trips. Delay in the system is reduced by almost 12%. The total system cost increases by less than one percent as reductions in travel time nearly offset the increased parking charges for suburban destinations. This alternative focuses on the provision of ubiquitous suburban transit service. As a result, this alternative has a more pronounced effect on the weighted auto path cost ratio, reducing the value from 4.1 in the base case to 1.6. This suggests for those routes on which the auto remains more competitive, transit service on average costs only 60% more. 7.3. The Schuylkill Valley Metro The Schuylkill Valley Metro (SVM) is a proposed 100-km long transit line to connect Center City Philadelphia and Reading, Pennsylvania serving essentially the I-76 and US 422 corridors. As shown in Fig. 5,

Table 11 Results of the combined auto disincentive and transit incentive model run Case

Total system cost

% Change in system cost

Transit modal splita (%)

Transit links useda

Total system delay (h)

% Change in delay

Transit competitiveness

Base case C1 – Combined fixed charges and suburban distribution

$631,499 $636,546

N/A 0.5

7.2 14.0

5 27

6109 5392

N/A 11.7

4.1 1.6

a

Modal split and transit links used is based only on suburban trips.

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Fig. 5. Schuylkill Valley Metro (SVM) alignment.

the proposed alignment would provide service between the centroids of Center City, City Line Avenue, Norristown and King of Prussia. The proposed line would also pass through the Plymouth Meeting activity center, but at a significant distance from the zone centroid. The locally preferred alternative is to operate high frequency (for transit units per peak hour) ‘‘MetroRail’’ service – a high efficiency (high platforms, self-service fare collection, etc.) electrified light rail system along ROW A for most of the proposed alignment. This operation would substantially reduce travel time for inter-center trips. This level of transportation investment, as well as the proposed implementation time, would certainly weaken the applicability of the model developed here. Regional land use and travel patterns would change considerably, making the assumption of a constant O–D matrix in light of the transportation improvement highly questionable. Despite these concerns, the model is coded to represent SVM service in the corridor, understanding the obvious limitations on the findings. The results are shown in Table 12.

Table 12 Results of Schuylkill Valley Metro service Case

Total system cost

% Change in system cost

Transit modal splita (%)

Transit links useda

Total system delay (h)

% Change in delay

Transit competitiveness

Base case SVM – Schuylkill Valley Metro

$631,499 $603,512

N/A 4.7

7.2 15

5 11

6109 5159

N/A 15.6

4.1 4.0

a

Modal split and transit links used is based only on suburban trips.

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Table 13 Model output for Schuylkill Valley Metro, suburban fixed charges and new suburban circulator transit service Case

Total system cost

Base case C2 – Combined SVM, fixed charges and suburban distribution

$631,499 N/A $588,788 6.7

a

% Change in system cost

Transit modal splita (%)

Total system % Change in Transit delay links useda delay (h)

Transit competitiveness

7.2 27

5 33

4.1 1.5

6109 3850

N/A 37.0

Modal split and transit links used is based only on suburban trips.

The model predicts that the SVM would capture 15% of the modal split with six additional transit connections being made. This is a result of transit capturing a much higher modal split for suburban to urban intercenter trips. In the base case, transit captures only 41% of these trips; with SVM, the model predicts transit would serve 79% of these trips. Specifically, the more frequent and higher speed connection between City Line Avenue and Center City would make transit the more competitive connection for all trips between this O–D pair. Also, the SVM would make transit the preferred mode for three suburban to suburban inter-center combinations: between City Line Avenue and Norristown (both directions) and from Norristown to King of Prussia. The SVM, by making transit competitive on high-volume inter-center trips, is also effective in reducing total system cost and total system delay, reducing these two measures by 4.7% and 15.6%, respectively. The total system delay is particularly noteworthy. This reduction is primarily a result of unloading the Schuylkill expressway, specifically the section between City Line Avenue and the I-676 interchange. This section of roadway operates with a volume to capacity (v/c) ratio of 1.3 in the base case; with SVM this ratio is reduced to 1.1. This translates into greater than 70% travel time reduction over this link with SVM compared to the base case. The SVM is intended to provide high quality corridor service. As a result, its construction alone would not significantly improve the weighted competitiveness index for trips within suburban activity centers. This is indicated by the weighted statistic in Table 12. With the SVM, transit remains nearly four times more costly than automobile for routes on which auto is less expensive. One final case is considered. The SVM alternative is tremendously effective in capturing inter-center trips along the I-76 corridor. The SVM may be more effective in conjunction with the combined suburban transit service enhancements and auto disincentives. This case, the combination of SVM, suburban fixed charges for auto use and the introduction of high quality suburban circulator transit is considered here. The model is coded to reflect these three policies together and rerun. The results are shown in Table 13. This very high level of investment in transit produces the highest transit modal split, 27% and the highest number of transit links used, 33. More importantly, the model predicts that these transit investments and auto disincentives produce impressive system performance improvements. This combination reduces total system delay by 37%. Reducing driving delay and lowering transit costs outweigh the introduction of parking charges such that total system cost is reduced in this alternative by 6.7%. Moreover, on the routes for which the automobile is less expensive, transit averages only 50% more expensive – down from more than 400% in the base case. 8. Policy implications of results The output statistics for each set of measures are shown in Table 14. The total system cost, the transit modal split and the delay statistics are measured as per cent changes from the base case. The weighted competitiveness ratios for auto-dominated O–D pairs are given as actual values. From Table 14, the following observations can be made. Applying auto disincentives without improving transit alternatives in each case increases total system cost. For example, increasing the cost per unit distance traveled by automobile by 100%, as in policy A1, increases total system cost by 17.5%, but has little effect on transit modal split. The results for system delay are somewhat more positive, a decrease of 6.4%. The transit competitiveness index for this policy shows that transit does not provide attractive service for users on most

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Table 14 Summary of policy and operational changes and their system impacts Measures

Total system cost (% change)

Transit modal split (% change)

Delay (% reduction)

Transit competitiveness

Base case A1 – Increased cost per unit distance A2 – Cordon toll A3 – Suburban fixed charge T1 – Free transit T2 – Increased operating speed T3 – Suburban distributor service SVM – Schuylkill Valley Metro C1 – Suburban fixed charge and new suburban service C2 – SVM, suburban fixed charge and new suburban service

N/A 17.5 2.7 2.7 4.1 1.6 0.9 4.7 0.5 7.1

N/A 1.9 0.6 0.6 5.4 1.4 0.7 7.8 6.8 19.8

N/A 6.3 4.6 2.1 10.1 6.4 4.1 15.6 11.7 37

4.1 3.3 3.9 2.0 3.5 3.8 2.8 4.0 1.6 1.5

routes; on average transit remains 3.3 times as expensive as auto on auto-dominated routes. Similar results are shown for policies A2 and A3. These findings suggest that road pricing solutions alone, when not combined with improved transit services, may not achieve the desired system benefits of reduced congestion or improved overall system performance. Each of the three transit incentives measures – eliminating transit fares (T1), increasing operating speeds (T2) and introducing new transit service (T3) reduces the total system costs, increases transit modal split and decreases the system delay. Measure T1 is most effective of these three policies in reducing delay and decreasing system cost, while T2 is second and T3 is least effective in these measures. The results suggest that policy T3 will have only a marginal impact on modal split, but a more significant impact on transit competitiveness. The weighted index with measure T3 is 2.8, the lowest index of all transit incentive policies. Generally, transit incentives produce superior results compared to auto disincentives in terms of total system delay. In each case, transit incentives include reducing the cost of transit while auto disincentives suggest increasing the cost of personal auto use. As a result, transit incentives lower the system cost in each case while auto disincentives tend to increase the system cost. Measure C1 emphasizes changing the cost structure for both modes in suburban areas by combining A3 and T3, with very positive results. System delay is reduced by 11.7% with C1 versus 2.1% and 4.1% for A3 and T3 (respectively) implemented alone. Similarly, the competitiveness index is 1.6 with C1 versus 2.0 and 2.8 with A3 and T3. Most importantly, the total system cost with C1 increases only slightly, by 0.5%, versus an increase of 2.7% with policy A3 alone. This result suggests that the increased auto cost experienced by some auto users is offset by the benefits in reduced auto travel time and improved transit service. As a result, the total system cost does not increase when a suitable transit alternative is provided. The model output suggests that the Schuylkill Valley Metro would be very positive for the regional transportation system. The benefits include a 12% reduction in system-wide delay and a 5% decrease in system cost. Transit is far more competitive regionally, with an index of 1.6 with SVM in place. Ideally, a transportation network provides high capacity links between densely developed activity centers which are in turn served by high-frequency, medium-capacity localized transit service (Schumann, 1997). This integrated network is consistent with modern land use and transportation planning philosophies. The final policy considered, C2, represents this type of transportation network within the region. The Schuylkill Valley Metro system provides high-frequency service between seven of the eight activity centers (Norristown is not served directly). Combining the SVM service, localized suburban distribution, and auto disincentives in suburban areas results in a phenomenally efficient transportation network. Total system cost is reduced by 7.1%, despite implementing the suburban fixed charge. The SVM project independently only decreased total system cost by 4.7%; thus combining the SVM with A3 and T3 reduced the system cost by an additional 2.4%. Delay in the region with policy C2 is reduced by 37%, with I-76 and suburban congestion nearly eliminated (in the short term only). Transit region-wide becomes more competitive with the weighted index reduced to its lowest value of 1.5. The improvements gained by the integrated policy C2 over the SVM by itself identify the need for integrated regional transportation planning and operation.

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9. Conclusion This paper presents a multimodal traffic assignment approach to analyzing system performance changes based on increased transit modal split. Origin and destinations are preselected based on their location in activity centers, areas of high employment and trip attracting activity. Trip patterns are chosen such that those travelers who shift from private automobile to public transportation reduce vehicular volume on the region’s most highly traveled, and most congested links. The results of the model suggest that two levels of integration are necessary. First, policies and operations must be designed such that marginal improvements in transit performance are accompanied with similar auto disincentives. Only when these two approaches are combined, can substantial improvements be realized. Second, transit service improvements and auto disincentives must be planned and designed such that modes are competitive for travel between activity centers and within activity centers. 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