JOINT HIGHWAY RESEARCH PROJECT FHWA/IN/JHRP-80/9 MODELLING TECHNIQUES IN TRANSPORTATION PLANNING FOR SMALL URBAN AREAS INDIANA UNIVERSITY

Transcription

1 SHOOL OF IVIL ENGINEERING PURDUE MDIANA STATE UNIVERSITY HIGHWAY OMMISSION JOINT HIGHWAY RESEARH PROJET FHWA/IN/JHRP-80/9 USE OF SYNTHETI DEMAND MODELLING TEHNIQUES IN TRANSPORTATION PLANNING FOR SMALL URBAN AREAS IN INDIANA K.. Sinha H. S. Mahmassani J. R. Mekemsn E. W. Hansm

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3 Final Reprt USE OF SYNTHETI DEMAND MODELLING TEHNIQUES IN TRANSPORTATION PLANNING FOR SMALL URBAN AREAS IN INDIANA TO: Harld L. Mihael, Diretr Jint Highway Researh Prjet FROM: Kumar es. Sinha, Researh Engineer Jint Highway Researh Prjet August 26, 1980 File: Prjet: L Attahed is the Final Reprt n the HPR Part I Study titled "Use f Syntheti Demand Mdelling Tehniques in Transprtatin Planning fr Small Urban Areas in Indiana". The researh has been nduted by Messrs. Hani S. Mahmassani, James R. Mekemsn and Edward W. Hansm, Graduate Instrutrs in Researh f ur staff under the diretin f Prfessr K.. Sinha, Researh Engineer. The Reprt here is a summary f the findings f the prjet. It inludes suggested guidelines and predures fr the use f the syntheti apprah in travel demand freasting in small urban areas in Indiana. This Reprt is frwarded fr review, mment and aeptane by the ISH and FHWA as fulfillment f the bjetives f the researh. Respetfully submitted, K.. Sinha Researh Engineer KS : ms : A. G. Altshaeffl W. L. Dlh R. L. Eskew G. D. Gibsn W. H. Getz M. J. Gutzwiller G. K. Hallk D. E. Hanher. F. Shler K. R. Hver K.. Sinha J. F. MLaughlin. A. Venable R. D. Miles H. P. Wehrenberg P. L. Owens L. E. Wd G. T. Satterly E. J. Yder S. R. Yder

4 Digitized by the Internet Arhive in 2011 with funding frm LYRASIS members and Slan Fundatin; Indiana Department f Transprtatin

5 Final Reprt USE OF SYNTHETI DEMAND MODELLING TEHNIQUES IN TRANSPORTATION PLANNING FOR SMALL URBAN AREAS IN INDIANA: EXEUTIVE SUMMARY by Kumar es. Sinha Researh Engineer and Hani S. Mahmassani James R. Mekemsn Edward W. Hansm Graduate Researh Assistants Jint Highway Researh Prjet Prjet N.: L File N.: Prepared as Part f nduted by an Investigatin Jint Highway Researh Prjet Engineering Experiment Statin Purdue University in peratin with the Indiana State Highway mmissin and the U.S. Department f Transprtatin Federal Highway Administratin The ntents f this reprt reflet the views f the authr wh is respnsible fr the fats and the auray f the data presented herein. The ntents d nt neessarily reflet the ffiial views r pliies f the Federal Highway Administratin. This reprt des nt nstitute a standard, speifiatin, r regulatin. Purdue University West Lafayette, Indiana August 26, 1980

6 ii 1. Reprt N. FHWA/IN/JHRP-80/9 7, G vrnmen t Aessin N TEHNIAL REPORT STANOARD TITLE PAE 3. Reipient's tlg N. 4. TitU and Subtitle USE OF SYNTHETI DEMAND MODELLING TEHNIQUES IN TRANSPORTATION PLANNING FOR SMALL URBAN AREAS IN INDIANA 7. Authr(s) Kumar es. Sinha, Hani S. Mahmassani, James R. Mekemsn and Edward W. Hansm 9. Perfrming Organizatin Nrn* and Address Jint Highway Researh Prjet ivil Engineering Building Purdue University West Lafayette, Indiana Spnsring Ageny Name and Addres* Indiana State Highway mmissin State Offie Building 100 Nrth Senate Avenue Indianaplis, Indiana Reprt Dale August 26, P«rtnnina, Orgnixatin 4* 8. Perfrming Organiztin Reprt N. JHRP Wrk Unit N. ntrat r Grant N. HPR-K17) Part I 1.1. Type f Reprt nd Perid vered Final Reprt 14. Spnsring Ageny de 15. Supplementary Ntes nduted in peratin with the U.S. Department f Transprtatin, Federal Highway Administratin. Frm Planning Study titled "Use f Syntheti Demand M delling Tehniques in Transprtatin Planning fr Small Urban Areas in Indiana". 16. Abstrat This reprt presents an exeutive summary f the researh dumented in three earlier interim reprts vering trip generatin, trip distributin and traffi assignment phases. The researh develped a predure fr the use f syntheti generatin f travel demand infrmatin that an be used in small urban areas in Indiana. The results indiated satisfatry perfrmane f the predure. A set f guidelines was develped fr appliatin f the researh results in new transprtatin studies r in updating f existing transprtatin studies. 17. Key Wrds Urban Transprtatin Planning; Trip Generatin; Trip Distributin; Traffi Assignment; Syntheti Demand Mdelling 18. Distributin Statement N restritins. This dument is available t the publi thrugh the Natinal Tehnial Infrmatin Servie, Springfield, VA Seurity laslf. (f this reprt) 30. Seurity lsslf. (f this ps' 21. N. f Pgee 22. P'lee Unlassified Unlassified 42 Frm DOT F le-esi

7 iii TABLE OF ONTENTS Page LIST OF TABLES LIST OF FIGURES PREFAE iv v vi INTRODUTION SYNTHETI TRIP GENERATION General harateristis Of The Study Areas 4 Distributin Of Trips By Purpse 4 Areawide Trip Frequeny Trip Generatin Analysis At The Znal Level Trip Generatin Analysis At The Disaggregate Husehld Level Bayesian Updating Of Trip Generatin Infrmatin 12 nlusins On Trip Generatin Phase 13 Remmended Predure Fr Syntheti Trip Generatin Analysis In Small Urban Areas 14 SYNTHETI TRIP DISTRIBUTION 17 Travel Time harateristis 18 Gravity Mdel Sensitivity Analysis 18 Develpment And Testing Of Predures Fr Syntheti Self-alibratin Of Gravity Mdels 22 Areawide Apprah T Syntheti Self-alibratin Of A Gravity Mdel 22 Origin Zne Speifi Apprah T Syntheti Self-alibratin Of A Gravity Mdel 23 Inrpratin Of The Estimating Equatins Int A Gravity Mdel. 25 nlusins On Trip Distributin Phase 27 mputer Prgram Fr The Remmended Predure 28 EFFETS OF SIMPLIFYING ZONE AND NETWORK SYSTEMS ON THE AURAY OF TRAFFI ASSIGNMENT 28 Predures Fr Data lletin And Analysis Analyses Of Test Results Guidelines Fr Zne System And Netwrk Develpment 33 ONLUSIONS 34 REFERENES 35 APPENDIX 36 mputer Prgram Fr Syntheti alibratin Of Gravity Mdel 36

8 lv LIST OF TABLES Table Pa «e 1. Study Areas' General harateristis 5 2. Distributin f Ttal Vehile Trips by Purpse 3. Indiana Study Areas' Travel Time Statistis by Trip Purpse (in minutes) 19

9 LIST OF FIGURES Figure Page 1. All Vehile Trips Per Husehld By Husehld Size and Aut Ownership-Lafayette All Vehile Trips Per Husehld By Husehld Size and Aut Ownership-Evansville Generalized Flw hart f Synthetially Self-alibrated, Origin Zne Speifi Gravity Mdel as Applied t a Typial Zne 26

10 vi PREFAE This final reprt presents an exeutive summary f the three interim reprts prepared fr the prjet "Use f Syntheti Demand Mdelling Tehniques in Transprtatin Planning fr Small Urban Areas in Indiana". The titles f these three reprts are: I. "Framewrk fr the Transferability f Trip Generatin Parameters f Small Urban Areas" II. "Evaluatin and Develpment f Syntheti Trip Distributin Mdelling Tehniques as Applied t Small Urban Areas in Indiana" III. "Effets f Simplifying Traffi-Zne and Street-Netwrk Systems n the Auray f Traffi Assignments in Small Urban Areas in Indiana" This summary will serve as a set f guidelines fr the appliatin f syntheti demand mdels in small urban areas. Remmendatins have als been made as t the use f these guidelines. The graduate researh assistants wh wrked n this prjet were: Hani S. Mahmassani, James R. Mekemsn, and Edward W. Hansm. Anther graduate researh assistant, Karen L. Pikett, assisted in the preparatin f the final reprt. The fllwing planning divisin staff f the Indiana State Highway mmissin prvided muh help and assistane: Mike Faris, Jhn Mik and James Barr. The typing f the reprts was mst ably perfrmed by Marian J. Sipes and Meldy Gray. The nstant guidane f Prfessr Harld L. Mihael is gratefully aknwledged.

11 INTRODUTION Initiating a ntinuing, mprehensive, and rdinated transprtatin press is a nsiderable task that faes small metrplitan areas as their ppulatin reahes 50,000. Full-sale mprehensive transprtatin studies used in large areas have tehnial and finanial requirements that ften exeed the apabilities f these smaller areas. In mst ases, suh sphistiatin is nt needed beause the prblems faed by smaller areas are different in nature and in mplexity frm thse in larger areas. It is therefre imperative t develp and implement simplified alternative planning apprahes whih wuld greatly redue the time and st urrently required by the "lassial" transprtatin planning press. One apprah t simplifying the transprtatin planning press is t eliminate the need fr a full sale rigin-destinatin (0-D) survey. This an be ahieved by use f a syntheti travel demand mdelling apprah, whereby infrmatin traditinally btained frm the 0-D survey is fabriated r synthesized thrugh the use f parameters "brrwed" frm "similar" areas. The first phase f this study was aimed at reduing the st and mplexity f the nventinal planning predures by develping a framewrk fr transferring trip frequeny parameters, thus eliminating the need fr nduting full-sale travel surveys. T this end, the bjetives were: 1. mpilatin and interpretatin f varius travel harateristis fr the several small metrplitan areas in Indiana in whih 0-D studies have been nduted. 2. mparisn f these travel harateristis and identifiatin f the ther urban variables that explain these harateristis. 3. Detailed examinatin f trip generatin parameters and mdels fr thse areas at three levels f aggregatin: areawide, znal and husehld; assessment f the stability and transferability f these parameters fr syntheti mdeling appliatins.

12 4. Develpment f a generalized framewrk fr the transferability f trip frequeny (generatin) parameters and mdels. The send phase f the study nentrated slely n the trip distributin part f the mdelling press. The mst mmnly used trip distributin mdel is the gravity mdel in whih alibratin f the mdel primarily invlves the establishment f the apprpriate travel time impedane funtin. This phase f the researh evaluated the perfrmane f sme f the varius suggested syntheti gravity mdel alibratin tehniques and develped an imprved predure fr syntheti self alibratin f the gravity mdel. The same small urban areas f Indiana, as were examined in the trip generatin phase, were used here. In additin, the mplete transprtatin mdelling data sets frm three f these urban areas were used fr detailed analysis and testing. The five majr tasks were t: 1. Summarize the travel time statistis f the study areas and relate these statistis t ther urban area harateristis. Determine if these statistis will be f any use in ther urban areas in whih the transprtatin planning press is t be implemented r updated. 2. Investigate the sensitivity f the gravity mdel with respet t fritin fatr errrs intrdued by the use f brrwed r standardized fritin fatrs. 3. Investigate the pssibility f using areawide travel time statistis and ther variables in a self-alibrating gravity mdel. 4. Investigate the pssibility f using rigin zne speifi travel time statistis in a synthetially self-alibrating gravity mdel. 5. Evaluate and make remmendatins as t what methd f syntheti trip distributin mdelling shuld be used in the transprtatin planning press fr small urban areas.

13 In the third and final phase f this endeavr, a study was made t determine if the mplexity f urban travel freasting uld be redued by simplifying traffi-zne and street-netwrk systems withut sarifiing auray in traffi assignments. This phase f the study nsidered simplifiatin f several frms using transprtatin netwrk and travel data frm tw small Indiana urban areas. Alternative traffi-zne and street netwrk systems were develped and tested fr eah ity, then test results were mpared by verall measures f traffi distributin and assignment auray, and by statistial analyses f netwrk links stratified by vlume grup. In the fllwing setins are briefly summarized the majr findings f the study inluding a set f guidelines fr the appliatin f syntheti demand mdels fr estimating travel demand in small urban areas in Indiana. The guidelines indiate the remmendatins fr new transprtatin studies r updating existing studies. SYNTHETI TRIP GENERATION The need fr simplifiatin f the transprtatin planning press in small urban areas is apparent. Varius effrts have been undertaken in this diretin. Reent effrts have examined the use f rss lassifiatin disaggregate mdels fr generatin analysis. This tehnique has been strngly suggested fr syntheti mdelling appliatins, espeially in small urban areas. It nsists f develping husehld trip rates fr eah husehld si-enmi ategry. Sine the husehld is taken t be the basi unit f analysis, these mdels are behaviral in nature and shuld therefre be transferable frm ne area t anther.

14 . The stability and transferability f husehld trip rates between small and medium areas is ne f the matters examined. In ntrast t previus wrk dne in this area, this study develped a framewrk fr transferring travel frequeny parameters at three levels f aggregatin: areawide, znal, and husehld. This theretial framewrk is supprted by empirial evidene derived frm the mparisn f parameters btained frm varius study areas fr eah f the relevant levels f aggregatin. General harateristis Of The Study Areas This study was aimed at mpiling infrmatin frm thse small and medium sized metrplitan areas in Indiana in whih a detailed travel survey has been nduted and a transprtatin study perfrmed. Seven metrplitan areas with ppulatin in the range f 50,000 t 250,000 were thus inluded in this study. These are: Andersn, Evansville, Frt Wayne, Lafayette, Munie, Suth Bend, and Terre Haute. These areas are Standard Metrplitan Statistial Areas (SMSA's) as defined by the U.S. Bureau f ensus, and the general harateristis f these urban areas are shwn in Table 1 Distributin Of Trips By Purpse Trips are usually stratified int three types in urban transprtatin planning studies. These are: - internal trips whih are thse with bth ends within the study area rdn line, - internal-external trips, with ne end inside the study area and the ther utside the rdn, and - external-external r thrugh trips, with bth ends utside the study area.

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16 On the average, 82.2 perent f the vehile trips were fund t be internal, 15.2 perent external-internal, and 2.6 perent thrugh trips. These perentages are the (unweighted) averages f the respetive perentages fr eah f the seven urban areas. The hme interview survey, the mst stly and time nsuming element f the 0-D survey, is the basis fr determining internal trips fr the base year. Inasmuh as the aim f this researh effrt was t examine syntheti tehniques whih wuld minimize r eliminate the need fr the hme interview survey, it essentially fused n internal trips. Number and Definitin f Trip Purpses. Fr smaller urban areas, stratifiatin f internal trips int three purpses is usually adequate. These are: Hme-Based Wrk (HBW), Hme-Based Other (HBO), and Nn-Hme Based (NHB). In Table 2 are given the ttal internal vehile trips by purpse alng with their perentages. It was bserved that the distributin by purpse f internal vehile trips was nt the same fr all urban areas in Indiana. Hwever, fr an initial assumptin (t be adjusted later in the press) in a syntheti mdelling effrt the fllwing unweighted vehile trip averages an be made: HBW %; HBO %; NHB %. Variatin Within Urban Areas. A husehld level f analysis was used t investigate sme f the fatrs behind the variatin f trip purpse distributin between urban areas. A hi-square test was used t test the hypthesis that the distributin by purpse f trips made by husehlds was independent f the si-enmi harateristis f husehlds (as refleted in aut wnership status), and the results led t the rejetin f the independene hypthesis with 99.5% nfidene. Therefre, the distributin f vehile trips by purpse turned ut t be signifiantly different between aut wnership grups. Inasmuh as the trip distributin by purpse is signifiantly different fr eah si-enmi grup, it wuld appear reasnable t assume that the verall trip distributin by purpse in tw different urban areas wuld be the same,

17 Table 2. Distributin f Ttal Vehile Trips by Purpse ity HBW HBO NHB* TOTAL ANDERSON 47,105 (16.2) 145,958 (50.3) 97,139 (33.5) 290,202 (100) EVANSVILLE 74,586 (16.5) 188,285 (41.6) 189,923 (41.9) 452,794 (100) FORT WAYNE 90,203 (23.3) 146,990 (38.0) 150,029 (38.7) 387,222 (100) LAFAYETTE 44,337 (15.0) 154,067 (52.1) 97,306 (32.9) 295,710 (100) MUNIE 38,591 (13.4) 159,017 (55.2) 90,641 (31.4) 288,249 (100) SOUTH BEND 79,672 (15.1) 208,452 (39.3) 241,896 (45.6) 530,020 (100) TERRE HAUTE 36,745 (15.7) 121,836 (52.1) 75,130 (32.2) 233,711 (100) inluding Truk trips (Numbers in parentheses are perentages) Unweighted Average Perentages: HBW: 16.5% HBO: 46.9% NHB: 36.6%

18 8 if the si-enmi mix in these areas were similar. This implies that "brrwing" areawide perentages withut first mparing the si-enmi mix f the tw areas uld lead t errneus results. Areawide Trip Frequeny "Areawide", as it is used in this ntext, desribes the gegraphi unit f analysis whih is the ttality f the urban area fr whih a transprtatin study is being nduted. Areawide frequeny measures are t be dedued based n aggregate areawide harateristis f the urban area. The fllwing rates are the mst mmnly used desriptive measures f trip frequeny: - average number f trips per husehld - average number f trips per persn - average number f trips per autmbile. The vehile trips per husehld ranged frm 5.64 t 9.94 per day, while vehile trips per persn varied frm 1.90 t This variability is t be expeted sine areawide travel demand reflets varius urban area harateristis whih are mst likely different between the study areas. Areawide husehld trip frequeny parameters depend n the si-enmi mix f the husehlds in the urban area under study. Thus, in rder t transfer aggregate areawide parameters frm ne area t anther, are must be taken t thrughly mpare the si-enmi harateristis f the husehlds f thse tw areas. Fr grss areawide estimatins, transferring aggregate trip frequeny parameters between urban areas with rughly similar si-enmi harateristis is feasible. Mre detailed analysis and freasting at a finer level f aggregatin wuld mst definitely nt rely n brrwed aggregate areawide travel frequeny parameters, but shuld try t brrw parameters develped at a finer level f aggregatin, suh as the traffi zne r the individual husehld.

19 Trip Generatin Analysis At The Znal Level Multiple regressin tehniques are generally used t relate average znal trips t the znal si-enmi and land use harateristis. All seven study areas develped znal trip generatin equatins. The trip purpses fr whih these equatins were develped varied between the study areas; hwever, these equatins were mbined arding t the three purpses defined fr this study (HBW, HBO, and NHB) in rder t have a basis fr mparisn. The transferability f the internal vehile trip prdutin equatins were investigated at tw levels f detail: 1. Fr eah urban area, the mean znal value f trips prdued fr eah purpse were predited using the equatins alibrated in ther areas. Areawide mean znal values f the independent variables were used fr these preditins, whih were in turn, mpared t the rrespnding bserved values (frm the 0-D survey). The perent differene f the mean (differene between mean f estimated znal trips and mean f bserved znal trips as a perent f the bserved mean) was then alulated as a measure f the verall preditive ability f eah equatin in eah urban area. 2. A mre detailed evaluatin was made fr seleted urban areas (based n data availability) by prediting znal trips fr eah purpse using znal values f the independent variables. These preditins were mpared n a zne-by-zne basis t the rrespnding 0-D bservatins. The perent standard errr f the estimate, expressed as a perentage f the mean value, was used t measure the verall preditive ability f an equatin.

20 10 The investigatin f the transferability f the trip attratin regressin equatins was limited t mparisns f the mean znal trips (bserved vs. estimated) fr three f the study areas (Lafayette, Frt Wayne, Munie). Overall, trip attratin equatins an be transferred fr the purpse f develping rugh estimates that wuld be redefined later n in the press. The results btained in this study shwed that znal aggregate mdel parameters tend t be dependent n the partiular znal sheme fr whih the mdel was develped. They are therefre nt readily transferable t a different znal sheme in anther urban area unless this sheme is very similar in size and mpsitin t the initial ne. The disadvantages f aggregate znal regressin equatins are f even greater nern in the ase f hme-based prdutins (HBW and HBO). These equatins use znal aggregate data and assume that the znes are hmgeneus in terms f the si-enmi harateristis f the husehlds in the zne; in many instanes, this might nt really be the ase. Aggregatin t the znal level tends t neal a nsiderable amunt f the variatin between individual husehlds within a zne, thus masking the ausal nature f the explanatry variables. These mdels are therefre nt behaviral in nature, and, as a nsequene, they are nt sensitive t hanges urring at the basi deisin level f tripmaking. Trip Generatin Analysis At The Disaggregate Husehld Level Within the framewrk f the established transprtatin planning press, tw types f disaggregate husehld trip generatin mdels an be used: regressin mdels alibrated with husehld data, and rss-lassifiatin (ategry analysis) mdels. The latter stratify husehlds arding t their si-enmi harateristis and prvide estimates f the trip rates fr eah f the husehld ategries. These mdels have numerus advantages ver znal

21 11 aggregate mdels. In additin, ategry analysis ffers the fllwing advantages ver the regressin tehnique: - ability t express nn-linear relatinships - distributin-free harateristi whereby n limiting assumptins as t the statistial distributin f the variables have t be made as in the ase f regressin analysis - ease f understanding and simpliity f appliatin - ease f mnitring and updating using small disaggregate samples r even subjetive judgment. These reasns make rss-lassifiatin mdels preferable t regressin mdels, espeially fr simplified transprtatin planning fr small urban areas. rss-lassifiatin Predure. The rss lassifiatin tehnique fr husehld trip prdutin analysis stratifies husehld trip rates arding t the majr husehld si-enmi harateristis affeting trip making behavir. Extensive empirial evidene was examined t indiate that the tw majr determinants f husehld trip generatin are aut wnership and husehld size. Husehlds an therefre be ategrized arding t these tw harateristis, and average trip rates fr eah husehld ategry (r ell) are then develped. In rder t d s, trip rates (by trip purpse) fr the individual husehlds are mputed and aumulated within eah ell. The average number f trips made by husehlds as well as the standard deviatin are then mputed fr eah husehld si-enmi grup. These rates an be pltted and a smth urve an be fitted t the bservatins. rss-lassified Vehile Trip Rates fr Lafayette and Evansville. Sine apprpriate disaggregate husehld data was available nly in tw f the study areas, Lafayette and Evansville, husehld rss-lassified trip rates were develped nly fr these tw urban areas. Trip rates were determined fr eah purpse (HBW, HBO, and NHB). Als, trips were aumulated within eah husehld

22 12 si-enmi ategry, and average trip rates, as well as standard deviatins, were mputed fr eah ategry. In Figures 1 and 2 are shwn ttal internal vehile trip rates fr Lafayette and Evansville, respetively. ell-by-ell mparisns f trip rates by purpse between the tw urban areas were made using a series f t-tests. Only a few ells fr all three purpses exhibited signifiant differenes between the urban areas. Thus, the mparisns made strengthened the belief that behaviral disaggregate husehld mdels are transferable frm ne urban area t anther, espeially in the ase f small urban areas, regardless f the si-enmi differenes between them. In spite f several suh differenes between the urban areas f Lafayette and Evansville, the husehld trip rates were nt signifiantly different, whih means that the trip making behavir is in essene similar in these ities even thugh their aggregate travel patterns are different. Bayesian Updating Of Trip Generatin Infrmatin Even thugh trip rates have been shwn t be stable between small urban areas, it might be desirable t mdify them s that they reflet lal nditins mre aurately. This an be dne by updating the brrwed parameters using infrmatin partiular t the area under study. This infrmatin may be either bjetive r subjetive. Bayesian tehniques an be used t update trip rates develped by ategry analysis in a ertain area befre applying them in a different area. In this study, the methdlgy, as well as example appliatins, were desribed fr tw ases: 1. New infrmatin btained frm small disaggregate sample upled with the prir infrmatin based n the brrwed travel data frm a similar urban area. 2. New infrmatin derived frm subjetive judgment upled with the prir infrmatin based n the brrwed travel data frm a similar urban area.

23 13 nlusins On Trip Generatin Phase I The distributin f trips by purpse is signifiantly different between the study areas, and within an urban area, this distributin varies between si-enmi grups. 2. Fr small urban areas, differenes in si-enmi harateristis nstitute the majr differentiating harateristis affeting trip frequeny; the si-enmi mix in the study areas was indiated by the aut wnership distributin between husehlds. 3. autin shuld be exerised when brrwing znal trip generatin equatins fr use in urban areas ther than the ne where they were develped. 4. Hme-Based Wrk znal prdutin equatins have mre ptential fr transferability than Hme-Based Other equatins beause f the nature f the wrk trip. 5. Nn-Hme Based znal prdutin equatins have gd ptential fr transferability and thus an be used suessfully in syntheti appliatins. 6. Znal trip attratin regressin equatins an be used t synthesize znal attratins in areas different frm the nes where they were develped. Nn-Hme Based and Hme-Based Wrk equatins predit mre aurately than Hme-Based Other equatins even thugh the latter predit within reasnable limits. Hwever, it is remmended that the synthesized attratins be balaned with ttal prdutins as btained by mre aurate syntheti tehniques. 7. Disaggregate husehld trip generatin rates have strnger theretial justifiatin fr transferability beause they are behaviral in nature. 8. The number f levels needed within eah variable in the rss-lassifiatin trip generatin tehnique has been determined t be:

24 Aut wnership: ars, 1 ar, 2 ars, 3 r mre ars. - Husehld size: 1, 2, 3, 4, 5 r mre persns. 9. Bayesian statistis an be effetively used t update rss-lassified husehld trip generatin rates. Objetive r subjetive infrmatin an be used t update trip rates spatially as well as temprally. Remmended Predure Fr Syntheti Trip Generatin Analysis In Small Urban Areas This predure is limited t internal vehile trips. Befre detailed trip generatin analysis is undertaken, rugh estimates an be btained using the trip rates given in Table 2 and in ther tables in hapters 3 and 4 f the reprt, "Framewrk fr the Transferability f Trip Generatin Parameters f Small Urban Areas in Indiana" (1) The fllwing steps uld next be fllwed: a. Establishing ntrl Ttals This uld be dne using the disaggregate trip rates as shwn in Figures 1 and 2 whereby areawide ntrl ttals an be btained by multiplying the apprpriate rate by the areawide number f husehlds in eah ategry. Mre detailed infrmatin an be btained in Referene 1. ntrl ttals are btained fr eah purpse separately. It shuld be nted hwever that the Nn-Hme Based trip rates reprted in hapter 6 f Referene 1 d nt inlude truk trips. The missing infrmatin an be supplied either by using the areawide vehile trip rates reprted in hapter 4, r by using lal survey infrmatin, r by multiplying the number btained frm the trip rates by a rretin fatr between 1.4 and 1.6 depending n the area being studied (this range is based n figures btained frm the study areas) b. Trip Prdutins Znal values f the trip prdutins are needed. These an be btained in the fllwing way:

25 15 01 <A 8 x 3 4 Husehld Size ALL VEHILE TRIPS PER HOUSEHOLD BY HOUSEHOLD SIZE AND AUTO OWNERSHIP - LAFAYETTE FIGURE

26 16 n > in 3O I I 2 3 Husehld Size ALL VEHILE TRIPS PER HOUSEHOLD BY HOUSEHOLD SIZE AND AUTO OWNERSHIP- EVANSVILLE FIGURE 2

27 17 - HBW and HBO: using apprpriate rss-lassifiatin tables and multiplying by the znal number f husehlds f eah ategry. HBW prdutins an alternatively be determined using the equatin remmended fr that purpse in hapter 5 f Referene 1. - NHB: using syntheti equatins as remmended in hapter 5 f Referene 1. The abve znal prdutins shuld be adjusted t agree with the ntrl ttals established earlier.. Trip Attratins Regressin equatins an be used t synthesize znal attratins prvided that the ttal attratins are balaned by ttal prdutins fr eah purpse. In additin, speial analyses might be needed fr speial generatrs unique t the study area. Alternatively, attratin rates derived frm natinal data an be used as desribed in hapter 5, again with the nditin that the ttal attratins are nstrained t equal the ttal prdutins. Finally, the whle press wuld have t be "tuned" s that the vlumes assigned t the netwrk (after the trip distributin and trip assignment steps are exeuted) mpare favrably with the bserved vlume unts. SYNTHETI TRIP DISTRIBUTION The send phase f the study nentrated n the trip distributin part f the demand mdelling press (2). The perfrmane f sme f the varius suggested syntheti gravity mdel alibratin tehniques were evaluated and an imprved predure fr syntheti self-alibratin f the gravity mdel was develped. Data frm a wide range f small urban areas were used in determining variability f different travel harateristis. In additin, detailed travel data frm three urban areas in Indiana, Lafayette, Andersn and Munie, were used in the develpment f the remmended predure.

28 18 Travel Time harateristis In Table 3 are given the relevant travel time harateristis fr eah f the study areas. A detailed analysis was nduted t evaluate the travel time harateristis and it was nluded that the preditin f average travel time based n the ppulatin size f the urban area alne wuld be f little pratial use. This nlusin was derived frm the fat that even thugh a psitive rrelatin existed between average travel time and ppulatin size, the variane f the data abut the regressin line was substantial as measured by lw R-squared values. This results in a regressin equatin with a nfidene interval t large t be f pratial use. A mparisn f hme based wrk travel time frequeny distributins fr the Indiana study areas suggested that mre areful seletin f znal terminal times might enable better mparisns t be made between the varius study areas. Even thugh tw distributins may lk similar, hwever, the average travel times an differ signifiantly. Using travel time frequeny distributins f ne urban area t mpare with the distributins generated frm anther urban area's syntheti trip distributin mdel is therefre f little benefit, unless it is previusly knwn that the average travel time in the tw areas are very lse. The abve pints verify the imprtant fat that travel time statistis f an urban area are a unique result f the area's harateristis and are therefre diffiult t mpare with, r use fr preditin in anther study area. Gravity Mdel Sensitivity Analysis Standard Gravity Mdel Frmulatin. The gravity mdel has been the mst widely used trip distributin tehnique, mainly beause it is simple in nept and the predure is well dumented. Mathematially, the gravity mdel is stated as fllws:

29 19 m * 01 * O vo r-t vo ^-t M vo O in LO <r r-~ M ih O w rh M vo Ov O 00 rh v h.h.-1 in <N -* v m > en 0) 4J 3 0) 60 O M O) > v v m O th s 01 O. u3 PL. u H >v.o m 01 «* * * * u O 00 -» 00 r-~ 00 r~ v VO -* <M Ov m v r^ O H r-{ vo v m i-i V-i rh rh i-h -* M H O >* fl > 0) ri 0) > K * * * * O VO O 00 v m Ov vf> O m in N O Ov N Ov O vo O rh 05 a ih 4J 0) 4-) O D s i > fl ri H en O 0) M < 3 T3 en <u ih O H SB W s 1-1 O dj O O P9 01 J fl H l-i O > 0> O u 0) > < >v J-> J m -* r rh M m Ov O r- M O m mo -* in >* O v O VO vo >-\ p- M 0\ av O M av v r** v 0) fl <4H fl J fl u T3 3 fl X 0) Vl l-l 01 E-i O a 0) vo -a- M in r^ * «* ih M N r^ H 4J fl H X> a 0) -0 3 rh J ih O U 4-1 H rh u rh 5 O 01 ^ >> <u O H fl «fl JZ ^i > 3 60 J O J fl H 3 j-i JJ H 0) rl fl M 3 XI 3 H > O O * U lb <*> M * * O fl XI 0) [fl 3 H rl O >

30 2', T ij p ivn K n m where: T = trips prdued in zne i and attrated t zne j, P. = trips prdued by zne i, A. = trips attrated by zne j, and F. = fritin fatr whih expresses the effet f spatial separatin n trip interhanges. K.. = zne-t-zne adjustment fatrs. (These fatrs are ignred in syntheti tehniques beause rigin-destinatin data is needed t determine these values.) Fritin fatrs an be represented by a set f values at ne minute time inrements r by an equatin f the frm: where: F(t) = t a e 6t F(t) = fritin fatr at time t, = nstant f prprtinality, a = alpha, alibratin parameter, 3 = beta, alibratin parameter, and e = base f the natural lgarithms. Of partiular interest t this study is the alternate frm f this equatin knwn as the negative expnential funtin where: F(t) = e" 6t with the variables defined as abve. Gravity Mdel alibratins. The alibratin f a gravity mdel basially invlves repeated adjustments f a set f fritin fatrs until the generated

31 21 gravity mdel travel time frequeny distributin repliates the 0-D survey travel time distributin within a predetermined errr limit. This predure, hwever, requires that an 0-D survey has been perfrmed in rder t ndut the auray tests and the alibratin itself. In rder t eliminate the st and time invlved in nduting an 0-D survey, researhers and transprtatin planners have asinally brrwed fritin fatrs frm ther urban areas pssessing similar harateristis, and then applied them t a new study area. An analysis was perfrmed t evaluate the perfrmane f the gravity mdel utilizing varius syntheti tehniques t assess pssible errrs that might be intrdued by the brrwing f fritin fatrs frm a similar urban area r by the use f standardized fritin fatrs r ther syntheti tehniques. The transprtatin study areas f Lafayette and Andersn were used fr the purpse f this evaluatin. The ppulatin sizes f these tw areas are apprximately equal, but spatial ativity distributins and netwrk impedanes are quite different frm eah ther. This prtin f the researh nsisted f fur parts. First, tests were nduted t measure the sensitivity f the gravity mdel with respet t small errrs in the fritin fatrs. Simultaneusly, these tests revealed the sensitivity f the gravity mdel with respet t the netwrk harateristis and land use ativity distributins f the individual study areas. Send, the variatin f fritin fatrs with respet t ppulatin size was investigated, This investigatin invlved the examinatin f the nept f similar urban areas frm whih fritin fatrs may be brrwed. Third, an evaluatin was perfrmed using standardized a parameters in the gravity mdels fr the Lafayette and Andersn urban areas. Furth, the fritin fatrs f Lafayette and Andersn were exhanged r brrwed frm eah ther in rder t test hw brrwed fritin fatrs may affet trip distributin mdelling auraies.

32 22 Sensitivity analysis f the gravity mdels fr Lafayette and Andersn resulted in three imprtant bservatins in regards t syntheti trip distributin mdelling. 1. Aeptable mdel results will ur when the brrwed, r standardized fritin fatrs are very similar t thse that wuld have been nrmally alibrated frm 0-D survey data. 2. Aeptable mdel results may ur when syntheti methds are used beause the urban area's gravity mdel is nt sensitive t fritin fatr errrs. 3. Aeptable mdel results may ur when ne individual trip purpse errr is ffset by an ppsite errr in ne r mre f the ther trip purpses. learly, the first situatin is the mst preferred with the send als being aeptable. The third situatin, hwever, is unaeptable beause hane plays a larger rle in the mdel's utme than the predure by whih the fritin fatrs were btained. In additin, analysis f data frm a number f small urban area transprtatin studies resulted in ne ther imprtant nlusin: ppulatin size is an inadequate riterin by whih t selet a similar urban area fr brrwing fritin fatrs r their parameters. Develpment and Testing Of Predures Fr Syntheti Self-alibratin Of Gravity Mdels Areawide Apprah t Syntheti Self-alibratin f a Gravity Mdel. It is pssible t relate mathematially the parameter f a fritin fatr equatin t the spatial land use ativity distributin and netwrk impedanes thrugh the use f the areawide desriptrs f mean travel time and travel time variane. This is beause the fritin fatr equatin f a gravity mdel represents the trip makers' destinatin hie press with respet t the spatial distributin f the land use ativities as represented by the destinatin hie variable f

33 23 interznal travel time. The aggregate effet f all the trip makers' destinatin hies result in a ttal travel pattern that an be apprximately desribed in terms f the areawide travel time statistis f mean and variane. Data used in the study f the areawide apprah nsisted f the 8 parameters f the negative expnential fritin fatr funtin, areawide average travel times and travel time varianes, study area ppulatin, and study area density as mputed r reprted fr eighteen small urban area transprtatin studies. Regressin analysis was used t searh fr mathematial relatinships between the 8 parameters and the areawide travel time statistis and any ther variables f pssible interest. The self -alibrating 8 parameter predure using the regressins equatins was fund t be nt as reliable a predure as the traditinally alibrated gravity mdel tehnique. Fatrs related t the pr perfrmane f this predure were nsidered in the develpment f the send apprah t syntheti self -alibratin f a gravity mdel. Origin Zne Speifi Apprah t Syntheti Self-alibratin f a Gravity Mdel. The prpsed syntheti predure invlves an rigin zne speifi selfalibrating mdel in whih the nly input data required are the znal prdutins and attratins, a zne-t-zne travel time matrix (skim tree), and the rigin zne terminal times. A travel time distributin is n lnger needed fr alibratin, thereby eliminating the neessity fr an 0-D survey fr internalinternal trips. The study areas f Lafayette, Andersn, and Munie, Indiana, were used t generate a data base beause f data availability and the wide sampling spae prvided by the differing harateristis f these areas. The gravity mdels f these three areas were alibrated at the znal level t generate the fllwing infrmatin fr eah rigin zne and fr eah trip purpse - HBW, HBO, and NHB trips:

34 ., = negative expnential fritin fatr parameter fr zne i f the rigin zne alibrated gravity mdel, 0. = trips riginating frm zne i, AVEO. = pprtunity average travel time fr rigin zne i when 8 equals 0.0, that is, fritin fatr f nstant value 1.0, VARO. = pprtunity travel time variane fr rigin i when 8. equals 0.0, AVE. = rigin speifi average travel time fr zne i f alibrated gravity mdel (same as 0-D survey value) VAR. = rigin speifi travel time variane fr zne i f alibrated gravity mdel, and TERM. = rigin zne terminal time. The abve variables were then used in a urve fitting regressin predure s as t find a relatinship f the fllwing frm: 8. = f (AVEO., AVE., VARO., VAR., TERM. ) l l l i i l The initial regressin analysis resulted in an equatin frm pssessing very aeptable statistis fr the urve fitting f the rigin zne speifi 8 parameters t their respetive znal travel time statistis f mean and variane. Hwever, inspetin f the residual plt revealed a greater than expeted number f utliers. areful review f the znal data and a sensitivity analysis led t the fllwing mdifiatins: - remval f the variable VAR. frm the analysis, and - eliminatin f the simultaneus inlusin f bth AVE. and AVEO.^ in the regressin equatin. A high rrelatin bserved between AVEO. and AVE. indiated that an indiret tehnique t estimating the 8 parameter uld be pssible. nsequently, a revised predure was develped with AVEO. as the key variable.

35 25 Regressin analysis f AVE n AVEO and TERM was amplished by weighting the znal data in rder t prperly balane the data by trip prdutins (rigins) and by trip purpse. The regressin analysis ill result fr trip purpse HBW fllws: AVE,, = TERM (AVEO. - TERM.) i R 2 =.865 Similar equatins fr HBO and NHB trips were fund, and all equatins pssessed R-squared values higher than thse ahieved in earlier attempts. The strng statistial fit f the equatins shws the imprtane f AVEO., the pprtunity average travel time, n the destinatin hie press. Inrpratin f the Estimating Equatins int a Gravity Mdel. The remmended predure fr syntheti alibratin was based n three assumptins regarding the interatin f rigin zne average travel time and the znal 3 parameter: - The rigin zne average travel time varies linearly with the rigin zne 3 parameter. - Znal average travel time is independent f the B parameters f ther znes. - Znal average travel time is independent f the attratin nstraint iteratin predure. The estimating equatins f AVE., znal average travel time, and the three assumptins listed abve were inrprated int a gravity mdel predure desribed belw in njuntin with Figure 3.

36 I 26 STEP Step 1 - Average at ft equal zer (0.0) - AVEO Step 2 - Average at ft equal t a set value. Step 3 - Average frm regressin equatin, AVE Step 4 - Interplate fr new ft 0.0 P> SET VALUE Origin Zne ft Parameter Figure 3. Generalized Flw hart f Synthetially Self-alibrated, Origin Zne Speifi Gravity Mdel as Applied t a Typial Zne

37 27 First, trips are distributed with all znal 6 parameters equal t 0.0, and then the resulting znal pprtunity average travel times, AVEO, are mputed. Send, trips are distributed with all rigin zne 6 parameters equal t a preseleted value, and then the rigin zne average travel times are mputed. Third, using the estimating equatin fr the apprpriate trip purpse and the AVEO. values mputed in step ne, the rigin zne average travel times, AVE, are determined. Furth, using the data pints frm steps ne and three and the estimated rigin zne average travel times, AVE., the znal 3 parameters are btained thrugh interplatin. All trips are then distributed using the interplated rigin zne speifi 8 parameters. Tests nduted n three separate study areas indiated that the prpsed mdel is able t reprdue trip patterns as aurately as the traditinally alibrated gravity mdel predure whih is alibrated n 0-D survey data. The pprtunity average travel time, AVEO., is the key variable n whih the mdel was develped and upn whih the very aeptable mdel results are due. nlusins On Trip Distributin Phase 1. The need and imprtane f fritin fatrs was established by the values f average travel time and traffi assignment statistis generated by trip distributin mdels withut suh time dependent fatrs. 2. The sensitivity f the gravity mdel t fritin fatr errrs may be highly dependent upn the partiular urban area being mdelled. 3. A large variability exists in the rrelatin between ppulatin size and fritin fatrs. 4. Beause the variability f average travel time with respet t ppulatin size is large, the validatin f a syntheti trip distributin mdel by mparing nly the average travel times is nt a valid apprah.

38 28 5. While general shapes f travel time frequeny distributins between tw areas may appear quite similar, even a small variatin r shift in a distributin an signifiantly alter average travel time and therefre als the auray f the mdel. 6. The mst reliable syntheti trip distributin mdelling tehnique is the rigin zne speifi synthetially self-alibrating gravity mdel. 7. The mst imprtant feature f the prpsed predure is its mre diret linkage t the spatial distributin f land use ativities within an urban area as quantified by the rigin zne speifi pprtunity average travel times. 8. N lletin f 0-D survey data is needed fr this predure, nr is it neessary t find a similar urban area frm whih t brrw infrmatin r t whih mdel validatin mparisns are t be made. mputer Prgram Fr The Remmended Predure A mputer prgram was written fr the purpse f using the remmended predure with the UMODEL prgram f the UTPS pakage. A PLANPA versin f was als ded. A FORTRAN IV prgram is als available fr inrpratin int ther pakages. In Appendix is given a listing f this prgram. EFFETS OF SIMPLIFYING ZONE AND NETWORK SYSTEMS ON THE AURAY OF TRAFFI ASSIGNMENT The third phase f the study inluded an evaluatin f the effets f simplifying zne and netwrk systems n the auray f traffi assignment (3). majr fatrs f interest in this phase were average traffi zne size, use f ensus trats as traffi znes, level f detail used in street netwrks, and methd f traffi assignment used. Sine a statistial design f experimentatin was needed t test these fatrs and the pssible interatins amng them,

39 29 atual transprtatin netwrk and travel data frm tw small urban areas in Indiana, Lafayette and Andersn, were used. Alternative Traffi Zne Systems. Fr eah urban area, alternative traffi zne systems were develped by substituting existing znes with smaller numbers f large znes. Only the internal znes were hanged; all external znes were left intat. Larger znes were develped by mbining existing znes int larger units. Only adjaent existing znes with similar land uses were mbined int a larger zne. The numbers f vehile trip prdutins and attratins (P's and A's) fr a new zne were mputed simply by adding tgether the P's and A's f its mpnent znes. New zne systems were als develped frm ensus trats. When a new zne was reated, the entrid-nneting links t the ld znes were hanged. Only entrid nnetrs that were near the new zne entrid and lletively prvide aess t all nearby arterial and lletr streets were hsen fr the new zne. Terminal Time and Intraznal Travel Time Estimatins. New terminal times and intraznal travel times were established fr eah new zne. The terminal time fr a new zne was set as the terminal time predminant amng the existing znes frm whih the new zne was frmed. The fllwing equatin was develped t mpute the intraznal travel times fr new znes: where: I (ITT * p ) ITT -w = TF=- z=1 > Jz p z 2 n ITT = intraznal travel time fr new zne, new ITT = intraznal travel time fr ld zne, n = n. f ld znes within new zne, and P = areal funtin f ld zne Z within new zne.

40 30 Alternative Netwrk Systems. Tw alternative street netwrk syste were develped fr eah urban area, ne being the full, existing ded netwrk fr an urban area. The send alternative was a redued versin f the full netwrk, with all links n lletr streets that arried less than 5000 vehiles per day remved. All links n arterial streets and high-vlume lletr streets were kept in the redued netwrks, as well as in the full netwrks, sine this level f street netwrk detail was a minimum level at whih all imprtant links wuld still be represented. Predures Fr Data lletin And Analysis Fr bth Lafayette and Andersn, eah alternative traffi-zne system was tested with eah alternative street netwrk. This resulted in ten znenetwrks tests fr Lafayette and eight fr Andersn. Eah test inluded a gravity mdel alibratin, an all-r-nthing assignment, and a apaity restraint assignment. After all tests had been mpleted, the data resulting frm the alibratins and assignments were analyzed. In nduting the zne-netwrk tests nsiderable street netwrk and travel survey data were required. The street netwrk data inluded a listing f all links with eah link's length, travel time, grund unt, and apaity. Travel survey data inluded vehile-trip P's and. A's fr eah zne, trip length distributins, and vehile trips interhanged between external- internal.and external-external ane. pairs. Eah test fllwed the same set f predures. These ativities were: - Preparatin f netwrk and travel data. - Gravity Mdel alibratin. - Traffi assignment. The final ativity invlved as its last step the mparisn f assigned vlumes with grund unt vlumes. This step prdued the data n whih mst f the analyses were based.

41 . 31 Analyses f Data. The data frm the test alibratins and assignments were analyzed by several methds. Aggregate methds were used t evaluate verall mdel alibratins and traffi assignments. Stratified methds were used t study traffi assignments in mre detail. Alpha values and intraznal trip measurements were used in the aggregate analysis f mdel alibratins and trip distributins f all tests. Alpha values were examined fr their stability with hanges in the zne system r street netwrk. Intraznal trips, measured as a perentage f all internal trips, uld be expeted t inrease as zne size inreased. Ttal vehilemiles travelled (VMT) and verall perent rt mean-square errr (% RMSE) were aggregate measures fr mparing traffi assignments. T study traffi assignments at a detailed level, the ttal ppulatin f links in a street netwrk was stratified int vlume grups based n grund unt vlumes Statistial analysis at the stratified level was nduted in tw phases. In the first phase, the % RMSE's f vlume grups in eah assignment were analyzed fr the main effets and ertain interatins. The interests lay with the main effets f and pssible interatins between ertain fatrs: the number f traffi znes, the type f zne bundary, the type f street netwrk, the methd f traffi assignment, and the vlume grup. Tw-way interatins were examined and multiple-fatr analyses f variane (ANOVA's) were run and mpared t determine main effets and interatins. Fr the send phase f stratified analysis, bservatins were the abslute differenes between the assigned vlumes and grund unts n individual links. Links n a netwrk are nt independent f eah ther and neither are their traffi vlumes. Withut independent bservatins, parametri statistial methds lak validity. Fr this reasn, these data were analyzed by nnparametri methds using Freidman rank sums.

42 32 Analyses Of Test Results Definite patterns appeared as t hw zne and netwrk fatrs affet values f the alpha parameter in gravity mdel alibratin. Reduing the number f znes seemed t redue the alphas fr HBW trips, while the alphas f the ther trip purpses were nly slightly affeted. Reduing the number f links in the netwrk seemed t inrease alphas fr all trip purpses. Alpha values were mre variable fr Lafayette than fr Andersn. This urrene may have been due t differenes in the harateristis f the tw ities, t differenes in the auray f the data frm the tw ities, r t the use f very large znes in Lafayette. The effets f zne and netwrk fatrs n verall VMT appeared small. Ttal VMT in apaity-restraint assignments generally varied less than 5% with hanges in zne size. Reduing netwrk detail inreased ttal VMT fr bth ities. Dereasing the number f znes tended t inrease the % RMSE. Assigning traffi by apaity-restraint always redued the % RMSE. The use f apaityrestraint redued the effet f the number f znes n the % RMSE. Redued netwrks generally shwed lwer % RMSE's, while n real trend uld be established fr the effet f zne bundary type n verall % RMSE. In the stratified analysis f % RMSE, three ANOVA's were run at a.05 level f signifiane. Observatins were the % RMSE's by vlume grup fr every traffi assignment. The ANOVA shwed that three fatrs, number f znes, assignment methd, and vlume grup, had partiularly signifiant main effets n the auray f traffi assignments. Interatins between fatrs were few and generally innsistent. The nnparametri, link-by-link analyses desribe the main effets mre fully.

43 33 The results f these analyses implied that several fatrs have effets n the auray f traffi assignments: - Using fewer (and larger) znes tends t redue the auray f traffi assignments; - apaity-restraint assignment is substantially mre aurate than all-r-nthing assignment; - Limited redutin in the detail f a street netwrk will nt materially affet the auray f traffi assignments n high vlume streets; - The bundaries f ensus trats will serve as a basis fr bundaries f traffi znes withut adverse effets n assignment auray. Guidelines Fr Zne System And Netwrk Develpment The purpse f this researh was t study the effets f reduing the number f traffi znes and simplifying street netwrks n traffi assignment auray. The fllwing prpsed guidelines fr establishing zne systems and netwrk nfiguratins in emerging metrplitan areas are the final results f this endeavr. Zne size is a signifiant fatr in the auray f traffi assignments. The average zne size fr a transprtatin study area shuld nt exeed 3 sq. mi., and n zne shuld exeed 15 sq. mi. in area. Otherwise, grss instability in fritin fatr parameters may ur. Use f 90 internal znes is suggested fr small urban areas suh as Lafayette and Andersn in rder t ahieve aeptable verall auray levels. ensus trat bundaries an be used suessfully as a basis fr establishing a traffi zne system. The innsistenies that may ur between ensus trat bundaries and land use bundaries have little effet n traffi assignment auray.

44 34 A redued street netwrk, mprised nly f lletrs with an ADT ver 5000 and all higher-lass streets and highways, shuld be adequate fr planning purpses. Finally, apaity-restraint traffi assignment is suggested fr assigning trips t aurately simulate grund unts. Nt nly is apaity-restraint assignment mre aurate than all-r-nthing assignment, but apaity-restraint assignment als redues the adverse impat that the use f a small number f znes an have n assignment auray. ONLUSIONS In this reprt was summarized the findings f the prjet n syntheti demand mdeling tehniques in transprtatin planning fr urban areas in Indiana. Only internal vehile trips were nsidered and guidelines were develped fr trip generatin, trip distributin and traffi assignment parts f travel demand freasting. Effrts are being made by the planning staff f the Indiana State Highway mmissin t implement the study results in several urban areas in Indiana. At present implementatin in three urban areas are being undertaken. Sme f the results f this prjet are being used in Blmingtn, Indiana. In partiular, the internal trip generatin infrmatin fr Blmingtn is being develped n the basis f the study results. Plans are being made t use the trip distributin part in the Lake-Prter (NIRP) area as sn as a netwrk bemes available. Use f the guidelines in the Suth Bend (MAOG) area will inlude bth the distributin and assignment phases. It is felt that the predures develped in the study will greatly redue the st and time invlved in data lletin and mdel develpment effrts assiated with nventinal travel demand mdeling press based n rigin-destinatin survey. The infrmatin an be used either in nnetin with a new transprtatin study fr an emerging metrplitan area r fr updating f existing transprtatin plans.

45 35 REFERENES 1. Mahmassani, Hani S. and Sinha, Kumares, "FRAMEWORK FOR THE TRANSFERABILITY OF TRIP GENERATION PARAMETERS FOR SMALL URBAN AREAS IN INDIANA", FHWA/ISH/JHRP-78/13, Shl f ivil Engineering, August 2, Mekemsn, James R. and Sinha, Kumares, "EVALUATION AND DEVELOPMENT OF SYNTHETI TRIP DISTRIBUTION MODELLING TEH- NIQUES AS APPLIED TO SMALL URBAN AREAS", FHWA/ISH/JHRP-79-5, Shl f ivil Engineering, July 11, Hansm, Edward W. and Sinha, Kumares, "THE EFFETS OF SIMPLIFYING TRAFFI-ZONE AND STREET-NETWORK SYSTEMS ON THE AURAY OF TRAFFI ASSIGNMENTS IN SMALL URBAN AREAS IN INDIANA", FHWA/IN/JHRP-79/15, Shl f ivil Engineering, September 5, 1979.

46 36 APPENDIX OMPUTER PROGRAM FOR SYNTHETI ALIBRATION OF GRAVITY MODEL PROGRAMMED BY JIM MEKEMSOM. PURDUE UNIUEPSITY, SEPT IMPLEMENTED AT THE INDIANA STATE HIGHWAY OMMISSION BY NIKE FARPIS THIS IS THE ODE FOR ENTRY POIHT M0D13B ZONAL UARIABLE S ZNE NUMBER ZONAL PRODUTIONS ZONAL ATTRATIONS (OS (OR ORIGINS) DESTINATIONS) TERMINAL TIME. ORIGIN ZONE INTRAZONAL TRAUEL TIME ZONAL BETA(IZ) GRAUITY MODEL DENOMINATORS NEW ITERATED UALUE OF Af J) :) SUM OMPUTED A(J) = SUM(IZ) T(I,J) ZONAL AUE. AT BETA=0.0 ZONAL UAR. AT BETA=0.0 ZONAL AUE. AT BETA=PBETA(TP) OR FINAL UALUE ZONAL UAR. AT BETA=PBETA(TP) OR FINAL UALUE USED ONLY AS WORKING STORAGE. TEMPORARY FRIITON FATOR INITIALIZE ZONAL MATRIX IF((ITN0.NE.1).0R.(IZ.NE.1))RETURN DO 30 I=5rl3 DO 30 J=l,OMAX 30 Z(I.J)=0.0 THIS IS THE ODE FOR ENTRY POINT M0D13 REAL«4 PEETA(3).FTR(99) PBETA(1)=0.15 PBETA(2)=0.30 PBETA(3)=0.30»«J"GM JMJM a 3 10 li S THIS IS THE ODE FOR ENTRY POINT MOD13D ORIGIN ZONE SPEIFI SYNTHETIALLY SELF-ALIBRATING GRAUITY MODEL INTERNAL-INTERNAL TRIPS ONLY4- USER PARAMETER INPUT UIA UPARMS UPARMS(l) = 1 HBW 2 HBO 3 NHB UPARMS (2) = 1 OMBINED REGRESSION EQUATIONS 2 LAFAYETTE REGRESSION EQUATIONS JMM J V GM S 5S GO

47 , ANDERSON REGRESSION EQUATIONS NUNIE REGRESSION EQUATIONS NOTE: OMAX=DMAX= NUMBER OF INTERNAL ZONES ASSUMED. REAL»4 AUE. UAR, ATT. TUAR. SUM, TRIP, RAUE. SX, SXX, TOT INITIALIZE OR ONTINUE IF((ITN0.EQ.1).AND.(IZ.EQ.1))G0 TO 101 GO TO 100 OMPUTE TOTAL TRIPS TOT=0.0 DO 20 J=l,OMAX T0T=T0T+Z(2.J) INITIALIZE SX=0.0 SXX=0.0 ATT=0.0 TUAR=0.0 ITER=0 AITER=2 DO 21 J=l,OMAX Z(8.J)=Z(3.J) Z(7.J)=T0T ITERATION FLAGS FOR DEISION TREE. ITER=ALIBRATION FLAG - BETA= BETA=PBETA(TP) 2 - BETA=ALIBRATED UALUE FROM REGRESSION EQUATIONS AND INTERPOLATION. AITER=ATTRATION FLAG - BEGIN ATTRATION ITERATION 1 - ONTINUE ATTRATION ITERATION 2 - ATTRATION ITERATION OMPLETE, I.E. LOSURE IFIZ.EQ.1)URITE(S,50)ITNO.ITER,AITER FORMAT(10X,7HITNO =, 15, 10X.8HITER =, 15, 10X.8HAITER =,15) IF((ITER.EQ.l).AND.AITER.EQ.0).AND.(IZ.EQ.l))GO TO 200 IF((ITER.EQ.2).AND.(AITER.EO.0).AND.(IZ.EQ.l))GO TO 300 IF(AITER. Q.0)GO TO 400 IF(AITER.EQ.l)GO TO 500 GO TO S00.. ITER=1,AITER=0,IZ=1 SET BETAD'S AND FRITION FATORS TO 200 TP=UPARMS(1) DO 201 J=l, FTR(J)=EXP(-PBETA(TP)»J) GO TO 99 El 62 G2 G4 G E9 70 jmgm JMM JMM B jrsm GIUEN

48 38.. ITER=2.AITER=0.IZ=1 FIND BETA(I) S FROM REGRESSION EQUA. 300 RTP=UPARMSQ) + (UPARMS(2)-1)«3 ATT=0.0 TUAR=0.0 SX=0.0 SXX=0.0 DO 18 J=1.0MAX IF(Z(2.J'.EO.0.0)GO TO 18 OMPUTE RAUE, AUERAGE FROM REGRESSION EQUATION HBU R»»2 = 0.8S5 1 RAUE= *Z(4.J)+0.S921«(Z(10.J)-Z(4,J» GO TO 19 HBO R»*2 = RAUE= *Z(4.J) »(Z(10.JW(4.J» GO TO 19 NHB R**2 = RAUE= «Z( 4. J)+0.G073»(ZQ0.J)-Z(4,jn GO TO 19 LAFAYETTE, INDIANA. HBU R»*2 =.893 AREAUIDE UAR=22.99: UAR0=24.99 AUE=10.G0: AUE0=12.32: UAR=12.17: UAR=14.00; TERM= RAUE=-1.0SS *Z(4,J) »(Z(10,J)-Z(4,J)) GO TO 19 HBO R»*2 =.893 AREAUIDE UAR=13.B3: UAR0=25.5^ AUE=8.55: AUE0=11.78: UAR=10.78S UAR0=15.63i TERM= RAUE= S»Z(4,J) »Z10.J)-Z(4,J» GO TO 19 NHB R**2 =.799 AREAUIDE UAR=14.09: UAR0=17.75 AUE=9.12; AUE0=11.42; UAR=9.94: UAR0=12.9l: TERM=1.93 S RAUE= *Z(4,J) »(Z(10.J)-Z(4,J)) GO TO 19 ANDERSON, INDIANA JHGH Y-^ 12S jnfi S AUE = ZONAL AUERAGE TRAUEL TIME. 137 AUEO = ZONAL AUERAGE TRAUEL TIME AT BETA = UAR = ZONAL TRAUEL TIME UARIANE AT ALIBRATED BETA UALUE. 139 UARO = ZONAL TRAUEL TIME UARIANE AT BETA = TERM = AUERAGE ORIGIN ZONE TERMINAL TIME. JMG^ 141 1^2 1-3 GO TO( 1.2, 3, 4, 5. 6, 7. 8, 9. 10, RTP J.-,3M 144 1^5 145 OMBINED REGRESSION EQUATIONS-LAFAYETTE, ANDERSON. AND MUNIE IND., jmgm JflGM JMM SS jmgm _ ITS 177 :"

49 Yi HBW R»«2 =.876 AREAUIDE UAR=9.61; UAR0=9.51 AUE=11.43; AUE0=12.10: UAR=6.53: U0=7.12: TERM= RAUE= »Z(4.J) »(Z(10.J)-Z(4,J)) GO TO 19 HBO R**2 =.664 AREAUIDE UAR=9.51: UAR0=11.10 AUE=10.04: AUE0=12.30: UAR=7.84: UAR0=5.20: TERM= RAUE=1.0G »Z(4,J) »(Z(10, J)-Z(4,J» GO TO 19 NHB R»»2=.210 AREAUIDE UAR=7.82; UAR0=9.20 AUE=9.50: AUE0=11.72; UAR=G.35: L'AR0=7.12; TERM=2.G4 9 RAUE= »Z(4.J) «(Z(10.J)-Z(4.J)) GO TO 19 MUNIE. INDIANA HEW R»»2 =.772 AREAUIDE UAR=11.16; UAR0=11.51 AUE=9.07; AUE0=9.82! UAR=7.06; UAR0=7.11: TERM= RAUE=0.8G »Z(4.J^ «(2(10,J)-2(4,J)) GO TO 19 HBO R**2 =.749 AREAUIDE UAR=9.27; UAR0=11.83 AUE=7.48: AUE0=9.58: UAR=6.40; UAR0=7.79: TERM= RAUE= «Z(4,J) »(Z( 10, J)-Z4,J» GO TO 19 NHB R*«2 =.506 AREAUIDE UAR=7.89: UAR0=9.28 AUE=6.93; AUE0=8.95: UAR=5.65; UAR0=G.G5: TERM= RAUE= «Z(4. J) «(Z 10. J)-Z(4. J)) 19 ONTINUE OMPUTE BETA, LINEAR INTERPOLATION. Z(G,J)=(PBETA(TP)/'(Z(12,J)-Z(10,J)))»(RAUE-Z(10.J)) IF(Z(12,J).GT.Z(10.J))Z(G,J)=»0.0 IFZtG. J).GT.0.5)Z(6.J)=0.S 18 ONTINUE GO TO 99.. AITER=0,IZ=1 INITIALIZE BEFORE ATTRATION 400 DO 401 J=1.0MAX Z(8.J)=Z(3.J) 401 2(9,J)=0.0 AITER=1 BEGIN ATTRATION ITERATION. SUMJ A(J)»F(I.J) 500 Z(7,IZ)=0.0 DO 501 J=1.0MAX IF(IZ.E0.J)INT(J.1)=Z(5.J) ITERATION BEGINS. 1~1 jnn 122 jmgm 183 jim :'ii jmgm 1 5 jr.n jmgm jmg;i J.-.GM 1SG jmgm 197 jmgm J*iGM 202 J'1GM G in E 217 J'iGM 218 J,",GM J^GM J"GM J'-GM

50 IF(IW(J. l).eq.0.0)go TO 501 Z(14p J) = 1.0 ADD TERMINAL TIMES TO SKIM TREE TRAUEL TIME. TiME=INT( J. 1)+Z(4, I2)+Z(4, J) IF(TIi1E.GT.99>TIME=93 IF(ITER.E0.1)Z(14,J)=FTF(TIME) IF(ITER.EQ.a)Z(14tJ)=EXP-Z(6.IZ)«TIME) SAF = SAF + A F Z(7.IZ)=Z(7,IZ)+Z(8,J)*Z(14,J) ONTINUE SUM OMPUTED ATTRATIONS DO 502 J=1.0MA!< IF(INT(J.l).EQ.0.0)GO TO 502 SA=SA+P»A»F/ SAF Z(3.J)=Z(3.J)+Z(2.IZ)*Z(8,J)»Z(14,J)/'Z(7,IZ) ONTINUE IF IZ.EO.OMAX. TEST FOR LOSURE IF IZ.LT.OMAX. ONTINUE ITERATION IF(IZ.EQ.OMAX)GO TO 700 RETURN 1 OMPUTE ERROR FOR LOSURE TEST SUM=0.0 DO 701 J=l,OMAX SUM=SUM+ABS(Z(9.J)-Z(3.J)) SUM=SUM/TOT ONE PERENT LOSURE TEST IFSUM.LT.0.0DGO TO 702 LOSURE TEST FAILED, D 703 J=1.0MAX IF(Z(8.J).EQ.0.0)GO TO 703 Z(8,J)=Z(3,J)»Z(8,J)/Z(9.J) 703 Z(9.J)=0.0 RETURN 1 OMPUTE NEW ATTRATIONS. LOSURE TEST PASSED. SET AITER=2 FOR AUE. AND UAR. ITERATION. AITER=2 IF ITER=2, SET ITNG=ITER-1 FOR LAST AUE. UAR. ITERATION. IF(ITER.EQ.2)ITN0=ITER-1 RETURN 1 AUERAGE AND UARIANE OMPUTATION. INITIALIZE ZONAL AUE. AND UAR. 2*1 2-'? JMM 343 2^4 245 J.1GM 24 S umgm J M GM JXM JMM S2 283.'3 M 234 JfIGM 235 2S E JrSGM _~:~ 2=3.. 23d S S

51 41 GOO AUE=O.0 UAR=0.0 DO 539 J=l. ZONES 5 " lf((iferteq.o).or.(iter.eo.a)).and.(iz.e0.1))o TO 598 GO TO ATT=0.0 TUAR=0.0 SX=0.0 SXX-0.0 BEGIN AUE. AND UAR. OMPUTATION IF ORIGIN ZONE PRODUTIONS. SKIP AUE. AND UAR. 597 IF(Z(2.IZ).LE.1.0)GO TO 605 DO SI J=1.0MAX ONFUTE ELL INTERHANGE. I.E. TRIPS. IF(IZ.EQ.J)INT(J.1)=Z5»J) IF(INT(J.l).EQ.0.0)GO TO 601 Z(14,J)=1 TIME=INT(J.1)+Z(4, IZ)+Z(4.J) IF(TIME.T.99)TIME=99 IF(ITER.E0.1)Z(14.J)=FTR(TIME) IFITER.EQ.2)Z14,J)=EXP(-Z(G.IZ)»TIME) TRIP = P * A F / SAF TRIP=Z(2.IZ)»Z(8.J)*Z(14.J)/-Z(7,IZ) SAUE TRIP IN TROUT IF LAST ITERATION, I.E. ITER=2 IF( (ITER.EQ.2).AND. (AITER.EQ.2) )TROUT( J, 1J-TRIP SUM ZONAL TRAUEL TIMES. AUE=AUE+TRIP«TIME BOl ONTINUE ADD ZONAL ONTRIBUTION TO AREAWIDE AUERAGE TRAUEL TIME IF(ITER.EQ.O). OR. (ITER.EQ.2)) IF((ITER.EQ.0).OR.(ITER.EQ.2))ATT=ATT+AUE/TOT OMPUTE ZONAL AUERAGE TRAUEL TIME AUE=AUE/Z(2.IZ) SAUE AUERAGE TRAUEL TIME AT BETA=0.0 IF ITER=0 IF(ITER.EO.0)Z(10,IZ)=AUE SAUE AUERAGE TRAUEL TIME AT BETA=PBETA(TP) OR FINAL UALUE. IF(ITER.NE.0)Z(12»IZ)=AUE OMPUTE ZONAL UARIANE AND DO 602 J=1.0MAX SAUE DATA FOR AREAWIDE UARIANE E 307 JiIGM S G G JriGM S 3G0

52 42 OMPUTE ELL INTERHANGE. I.E. TRIP IF(INT(J, l).eq.0.0)go TO SOS TRIP=2(2. IZ)«Z(8. J)«Z(14, JVZ7, IZ) ADD SUN OF SQUARES FOR ZONAL UARIANE UAR=UAR+TRIP»(TIME-AUE)2 ADD SUN OF TINE AND SUN OF TINE SQUARED FOR AREAWIDE UARIANE IF(ITER.EQ.0).0R.(ITER.EQ.2)) IF(INT(J.l).EO.0)GO TO G02 TINE=INT( J. 1 )+Z(4, IZKZ4, J) IF(TIME.GT.99)TIME=99 IF((ITER.EQ.0).0R.(ITER.EQ.2))SX=SX+TRIP»TIME IF((ITER.EQ.0).0R.(ITER.EQ.2'»)SXX=SXX+TRIP»TIME»«2 S02 ONTINUE SAUE ZNAL UARIANE AT BETA=0.0 IF ITER=0. IF(ITER.EQ.0)Z(11.IZ)=UAR/(Z(2. IZ)-1.0) SAUE ZONAL UARIANE AT BETA=PBETA(TP) OR FINAL UALUE. IF(ITER.NE.0)Z13.IZ)=UAR/(Z(2.IZ)-1.0) IF IZ=ONAX INREMENT ITER BY 1 AND SET AITER=0. STMT S03 IF(IZ.EQ.OMAX)GO TO G03 IF IZ.LT.OMAX. ONTINUE ITERATION. RETURN 1 G03 ITER=ITER+1 IF(ITER.EQ.1)URITE(6.SG)ATT.TUAR EOG FORNAT/10X.10HBETA = 0. (V10X, 22HAUERAGE TRAUEL TIME =.F10.3/ H0X.23HTRAUEL TIME UARIANE =.F9.3/0 IF(ITER.EG.3)URITES.604)ATT,TUAR G04 FORMAT(/10X.22HAUERAGE TRAUEL TIME =, F10.3/10X.23HTRAUEL TIME UAR-TGM 1IANE =.F9.3/) -MGM AITER=0 RETURN 1 _ -:-.-: - JT31 _~2~ -"- ' 351 2E2 3G3 25* 2 = G7 3G3 3G " S SS :ee 22 = :: :h :5

53

54 a I en O z