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Page 24
Suggested Citation:"Related Information and Impacts." National Academies of Sciences, Engineering, and Medicine. 2004. Traveler Response to Transportation System Changes Handbook, Third Edition: Chapter 9, Transit Scheduling and Frequency. Washington, DC: The National Academies Press. doi: 10.17226/23433.
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Page 24
Page 25
Suggested Citation:"Related Information and Impacts." National Academies of Sciences, Engineering, and Medicine. 2004. Traveler Response to Transportation System Changes Handbook, Third Edition: Chapter 9, Transit Scheduling and Frequency. Washington, DC: The National Academies Press. doi: 10.17226/23433.
×
Page 25
Page 26
Suggested Citation:"Related Information and Impacts." National Academies of Sciences, Engineering, and Medicine. 2004. Traveler Response to Transportation System Changes Handbook, Third Edition: Chapter 9, Transit Scheduling and Frequency. Washington, DC: The National Academies Press. doi: 10.17226/23433.
×
Page 26
Page 27
Suggested Citation:"Related Information and Impacts." National Academies of Sciences, Engineering, and Medicine. 2004. Traveler Response to Transportation System Changes Handbook, Third Edition: Chapter 9, Transit Scheduling and Frequency. Washington, DC: The National Academies Press. doi: 10.17226/23433.
×
Page 27
Page 28
Suggested Citation:"Related Information and Impacts." National Academies of Sciences, Engineering, and Medicine. 2004. Traveler Response to Transportation System Changes Handbook, Third Edition: Chapter 9, Transit Scheduling and Frequency. Washington, DC: The National Academies Press. doi: 10.17226/23433.
×
Page 28

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

9-24 Other Considerations A change in service hours introduces the issue of availability of service. Beyond the reach of operating hours there is simply no transit service available to the prospective customer. When the service hours issue is how late after the PM peak period to operate, the potential for riders to be “trapped” without service when they have to work late or try to squeeze in an after work activity becomes a concern. Persons faced with such trip scheduling uncertainties may simply elect not to use transit at all, although provision of an evening “guaranteed ride home” program may mitigate the deterrence. Similar situations arise when there is no midday service, and a commuter is faced with an emergency need to return home. When attendees were polled at a St. Louis public hearing, only 24 percent were concerned with obtaining improved rush hour service, while nearly all desired service improvements in other time periods (Holland, 1974). Commuters to New York City listed midday and evening service improvements, which involved both speed and service frequency, as the most important changes wrought by a demonstration project involving the New York Central Railroad (Tri-State, 1966). Where and when transit service already exists, as is always the case when service frequency improvements are being considered, those who are most dependent on public transportation (“captives”) are among the transit riders already being served. Thus the riders attracted by frequency improvements tend to be discretionary (“choice”) transit riders, more prevalent among middle and upper income groups (Holland, 1974). This has recently been observed in the case of the Santa Monica “Big Blue Bus” frequency improvements examined under “Response by Type of Strategy” — “Bus Frequency Changes” — “More Recent Experience.” The ridership increase has drawn especially on trip makers within the $40,000 to $50,000 household income range. Persons in this income bracket constitute some 20 percent of current Santa Monica Municipal Bus Line ridership (Catoe, 1998). RELATED INFORMATION AND IMPACTS Mode Shifts and Sources of New Ridership When transit riders are attracted or repelled by transit service frequency increases or decreases, shifts between travel modes take place along with some occurrences of new trips or trips no longer taken. Such effects define the sources of new ridership when frequencies are improved. In available surveys of new riders attracted by increased service frequency, “trips not made monitoring with electronic signs at the 400 stops along 40 day and 12 night bus routes, giving passengers closely estimated wait times for approaching buses. Results of this “Countdown” system were sufficiently promising that fleetwide AVL implementation was programmed for the next 3 years, with provision of “Countdown” signs at all 4,000 bus stops over the next 10 years (London Transport, 1998). The information on expected wait time is reported to make passengers less anxious, to reduce their perception of the amount of wait time even though nothing else has changed, and to have a positive although probably modest effect on actual ridership. “Countdown” results are further explored in Chapter 11, “Transit Information and Promotion,” under “Traveler Response by Type of Program” — “Real-Time Transit Information Dissemination” — “Results of Real-Time Train and Bus Arrival Information.”

9-25 previously,” reflecting changes in trip frequency or destination choice that result in “new” trips, were apparently not identified. The percentage of such trips is probably comparable to the 10 to 20 percent reported in connection with combined fare and service increases. (See “Related Information and Impacts” — “Sources of New and Lost Ridership” in Chapter 12, “Transit Pricing and Fares,” for the specific data and further discussion.) Bus and commuter railroad riders attracted from other travel modes by increased frequency were, in various Massachusetts experiments, distributed among the prior modes as shown in Table 9-10 (Mass Transportation Commission et al., 1964): Table 9-10 Prior Travel Modes of Transit Users Attracted by Increased Frequency Bus Users Attracted by Various Massachusetts Bus Frequency Increases Rail Users Attracted by Boston Area Commuter Rail Frequency Increases Prior Mode Percentage Prior Mode Percentage Own car 18 to 67% Own car 64% Carpool 11 to 29 Carpool 17 Train 0 to 11 Bus 19 Taxi 0 to 7 Walking 0 to 11 Source: Mass Transportation Commission et al. (1964). When frequencies were reduced on the Mt. Pleasant Road trolley bus route (Route 74) in Toronto, Canada, choice of that particular route relative to all other possible travel options went down by 12.5 percent among panelists selected at bus stops prior to the change. However, choice of public transit as the selected travel mode went down only 1.7 percent. The indication was that in Toronto’s relatively dense transit network, shifts among routes were dominant, with relatively little shifting to non-transit modes taking place. Overall trip rates for worker and student trips were relatively impervious to the service decrease, but reported non-worker and non-student trips by all modes dropped by 14 percent, suggesting travel foregone (Miller and Crowley, 1989). (See the case study “Mt. Pleasant Bus Route Service Reduction in Toronto — Panel Survey” for further detail.) Temporal Ridership Patterns The potential of transit frequency improvements for attracting additional ridership is demonstrably greatest percentagewise in the off-peak periods of the day. A likely reason, in part, is the typical existence of lesser service frequencies in the off-peak hours. Another likely factor is the off-peak prevalence of discretionary travel. In the Detroit center city Grand River Avenue demonstration of the 1960s, off-peak elasticities were almost 100 percent above the peak hour headway elasticity of -0.13. In Virginia, the Chesapeake to Norfolk suburban service off peak elasticities were over 50 percent above the morning peak -0.58 elasticity. Bus headway observations previously discussed with respect to Table 9-2 are stratified in Table 9-11 by time period (Mayworm, Lago and McEnroe, 1980). This

9-26 stratification also displays the existence of higher off-peak sensitivity to frequency improvements, although to a lesser degree than the individual instances cited first. Table 9-11 Bus Headway Elasticities Stratified by Time of Day Time Period Number of Observations Arc (Mid-point) Elasticity Standard Deviation Peak Hours 3 -0.37 ±0.19 Off-peak Hours 9 4 -0.46 ±0.26 Weekends -0.38 ±0.17 All Hours 7 -0.47 ±0.21 Source: Mayworm, Lago and McEnroe (1980). Only in the Stevenage, England, and Mt. Pleasant trolleybus of Toronto observations were elasticities observed or estimated to be lower in the off-peak than in the peak. Analytical issues affecting the Toronto off-peak estimate were previously noted. Experimental train frequency increases on Boston & Maine service into Boston of 82 percent in the peak and 92 percent in the off-peak induced an 18 percent Phase 1 ridership increase in the peak and a 60 percent increase in the off-peak. In this experiment, “off-peak” was defined as including not only midday and evening trains and patronage, but also trains and patrons moving reverse to the predominant flow during the peak hours. The experiment did not employ off-peak fare discounts until after Phase 1 (Mass Transportation Commission et al., 1964). The results imply peak and off-peak service elasticities of +0.3 and +0.7, respectively. Traveler Response Time Lag The effects of service frequency and fare changes require time to fully develop. Existing and prospective transit riders need time to assess the ramifications of a change and sometimes to terminate old travel arrangements and make the different arrangements required by shifting to a new mode. In the case of the 1960s Massachusetts experiments, some frequency improvements elicited ridership increases that stabilized within the first month. This was particularly true of the bus service experiments oriented to urban, off-peak travel. Other frequency improvements elicited a response that grew throughout the course of the 9 to 12 month experiments. For example, a suburban route into Boston exhibited a 27 percent ridership increase over the prior year in the fourth quarter compared to 18 percent in the first, while a suburban route into Worcester showed a 16 percent increase in the third quarter compared to none in the first (Mass Transportation Commission et al., 1964). Commuter railroad service frequency improvements attracted steadily increasing ridership over 16 to 18 month periods (Mass Transportation Commission et al., 1964; Southeastern Pennsylvania Transportation Authority, 1971; Tri-State, 1966).

9-27 VMT, Energy and Environment Table 9-12 Hypothetical Corridor Bus Frequency Impacts on VMT and Emissions Transit Headway Bus Emissions (kg/hr) from Buses Automobile Emissions (kg/hr) from Automobile Emissions (kg/hr) from All Vehicles (minutes) VMT CO HC NO VMT Trips CO HC NO CO HC NO 30 24 1.23 0.18 0.70 2,360 708 193 18.6 6.63 194 18.8 7.33 15 48 2.46 0.37 1.40 2,160 649 177 17.1 6.06 179 17.5 7.48 5 144 7.39 1.11 4.20 2,070 622 170 16.4 5.83 177 17.5 10.0 Source: Joel Horowitz, Air Quality Analysis, The MIT Press, 1982, as cited in Cambridge Systematics (1992). An earlier study indicates that within certain travel markets, increased transit fuel consumption may largely or completely offset the automobile energy saved by attracting trips to transit with frequency increases. To illustrate with an example from the most disadvantageous end of the spectrum, the impact of decreasing Chicago rail rapid transit wait time by 20 percent was estimated to be a 1.8 percent ridership gain accompanied by a net increase in urban transportation energy use equivalent to 0.5 percent of areawide automotive fuel consumption (Pratt and Shapiro, 1976). More comprehensive examination of bus frequency increases in combination with increases in service coverage have indicated that net energy savings are attainable in a number of travel markets, but not in others (see “Related Information and Impacts” — “Energy and Environmental Relationships” in Chapter 10, “Bus Routing and Coverage”). An analysis of bus transit in Portland, Oregon, found that for service-level changes in suburban areas, the range of ridership development times was from 1 to 5 months. In the urban area, the service-level change response time range was 8 to 10 months. In contrast, fare change effects typically stabilized in about 3 months (Kyte, Stoner and Cryer, 1988). While the suburban versus urban differentiation appears to be reversed comparing Massachusetts and Portland, Oregon, it may nevertheless be concluded that ridership response to frequency and schedule changes often stabilizes at least somewhat faster than response to new transit routes. The two or up to three years that it takes to reach equilibrium with new routes is discussed in the “Related Information and Impacts” — “Service Development and Time Lag” subsection of Chapter 6, “Demand Responsive/ADA,” and the corresponding “Traveler Response Time Lag” subsections of Chap- ter 10, “Bus Routing and Coverage,” and urban rail Chapters 7 and 8. Modeled rather than observed traveler response is the only available basis for evaluation of the impacts of transit service frequency changes acting alone on vehicle miles of travel (VMT), energy consumption and pollutant emissions. A hypothetical example of changes in vehicle headways for a corridor with 4 bus stops per mile and 1,000 person trips per hour indicates the potential VMT reduction benefits and air quality impacts that might accrue at the corridor level. Table 9-12 shows the results of the analysis, which suggest that in the context of early 1980s emissions controls, transit frequency improvements would reduce carbon monoxide (CO) and hydrocarbon (HC) emissions, but increase nitrous oxide (NO) emissions (Cambridge Systematics, 1992). Changes in emissions control technology and increased use of low or no emissions autos and/or buses may markedly alter the emissions and trade-offs shown.

9-28 Notably, the net energy savings resulting from combining improved frequency with decreased fare is in most cases greater than the sum of the individual actions. This same synergistic effect is also evident when improved transit service is combined with auto use disincentives. In both cases the complementary actions assist in filling the additional transit vehicles required by virtue of the frequency improvement strategy, thereby increasing both transit and total energy efficiency (Pratt and Shapiro, 1976). (See also “Related Information and Impacts” — “Impacts on VMT, Energy and Environment” in Chapter 12, “Transit Pricing and Fares”.) Costs and Revenues Transit service frequency increases will attract transit trips and thereby increase gross farebox revenue, but will seldom lead to a decreased net cost of transit operation. In any case, the net cost of a carefully designed service frequency increase may be found acceptable to the operating agency involved when examined in the context of mobility and other objectives. For example, see the new- and established-service farebox recovery ratio standards used by New Jersey Transit, described in Chapter 10, “Bus Routing and Coverage” under “Related Information and Impacts” — “Costs and Feasibility.” Note that schedule regularization to provide greater public convenience and easy recollection of departure times may involve not much more than the start- up costs of rescheduling, which necessarily include resolution of any interlining issues and route redesign requirements. Service frequency reductions are, on the other hand, a means to lower costs and increase net revenue, albeit at the expense of service quality and reduced patronage. Deficit reduction needs have forced this action, often taken together with fare increases, where economic circumstances required (Washington Metropolitan Area Transit Authority 1995; Allen, 1991). It is possible to reach a point of diminishing returns, however, when service quality drops below a certain point (Pratt and Bevis, 1971). The marginal cost of off-peak service may be significantly less than the average systemwide full operating cost. Peak ridership demands determine the number of vehicles and heavily influence the number of drivers needed to provide service. Off-peak costs are thus closer to being determined by direct vehicle operating costs alone, particularly where full time drivers are not actually driving full shifts. To quantify the lesser cost of off-peak service it is necessary to develop a cost model that differentiates between peak and off-peak costs. This was done for the Twin Cities of Minneapolis-St. Paul based on 1984 cost and ridership data. The result for the public carrier was the formula: CST = $1.065 x VM + $20.255 x BVH + $30.799 x PVH + $19,941 x PV where: CST = system or route cost VM = vehicle miles for route or system BVH = base vehicle hours for route or system PVH = peak vehicle hours for route or system PV = peak vehicles in route or system and the cost of each peak vehicle is expressed as annual cost which does not include capital costs (Regional Transit Board, 1987).

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TRB’s Transit Cooperative Research Program (TCRP) Report 95: Chapter 9 – Transit Scheduling and Frequency examines scheduling changes made to conventional bus and rail transit, including changes in the frequency of service, hours of service, structuring of schedules, and schedule reliability.

The Traveler Response to Transportation System Changes Handbook consists of these Chapter 1 introductory materials and 15 stand-alone published topic area chapters. Each topic area chapter provides traveler response findings including supportive information and interpretation, and also includes case studies and a bibliography consisting of the references utilized as sources.

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