A decision model for evaluating transit pricing policies

TronspnRes 4 Vol ISA pp 125-1381981 Prmtedm GreatBntam

0191-?M)7/81/02012~-14502WiO PergamonPressLtd

A DECISION MODEL FOR EVALUATING PRICING POLICIESIDONALD School of Busmess,

TRANSIT

P BALLOU and LAKSHMI MOHAN

State Umverslty

of New

York

at Albany, Albany

NY I??22 U S 4

(Recerred 23 hlav 1979 III rewed form 7 Februarr 1980) Abstract-Declrion models that emphasize the relatlonshlp between control variables quch rfs transit fares and performance measures hke revenue and rIdershIp can be of slgmficant value to the trdnsrt manager’s declrlonmaking process Since transit prlcmg has become a pohtlcal IFsue. any tran~lt fdre model needc to eyarnme the eqmtv lmphcatlons of proposed fare policies To this end, the trdnslt prlcmg decision model pre\ented m thl\ paper not only provides mformatlon on aggregate quantities such as revenue and ridership but more lmportdntly through use of the micro-\imuldtion technique, facditates andlysi\ of the Impact thdt various fdre policies aould have on selected groups of riders The potential usefulness of the model IF IllWtrdted through dn application to evaluate the lmpdct of distance-bdsed fdre policies The design of the model dnd hupportmg computer prognm\ dre however flexible enough to test a variety of fare structures and permit the model to be cuvtomlzed to ‘1 specific user’\ need\ and data constraints

purpose 1s to assist d transit manager in deciding among alternatlve courses of action This puts some special requirements on the desrgn of the model so that It can indeed be used by managers This paper presents a declslon model developed for evaluatmg transit fare pohcies The methodology proposed by us was designed to fulfil the Fpecdic requirements which have been Identified as critical for a genuinely usable trdnslt prlcmg decision model The basrc idea of our methodology 15 drawn from a management science approach that has been successfully applied to marketmg decision problems Smce transit pricing problems exhibit many of the same mtrmslc characteristics as marketing problems. we feel that this approach should yield frmtful results The proposed model hds been applied to evaluate the Impact of distance-based fares m the Albany, New York Metropohtan area, ucmg data from dn on-board ridership survey conducted by the local transit agency The design of the methodology IS, however, flexible enough to permit its use by transit operators m other regions for testing a variety of fare structures A general set of computer programs IS avadable which can be customized to a specific user’s needs The paper IS organized as follows design considerations for transit pricing declslon models, the proposed approach for model development, the trdnslt pncmg decision model, dn dlustratlve npphcatlon of the model, and, conclusions

INTRODUCTION A review

of the literature

mdlcates

that the conventlondl

demand forecasting IS a macro model of some ,ort, which operates on aggregate data and relates a measure of travel demand, such as number of passengert or number of trips, to a set of Independent variables Depending on the purpose for which the forecast was developed, avadablhty of data and model structure, this set would mclude variables from some or all of the followmg categories (I) DemographIc variables hke total population. total employment by type, median Income, number of cars per household, etc (a) Trip characteristics hke length of trip, travel time. purpose of trip, peak/off-peak, etc @I) Decision (or control) variables like frequency of service and fares Models that do not Include any control variables cannot, by their very nature, assist transportation planners m evaluatmg the consequences of alternatlve proposals or pohcles Such models are usually developed to provide a better understandmg of the impact of non-policy variables such as city size and distance on travel demand Whde these descriptive models can be used for forecasttng, the results would be passive or status quo prolectlons m the sense of being bdsed on a sumlar future assumption with regard to transit services and policies In contrast to descrlptlve models, a decrslon model IS d relation between control variables like transit fares and performance measures like revenue and rIdership Its tool fol travel

DESIGN CONSIDER4TIONSFOR TRANSIT PRICING DECISION MODELS

The major characteristics of the transit pricing problem which should temper the design of models that purport to address It are outlined below (1) The system to be modeled IS Ill-structured because It deals with behavloral phenomena (u) Available ddta are inadequate and “dirty” because

tThls resedrch wds supported m part by a grant from the U S Dept of Transportah& UrbanMass Transportation Admmlstration (No NY-I l-0016) The material found m this nauer . . served as the basis for an mvlted talk to the Forum on Transit Pricmg Innovation held m Virgmia Beach, VA, 28-29 March 1979 125

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of severe measurement dlfficultles associated agam with behavioral phenomena Contrast this situation with that of the traditional operations research models which have been developed for production and Inventory control The latter models rely prlmardy on data from physical systems wlthm the firm On the other hand, transportation models often require data that must be recalled by the survey respondent Frequency of transit usage 1s one such key example Analogously, as noted by Buzzell (1964, P 74), “ inventory models would be far more difficult (to use) If It were necessary to rely on estimates of stocks on hand as recalled by stock clerks ” (m) Demand relatlonshlps are complex They tend to be highly non-linear, exhibit threshold effects (1e some mmlmum level of stimulus 1s required for there to be any response at all) and delayed response (1e effects of pohcy changes are frequently carried over mto future time periods), and, mvolve a multitude of interacting vanables of which transit fare 1s lust one (IV) The final outcomes of transit fare deacon\ depend on competitor’s actions about which transit planners have imperfect or httle advance mformatlon The above problem characterlstlcs are literally the same as those encountered m marketing on account of the slmllarlty between the two problem environments Given these charactenstlcs, It 1s not surprlsmg to find Judgments playing a major role m both the transit prlcmg and marketing deaslon-making processes This observation leads us to the first conslderatlon for model design a transit pricing declslon model should exploit the “ludgmental data” derived from experience and mtuition which managers carry m their heads and which they use m any event to arrive at the final decision The next pomt that we make about model design concerns Its structure Traditional aggregate models of travel demand can yield estimates of only overall economic effects of a proposed fare pohcy m terms of, say, total ridership and revenue However, since transit systems are increasingly being viewed as a public service, the pohtlcal dimension cannot be ignored m the determination of transit pricing pohcy Speafically, m addition to the aggregate economic effects, the equity lmphcatlons of a fare pohcy need to be examined m terms of how different classes of riders are affected by the proposed pohcy For example, would a price increase hurt mner city residents more or less than suburban commuters? Would the loss m patronage be greater off-peak than peak? Would lightly used routes be lmpacted more than heavily frequented ones? Would a fare reduction benefit work tnps more than shopping trips’ Would lowering the fare during the off-peak period generate significantly more riders on routes with considerable excess capacity? To answer detailed questions such as those posed above, the analysis needs to be carried out at a micro level, where riders are dlstmgulshed by age, sex and other relevant characterlstlcs The micro approach predicts mdlvldual travel behavior, which can be subsequently aggregated by a variety of ridership characteristics to provide a complete picture of the impact of a proposed fare pohcy Thus, with regard to model struc-

MOHL\N

ture, a transit pricing declslon model {hould be dlsaggregate, 1e based on individual obrervatlons of travel behavior, If a detailed assessment of impact 1~needed m terms of which groups of nders are affected by a change m the fare structure An aggregate model IS inherently incapable of provldmg such an assessment Atherton et a/ (1976) adopted a slmlldr disaggregate methodology for the same reasons to analyze the effects of car pooling policy incentives The use of a disaggregate methodology 1s also underscored by another factor that IS gaming importance m the design of a travel demand model, namely, the model’s potential for transferablhty The reason for this 1s twofold (a) the smaller tranrlt agencies are often not able to develop models of their own due to budget and time constraints, and, (b) the cost incurred m developmg a travel demand model could yield a multlpher benefit d the model 1s used to forecast demand m other areas On the question of transferability of a disaggregate model, the following observations by Atherton and Ben-Aklva (1976) are pertinent “Disaggregate models are most likely to be trdnsferable because they represent the average behavior of the mdlvldual traveler, and It IS reasonable to expect mdlvldual travel behavior to be essentially the same m one area as m another Moreover, the estlmatlon of disaggregate models does not rely on a particular zonal aggregation. 50 that a correctly specified disaggregate model that properly explains travel behavior in one area should be valid (at least more valid than a comparable aggregate model) for predlcbons of travel behavior m other areas ” The last point we wish to make on the model design issue 1s that it should be understandable to transit managers for them to use It A well-known management scientist has diagnosed lack of understanding by managers to be the major obstacle to lmplementatlon of models based on the followmg argument (Little, 1970) “People tend to reject what they do not understand The manager carries responshhty for outcomes We should not be surprised if he prefers a simple analysis that he can grasp, even though it may have a quahtatlve structure, broad assumption\, and only d httle relevant data, to a complex model whose assumptions may be partially hidden or couched in Jargon and whose parameters may be the result of obscure statIstical mampulatlons ” Ease of understanding IS even more critical for transit pricing models as compared to, say, marketing models due to the more complex decision-makmg environment of the transit manager The marketmg manager has greater freedom to make declslons. with a well-defined responslblhty to produce desired financial results In contrast, the transit manager has to negotiate his choice of a fare pohcy with several other parties who are mvolved m Such pubhc sector pohcy matters Gettmg the acceptance of these other parties would be facilitated if they also understood the rationale for the selection of the particular fare pohcy, mcludmg the model underlying the analysis The need for understanding dictates that the model should be simple and include only important phenomena The pressure to put more detail mto a model to improve Its accuracy should be re5lsted until the users demon-

A deaslon

model

for evaluatmgtramIt prlclng pollcler

strate they are ready to assumlate it Apart from the mtellectual cost of understanchng a complex model, the time and financial costs of model development and data collectIon Increase rapldly with model detail An evolutionary model IS the Ideal where the first cut IS a simple model which IS later expanded m detail, If It appears worthwhile to do 50 In summary, we beheve the followmg conslderatlons should Influence the design of transit prlcmg declslon models (1) Manager’s ludgements are an important data source which should be explolted by the model (11)A disaggregate model structure IS called for d a detalled evaluation of the Impact of a fare pohcy IS required m terms of who IS affected by the proposed change The other advantage of a disaggregate methodology IS Its potential for transferabihty (m) The model should be understandable to managers smce it IS used as an ald to declslon-makmg and, consequently, managers wII not risk using It unless they have confidence m Its output This confidence comes about from understandmg what to expect from the model and, equally Important, what not to expect The understandablhty factor IS especrally Important m models deslgned to assist m pubhc sector pohcy determmatlon because of the multlphclty of parties mvolved m the policy-makmg process The negotlatlon of a particular prlcmg pohcy selected by the transit manager reqmres, at a mmlmum. the commumcatlon of the ratIonale underlymg the choice to these other pa&es, which would be hmdered by the use of a complex model (IV) A simple parslmomous model should be the startmg point, and an evolutionary approach 15recommended for expandmg it m detail based on a trade-off of costs agamst the value of Improved forecasting accuracy of a more complex model

127

LIttIe comed the term declslon calculus to describe a model that fulfils the above reqmrements and defined It as a set of numerlcal procedures for processmg data and Judgments to assist a manager m his declslon-makmg The goal of such a model IS simply to help the manager do his lob better This should not be difficult If the manager IS currently basmg his decisions prlmarlly on Judgment wlthout the ald of any model The crux of the declslon calculus approdch IS the Integration of Judgments with avallable hard data m an exphclt model which would (a) permit exammation of a much larger number of pohcy alternatives than would otherwIse be feasible, and, (b) enable a sensltlvity analysis to be carried out with regard to the soft data Inputs used m the analysis The mathematical model and the user act cooperatively m such a model The manager usually has a good understandmg of the dynamics of the market based on experrence and can make subJectlve assessments of reactions to, say, a fare change for 5pecdic segments of the rider population However, It IS not easy for him to evaluate the total impact of a proposed fare pohcy for all the segments taken together and balance the trade-offs between rIdershIp and revenue for a number of alternative fare pohcles-these are tasks that a mathematical model can perform very efficiently Thus Judgment alone IS inadequate as IS a mathematical model alone, but the two together may be most effective This IS the essence of the declslon calculus approach A key element of ttus concept IS Its approach to cahbratlon Rather than relymg entuely on past data and econometric procedures to estimate a model’s Little’s approach 13 “unabashedly parameters, (smce) we would mamtam that, for a real eclectic declslon problem, exclusive rehance on previous data bases ~111usually be mlsleadmg, the cahbratlon task IS to estimate what IS gomg to happen, not what has already happened” (see Little. 1975) In Wtle’s view, managerial Judgments are an Important data source for cahbratmg a THE PROPOSED APPROACH FOR MODEL DEVELOPMENT model especially when hlstorlcal data are either mA new approach for deslgmng decision models was adequate or duty The best data for estlmatmg ploneered by John Llttle of M I T m the late sixties m an parameters 1s probdbly through controlled expenmenty, attempt to overcome the lmplementatlon d~fficultles but this IS expensive and time-consurmng and m the case which plagued the large number of marketmg models of transit pnang, probably lmposslble In any event, that had been developed at that time H15 analysis of the managers who make declslons about, say, prices are reasons why managers did not use a&able models led tmphcltly makmg Judgments about response Hence, if him to prescribe the followmg requirements for an the model 1s cahbrated with key people’s Judgments, we effective decision model (LIttIe, 1970) should at least be as well off as before and, usually, better off, smce Judgments can be obtained m an “A model that IS to be used by a manager should be orgamzed way and from more than one person More simple, robust, easy to control, adaptive, as complete Importantly, a sensltlvlty analysis can be performed with as possible and easy to commumcate with By simple respect to alternatlve SubJectlve estimates to test then IS meant easy to understand. by robust, hand to get Impact on the model’s output Cleaner, more extensive absurd answers from, by easy to control, that the user data could then be gathered only for those inputs which knows what input data would be reqmred to produce slgmficantly affect the conclusions In any event, at a desired output answers, adaptive means that the given pomt m time, subjective e&mates are valuable for model can be adjusted as new mformatlon IS dcqmred, parameters that are difficult to measure or w&h cannot completeness lmphes that Important phenomena ~111 be measured m the time available before a decision must be Included even if they require Judgmental estimates be made of theu effect, and, finally, easy to commumcate with In this context the requests to the user for Judgmental means that the manager can qmckly and easily change data should be for parameters with a clear operatlonal inputs and obtam and understand the outputs ” Interpretation Consider, for example, the sales advertis-

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P

BALLOII

mg model S = M( 1- exp (- KA)) The parameter, M, IS the market potential which managers can estimate directly through a combmatlon of market research data and Judgment However, the sale response constant, K, 1s not capable of such an operatlonal mterpretatlon In this case, It IS better to ask the manager to estimate ludgmentally sales for, say, a 50% increase from the current advertlsmg level and infer the value of K from the equation In the short time since Its mtroductlon. the declslon calctilus approach has been widely apphed m deslgmng marketing models which have achieved a track record of successful lmplementatlon m a number of companies Because of the \Imllarltles between marketing and transit prlcmg problems, we decided to adopt a slmdar approach m the design of a lode1 that would be useful to transit managers m evaluating dlternatlve fare pohcles The goal and style of a model produced by this dpproach are different from those of the travel demand forecastmg models m the literature where the trend appears to be “the bigger the better” Note that the more eldborate the model. the greater 1s the cahbratlon problem, which IS one of the factors responsible for the patchy Implementation record of some of the sophlstlcated operation\ research models Our efforts have been totally dommated by the desire to produce a simple model that transit managers can readdy understand and use, yet one that 15not slmphstlc, a model thdt 15 complete on Issues that are Important for assessing the impact of d proposed fare structure, a model that does not rely only on hlstorlcal ddta for Its cahbratlon. dnd. finally, d model thdt is easy for the user to commumcate with m the \ense of bemg able to change inputs easily and obtam outputs that provide a full range of mformatlon for a5sessmg the effects of d change in fare structure All of these are lmportdnt features that characterize the declzlon calculus approach

THE TRANSIT

PRICING

DECISION

MODEL

Model structure

As noted ahove, d prime requirement of d trdnslt pricing decision model 1s the capablhty to forecast travel demand by vdrlous classes of riders m order to determme the revenue and equity lmphcatlons of proposed changes m the fare structure Further, the vahdlty of the forecasts 1s dependent on the extent to which the model 1s complete m terms of mLorporatmg Important factors that affect trdnslt usage such as purpose of the trip peak or non-pedk hour of the day, age, sex, etc We adopted the micro-slmulatlon modelmg technique since conslderable detail has to be captured at the level of the mdlvldual rider to enable subsequent aggregation by any desired rider charactenstlc for the eqmty analySIS The other ddvantdge of usmg micro-simulation IS that, by operatmg at the mdlvldual rider’s level, we can Isolate the behavior of interest, namely, how the mdlvldual’\ travel demand IS affected by a change m the fare pohcy All the other factors that affect travel behavior hke frequency of bus service, proxlmlty of the bus stop to the rider’s residence or destmatlon, etc will

and L

MOH~N

be held constant If we study transit demand at the level of the mdlvldual rider This ehmmates the need to exphcate these other factors since their combined effect has already manifested itself m the decision of the mdlvldual to use the bus and m his/her present frequency of rldershlp What we wish to study 1s the extent to which this frequency of transit usage ~111change due to a change m the fare pohcy The basic equation used m our micro-slmulatlon model for forecastmg the travel demand of an mdlvldual rider 1s simple and qtralghtforward

(1)

where D, = present transit usage by rider I. 0: = forecast usage under the proposed fare pohcy by the sdme rider 1 F, = current fare pald by rider 1, F: = new fare that WIII be paid by the same rider under the proposed fare pohcy. and e, = fare eld\tlclty dpproprlate for rider 1 In essence, the above equdtlon calculates the new demand for the proposed fare pohcy 0:. by adlusting the current demand, D,, through a ratio estimate based on the fare elasticity that 1s appropriate for that rider We feel that this approach IS preferable to a regresslon model which estimates 0: directly as a function of the proposed new fare, F: It emures that the model 1~ robust m LIttIe’s sense, reasonable values for e, cannot yield unreasonable value\ for 0,‘. which would not necessdrlly be the case if 0: were estimated from d regresslon equation Clearly. the elasticity would vary by factors \uch d\ trip purpose, time of day dnd \ocIo-economic dnd demographic characterlstlcs of the rider For example, affluent suburban residents will react differently to the fare change\ as compared to elderly inner city residents Peak hour commutmg trips may not be affected by a fare change m the same way aq non-peak shopping trips The micro-slmulatlon approach enables different fdre elastlcltles to be applied to mdlvldual riders thus mabmg our model complete on the important factors that dffect frequency of transit usage Although the above model IS focused solely on the Impact of fare changes. It can be easdy extended to encompass the effects of service changes as well through the mcorporatlon of service elastlcltles It should, however, be kept m mmd that the model m Its preqent structure 15 concerned with current rider5 only More 5peclficdlly. the model evaluates the change m the frequency of transtt usage on the dssumptlon that the mdlvldual rider mdkes the same trip (e g work trip or shopping trip) after d fare change ds before but with d different frequency The model does not Include the potential Increase m aggregate travel demand arlsmg from new riders or from current riders expandmg their transit usage for new trip purposes because of the mducement provided by the new fare structure Our ratlonale for 5tructurmg the model m Its present form can be traced partly to the constramts of the data dvailable for lmplementmg the model The most common source of data for transit demand studies at a micro level

A decwon model for evaluatmg transit pricingpohcles IS an on-board rIdershIp survey which cannot, by Its very nature, provide any data for current non-nders With regard to new trip purposes of current riders (other than the particular trip that the rider was makmg at the time of the survey), the pertment data could be gathered through addItIona questlons m the survey However, for the mltlal apphcatlon for which this model was developed m the Albany area, the on-board ridership survey conducted by the local transit agency did not include these questions The present model structure was thus Influenced slgmficantly by the data avallable to drive It m the mltlal application We were also influenced by the requlrements of a decision calculus model dlscussed m the previous sectlon In particular, the Importance of the model being understandable to transit managers required the mltlal model structure to be slmple However, m keeping with the evolutionary nature of decision calculus models, the model IS currently bemg expanded to handle both new riders and new trip purposes of current riders As 1s to be expected, Its Implementatlon will require more data than 1s provided by the standard on-board rldershlp surveys Whether It IS worth the time and cost to generate the new data will depend on the tolerance of the transit manager to accept the error ansmg from the mltlal simple model Note that this error IS one of underestlmatatlon of aggregate travel demand and, hence, aggregate revenue

Culrbratlon

The cahbratlon of eqn (I) mvolves the estlmatlon of only one parameter-the fare elastlclty To slmphfy the task, the user may segment the market mto groups whose members are expected to be relatively homogeneous m terms of their responses to changes m the fare structure, 1e whose members have a common fare elastlclty The fare elastlclty then needs to be estimated only for each of these market segments It should be pomted out that the elasticity of demand represents the percentage change m frequency of transit rIdershIp for a 1% change m fare paid by that rider for that trip For example, suppose a rider IS using the bus 5 times a week for shopping trips and pays a fare of 25 cents per trip An elastlclty of -0 25 for this rider lmphes that a fare increase to 35 cents for this trip, representing a 40% increase m fare paid, wdl reduce demand by 10% Based on the declslon calculus prmclples outlined m the previous sectlon. an eclectic approach IS recommended for the cahbratlon task The key feature of our model 15that Its only parameter, the fare elastlclty, has a clear operational meaning and 1s umversally understood The user could, m fact, estimate the percentage mcrease/decrease m transit usage for a given type of rider for, say, a 25% Increase/decrease m fare paid by that rider, from which the value of e could be inferred Hence, It IS feasible for transit managers to partition Judgmentally the market and estimate the fare elastlcltles for each segment, If a clean data base 1s not available for this exercise Also, transit managers should use elastlclty estimates from other studies with Judgmental adlustments,

129

d necessary, to reflect their beliefs about the market response for their particular areas This approach IS better than relying on Judgment alone since It would dvoid the pltfall of usmg grossly inaccurate elasticity values A blbhography prepared by Kemp and Rea (1976) contams an extensive hst of articles dealing with fare elastlcltles Recent studies m this area include the work of Goodman (1979) and Smha (1979) To Illustrate the approach, we cite our own mltlal work m applying the model to the Greater Albany, New York, area (Ballou and Mohan, 1977) The market was first segmented mto three income groups based on the Judgment that the fare elastlclty would be relatively more homogeneous for members of the same income group Two sets of elastlclty estimates were then employed m running the micro-slmulatlon model One set was obtained by fitting regression models to the sample observations m each income group An alternative set of estimates was taken from a recent report prepared by Peat, Marwick, Mitchell and Co (1977) and Judgmentally adjusted for the situation at hand This example also dlustrates another important feature of our modeling approach-the use of alternative parameter estimates whose effects on the conclusions of the pohcy evaluation can be tested through sensltlvlty analysis This testing of alternative elastlclty sets circumvents. to a degree, the need for precise elastlclty values In the context of the sensltlvlty analysis, we wish to pomt out that, although the Impact of changing elasticity values on the transit usage of an mdlvldual rider IS obvious from eqn (1). different eldstlclty values are applied to different riders depending on the market partltlonmg used m the analybls It IS not, therefore, lmmediately obvious to what degree the changes m the elastlclty values would affect ridership and revenue m the aggregate as well as for the specific customer groups of interest for the equity analysis Before concludmg this subsectlon, It must be noted that Judgment IS not the only means available for partltlonmg a market Statlstlcal analysis of sample data using techniques like AID (Automatic Interaction Detection) can also be employed for this purpose (see Ugohk, 1978) Data requirements

The mformatlon required to drive the model IS easily avallable through an on-board ridership survey The survey should Include questions on the key variables m eqn (1). the current fare (F,) and frequency of transit usage (0,). m addition to other useful ridership characterlstlcs such as destmatlon purpose and time of day of the trip together with demographic factors such as mcome, age, sex, famdy size, county of residence and race Information on trip orlgm and destmatlon will also be required to determine the fare that wdl be paid by that rider under a proposed fare pohcy change (F: of eqn 1) For situations where ongm-destination data are not available, changes m fare pohcy could be proposed for specific subgroups of riders, e g senior cltlzens, off-peak travelers, and the like This would m effect specify F: for a given nder--F: would be set equal to

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F, If rider I does not belong to any of the specific subgroups covered by the proposed fare pohcy. it would be set at the new fare d he/she does In this case mformatlon on rldershlp charactenstlcs and demographic factors would be essential to ascertain the membership status of rider I with regard to the specdied subgroups Also, if the transit manager wishes to segment the market with the idea of usmg a common fare elastlclty for members of a group, the partltlonmg variables (e g mcome) will have to be selected from the variables covered by the survey A slmdar constraint applies to the ridership categories of interest m the equity analysis The vanables defining these categories have to be limited to those covered by the survey since the mdlvldual riders of the sample need to be dlstmgulshed m terms of these vdriables The sampling plan for the survey should encompass all malor routes and hours of the day when the transit service IS m operation Smce population ridership data by route and time of day are usually available, the sample can be projected on the basis of appropriate welghtmg factors to reflect the entire ridership population Model flowchart

The gist of our micro-slmulatlon model IS to forecast transit usage and the correspondmg revenue for mdlvldual riders m the sample, project the results to the population through weighting factors, and, finally, aggregate the forecasts by the desired ridership cate-

w

MOHAN

gories to assess the revenue and equity effects of a proposed fare pohcy The detailed structure of the model 1s outlined as a flow-chart m Fig I and described below as a sequence of steps (I) The user partltlons the market mto homogeneous groups which are assumed to have a common fare elast1c1ty (2) The user supplies elasticity estimates for each of the market partltlons Alternative sets of elasticity estlmates may be provided to reflect the inherent uncertainty of market response to a fare change These will be used m separate runs of the model m the sensltlvlty analysts phase (3) The user specifies the fare pohcy to be evaluated by the model (4) The user defines the ridership categories for aggregating the results of the micro-slmulatlon m the output of the model (5) An mdlvldual rider’s record (from the sample) IS read which yields the F, and D, values for this rider m addition to mformatlon on the ridership characterlstlcs This record IS then assigned to the appropriate market partition whose fare elasticity 1s input as the e, value for this rider (6) Depending on the nature of the pohcy being evaluated, the new fare to be paid by this rider, F:. IS determmed using the zonal structure or trip length, if ongm-destination data are available. or the ridership characterlstlcs (7) This rider’s frequency of usage under the proposed

FORECAST

NEW DEMAND,

D,‘= D,(

I+E,

(F:-F,)/

FI)

USER PARTITIONS THE MARKET INTO HOMOGENEOUS GROUPS ASSUMED TO HAVE A COMMON FARE ELASTICITY

1 USER SUPPLIES ESTIMATES OF FARE ELASTICITY FOR EACH OF THE MARKET PARTITIONS

-1 USER

SPECIFIES

THE

FARE POUCY

TO

t WEIGHT REVENUE AND DEMAND BY THE RECORD’S EXPANSION FACTOR TO PROJECT SAMPLE RESULTS TO POPULATION

ACCUMULATE WEIGHTED REVENUE ANDDEMAND ESTIMATES IN THE COUNTERS OF THE APPROPRIATE RIDERSHIP CATEGORIES

;, CATEGORIES

FOR AGGREGATING

THE RESULTS

t READ ONE RECORD (INCLUDING CURRENT FARE PAID, F, , AND TRANSIT USAGE, D, )

ASSIGN RECORD (RIDER) TO AFPROPMATE PARTITION WHOSE FARE ELASTICITY IS

fg

I

Flow

chart

of micro-Slmul&on

lYES CALCULATE PERCENT CHANGE IN TOTAL REVENUE, DEMAND AND AVERAGE FARE PAID FOR FARE POLICY TESTED VS CURRENT FARE POLtY FOR EACH OF THE RI DERSHIP CATEGORIES

model for evaluatmg transit fdre polues

A declslonmodelfor evalualtmgtransit prlcmgpolicies pohcy, D:, IS then forecast using eqn (I) The corresponding revenue IS computed as D:F: (8) The current and forecast demand and revenue of this rider are welghted by an appropriate factor to prolect the sample results to the ridership population m question (9) The weighted revenue and demand estimates are accumulated m the counters of the appropriate ridership categories defined m Step (4) above (10) The computatlonal procedure recycles to Step (5) until all sample records have been read at which pomt the followmg performance measures are captured m the counters for each of the rIdershIp categories, namely, total demand and revenue for both the current and proposed fare pohcles Percentage changes to facdltate “before and after” comparisons are then calculated for the various categories (II) The results are printed out for each of the ndership categories as per the format given m Fig 2, with T’S representmg output values Software package

An interactive software package has been developed to Implement the micro-slmulatlon model with the software design being general enough to permit customlzatlon to a specific user’s needs A modular structure has been employed m programmmg so as to endow the package with maxlmum flexlblhty thereby enhancmg the model’s potential for transferablhty (Recall that a microslmulatlon model has a comparative advantage of transportablhty over a macro model ) An example of the generahzed design of the software

DEMAND

CURRENT POLICY

RIDERSHIP

CATEGORY VARIABLE

1s provided by the output module of the program whtch allows the user to define the rIdershIp characterlstlcs such as age, sex, trip purpose, etc for aggregatmg the results of the micro-slmulatron The program has the ablhty to handle d maxlmum of I5 variables for this aggregation, wherein each variable can be classified up to a maxlmum of IO categories Thus, the user IS not locked mto using predefined variables for the eqmty analysis for which data may not be locally avadable The ablhty provided by the micro-slmulatlon approach to accommodate a variety of needs m pohcy analysis IS highlighted by the ease of tackhng special sltuatlons For example, the software package has the built-m flexibdlty to handle special fares for particular subgroups of riders such as the elderly, handicapped and school chddren Further, the program can evaluate separate fare pohcles for peak and off-peak riders An optlon IS also dvallable to use “forward” and “backward” elastlcltles for representmg the sltudtton where a greater varlatlon m demand IS caused by a fare Increase (“forward’ ) as compared to a fare decrease (“backward”) The mteractlve nature of the package also enables the user to exercise his Judgment to the fullest with regard to the data Inputs for the fare elastlcltles The user 1s completely free to define the market partltlons whose members, m his Judgment, are antlclpated to have a common fare elastlclty Furthermore, the elastlclty estimates to be used m the model are left under the control of the user The mteractlve features of the software should stimulate extensive sensltlvlty analysis with alternatlve sets of elastlclty estimates thus enablmg the user to be less dependent on time-consuming,

SUMMARY

PROPOSED POLICY

REVEMJE % CHANGE

131

CURRENT POLICY

SUMMARY

PROPOSED POLICY

AVERAGE

% CHANGE

CURRENT POLICY

FARE

PROPOSED POLICY

PAID

% CHANGE

1

(say. age1 1

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

2

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

TOTAL

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

1

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

2

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

xxx

VARIABLE (say,

2

trip

TOTAL

purpose)

FIN 2 Format of the output report

132

D P B~LLOU and L MOHAN

econometric cahbratlon procedures which are not easily understandable In sum, our transit prlcmg model fulfils the major prereqmwtes of a declslon calculus simple m structure and, hence, easy to understand, complete on important Issues, easy to control, and. finally, easy to use 4N ILLUSTR4TIVE APPLICATION OF THEMODEL The potential usefulness of the above transit pricing model IS illustrated m this section by an apphcatlon made of the model m the Greater Albany, New York area to study the ridershIp, revenue and eqtuty lmphcatlons of a #peclal class of fare pohcles, VIZ distance-based fares Further apphcatlons are currently under way m three more cities m New York State-Buffalo, Elmua and Syracuse Only the gist of the Albany study IS presented here, since our aim IS to describe the practical utility of the model Fuller details of this study are available m several papers noted m the references The fare structure currently employed by mo$t local or reglonal transit agencies consists of large zones within which passengers are charged the same amount regardles5 of the distance traveled Although the fare IS mcrea$ed each time n zone boundary IS crossed, the boundaries of the zones encompass very large areas $0 that it IS possible to travel several miles from the outsklrtq of the city to the city center for the flat base fdre This has the effect of favoring long trips at the expense of short ones and results m those u\mg the system for long distances paying a considerably lower rate per mile thdn the short-distance rider Under the existing spatial layout of urban areas. the former dre more likely to be affluent suburban commuters traveling during peak hours while the latter tend to be Inner-city users from lowincome ghettos who travel off-peak Thus, while flat fares are popular because of then slmphclty and ease of operation, they dlscrlmmate against the short-distance riders, with distance mequltler often implying social mequltles Further, they dlqcourage off-peak nelghborhood trips which could utlhze the substantlal excess capacity that I$ available durmg non-rush hours If the flat fare was reduced to utilize this capacity. it would undercharge passengers who are wllhng to pay more for longer trips and place an even greater burden of the system’s operatmg cost on short-distance riders It might also reduce total revenues since transit demand tends to be price-melastlc A distance-based fare policy thus merits conslderatlon as dn alternatlve to the present basically flat fare structure from an equity point of view However, there has been no analysis made of (a) whether such pohcles do indeed improve the inequity picture, and, (b) their Impact on revenue and ridership levels Recently the Planning Research Unit of the New York State Depdrtment of Transportation (NYSDOT) became concerned about this issue To evaluate the rIdership, revenue and eqmty effects of alternatlve distance-based fares, the transit prlcmg decision model was developed and applied using data from an on-board rldershlp survey admmlstered by the Capital District Transportation Authority (CDTA), the bus transit system for the Al-

bany, New York, Metropohtan area comprising four urban centers (Albany, Schenectady, Troy and Saratoga) Personnel from CDTA and NYSDOT coded the data and, m particular. used the mformatlon provided by the respondent on the exact origin and destination of the trip together with the bus transit routes to determine the distance travelled for each of the 1190 riders in the sample A prehmmary analysis was performed on the data to test the hypothesis that mequltles exist in the present flat fare structure It emerged that the fare per mile paid by riders spanned a wide range 32% pald IO cents or less, 44% paid between II and 25 cents, dnd the remaining 24% pald between 26 and 76 cents Also. the structure of the mequltles was found to be highly correlated with demographlcs and other factors like the purpose of the trip dnd time of day The findmgs dre fully documented m Ugohk and Leutze (1979) NYSDOT wds Interested m exploring two types of fare structure5 The first, referred to as d “pure dlstancebased fare”, IS defined by the addition of a mmlmum charge (possibly zero) to the product of distance travelled and rate per mile. subject to a prespeclfied maximum fare The second type, a “step-fare” structure, consists of a series of fare increments, which are charged after a specified distance has been travelled, 3ubJect also to a prespeclfied maximum fare Further, two of the seven pohcles tested represented a dlfferentlal fare structure for peak and off-pedk hours The partltlonmg of the YampIe was undertaken by NYSDOT usmg the statistical technique of AID Five market segments were Identified for which the fare eldsticities were estimdted by appropriate multiple regresslon models that were fitted to the disaggregate sample data The results are dlscu$sed by Ugohk (1978) However, because of the inherent hmltatlons of the CDTA data base, Ugohk notes that “the data does not fit the regression equations very well” dnd the elasticity estimate? should be used as an mdtcator of “the trend of frequency of use with varying cost-per-mile” This illustrates the point made m this paper dbout the difficulty of getting clean elastlclty estimates with the kmd of data that are usually dvallable in transportation planning To compensate for the shortcommgs of the calibrated regressions, NYSDOT provided three alternative sets of elastlcltles based on Judgment for runnmg the micro-slmulatlon model Given seven fare pohcles to be analyzed and four sets of elastlclty estimates, the model was run 28 times The vdrlable\ used for aggregatmg the results of the micro-slmulatlon for the equity analysis Included 12 ridership characterlstlcs which are listed m Table I For each ridership category, the computer prmt-out provided before dnd after comparisons in terms of percentage change of demand, revenue, average fare pald, and an addltlonal performance measure that was added to the output for the sake of evaluating diutdnce-based fares, the average fare-per-mile A typical specimen of the computer output 1s presented m Table 2 As expected, certain groups of riders were affected much more by a particular distance-based fare pohcy than others An analysis of the impacts on the various

A deuslon Table Code 9

model for evaluatmg transit prung

poluea

133

1 Variables used for aggregatmg the results of the pohcy analysis for the CDTA bus system Vanable

Name

Descrlptlon

Peak Hour

1 = Peak

2 = Off-Peak

12

Fare Pad

1 2 3 4 5

= = = = =

Cash School Pass conmluter Transfer Emplovee

6 7 8 9

= = = =

Other Token Sr Cltlzen Handicapped

17

Trip Purpose (Destlnatlon)

1 2 3 4

= = = =

Home Yedlcal Shoppng School

5 6 7 8

= = = =

Personal Business Work Social/Recreation Other

19

Frequencv of Transit Usage (trips/week)

1=0-l 2=2-4 3=5-7

4=8-10 5 = 11 or more

28

Countv of Resldence

1 = Albany 2 = saratoga 3 = Rensselaer

4 = Schenectady 5 = Other

29

Sex

1 =

30

Age

1 = 17 or below 2 = 18 - 24 3 = 25 - 44

4 = 45 - 64 5 = 65 or older

31

Number of Cars

1 = No car 2 = 1 car

3 = 2 cars 4 = 3 cars, etc

32

Auto Avallablllty

2 = Female

Male

2 = No

= Yes

34

Family Sue

Category il= No

44

Trip Tune (IIIrnnutes)

l=O-19 2 = 20 - 39

3 = 40 - 59 4 = 60 - 79

52

Trip Length

1 = 1 nnle or less 2 = 1 - 3 miles

3 = 3 - 8 miles 4 = Over 8 nnles

rIdership

categories was made from the computer outputs and presented graphically as m Fig 3 m terms of the ten groups helped most and the ten groups hurt most Further, certam special groups of Interest, If not already included m the previous twenty groups, were added to

in Household

the charts, namely, male, female, peak nders. off-peak nders, riders aged 17 or under and riders aged 65 or over For a particular fare pohcy charted in Fig 3, it R apparent that both the young and the old benefit from ttns pohcy Also, female riders are not hurt as much as

SAMPLE OF GRAPHICAL REPRESENTATION OFTHE IMPACTOF A FARE POLICY ON SELECTED GROUPS OF RIDERS

Frg 3 Sample of graphical

representation

on the Impact of a fare pohcy on selected groups of riders

134

D P BALLOUand L MOHAN

.

“““”

“““““M””

““_““““““_”

..,“3”“3””

u

c-J-3 . . . v\Lh-LP*

< .J

cr.C-I

r,

0.30 .

.

. -f

I“.

_

u\

J

.Y

CJ

.

.

*\

-t

CrCrCrCC.

.

.

.

.

.

.

. . . . . .

:

I

I

4756.6

2Q42.4

ln39.5

too.1

257.5

5

e 7

P

9

139Ql.5

2L97.7

4339C.R

3

TOTAL

5: 6414.6 20497.1

VARIABLE 1 2

4

4379O.P

.3 .o

e 5

TOTPL

.‘)

7

273.?

4

:“o

acc9.9 QL9.2

5 6

34150.8

2 3

44

1

VARIABLE

63’90.9

. 1@2

1

I

4P54.3

1OTAL

. 121

1

6625.9

I I

1

I I

1

I

I I

1

I 1

I

I I

I

1

1

1Z.l

24.2

101.1 193.7

. 15" .14"

.n:1

.052 ,117

.4E2 .192 .nc7

. 117

.oco .OCO

.oco

.C .D .@

.cor . COP

. CC? .14c

I

2L.C

14t

6t.4 lR5.1

.146

.149

.

I

-69.5 -?h.Z

.147 .lL'

24.0

.@ .O

nCC :ccc

292.E

-13.8

24.c

47.4

.147

.14c

.15"

.15r

63.4

16iC2. 227479.

7CC'9.

26865. lO?3C4.

227479.

0.

0.

@.

0.

L.

n

0. " u.

13691. 226763.

-.3

-15.

3. -4.

73316.

7.

28709.

-.7

111067.

2?678T.

0.

0. 0.

0. 0.

0.

0.

0.

-54.

732.

15$5.

93851.

31831. 10217.

10356. 41308.

93851.

0.

0. 0.

0. 0.

1044.

4841.

19aR6.

-16.

6t'ORl.

93852.

6CE.

109882.

51581. 2'805.

3364. 31151.

looaR2.

0.

0. 0.

0. 0.

1878.

11299.

37eaO.

58825.

109aP2.

689.

7369.

17.1

133.0

61.8

-24.0

-67.5

17.1

.C

.O

.O

.O .O

79.9

90.5 133.4

-13.6

17.1

24.1 46.4

45.a

4777.

322". 1919.

I

I

I

I

1 1

1

I

I

I

I

I

1 I

I

I

I

I

I

I

I

I

I

I

I

1

I

7.7

10770.

94oe.

30.2

21.c 13.8

13990.

10743.

I

1

643E.

lQ567.

1534=.

5.7 21.c

I

5977.

2'147. 27095.

22327.

I

17.1

109a*2. I

1

10.4

I

40.9

2h844. alo77.

24245.

2. I

23470. 73381. 97851.

-5.

-.T

0. -0.

71?6.

18013C.

276783.

1746.

4t39.

T99OP. 6OC7.

42C2?.

176670.

727490.

1349.

46'0.

f9Qi.

14797. 6733.

lLYP5.

1F.P

.144

.0rc . nr@

-1.

22427.

22611.

lh.9 -1. -4.

-1.

2477P.

75?5T.

S5362.

36iPl. 2ClPl.

0. -1.

6OQLQ.

5<2I-3.

1.

-0.

6C498.

22678'.

.15" . 15P

.c73

. 03P

47119. 179664.

. 15r

I I

4719E. 1an2Ql. 227479.

7L.C 37.9

20.2

1

Table 2 (Contd)

.14r .14'

.14L

.13'

17.t

.14q .14( 24.0

46.2

.14Q

. 167

. 117

.1?5 .OQ?

. 12p

.lC7

,115

,114

4

1

3

. 124

.1;3 .117

.1r1

9'11.1

I

1 1

1

134t3.E

.2

2

34

75'22 L3390.9

32 8068.6

1

VAPIAALE

2 TOTAL

VARIABLE 1

.4&O ,435

.4lQ .401

.385

.413

.631

.419

.385 .382

.413

.ooo .ooo

.ooo

.DOO .@OO

.654

.t74

.4?3

.327

.4E5

1.739

.704

.280

.117

.LSS

.@OO

.ooo

.OOO

.ooo .ooo

2.564

1.861

.949

17.4

67.8 175.7

-26.6

-69.6

17.4

.O

.O

.O

.O .O

291.E

179.2

100.6

-15.3

17.4

.661

.450

.4a5

23.9 46.8

,511 .412 .413

51.4

8.5

14.7

22.1 31.9

.699

.462

,565

.511

,424 .42F

3.1 20.6

.417

17.4

lo.?

.488

.485

.413

41.1

.401

.451

.437

.LO4

.612

.434

77r?.c

2'25.7 4779r.1

4

5

.n

llC30.7

4212.4

41071.7

5

6 TOTAL

TOTAL

4379c.9

57.6

5

e

14.G ."

7

I

I

1 I

I

1 I

2451.4 lF6.7

51.4

I

9re7.9

7 4 <

1 I

14Fl3.1

167L2.P

. 1: ,-

. 14P

-5L.2 24.C

.1:i-

.lLfi

.117

.C

171.1

1.6

.OCr! .7l4

. CCC

.15n

. 15r

.rc7

.c?c:

'2.f

. 15f 54.4

37.L

7.4 3;.;

.? 24.1

-15.E

2L.4

3 J.5

3t.c

-3.3

.15'

.I:< .14c

.rcr .14c

.C7'

.15 .15r

.15'

. 1co . 11'

.112

.l"

.117

I

.lil

.llK

.l5r

24.G

.14f

.ll'

.155 .ll"

74.1 19.5

.14t

.lLC

'4.0

.14t

.117

.1r9

J?.O -4.7

.144 .147

.lil

7t.t

.147

I

I

I

1 I

2274T9.

?75.

0.

1re.

?Cl.

lOlOT. 12'0.

4'2'3.

771". 94766.

22r473.

C.

l&(75.

51C92.

57@44. 5ccc3.

:lL';.

p.

2247eT.

746.

l6P. ".

'61.

116:.

996n.

L2P14.

"4623.

'7349.

21974t.

17063.

59936. E574P.

5ClC4.

'097'.

226783.

227479.

66fl6.5. 159917.

150859.

t7620.

226787.

-.3

13.

0. c.

0.

-7.

-1. -2.

c. -;.

-.'z

L.

2.

c. -1.

-2.

-0.

0. -.1

-1.

0. -.3

4262.

42'1. 227479.

13237. 1598.

ll5oe. 1801.

I

.4&5

. 417 1

.lE5

.COfl .3a9

.@CO

.940

.3t6

.526 .589

.567

.532

.375

.4P4

. OCD

1

.1!3p

.39@

. 417 .'9P

. 429

. 395 . 420

.417

.con

.199

. 497 .2ST

. 422

I

I

I

I

I

I

I

17.1

-47.6

.3

1.6 121.1

24.5 77.c

3C.P

I

I

I

159992.

64.

3.

141.

139.

6P5.

?42tb. 5235.

50362.

-4.: 26.5

1 I

I

97e51.

172.

c.

64.

177.

5ro.

18547. 42';L.

30?25.

2P9e9.

.3 17.0

I I

.566

.'71 .536

.434

.4a5

.4bo

.542

.4a5

.4ae .375

.42P

.:96

.413

.407

.426

,413

.424

I

I I

I

I I

I

I

I

I

I I

33471.

0.

.604

.425

.430 .556

.coa .415 .414

1 I

1

lC6431.

17.1 -2R.C

25.6

2'.3

-6.5

I

3.

'394.

27707.

321C2.

31741.

1

9p956.

4716.

23CA'.

25554.

247x3.

I

,

1

I

I 1

I

1 114Pb.

1 I

1229c.

I I

1

109PF2.

93851.

13.2 17.1

25.8

1

77615.

c5031.

I

I I

36266.

I 1 I I I

2aa2c.

I I

I

17.1

-11.3

14.e

34.9

6.5 41.5

I

109882.

39833.

205'C.

93851.

51472. 3772.

cp345. 2667.

1

I

I

I

0.

27076.

27071.

713'6.

1

0.

I I

-1. -3.

.14<

t242.

117507.

Table 2 (Cord)

71697.

llP3A8. 6423.

l!.h 51.2

.lLC

. lC7 .1-o .lCP

. OFQ .Pf?

I

;

e

.1:0

.rCt

I

1

Jl

9'53.7

4

VARIAPLF

I

9Q12.7

2 3

I

I

6762.6

1

1

I

437CP.k

3r

I

vARIARLE

I

12029.6

70461.2

2 TOTAL

i I

I

I 1

I

1

VAQIAHLE

2"

1277.6 ll'h9.2

TOTAL

21'15.7

2 7

2h

1

VARIABLE

17.4

-52.3

.cI

121.1

1.6

4P.O

27.3

32.2

26.7

-5.0

.o 17.4

-29.7

17.:

25.2

30.5

-6.4

13.2 17.4

27.3

17.4

-11.5

14.7

34.3

7.2 45.5

A declslon model for evaluatmg transit

males under this pohcy Further, m addition to short-tnp users, the groups helped the most use the transit system for non-work purposes On the other hand, the peak-hour travelers pay more as do the long-&stance riders The detarled analysis of the seven fare pohcies 1s presented m a separate paper by Ballou et al (1978) The results mdlcated that distance-based fare pohcles can be developed which wdl mamtam revenue and rIdershIp levels while lmprovmg the eqmty picture Further, the sensltlvlty analysis showed that the conclusions drawn from the analysis were quite robust with regard to the fare elasticltles used m the ~lmulatlon CONCLUSIONI

Declslon models that emphasize the relationshIp between control variables such as transit fares and performance measures like revenue and rlderslup can be of slgmficant value to the transit manager’s declslon-making process However, such models are not easy to design because of the behavIoral nature of travel, data hmltatlons and complex Inter-relationshIps among the varlables The declslon calculus concept pIoneered by John Little for addressmg marketmg problems which have the same problem characterlstlcs as transit prlcmg problems offers a tested methodology for deslgmng transit prlcmg decrslon models A key feature of the decision calculus approach 1s the recogmtion of a manager’s Judgments as an Important data source for cahbratmg the model especlally when hIstorIcal data are either Inadequate or dirty Smce transit prlcmg 1s a pohtlcal issue, the equity lmphcatlons of a proposed fare pohcy need to be examined m ad&Ion to the overall econormc consequences of lmplementmg that pohcy This calls for a disaggregate model structure since traditional aggregate models of travel demand are inherently incapable of assessing which groups of riders are affected by a change m the fare policy Another important design conslderatlon 1s that the model should be understandable to transit managers Since its purpose ICto assist them m decision-makmg and they are unlikely to use it d they do not have confidence m the mechanism producmg the output Further, because of the multlphclty of parties who are mvolved m the policy-making process, the transit manager has to negotiate his choice of d fare pohcy with several other parties, which would be hindered by the use of a model that IS ddiicult to understand The need for understandmg dictates a simple model structure mcorporatmg only the important phenomena Based on the above design considerations, the microsimulation technique was used m designing the model since It isolates the behavior of interest. namely, how the mdividual rider’s frequency of transit usage IS affected by a change m the fare structure The slmulatlon model begins by forecasting the revised demand for each rider m the sample If a new fare policy were to be introduced This IS based on an elasticity estimate provided as a data mput to the model The micro-simulation approach enables different fare elasticities to be applied to mdividual riders, thus making the model complete on the Important factors that affect travel demand such as trip TRAVol

!(A

No 2-B

pr~cmgpohcles

137

purpose, peak or non-peak hour of the day, age sex, etc The total fare correspondmg to this revised demand and fare schedule 1s also calculated The resultant mdivldual es&mates of ridership and revenue are proJected to the population usmg available weightmg factors and aggregated accordmg to ridership charactenstlcs of interest The results yield estimates of transit demand, revenue and average fare pald by classes of riders for the particular fare pohcy that was simulated The computer output presents the results m terms of before and after comparisons to facilitate the user’s mterpretatlon of the results The data needed to drive the model are easily avadable through standard on-board ridership surveys Indeed, the data avadabdity constraints had a sigmficant influence m designing the model m its present form Any refinement of the model to handle, say, the potential increase m aggregate demand drlsmg from new riders, who are attracted by a change m fare pohcy, would require additIona data pertaining to current non-nders This segment of the population, by its very nature, cannot be covered by an on-board ridershIp survey The time and dollar costs of gathering this additional data, not to mention the mtellectual cost of understandlng a more complex model, have to be traded off against the value of improved predictions of ridershIp and revenue from a more comprehensive model The practical utility of the transit pricing model has been dlustrated by Its application to evaluate a special type of fare policy-distance-based fares The model and supporting software have, however, been designed to be flexible enough to facdltate testing of a variety of fare structures and customlzatlon to a specific user’s needs and data constraints Acknowledgements-The authors gratefully acknowledge the stimulus and assistance provided by personnel from the New York State Department of Transportahon and the Camtal Dlstrlct

TransportationAuthority In-partlculdr,the suppori providedby David T Hdrteen. Planmne Research Unit during the entire ‘research prolect deserves knowledgement

of NYSDOT. a special ac-

REFERENCES

Atherton T J dnd Ben-Aklva M E (1976) TransferabIlIty and updating of disaggregate travel demand models Trunspn Re3 Ret 610. 12-18 Atherton T J Suhrbier J H and Jessiman W A (1976) Use of dl$aggregate travel demand models to analyze car pooling policy incentives Trunspn Res Ret 599. 35-40 Ballou D P and Mohan L (1977) Evaluafron of Revenue and Equity Implrcatrons of Distance-Based Fares for Transit SWterns Status Report on Transportation Research ProJect Prepared for Department of Transportation, Urban Mass

TransportationAdnumstratlon Ballou D P . Hartaen D T and Mohan L (1978) Dutance-Based Transit Fares Robrn Hood or Shenff of Notlrngham? Preliminary ResearchRep No 145 New York State Department of Transportation Planning Research Unit Albany, N Y Buzzell i (1964) Math~matrcal Models and- Marketrng Munaaement I)lvision of Research,GraduateSchool of BUSInebs Admmistratlon, Harvard Umverslty Goodman K M (1979) Impacts of recent fare Increases Paper presented at the Forum on Transit Pncmg Innovatrons sponsored by UMTAIn VlrgmiaBeach,VA, March 1979 Kemp M and Rea L (1976) The consequences of transit fare and service pohcles a classdied bibliography Workmg paper 5050-1-2, The Urban Institute, Washmgton. D C

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Little J D C 11970) Models and managers The concept of d declslon calculus Mgmf Scr 16, 466-485 Little J D C (1975) Brandaid A marketing mix model Ops Rey 23 628-673 Peat. Marwlck. Mltchell & Co (1977) Public transportation fare policy U S Depdrtment of Transportation. Rep. #DOT-TPI10-77-19 Smha K C (1979) Trdnslt \ervlce ela\tlcmes Paper presented at

the Forum on Transit Pncmg Innovations, sponsored by UMTA held m Vnguna Beach, VA, March 1979 Ugohk W (1978) Demand elastlcmes of per-mile transit fares Preliminary Research Rep No 138 New York State Department of Transoortatlon Plannmg Research Unit. Albanv. N Y Ugohk W and Leutze C B (197%) Who pays the highest and lowest per kllometer transit fares? Transpn Res Ret 719