Reduction of CO2 Emissions from Road Transport in Cities: Impact of Dynamic Route Guidance System on Greenhouse Gas Emission

186 Pages·2017·9.156 MB·English

by  Michal Markiewicz (auth.)

Michal Markiewicz
Reduction of CO
2
Emissions from Road
Transport in Cities
Impact of Dynamic Route Guidance
System on Greenhouse Gas Emission
Reduction of CO Emissions from Road
2
Transport in Cities
Michal Markiewicz
Reduction of CO
2
Emissions from Road
Transport in Cities
Impact of Dynamic Route Guidance
System on Greenhouse Gas Emission
Michal Markiewicz
Bremen, Germany
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ISBN 978-3-658-16318-1 ISBN 978-3-658-16319-8 (eBook)
DOI 10.1007/978-3-658-16319-8
Library of Congress Control Number: 2016956285
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Acknowledgement
I would like to thank all of those who have helped in carrying out the
research. My supervisors prof. dr. Michael Lawo, prof. dr hab. inż.
MarekSkomorowski. IwouldalsoliketothankKrzysztofWroński,Jeffand
Martina Baumgart for proofreading.
I gratefully acknowledge the funding sources that made my Ph.D. work
possible:
• Ph.D.Scholarship“MałopolskieStypendiumDoktoranckie”–Regional
Fellowship for Ph.D. Students,
• Ph.D. Scholarship “Doctus – Małopolski Fundusz Stypendialny dla
doktorantów” – Regional Fellowship for Ph.D. Students,
• Center Regional Analysis Foundation Scholarship (“Fundacja Cen-
trum Analiz Regionalnych”),
• Cracow City Council Scholarships for gifted students (“Stypendium
naukoweMiastaKrakowadlaszczególnieuzdolnionychstudentóworaz
uczestników studiów doktoranckich krakowskich uczelni wyższych”),
• Ph.D. Scholarship at Divisions of Cognitive Science, Institute of Phi-
losophy, Jagiellonian University.
ThedevelopmentofthecomponentsmentionedinAppendixBwouldn’t
be possible without the involvement of the following people: doc. hab.
Marian Jabłonski, prof. dr hab. Piotr Zgiczyński, prof. Lech Konieczny,
prof. Irena Roterman-Konieczna, prof. dr hab. inż. Jerzy Mikulski, dr
hab. inż Wiesław Starowicz, Tomasz Koźmiński, Aneta Ruzik, Robert
Gilewski, Aleksandra Gilewska, Agata Markiewicz-Górska, Paweł Górski,
Maciej Mindziak, Marzena Bryła, Jadwiga Jędrzejewska, Mirosław Jędrze-
jewski, Andrzej Woch, Piotr Białas, Piotr Gaździcki, Wojciech Przybylski
and Jacek Legendziewicz. I would like also thank Vatico company, espe-
ciallyitsownerWacławSzczepanik,MichałKurowskiandJarosławSzkarłat
andtheTrafficInfrastructureManagementOffice(ZIKiT),especiallyPiotr
Trzepak,AdrianMalik,JarosławKołodziejandŁukaszGryga. Iwouldlike
to thank Roger and Loni Werne and my family, especially my mother.
Abstract
Thisthesisproposesatrafficoptimizationsystemwhichreducestheamount
of greenhouse gas emissions. Decisions made by the system are based on
the current traffic situation in the city. It is shown that drivers who have
access to information about optimal routes computed by the system are
able to reduce the amount of emitted greenhouse gases. The research has
been conducted by finding the answers to several research questions.
The first research question is about the methods of acquiring infor-
mation about traveling times and in that way – indirectly about conges-
tion. This thesis compares two groups of methods of measuring traveling
times. The first contains techniques which acquire data from sensors in-
stalled in–roadway or over–roadway, for example license plate recognition
systems that analyze video streams from traffic monitoring cameras. The
second group analyzes the movement of a control test vehicle. It includes
methods like Floating Car Data (FCD). This relies on the wireless trans-
missionofvehiclepositioningmeasuredbyanon–boardpositioningmodule
like a Global Positioning System (GPS). Reliability and stability of the
measurements and also the cost of deployment are studied.
Thesecondresearchquestionishowtomodelcarbondioxideemissions,
possibly using efficient computational models, to be able to reliably and
quicklysimulateoverallCO emissionswhensomeorallofthedrivershave
2
access to the information about optimal routes. Dedicated simulation soft-
ware is proposed that integrates a CO emission model with a cellular au-
2
tomatamodelthatisalsoabletomapthe roadnetworkofanycityaslong
as its topology is stored in a geospatial database.
The third research question is more technical in nature. It is how to
deploysuchasysteminrealworldbyre-usingexistingtrafficinfrastructure.
The proposal of such a system is presented. It includes the description of
hardwareandsoftwaredevelopedtotestsomeoftheissuesrelatedtotraffic
optimization in the city of Cracow.
The main outcome of this research is a proof that greenhouse gas emis-
sions of the vehicles in the city can be reduced by providing drivers with
information of optimal routes.
Zusammenfassung
DieseArbeitbefasstsichmitderEntwicklungeinesSystemszurVerkehrsop-
timierungzurReduzierungvonTreibhausgas–Emissionen.DieEntscheidun-
gendesSystemsberu¨cksichtigendabeidiejeweilsaktuelleVerkehrssituation.
Es wird gezeigt, dass Verkehrsteilnehmer, mithilfe von Informationen u¨ber
optimale Routen, die Emission von Treibhausgasen reduzieren ko¨nnen. Die
BeantwortungverschiedenerForschungsfragenstanddabeiimVordergrund.
Die erste Forschungsfrage bezieht sich auf Methoden zur Akquirierung
vonReisezeitenunddamitindirektaufInformationzurVerkehrsbelastung.
Einige Methoden davon behandeln Techniken, welche Daten von Senso-
ren auswerten, die in oder u¨ber der Fahrbahn installiert sein ko¨nnen. Dies
ko¨nnen Systeme zur Erfassung von Fahrzeugkennzeichen sein, die auf der
Auswertung von Videobildern von Verkehrsu¨berwachungssystemen basie-
ren.WeitereMethodenbehandelndieAuswertungvonBewegungsdatenei-
nes Testfahrzeugs. Hierbei werden auch Methoden wie Floating Car Data
(FCD) beru¨cksichtigt. Diese basieren auf der Funku¨bertragung von Fahr-
zeugpositionsdaten,welchevoneinemimFahrzeuginstalliertenSystemwie
dem Global Positioning System (GPS) bereitgestellt werden ko¨nnen. Hier-
beiwerdensowohldieVerla¨sslichkeitundStabilita¨tderMessdatenundder
Kosten des Systems untersucht.
DiezweiteForschungsfragewidmetsichderModellierungvonKohlendi-
oxid -Emissionen. Es werden mo¨gliche Berechnungsmodelle untersucht, um
schnell und verla¨sslich die Emissionen von CO zu simulieren, wenn Teile
2
oder die Gesamtheit aller Verkehrsteilnehmer Zugang zu Informationen zu
optimalen Routen haben.
Es wird eine dedizierte Simulationssoftware vorgestellt, welche ein CO
2
Emissionsmodell in einen Zellularautomaten integriert, der in der Lage ist,
das Straßennetz einer beliebigen Stadt abzubilden, welches durch eine Da-
tenbank von Geodatensa¨tzen zur Verfu¨gung gestellt wird. Die dritte For-
schungsfragehateinentechnischenSchwerpunkt,dersichumdieInstallati-
on eines solchen Systems in einer realen Umgebung dreht. Die Verwendung
vonbereitszurVerfu¨gungstehenderVerkehrsinfrastrukturwirdhierbeibe-
sonders beru¨cksichtigt.
DieseArbeitzeigteinenVorschlageinessolchenSystems.Erumfasstdie
Beschreibung von Hardware und entwickelter Software um Verkehrsproble-
Zusammenfassung x
me am Beispiel der Stadt Krakau testweise zu l¨osen.
DasErgebnisdieserArbeitbeweist,dassdieEmissionenvonTreibhaus-
gasen von Fahrzeugen in der Stadt, durch die Bereitstellung von Informa-
tionen u¨ber optimale Routen, reduziert werden ko¨nnen.
Contents
1 Introduction 1
1.1 Controlling traffic lights . . . . . . . . . . . . . . . . . . . . . 1
1.2 Data acquisition methods . . . . . . . . . . . . . . . . . . . . 2
1.3 Airborne sensors . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Self–Organizing Traffic Lights . . . . . . . . . . . . . . . . . . 4
1.5 Traffic optimization goals . . . . . . . . . . . . . . . . . . . . 5
1.6 Greenhouse gas . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.7 Vehicles’ emissions . . . . . . . . . . . . . . . . . . . . . . . . 6
1.8 Hypothesis and the research questions . . . . . . . . . . . . . 6
1.9 Organization of the thesis . . . . . . . . . . . . . . . . . . . . 7
2 Comparison of travel time estimations using intelligent
infrastructure and floating car data 9
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Simulation environment . . . . . . . . . . . . . . . . . . . . . 10
2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.6 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Integration of cellular automata traffic simulation with
carbon dioxide emission model 21
3.1 Traffic simulators . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Greenhouse gases and carbon dioxide emission . . . . . . . . 21
3.3 CO emission model . . . . . . . . . . . . . . . . . . . . . . . 22
2
3.4 Microscopic traffic simulator . . . . . . . . . . . . . . . . . . . 26
3.5 Cellular automata traffic simulator . . . . . . . . . . . . . . . 26
3.6 Integrationofcellularautomatatrafficsimulationwithemis-
sion model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.6.1 Data acquisition . . . . . . . . . . . . . . . . . . . . . 28
3.6.2 Data preparation . . . . . . . . . . . . . . . . . . . . . 29
3.6.3 Vehicles’ acceleration rate . . . . . . . . . . . . . . . . 31
Contents xii
3.6.4 Data analysis . . . . . . . . . . . . . . . . . . . . . . . 33
3.6.5 Distribution of the acceleration . . . . . . . . . . . . . 38
3.6.6 Deceleration. . . . . . . . . . . . . . . . . . . . . . . . 45
3.6.7 Adaptation of Rule 184 . . . . . . . . . . . . . . . . . 49
3.7 Rule 184-based CO traffic emission model . . . . . . . . . . 60
2
3.7.1 Implementation note . . . . . . . . . . . . . . . . . . . 63
3.8 NaSch-based CO traffic emission model . . . . . . . . . . . . 64
2
3.9 Critics, discussion and future work . . . . . . . . . . . . . . . 69
4 The impact of dynamic route guidance system driven by
travel time measurements on carbon dioxide emission 71
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2 Dynamic route guidance system architecture . . . . . . . . . 72
4.2.1 Shortest Paths Algorithm . . . . . . . . . . . . . . . . 73
4.2.2 Cost function . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.3 Path with minimal cost . . . . . . . . . . . . . . . . . 76
4.3 Methodology and measurements . . . . . . . . . . . . . . . . 76
4.4 Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.1 Virtual cities . . . . . . . . . . . . . . . . . . . . . . . 81
4.5 Test scenarios and results . . . . . . . . . . . . . . . . . . . . 82
4.5.1 First scenario . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.2 Second scenario . . . . . . . . . . . . . . . . . . . . . . 87
4.5.3 Third scenario . . . . . . . . . . . . . . . . . . . . . . 87
4.5.4 Fourth scenario . . . . . . . . . . . . . . . . . . . . . . 93
4.5.5 Fifth scenario . . . . . . . . . . . . . . . . . . . . . . . 97
4.5.6 Sixth scenario . . . . . . . . . . . . . . . . . . . . . . . 97
4.5.7 Seventh scenario . . . . . . . . . . . . . . . . . . . . . 104
4.5.8 Eighth scenario . . . . . . . . . . . . . . . . . . . . . . 104
4.6 Summary, critics and future work . . . . . . . . . . . . . . . . 109
5 Conclusions and future work 113
A Naxos Traffic Simulator 115
A.1 Simulator description. . . . . . . . . . . . . . . . . . . . . . . 115
A.2 Graphical user interface . . . . . . . . . . . . . . . . . . . . . 117
A.2.1 Key shortcuts . . . . . . . . . . . . . . . . . . . . . . . 117
A.3 Simulator configuration . . . . . . . . . . . . . . . . . . . . . 120
A.4 Virtual cities . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
A.4.1 City generators . . . . . . . . . . . . . . . . . . . . . . 122
A.4.2 Importing city layouts from GIS . . . . . . . . . . . . 125
A.5 File formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
A.6 Distributed computing . . . . . . . . . . . . . . . . . . . . . . 133
A.7 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 134