Speurwerk. Colloquium Vervoersplanologisch INNOVATIE IN VERKEER EN VERVOER . _,..._ -._.._ _..^

Transcriptie

1 Colloquium Vervoersplanologisch Speurwerk 1992 Budding van bijdragen van bet colloquium gehouden te Rotterdam op 26 en 27 november 1992 INNOVATIE IN VERKEER EN VERVOER Redaclie P.M. Blok Dee1 3. _,..._ -._.._ _..^

2 Inhoudsopgave Samenvatting s-ary 1 Inleiding 2 Doelstellingen van de Nederlandse overheid 3 Omvang autopark en uitstoot aan emissies ín 2015 volgens FACTS Aanpassingen op het FACE-model 3.2 Resultaten van simulaties met FACTS 4 Maatregelen om overheidsdoelstelhngen te realiseren 5 Conclusies Bijlage 1 De nieuwe lange termijn-scenario s van het CPB Literatuur 1018

3 Samenvatting Het personenautopark in 2025 volgens FACTS 2.0 en de gevolgen voor de overheidsdoelstellingen Met behulp van het NELmodel FACTS 2.0 zijn simulaties uitgevoerd tot en met het jaar 2015 betreffende de omvang van het personenautopark, het afgelegd voertuigkilometrage, het brandstofverbruik alsmede de uitstoot aan emissies. Deze simulaties zijn uitgevoerd binnen de context van de drie nieuwe CPB-scenario s. De resultaten zijn vervolgens vergeleken met de doelstellingen van de Nederlandse overheid ten aanzien van het personenautoverkeer. Het blijkt dat in alle drie de CPB-scenario s de overheidsdoelstellingen worden overschreden. Ook bij het doorvoeren van verschillende maatregelen blijkt het niet bijzonder gemakkelijk het autobezit en -gebru& en de daaruit voortvloeiende consequenties voor brandstotierbruik en emissies af te remmen. Wanneer de overheid in de buurt van de gestelde doelen wil komen zijn zeer drastische maatregelen noodzakelijk. Cas owtzership in 2015 simulated with FACTS 2.0 and the implications for the gooernment targets With the FACTS 2.0 model forecasts have been made of the ownership and use of passenger cars, energy consumption and emissions up to the yea.r These forecasts are made within the context of the three recent Central Planning Bureauscenarios. The results of these forecasts are then compared with the govemment targets for car traffic. This comparison shows that the government targets in all three CPB scenarios will not be attained. Calculations of the effects of several measures shows that it is not easy to reduce car-ownership and -use and thus energy consumption and emissions. Therefore to attain the government targets for the year 2010 drastic measures wilt be necessary. 1019

4

5 COLLOQUIUM VERVOERSPL4NOLOGISCH SPEURWERK Het doel van het Colloquium Vervoersplanologisch Speurwerk is een ontmoetingsplaats te vormen waar nieuwe inzichten en ideeën met betrekking tot de vervoersplanning en de raakvlakken hiervan met de ruimtelijke planning worden gepresenteerd en besproken. De auteurs bezitten auteursrechten van hun bijdragen. Bestelling van boeken: Dit verslag, dat uit drie delen bestaat, kan worden besteld door overmaking van f.120,-- op girorekening ten name van de penningmeester van het Colloquium Vervoersplanologisch Speurwerk, Geerdinkhof 237, 1103 PZ Amsterdam, onder vermelding van CVS 1992 en de naam en adres van de besteller. Aanbevolen literatuurverwijzing: P.M. Blok (red.) Colloquium Vervoersplanologisch Speurwerk Innovatie in Verkeer en Vervoer, Delft, C.V.S., 1992.

6 TRANSPORTATION PLANNING RESEARCH COLLOQUIUM The purpose of the Transportation Planning Research Colloquium is to provide a meeting ground for the presentation and discussion of new insights and ideas in the field of transportation and its relationship with spatial planning. Authors retain al1 rights in their papers. Orders for books: Copies of this publication CVS-1992, which exists of three volumes, can be ordered from the treasurer of the Transportation Planning Research Colloquium, Geerdinkhof 237, 1103 PZ Amsterdam, The Netherlands. Suggested citation: P.M. Blok (red.) Colloquium Vervoersplanologisch Speurwerk Innovatie in Verkeer en Vervoer, Delft, C.V.S.,

7 LIST OF PUBLISHED PROCEEDINGS OF THE PREVIOUS COLLOQUIA 1. P.H.L. Bovy et al (red.) Colloquium Vervoersplanologisch Speurwerk modellen en methoden in de vervoersplanologie. 2. F. le Clercq et al (red.) Colloquium Vervoersplanologisch Speurwerk praktijk en model in de vervoersplanning. 3. J.P.J.M. van Est et al (red.) Colloquium Vervoersplanologisch Speurwerk planevaluatie, vervoersmodellen en ruimtelijk keuzegedrag. 4. G.R.M. Jansen et al (red.) Colloquium Vervoersplanologisch Speurwerk veranderingen in en om vervoersplanologisch onderzoek. 5. G.R.M. Jansen et al (red.) New developments in modelling travel demand and urban systems: some results of recent Dutch research. Farnborough, Saxon House, 1979 (alleen via boekhandel). 6. F. le Clercq et al (red.) Colloquium Vetvoersplanologisch Speurwerk-1979-vervoersstudies, -modellen en methoden. Delft, P.H.L. Bovy et al (red.) Colloquium Vervoersplanologisch Speurwerk mobiliteit, ruimtelijke interactie en vervoerssysteemgebruik. Delft,

8 8. C.J. Ruijgrok & J.P.J.M. van Est (red.) Colloquium Vervoersplanologisch Speurwerk nieuwe tendensen in de vervoersplanologie. Delft, J.G. Smit & F. le Clercq (red.) Colloquium Vervoersplanologisch Speurwerk openbaar vervoer, kompakte stad en mobiliteit. Delft, P.H.L. Bovy (red.) Colloquium Vervoersplanologisch Speurwerk Transportation and stagnation; challenges for planning and research (2 volumes). Delft, ll. G.R.M. Jansen et al (red.) Transportation and Mobility in an Era of Transition Elsevier/North-Holland, 1985 (alleen via de boekhandel). 12. J.P. van Est (red.) Colloquium Vervoersplanologisch Speurwerk Mobiliteit in beweging (2 delen). Delft, F. le Clercq (red.) Colloquium Vervoersplanologisch Speurwerk Dynamiek in verkeer en mobiliteit (2 delen). Delft, G.R.M. Jansen (red.) Colloquium Vervoersplanologisch Speurwerk Mobiliteit, transport en technologische vernieuwing (2 delen). Delft, IV

9 15. E.J. Verroen (red.) Colloquium Vervoersplanologisch Speurwerk miljoen auto s in 2010 (3 delen). Delft, P.M. Blok (red.) Colloquium Vervoersplanologisch Speurwerk Nederland in nota s, strategie en pragmatisme en beleid en onderzoek (3 delen). Delft, H.J. Meurs (red.) Colloquium Vervoersplanologisch Speurwerk Vervoerbeleid tussen rand en stad, naar een integrale aanpak op regionaal niveau (3 delen). Delft, J.M. Jager (red.) Colloquium Vervoersplanologisch Speurwerk Meten-modellerenmonitoren (2 delen). Delft, P. T. Tanja (red.) Colloquium Vervoersplanologisch Speurwerk De prijs van mobiliteit en van mobiliteitsbeperking (3 delen). Delft De publikaties (met uitzondering van de nummers 5 en 11) kunnen worden besteld door overmaking van het bijbehorende bedrag op girorekening ten name V

10 van de penningmeester van het Colloquium Vervoersplanologisch Speurwerk, Geerdinkhof 237, 1103 PZ Amsterdam, onder vermelding van CVS, jaartal en nummer, en de naam en het adres van de besteller. Het over te maken bedrag is voor de publicaties: - 1 t/m 8 f 25, t/m 14 f 55, t/m 17 f 85, f 75, f 120,-- Copies of these publications can be ordered from the treasurer of the Transportation Planning Research Colloquium, Geerdinkhof 237, 1103 PZ Amsterdam, The Netherlands. VI

11 VOORWOORD Voor U ligt een bundeling van bijdragen aan het negentiende Colloquium Vervoersplanologisch Speurwerk, dit keer gepland op 26 en 27 november te Rotterdam. Het Colloquium heeft een drietal doelstellingen: 1. Het presenteren van recent speurwerk op het gebied van vervoersplanning inclusief de raakvlakken daarvan met de ruimtelijke planning, aan collegaonderzoekers, alsmede het verspreiden ervan. 2. Het creëren van een forum, waar dit speurwerk ter discussie kan worden gesteld. 3. Het vormen van een ontmoetingsplaats voor onderzoekers. Daartoe is voor de bijeenkomsten gekozen voor de opzet waarbij iedere deelnemer tevoren een schriftelijke bijdrage indient en waarbij geen nauwe grenzen aan de onderwerpen van de bijdragen worden gesteld. Om een bepaald onderwerp, waarvan het bestuur van het Colloquium meent dat het gezien het belang of de actualiteit speciale aandacht verdient, meer te belichten, kan een centraal thema worden aangegeven. Dit COLLOQUIUM-jaar 1992 heeft het bestuur als centraal thema gekozen: INNOVATIE IN VERKEER EN VERVOER. De groei van zowel het personenvervoer als het goederenvervoer blijft aanhouden. Dit brengt een steeds toenemend beslag met zich op de capaciteitsruimte en de milieuruimte. Er worden thans in verschillende richtingen maatregelen en instrumenten overwogen danwel ingezet om te komen tot een zo effectief mogelijke VII

12 benutting van die ruimte. Bij het thema van 1992 staat de vraag centraal in hoevere innovaties betreffende strategische, organisatorische of meer operationele zaken een probleemoplossend vermogen hebben. Verkenning van de bijdragen leert dat een relatief bescheiden aantal papers werkelijk als thema-bijdrage kan worden aangemerkt. Dit kan zou kunnen worden opgevat als een signaal dat Innovatie in ons vakgebied nog onvoldoende leeft. De themabijdragen en de overige bijdragen zijn, in alfabetische volgorde naam auteur, opgenomen in de delen 1, 2 en 3. De organisatie van het Colloquium 1992 was in handen van ondergetekenden, die daarbij volledig a titre personel optreden. Op deze plaats willen we onze dank uitspreken aan onze werkgevers: - Projectbureau IVVS, Den Haag - Instituut voor Ruimtelijke Organisatie, INRO-TNO, Delft - MuConsult, adviesbureau Vervoer en Ruimte, Utrecht - NE1 Nederlands Economisch Instituut, Rotterdam - Gemeente Breda - AGV Adviesgroep voor Verkeer en Vervoer BV, Nieuwegein Een bijzonder woord van dank verdient Dick den Adel van het OSPA van de Technische Universiteit Delft. Hij droeg ook dit jaar weer zorg voor de vele praktische zaken, die de organisatie van de bijeenkomst en het samenstellen van de Colloquium-boeken vereist. Peter Blok Joke Jager Henk Meurs Arnoud Mouwen Theo Reijs Pieter Tanja Erik Verroen Delft, oktober 1992 VIII

13 INHOUDSOPGAVE Voolwoord Inhoudsopgave Deel 1 1. Aarts, H., C. van Knippenberg & B. Verplanken Vervoermiddelkeuze en gewoontegedrag Baerveldt, P.W. & K.Tj. Joustra Sturen op verstoring - actuele reisinformatie voor de klant Batenburg, R.S., F.C. Kuik & M. Kockelkoren Van hot naar her: De groei van taakcombinatie en mobiliteit in Nederland, Beek, P. van, K.H. van Eek & F. ter Welle Mobiliteit en dynamiek van tweeverdieners en andere typen huishoudens Ben, L.J.C. de, A.M.T. Mouwen & R. Weenink Draagvlak en systeemkeuze openbaar vervoer Binsbergen, A. van & H. Flikkema Distributie in stedelijke gebieden van morgen: Een verkenning Boneschansker, E. & A.L. t Hoen Externe kosten van het goederenvervoer X

14 8. Bos, W. Het Rijn-Corridor projekt. Pilot voor het uit- testen van RDS-TMC als medium voor radio-verkeers- informatie Bradley, M., F. Rosenberg Sr J. Veldhuis Een geintegreerde modelaanpak voor prognose van Schiphol-gebonden luchtverkeer Brohm, K.A. & A.M.T. Mouwen Kansrijke openbaar vervoerrelaties in de vervoerregio Amsterdam. 175 ll. Brouwer, M.Y., M.A. Gommers & R. de Boer Verkeersprognoses voor de Zuiderzeespoorlijn en de Hanzespoorlijn Bruin, R.A. de, J.C Salverda & C. Hartveld Waarom transferium doen als het P+R kan? Bruinsma, F., P. Rietveld & A. Loos Structurerende werking infrastructuur; oriëntatie Buffing, A.H.M. & H. de Hey ROA-RVVP: Het wankele evenwicht tussen milieu en bereikbaarheid Burgess, A. & H. Dijkman Logistieke en technologische verbeteringen in het Europees goederenwegvervoer; de effecten voor het milieu Bus, L. & J. Verkade Een verkenning naar de macro-economische baten van innovaties in het goederenvervoer Dekker, J.A.J., J.W. Houtman & P.J. Altena De meerwaarde van de corridorbenadering; theorie en praktijk. 309 X

15 18. Delsing, P.J. De P+ R-leasepas, van automobilist naar mobilist Dijkman, H. Kc W. Scheltes Flexibel conjunct meten (FCM) versus traditionele stated preferente Dijst, M. & H. van Hoogdalem Hoe complementair zullen elektrische voertuigen zijn? Contouren van een individueel-collectief vervoersysteem Egeter, B. & M.G. van den Heuvel Systeemopbouw openbaar vervoer in stedelijke gebieden. Een theoretisch model voor het toetsen van netwerk-concepten Emmerink, R., P. Nijkamp & P. Rietveld De effecten van informatie op de prestatie van transport netwerken Est, J.P. van & P. de Klein Kodering verkeersnetwerken en GIS: Matching segment- en linkbestanden. 411 Deel Ettema, D., A. Borgers & H. Timmermans Een simulatiemodel voor het genereren van activiteitenschema s Evert, H.C. van & L.H. Immers Kwaliteitszorg verkeersprognoses: Gewenste produkt kwaliteit Evert, H.C. van & J.M. Jager NRM versie X1

16 27. Ginkel, J.C. van & M.J.P.F. Gommers Matrixschattingstechnieken - een aanzet tot bezinning en discussie Ginkel, J.C. van, M.J.P.F. Gommers & P.J.A.M. Veeke Het onderdeel BASMAT binnen het NRM - de beoogde modelstructuur voor het schatten van basismatrices Ginkel, J.C. van, P.J.A.M. Veeke & D. Bergsma De NRM-gereedschapskist voor basismatrixbouw - een proeftoepassing Hagen, M. van & H. Meurs Bereikbaarheidsprofielen van dagattracties Hagen, M. van & J. Quik De potentie van Schiphol in relatie tot de Hoge-Snelheidstrein Hamerslag, R., E.C. van Berkum & N.J. Aardoom De invloed van de aanleg van auto(snel)wegen en raillijnen op de ruimtelijke ontwikkeling: Een model benadering Hilbers, H., E. Verroen & F. Kuik Het meten van bereikbaarheid. Aanknopingspunten voor een effectieve mobiliteitsgeleiding via het lokatie- beleid voor bedrijven, voorzieningen en woningen Huiberts, S.J.C. Concept 21: Een nieuwe vervoerconcept in een binnenstedelijke omgeving Jaarsma, C.F. Innovatie van plattelandswegen door verbouw : Structurele oplossingen voor verkeersproblemen met gebiedsgewijze aanpak. 645 x11

17 36. Jong, G.C. de, M.A. Gommers & J.P.G.N. Klooster De reistijdwaardering in het goederenvervoer: De uitkomsten Jong, M.A. de & J.H.M. van Rooden Parkeermanagement in de vervoerregio Utrecht Jorritsma, P. & Y.H.F. Cheung Effecten van de invoering van de studentenkaart: Doel, opzet en veldwerkresultaten Katteler, H., C. Louisse & H. van Vliet Kansrijke situaties voor een fietsgebruik-bevorderend beleid Konijnendijk, A.T. & R.J. Scheele Overstappen in het streekvervoer rond Utrecht Kreutzberger, E. Het doorstroomconcept op de hoofdwegen in de vervoerregio en de gemeente Den Haag. Hoe concretiseren? Kreutzberger, E. & J. Vleugel Infrastructuurgebruik in de binnenvaart en het lucht-, rail- en wegvervoer en mogelijkheden tot de vernieuwing van het personen- en goederenvervoer Kroon, M.C. Voertuigzelfbeheersing; vermogensbeperking als voorwaarde voor duurzaam en veilig verkeer Kropman, J., P. van Beek & F. Kuik Flexibilisering van tijdroosters: Een instrument voor beïnvloeding van mobiliteit? 831 X111

18 45. Laan, D.H. v.d. De economische betekenis van luchthavens: Schiphol. 851 Deel Lindveld, Ch.D.R., E.P. Kroes Sr H. de Ruiter DYNA; Real-time voorspellingen van congestie Maanen, T. van, E. Verroen & P. Heerema Mobiliteitsprofielen revisited: Een nadere analyse van de samenhang tussen bedrijfs-, lokatie- en mobiliteitskenmerken Meurs, H. & P. Bovy Nieuwe baan of ander huis: Verandering van vervoerwijze? Meijdam, J. & M. Tacken Fijnmazig telecomnet als aanvulling op grofmazig collectief openbaar vervoersnet en ruim bemeten wegennet Ooststroom, H. van Een translog-vraagfunctie voor het personenvervoer in Nederland Ooststroom, H. van & E. Bekker De spoorwegtarieven. Er is niets mis met het tariefbeleid van de Nederlandse Spoorwegen Peeters, P.M., C.D. van Goeverden & P.J.J. Heerema Na vijven met de auto? Pronk, M., J. Klooster & B. van Wee Het personenautopark in 2015 volgens FACTS 2.0 en de gevolgen voor de overheidsdoelstellingen XIV

19 54. Reisen, F. van Het grote optimisme rond telewerken: Gerechtvaardigd of gelogenstraft? Renes, G., H. Meurs AL J. Klooster Reiskostenvergoedingen en woon-werkverkeer. r Rooijers, T. Maatschappelijke acceptatie van mobiliteitsbeleid Rouwendal, J. & P. Rietveld Veranderingen in pendelafstanden van Nederlandse huishoudens Schoemaker, Th. & B. Egeter Van randstad tot metropool Schol, E. & J.M. Bais Marktpotentielen en milieu-effecten van nieuwe vervoerstechnologieen. Elektrische- en aardgasvoertuigen Schrijnen, P.M. Verkeersmanagement: Beheersen van mobiliteit én bereikbaarheid Slebos, I.C. Milieuvriendelijker zakelijk verkeer nodig en mogelijk Smits, C.A. & W. Korver De modellering van de kosten in het stedelijk openbaar vervoer Verhoef, E.T., P. Nijkamp & P. Rietveld De economie van parkeermanagement systemen. De (on-)mogelijkheden van een sturend parkeerbeleid xv

20 64. Visser, J.G.S.N. Stedelijk goederenverkeer als aandachtspunt Vlist, M. van der, J. Pommer & J. Benschop De ontwikkeling van de Applicator als onderdeel van het Nieuw Regionaal Model Wee, B. van & J. Klooster 2010 voorbij: Wat ligt er achter de horizon van NMP en SVV? Witbreuk, M.J.G. & M.F.A.M. van Maarseveen De mobiliteit van werknemers in de Twentse binnensteden Lijst van deelnemers 1301 XVI

21 CONTENTS Foreword contents Part 1 1. Aarts, H., C. van Knippenberg & B. Verplanken Transport mode choice and habits Baerveldt, P.W. & K.Tj. Joustra Sturen op verstoring, actuele reisinformatie voor de klant Batenburg, R.S., F.C. Kuik & M. Kockelkoren A hectic life: the growth of task combination and mobility in the Netherlands, Beek, P. van, K.H. van Eek & F. ter Welle Mobility of double income families compared with other families Ben, L.J.C. de, A.M.T. Mouwen & R. Weenink Basis and choice of system for public transport Binsbergen, A. van & H. Flikkema The transport of goods in urban area s of the future; a preview. 97 Boneschansker, E. & A.L. t Hoen External costs of freight transport. 117 Bos, W. Rhine-Corridor, an RDS-TMC pilot for radio traffic information. 137 XVII

22 9. Bradley, M., F. Rosenberg & J. Veldhuis An integrated modelling approach for predicting air traffic at Schiphol airport Brohm, K.A. & A.M.T. Mouwen Opportunities for public transport in the Amsterdam region. 175 ll. Brouwer, M.Y., M.A. Gommers & R. de Boer Traffic forecasts for the Zuiderzeespoorlijn and the Hanzespoorlijn Bruin, R.A. de, J.C Salverda & C. Hartveld The Dutch (potential) transferium-user Bruinsma, F., P. Rietveld & A. Loos The structuring effects of infrastructure Buffing, A.H.M. & H. de Hey The delicate balance between environment an accessibility Burgess, A. & H. Dijkman Logistic improvement and technological change in the European roadtransportation of goods; the effects on the environment Bus, L. & J. Verkade The macro economie benefits of freight transport innovations - an exploration Dekker, J.A.J., J.W. Houtman & P.J. Altena The surplus value of the corridor-approach; theory and practice Delsing, P.J. The P+R-leasecard, a change towards the use of multi-transportation modes. 331

23 19. Dijkman, H. & W. Scheltes Flexibel conjunct meten (FCM) versus traditionele stated preferente Dijst, M. & H. van Hoogdalem A new brief for the design of electricle vehicles: The IC-system Egeter, B. & M.G. van den Heuvel Design concepts for public transport networks Emmerink, R., P. Nijkamp & P. Rietveld The role of information in the performance of transport networks Est, J.P. van & P. de Klein Coding networks and GIS: Matching of transport networks and geographic base files. 411 Part Ettema, D., A. Borgers & H. Timmermans A simulation model of activity scheduiing behavior Evert, H.C. van & L.H. Immers Quality assurance regional forecasts; desired product quality Evert, H.C. van & J.M. Jager NRM version Ginkel, J.C. van & M.J.P.F. Gommers 0-D matrix estimation techniques, an impulse to reflection and discussion. 489 x1x

24 28. Ginkel, J.C. van, M.J.P.F. Gommers & P.J.A.M. Veeke The element BASMAT within the NRM; the model structure intended for the estimation of 0-D matrices for a basis year Ginkel, J.C. van, P.J.A.M. Veeke & D. Bergsma The NRM-toolbox for estimation of matrices for a basis year; an application for testing Hagen, M. van & H. Meurs Accessibility of resorts Hagen, M. van & J. Quik The potential of Schiphol Airport in relation to the High Speed Train Hamerslag, R., E.C. van Berkum & N.J. Aardoom The influence of highway and railway construction on spatial development: A modelling approach Hilbers, H., E. Verroen & F. Kuik Measuring accessibility: Point of departure for location planning in order to reduce car use Huiberts, S.J.C. Concept 21: Een nieuwe vervoerconcept in een binnenstedelijke omgeving Jaarsma, C.F. Innovation of minor rural roads by amelioration: Structural selutions for traffic problems with a regional approach Jong, G.C. de, M.A. Gommers & J.P.G.N. Klooster The value-of-time in freight transport: The results. 663 Xx

25 37. Jong, M.A. de & J.H.M. van Rooden Parkingmanagement in the transportregion Utrecht Jorritsma, P. Sr Y.H.F. Cheung Effects of the Public Transport Pass for students in the Netherlands Katteler, H., C. Louisse & H. van Vliet Favourable situations for a bike use stimulating policy Konijnendijk, A.T. & R.J. Scheele Transfer in regional public transport, the Utrecht case Kreutzberger, E. Ideas for the elaboration of the preferential lane concept in The Hague Kreutzberger, E. & J. Vleugel Use of infrastructure by passenger and commodity transport Kroon, M.C. Vehicle selfcontrol; engine downsizing as a prerequisite for sustainable and safe traffic Kropman, J., P. van Beek & F. Kuik Implementation of flexible schedules: An instrument to affect mobility? Laan, D.H. v.d. The economie impact of an airport: the case of Schiphol airport. 851 XXI

26 Part Lindveld, Ch.D.R., E.P. Kroes & H. de Ruiter DYNA; Real time congestion prediction Maanen, T. van, E. Verroen & P. Heerema Mobility profiles revisited: Additional research on the relationship between company-, location- and mobility characteristics Meurs, H. & P. Bovy A new job or an other house: Change in modal split? Meijdam, J. & M. Tacken The high density telecom network as completion to low density public transport network and ample road network Ooststroom, H. van A translog travel demand system for the Netherlands Ooststroom, H. van & E. Bekker Rail transit prices Peeters, P.M., C.D. van Goeverden & P.J.J. Heerema Driving by car after five o clock? Pronk, M., J. Klooster & B. van Wee Car ownership in 2015 simulated with FACTS 2.0 and the implications for the government targets Reisen, F. van The big optimism around telecommuting: Justified or falsified? 1035 xx11

27 55. Renes, G., H. Meurs & J. Klooster Commuting reimbursements, mobility and labour market Rooijers, T. Social acceptance of mobility policy and measures Rouwendal, J. & P. Rietveld Changes in commuting distances of Dutch households Schoemaker, Th. & B. Egeter From randstad to metropolis Schol, E. & J.M. Bais Market penetration and environmental effects of new transportation technologies Schrijnen, P.M. Traffic management, control of car use and traffic flow Slebos, I.C. Business traffic and the environment Smits, C.A. & W. Korver Modelling the costs of urban public transport Verhoef, E.T., P. Nijkamp & P. Rietveld The economics of parking management systems. The (im-)possibilities of parking polities in traffic regulation Visser, J.G.S.N. The focus on inner-city freight transport xx111

28 65. Vlist, M. van der, J. Pommer & J. Benschop The Applicator, one of the modules in the New Regional Model Wee, B. van & J. Klooster Innovations in goals and future research desired Witbreuk, M.J.G. & M.F.A.M. van Maarseveen The mobility of employees in the inner cities of Twente List of participants XXIV

29 XXV

30 XXVI

31 DYNA; Real-time voorspellingen van congestie Auteurs : Lindveld, Ch.D.R., Hague Consulting Group Kroes, E.P., Hague Consulting Group de Ruiter, H., Rijkswaterstaat : Dienst Verkeerskunde 875

32 Inhoudsopgave Samenvatting Introduction _ The Data Collection System..... _ _. _ Congestion Prediction Modelling: a review of methods.. _ Statistical and mechanica1 models Models describing drivers' route choice..... _ Proposed Real-Time congestion prediction system... _ System Environment.. _... _.... _... _.. _ Mainsystemcomponents _ Model-interdependence and flow of the measurements Tempora1 aspects , SConclusion....._.._...._ Literature

33 Samenvatting DYNA; Real-time voorspellingen van congestie Dit paper geeft een overzicht van de globale opzet van het Real-time Traffic Prediction System (RTPS), zoals omschreven in Drive 11 project V2036 'Dyna'. Dit systeem is bedoeld om de ontwikkeling van de verkeerssituatie op een autosnelwegen netwerk te modelleren en in real-time te voorspellen. Deze voorspellingen kunnen dienen om beheerders en (potentiele) gebruikers van dit autosnelwegennet te informeren t.a.v. verwachte congestie. Het systeem verwerkt verkeersgegevens (intensiteit, gemiddelde snelheid, en gemiddelde detector bezetting) zoals die worden aangeleverd door het Monitoring Casco van Rijkswaterstaat. Tevens zal gebruik gemaakt worden van een database van historische verkeersgegevens. Het RTPS omvat verscheidene deelsystemen: een statistische module (STA) om de ruwe gegevens te filteren en om voorspellingen te doen voor de zeer korte termijn (1-15 min.), een module voor dynamische verkeerstoedeling (DTA) voor de korte-termijn voorspellingen (15-60 min.), en een module voor schatting van O/D matrices (ODME) in real-time om de DTA module van de nodige gegevens te voorzien. Voorspellingen van de STA en de DTA module worden samengevoegd waar hun voorspellings-horizon overlapt. Zowel de actuele als de voorspelde verkeers situaties zullen voor de wegbeheerder in beeld gebracht worden. Deze kan desgewenst ingrijpen op basis van de hem gepresenteerde informatie. Het real-time aspect van dit systeem komt tot uitdrukking in de beperkte rekentijd en noodzaak de afzonderlijke modules te synchroniseren. De implementatie zal een multitasking omgeving vereisen. Sunmary DYNA; Real time congestion prediction This paper specifies the overall architecture of the Real-time Traffic Prediction System (RTPs) as proposed in Drive project no. V2036 "Dyna". This system is to model and predict traffic conditions on a highway network in real time, which can be used to provide information on (expected) congestion to a traffic operator and (potential) car drivers. The system receives traffic data such as traffic flow, average speed and average detector occupancy in real-time from the Rijkswaterstaat Monitoring Casco. Additionally, a database of historie traffic information wil1 be used. The RTPS contains several submodels : a statistical model (STA) for filtering noisy traffic data, and for very short term predictions (prediction horizon : 5-15 min.), a dynamic traffic assignment model (DTA) for short term predictions (prediction horizon : min.), and a real-time O/D matrix estimation model (ODME) to provide the DTAwith the necessary O/D information. Predictions from the STA and the DTA model are merged where their respective time horizons overlap. The present and predicted traffic conditions wil1 be displayed for evaluation by the traffic operator, who can take appropriate action if required. The real-time aspect of this system is reflected in the need for timing and reliable synchronisation of the various model components. A multitasking environment wil1 be required for implementation of the system. 877

34 4 1 Introduction In 1995 traffic flows, speeds and densities on the entire Netherlands motorway network wil1 be continuously measured by the so-called Monitoring System. This system wil1 be operated by Rijkswaterstaat (Ministry of Transport and Public Works), and consists of double loop detectors in the road surface, local computers to preprocess the collected information and a centra1 traffic control centre to which al1 information is sent in real-time. The collected information wil1 be used for various purposes: to create a database of historie traffic flow and speed information to be used for statistical purposes (to monitor broad trends) and for transport modelling and research (to analyse patterns in traffic and transport behaviour) to monitor the actual traffic flow conditions in real-time, mainly to detect incidents and identify congested links inside the motorway network (to inform the police and (potential) car drivers elsewhere on the network) to predict the expected near-future traffic flow conditions (in the immediate future, between 1 and 60 minutes ahead in time) in order to inform the police and car drivers. The ensuing incident warnings and information on expected future traffic conditions (e.g. congestion) can also be used to guide the use of dynamic traffic management measures, such as variable message signs, information provision by radio, etc. in order to reduce congestion and improve efficiency. Chapter 2 of this paper provides a short description of the Monitoring System. Congestion prediction modelling methods are reviewed briefly in Chapter 3. In Chapter 4 the model system that is being developed to predict traffic conditions and particularly congestion in real-time is presented. Initial specifications of the methods used in this systemwere defined during a Pilot Study conunissioned by Rijkswaterstaat to Hague Consulting Group in The Pilot Study was followed by the DRIVE-11 DYNA project for the European Cormnission, in which a prototype version of the model system wil1 be developed and implemented, and in which realtime field tests of the congestion prediction system wil1 be conducted. This paper contains some initial results from this ongoing DYNA project. 878

35 2 The Data Collection System The Netherlands. full Data For Collection test purposes, System about Rotterdam (sec on and 200 off kilometres ramps. Installation of motorway, The Monitoring System in the is features: about surface detector loops average corridors, ramps processors the detector loops and al1roadside aggregation processors of the are data conn to which collect wil1

36 6 The Data Collection System wil1 provide the following data items to the control centre on a 1-minute basis: date, time :; loop-counter identification traffic counts (1-minute flows) 2; measured speeds (l-minute average km/hr) standard deviation of speeds (on l-minute basis) 2; detector occupancy (1-minute average percentage) 7) congestion indication (O/l indicator) In some parts of the network, measurements wil1 be provided by the existing MCSS (Motorway Control and Signalling System); this system does not provide data items 5) and 6), but has a smaller inter-sensor distance. In other parts where the new Monitoring System is installed, measurements for items 3.4, and 5 wil1 be available for each lane and for each vehicle category (5 different vehicle length categories are distinguished). Occupancy wil1 be given per lane (for most of the network), and a separate congestion indicator wil1 be given per crosssection. The congestion criterion to be used is: about 4 consecutive vehicles move slower then a predetermined speed. Further information on the quality of the measurements is given for each time period. Additionally the Data Collection System provides some information on the quality and reliability of the measurements themselves. Part of the incoming data wil1 be stored in a traffic database. To keep storage requirements within reasonable limits, the following Standard Database scheme has been proposed: - 30-minute database (5-minute averages of data items 1-5 are kept for 30 minutes; lane and vehicle type information included) - 7-day database (15-minute averages of data items 1-5 are kept for 7 days; no lane or vehicle-type information; al1 data are converted into Passenger Car Units or PCU's) - 7-day moving average database (for this database, the week is divided into 7x24~4 = 672 time periods of 15 min. each; in this database the moving averages of data items 1-5 are kept for each of the 672 time periods; no lane or vehicle-type information is retained; al1 data are converted into PCU's). - l-year database (60-minute averages of data items 1-5 are kept for 365 days; no lane or vehicle-type information; the data are converted into PCU's). For the Pilot Project this Standard Database information wil1 be available. Additionally more detailed data can be stored separately for test purposes. In the longer run, however, it is hoped that the Standard Database as described wil1 provide sufficient statistics for most purposes. 889

37 3 Congestion Prediction Modelling: a review of methods 7 For the prediction of traffic flows and congestion using real-time measurements of traffic flows and speeds, a number of different modelling approaches exist. We distinguish three main groups of methods: 1 Statistical and mechanica1 models of traffic flows 11 Models describing drivers' route choice. Sometimes neural networks are presented as a third group of methods. However, neural networks are particularly wel1 suited to classify observations into categories or to identify patterns, while less is known about their use for prediction. Ripley (1992) notes that 'Comparison (of neural network applications) with other methods are rare, but when done carefully often show that simple statistical methods can outperform state-of-the-art neural networks'. Following Ripley, we wil1 not treat neural networks separately here. 3.1 Statistical and mechanica1 model8 Models in this category range from purely statistical regression models (without regard for the properties of traffic flow) to extremely complicated models based on stochastic differential equations. The 'Athena' model, described first by Danech-Pajou and Aron (1990) and later in MonicaO4 (19901, belongs in the first category. It predicts future traffic flows by first identifying a prototypical class of operating characteristics using recent flow measurements, and then applying the corresponding regression equation to obtain the predicted flow levels for a series of 30-minute intervals in the future. In total 48 prototypical classes are distinguished: 4 prototype days times 12 different months. At the other extreme there are models which typically describe the evolution of aggregate traffic flows through time from some starting condition. This category includes models that describe the evolution of traffic flow in terms of differential equations, like the wel1 known ones used by Payne (1971) and Lighthill (1955). An example of an operational version of such a model is METANRT, described in Papageorgiou et alia (1989) and further reported in Morin et al. (1991). This macroscopic modelling approach solves a discrete initial-value problem representing the evolution of a traffic flow on a stretch of road, with additional terms representing acceleration and merging phenomena at on-ramps or at lane-drop locations. This model, although originally developed for off-line applications, can operate in real-time. To account for the stochastic elements in traffic flow, stochastic differential equations, have been used by Van Maarseveen (1982), Smulders (1989) and Weits (1990). One of the problems that is identified by these authors is to determine an (initial) state on which to apply the evolution described by the equations. In Van Maarseveen (1982) and Smulders (19901, optimal estimators of traffic flow states are derived based on the representation of traffic through stochastic differential equations. It is noted by both authors that the optimal estimators (filters) must be truncated at some point, and even then require a very high computational effort. Modified, approximate filters are reported to bear a strong resemblance to the Kalman-Bucy filter, which brings US to the third statistical approach. This third statistical approach, is based on the use of the Kalman filter. The Kalman filter serves a dual purpose here : on the one hand it contains a part to estimate current traffic conditions from the measurements (the updating equations). on the other hand it is 881

38 particularly well-suited for making predictions through its statetransition mechanism (the transition equations). This method is also called adaptive-forecasting, because it relies on a continuous comparison of past predictions and realisations in order to adapt its forecasts to observed prediction errors. In this way prediction errors wil1 be damped out, instead of being amplified by subsequent applications of the model. This feature also makes such filters self-calibrating. Literature on the successful use of the Kalman filter for traffic flow prediction on highway networks is rather sparse. The most recent we know of is that of Okutani and Stephanides (1984). Willsky (1980) describes a Kalman filter, but this has not been implemented due to its strong data requirements. Furthermore, this research is primarily aimed at incident detection rather than flowprediction. More recently, in Whittaker (1991) an outline is given of a dynamic state space model with its associated Kalman filter for very short-term prediction of traffic on an highway network. 3.2 Model6 descrlbing drivers' route choice The second group of models is drastically different from the first in that it explicitly models driver's route choice. We choose to use aggregated models rather then do micro-simulation (too demanding), based on dynamic traffic assignment (DTA). DTA differs fundamentally from traditional static traffic assignment (STA) methods, in that it explicitly models the time dimension. This is typically done by cutting the time axis into a limited number of discrete time intervals. Traffic is loaded onto the network time interval by time interval, and progress of the vehicles in the network is monitored over time. In this way the time-varying nature of traffic is properly reflected, as wel1 as the phenomenon that traffic, which is held up at one bottleneck, cannot at the same time enter other bottlenecks downstream (as the STA allows). In application, the DTA based methods often rely on simulation, where progress of smal1 clusters of vehicles or even individual cars through the network is modelled in detail. The use of dynamic traffic assignment requires that a time dependent OD matrix (called ODt matrix) is available. One of the earliest and best known DTA models is CONTKAM (Leonard 1978), which was modified into MCONTHAM for application to motorways as described in Morin et al. (1991). That program has been validated against real data from the Boulevard Périphérique in Paris in an off-line application; the authors report good agreement between predictions and actual data. In Ben-Akiva and Kaysi (1991) an overview has been given of various DTA approaches that have appeared in literature. Merchant and Nemhauser (1978) were among the first researchers to model dynamic network flows and formulate a discrete-time, system-optimizing dynamic traffic assignment model for a single destination. Carey (1986, 1987) expanded on the Merchant-Nemhauser formulation, and Ho (1980) developed a solution algorithm to the same formulation consisting of successive linear optimisation (1980). More recently Ho implemented this on a parallel Hypercube Multicomputer (Ho, 1990). Other formulations include the utilisation of heuristic algorithms (Janson, 1990, 1991), optimal control theory (Wie et al., 1990), a three-dimensional network approach (Hamerslag, 1988) and a heuristic approach by Clarke et. al. (1992). Cascetta and Cantarella (1991), on the other hand modelled dynamic traffic conditions as a stochastic process while Vithoulkas (1990) adoptedthe stochastic equilibriumapproach proposed earlier by Ben-Akiva et al. (1986). 882

39 Despite hasbeen widely richthe context implementation (Ben-Akiva within and Kaysi, an from one or more of the followin 19 route Inappropriate choice decisions, modelling of use behaviour volume, Flawed presentation speed, of network Not-truly-dynamic traffic features: conditions, repeated assignment NO volumes exists solution algorithm is availa Modelling experience is limited

40 4 Proposed Real-Time congestion prediction system 10 The objective of the congestion prediction systemthat is currently being developed is to provide accurate short-term forecasts of traffic flows and travel times in real-time for inter-urban motorway networks using the monitoring information. More specifically, these forecasts should provide the following forecasts for each link in the network: - predicted total link flow in vehicles per minute by direction - predicted (or detected) congestion situation (congestion yes/no) by direction - predicted mean travel time (including queuing time where applicable) by direction. This information may be used for the following purposes: - to provide the traffic operator and the police with infonnation about the expected operating conditions inside the network, and particularly with early warnings about the expected start and/or continuation of congestion on specific links in the motorway network - to provide information on expected bottlenecks and delays to travellers already on the network, and to potential travellers before departure - to provide (commercial) route-planning systems with information which enables an optimisation of expected vehicle efficiency - to assist the traffic operator to use dynamic traffic management measures (eg. variable message signs, radio information to drivers in the network, radio information to travellers before departure) in order to modify the traffic flow pattern on the network. In practice, the forecasts wil1 be presented to the operator trough a number of computer screens, each displaying the predicted state of the network for a particular prediction horizon (current situation, 15 minutes ahead, 30 minutes ahead, and (if sufficient accuracy can be achieved)60 minutes ahead). Predicted speeds, flow/capacity ratios, queue indicators and other performance measures wil1 be colour-coded. The operator can then zoom in on those area's of the network that require closer attention. Note that 'now' is also a prediction, as the most recent information wil1 arrive in the control centre with a delay of at least 1 minute, and the prediction algorithm wil1 also need at least one minute before updated results can be produced. The objective outlined above is to be achieved subject to a large number of constraints, of which we mention the most important ones: - real-time operation: the main constraint is that the system wil1 need to provide its forecasts in real-time, which puts a heavy burden on speed of computation and data access, amount of data used, speed and complexity of algorithms to be used. - accuracy of the results: the prediction results are only useful when a satisfactory degree of accuracy can be achieved; clearly expected accuracy is smaller as the prediction horizon gets longer, and it remains to be seen whether or not the theoretically desirable 60 minute horizon can be reached with an acceptable accuracy at al1 - operating conditions: the system should provide meaningful results under very different operating conditions, and not just for an 'average day'; this includes situations with recurrent and incidental congestion, accidents, road-works, and so on - dynamic traffic management effects: the system must (ultimately) be able to include in its predictions the effects of dynamic traffic management measures taken by the operator; as a consequente the algorithms used in the system must be able to reflect possible changes in driver route choice behaviour in response to such traffic management measures. 884

41 System Environment In order to meet the specified objective given the constraints just mentioned the DYNA model system has been proposed. The environment diagram of the proposed system is given in Figure 3 below. Hlstoncal Information Infrastructure Data Data Coilection Traffic System Measurements Traffic Prediction Queries and Input Traffic Information Figure (3) : System Environment The DYNA Real-time Traffic Prediction System (RTPS) is fed with data from four sources: real-time data provided by the Data Collection System, consisting of direct measurements of volumes, speeds, occupancies and derived link based information including indications the presence of incidents; this information is provided every minute historica1 information, generally consisting of origin-destination information obtained from surveys, models and/or inferred from traffic counts using matrix estimation techniques; this historica1 information changes only slowly over time, but should be updated from time to time infrastructure data including a full network description with al1 relevant network attributes that are generally invariant with time, or change slowly with time; this includes the standard network topology, the geometrie attributes of al1 network elements, the control devices that are installed in the network, and so on; updates and temporary changes in the network attributes, for instance caused by road-works, should be indicated by the traffic operator. input from the Traffic Operator (accident reports, road maintenance, special events, etc.) The system wil1 provide its information to the traffic operator at request, as described before. 885

42 Main system componenta The main components of the DYNA RTPS are shown in Figure 4. HMoncal Infrastructure Information Data Real-time Traffic 1----,,,,,,,,,-,,-,-,,-1, Figure (4) : Structure of the RTPS Shown in the top-right and top-left corners are the data interface through which the measurement data enters the system, and the Operator Interface through which the Traffic Operator interacts with the system. Submodels of the prediction system are : - a statistical traffic model (SIM) - an O/D matrix estimator (O/D) - a dynamic traffic assignment model(dta) Due to this approach a merging procedure (M) is required to combine the results of the two methods, and a scheduler (S) which co-ordinates al1 the inter-process communication. Each of the five main components wil1 be discussed briefly below. As can be seen, a model structure has been chosen that does not rely on one single method to predict congestion, but on a combination of two differentmethods: a Statisticalmodel andthe Dynamic Traffic Assignment model. A separate model for the dynamic estimation of the O/D matrices needed by the DTA module is also shown. The statistical model approach was selected mainly for the very short prediction horizons: 1 to 10 (or 15) minutes ahead. The two main reasons for its selection are: its speed (it can run in 1 or 2 minutes) and its expected accuracy (for the very short term). For these very short prediction horizons route choice behaviour does not (yet) play an important role, so the statistical model's rather poor representation of driver route choice (and hence driver response to dynamic traffic management measures) does not count as a serious limitation. Following 886

43 recommendations has been selected, by Whittaker measurements the filter in - or the as filter necessary little to of the traffic stationarity the filter is (the self-calibrat - it can incorporate parameters c method proposed by Neither fixed at the the transition moment. equation as simple state evolution a canbe starting incorporated point. until predictions of into the tr which investigated. the various network burden and exploit the possibility

44 recommendations has been selected, by Whittaker measurements the filter in - or the as filter necessary little to of the traffic stationarity the filter is (the self-calibrat - it can incorporate parameters c method proposed by Neither fixed at the the transition moment. equation as simple state evolution a canbe starting incorporated point. until predictions of into the tr which investigated. the various network burden and exploit the possibility

45 Tempora1 aspect8 The tempora1 aspects of any real-time prediction system can be depicted graphically in the following way (source [Ben-Akiva and Kaysi]). L I TP I I l Real timet: I l t t3 Figure (6) : Rolling Horizon prediction On the horizontal axis the real-time T, is set out; on the vertical axis the projected time Tp for which predictions are made. On the line T, = T=, Projected-time equals real-time. Predictions are updated at times t,, t,, t,. etc.. i.e. the previous computational cycle ends and a new one begins. Predictions started at t, become available at time tiei. The stepfunction denotes the computing activity, the prediction horizon, and the availability of new predictions. The Tp value of the horizontal pieces denote the prediction horizon for the prediction currently being computed. Between times ti and ti,, (where the stepfunction stays constant) the system is busy computing the next prediction. At the step discontinuities ti, the current prediction becomes available, and a new prediction cycle is started. The prediction is made for some time in the future, the Prediction Horizon H. During a computational cycle only the predictions from the previous cycle are available. The life-time of these predictions : the time left between the actual time and the time for which the prediction was meant decreases continuously (as H-T,). The set of prediction horizons tp,, tp,,... together form the Rolling Horizon. As there are at least 2 different prediction modules (one for short-term 5-15 min. predictions and another for min. predictions) there wil1 be at least 2 different stepfunctions, each having its own starting points and its own Rolling Horizon. 889