Bottom-Up Estimation of Road Traffic Carbon Dioxide ...
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Technische Universität München TUM Department of Electrical and Computer Engineering Professorship of Environmental Sensing and Modeling Prof. Dr.-Ing. Jia Chen O C O H H H H C Bachelor’s Thesis Bottom-Up Estimation of Road Traffic Carbon Dioxide Emissions on the Mittlerer Ring in Munich based on Traffic Sensor Data Helena Hahne March 31, 2018 Supervisors: Prof. Dr.-Ing. Jia Chen Dr. Francisco Toja Silva
Transcript of Bottom-Up Estimation of Road Traffic Carbon Dioxide ...
Technische Universität München
TUM Department of Electrical and Computer Engineering
Professorship of Environmental Sensing and Modeling
Prof. Dr.-Ing. Jia Chen
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O
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Bachelor’s Thesis
Bottom-Up Estimation of Road Traffic CarbonDioxide Emissions on the Mittlerer Ring in
Munich based on Traffic Sensor Data
Helena Hahne
March 31, 2018
Supervisors:
Prof. Dr.-Ing. Jia ChenDr. Francisco Toja Silva
Abstract
As the transport sector contributes significantly to anthropogenic greenhouse gas emissions,research on this topic is important to be able to take appropriate actions. This bachelor’s thesisaims to estimate the carbon dioxide (CO2) emissions from road traffic on the Mittlerer Ring inMunich utilizing a bottom-up approach with the following variables: the driven distance, theaverage emission per car and kilometer and the number of vehicles measured at the respectiveroad segment. The calculation of the latter two by processing the vehicle fleet and traffic sensordata obtained from contacts at the city of Munich is presented and explained. A further focuslies on the uncertainties that come with the applied approach. Therefore, additionally to thefinal estimation result of about 164,000 tons of CO2 per year, the possible approximate rangewhere the real value is probably found is given and explained. The results are compared toother sources and discussed against the backdrop of more in-depth information about the currentsituation and outlook regarding the transport sector as well as about the specific case of Munich.
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I confirm that this Bachelor’s Thesis is my own work and I have documented all sources andmaterial used.
Gothenburg, March 31, 2018
Place, Date Signature
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Table of Contents
Page
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Preparatory Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 First Contact to the City of Munich . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Selection of the Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.1 Department of Public Order . . . . . . . . . . . . . . . . . . . . . . . 72.3.2 Statistical Office of Munich . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 Department of Traffic Planning at the Department of Urban Planning
and Building Regulation . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.4 Other important sources . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Working with Traffic Detector Data . . . . . . . . . . . . . . . . . . . . . . . 92.4.1 Induction Loop Traffic Detectors . . . . . . . . . . . . . . . . . . . . . 92.4.2 General Difficulties with Induction Loop Traffic Detectors . . . . . . . 102.4.3 Difficulties with Traffic Detector Data within the Scope of this Work . . 11
3 Calculation Methods, Data Processing and Intermediate Results . . . . . . . . . 12
3.1 The Estimation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Obtaining the Distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.3 Car Fleet Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.1 Calculation of the Average CO2 Emission . . . . . . . . . . . . . . . . 143.3.2 Results and Comparison with other Sources . . . . . . . . . . . . . . . 16
3.4 Traffic Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.4.1 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.4.2 Results and Comparison with Traffic Volume Map Values . . . . . . . 23
4 CO2 Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1 Calculation of the CO2 Emissions . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
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5 Current Situation, Outlook and Discussion . . . . . . . . . . . . . . . . . . . . . 33
5.1 Current Situation and Outlook Regarding the Transport Sector . . . . . . . . . 335.1.1 Misinformation and Insufficient Legislation . . . . . . . . . . . . . . . 335.1.2 Outlook for the Case of Munich . . . . . . . . . . . . . . . . . . . . . 34
5.2 Discussion of Final Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6 Conclusion and Suggestions for Future Work . . . . . . . . . . . . . . . . . . . . 37
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376.2 Suggestions for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
APPENDIX
A Additional Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
B Contact Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
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Chapter 1
Introduction
The levels of the main anthropogenic greenhouse gas carbon dioxide (CO2) in the atmosphereare increasing rapidly, in 2016, when they reached 403.3 ppm, they increased by more than3 ppm. Compared to the 2.21 ppm mean annual absolute increase over the last decade, thisis a 50% faster growth rate [1]. And while global CO2 emissions were stable from 2014 to2016, climate change experts expect them to rise again for the year 2017. The expected modestdecrease of CO2 emissions in Europe and the US is not enough to compensate for the increasesin China, India and other countries [2].
The rising greenhouse gas emissions and therefore global warming and climate change willprobably lead to drastic changes on the earth, if not stopped. In Bavaria, it is likely that weatherextremes, such as heavy rains and severe flooding will happen more often [3]. Globally, risingsea levels are expected to affect millions of people living in coastal areas and therefore leadto big population movements. And this does not only concern countries like Bangladesh butfor example also coastal states in the United States, like Florida [4]. When not only lookingat impacts of climate change on humanity but also on other species, there are many that willprobably be even more endangered or even go extinct, like for example the polar bear [5].
Emissions from the transport sector contribute significantly to the problem: According to theUN-Habitat World Cities Report from 2016, about 70% of the global anthropogenic greenhousegas emissions come from urban areas, where they mainly originate from fossil fuel consump-tion for energy supply and transportation [6]. The European Union (EU) wants to reduce itsgreenhouse gas emissions, to fight against climate change, but in 2015 they rose again for thefirst time since 2010. The European Environmental Agency states that this was mainly due toincreases in emissions from road transport, which make up about 20% of the total EU green-house gas emissions. More efficient new vehicles could not compensate the higher demand inboth passenger and goods transport. [7]
In Germany, the transport sector is the only sector that, as of 2015, saw a significant rise ofits CO2 emission share since 1990. In Table 1.1 the CO2 emissions from traffic in Germanyin million tons and their share of the total emissions according to the Umweltbundesamt (Ger-man Federal Environmental Agency) are given [8]. The CO2-equivalent values are taken fromthe section about the role of the traffic sector in climate protection in a report published bythe Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (BMUB, German
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Table 1.1: CO2 emissions from traffic in Germany and share of total emissions [8] [9]
Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety) [9].The value for 2016 is only based on first estimations, but it would further increase the share ofthe traffic sector of the total CO2 emissions in Germany and very likely also be the first rise ofthe value of yearly CO2 emissions from traffic above the 1990 value since 2004 [8].
The goal of the German government according to the “Climate Action Plan 2050” is “exten-sive greenhouse gas neutrality by 2050” respectively to “reduce greenhouse gas emissions by80 to 95 percent by 2050 compared to 1990 levels” [10]. The conclusion to this by the BMUBis that in order to achieve the 95% overall reduction goal the greenhouse gas emissions fromthe transport sector would have to decrease by 98,5%. This means that by the year 2050 thesector would have to become almost climate-neutral [11]. In the current situation, this mightseem like an unreachable goal: 98,4% of all licensed passenger cars in Germany are poweredby a combustion engine (January 2017) [9].
It is generally difficult to determine CO2 emissions from traffic at regional scales accuratelybecause of the limited availability of data. Instead of downscaling national-level data, and toavoid the possibly emerging errors that come with this method, it has been suggested to usebottom-up estimation approaches [12].
This thesis is a continuation of the bachelor’s thesis “Bottom-up estimation of traffic CO2
emissions in Munich city center (Altstadt)” by Dora Bali from summer 2017. In her work theyearly CO2 emissions on the Altstadtring in Munich were estimated on the basis of data foreight days: Wednesday, October 26, to Thursday, November 3, 2016, containing one nationalholiday. The results were that the road traffic CO2 emissions sum up to about 170 tons perweek or about 9000 tons per year. This corresponds to existing data. Also, it was stated that theemissions are, as expected, higher on workdays than on weekends. [13]
The goal of this work is to expand the previous thesis by Dora Bali and furthermore look intothe uncertainty that comes with the applied estimation approach. Chapter 2 describes the firstnecessary steps: contacting the cooperation partners at the city departments, the selection ofthe area to be looked at and the data collection. This chapter also covers the topic of workingwith traffic detectors and data from these devices and the difficulties that come with this. The
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estimation approach and the different calculation and data processing methods for all variablesare explained in Chapter 3. Furthermore, it contains the intermediate results and a discussionof those results. Chapter 4 describes the final steps of the calculation in order to obtain yearlyvalues of the CO2 emissions in the selected area and the final results are stated and visualized.Chapter 5 gives further information about the current situation regarding the transport sectorin Munich and Europe and the final results are discussed against this background. Chapter 6contains a final conclusion and some suggestions for future work are given.
Apart from that, another important goal was to keep up the contact to the civil servants at thecity administration and continue the good cooperation.
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Chapter 2
Preparatory Work
In this chapter the preparatory work which was necessary to begin with the actual processing ofthe data is stated and explained. This includes contacting the different departments of the ad-ministration of Munich, the selection of the area to be looked at in the work, the data collectionfrom different sources and information about working with data from traffic detectors.
2.1 First Contact to the City of Munich
The first essential step was to reach out to the existing contacts at the city of Munich, to be surethe necessary data would be provided, and the good collaboration as during the previous workfrom Dora Bali would be continued. Both contacts—at the Kreisverwaltungsreferat (Depart-ment of Public Order) and at the Statistical Office of Munich—confirmed to be available forcooperation on the topic. During the first meeting with Mr. Sven Nöldner, the contact person atthe Statistical Office of Munich, he provided information about another possible contact at theDepartment of Urban Planning and Building Regulation (Subdivision Traffic Planning HA I/3).The contact information is given in Appendix B.
2.2 Selection of the Area
Before requesting traffic sensor data, the area to be looked at had to be selected. The require-ment was that it could only be an area where the KVR has detectors or measurement pointsinstalled. In this step, the map mentioned above (see Figure 2.1), as well as the traffic viewingoption in Google Maps 1 was used to determine streets with a lot of traffic volume, which wouldbe interesting to assess. Both sources show, that the highways leading into and partly aroundMunich are very frequented, as expected, but the Mittlerer Ring also stands out and is even morefrequented than some of the highways. Furthermore, there was enough data available from the
1 Google Maps view of Munich → Menu → Traffic → change from “Live Traffic” to “Typical Traffic” andselect rush hour time
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measuring cross-sections positioned on the Mittlerer Ring, according to Mr. Träger. Thesemeasuring cross-sections of flowing traffic also have an advantage compared to data from lightsignal systems (which were used by Dora Bali): The data from all the lanes can just be summedup and does not have to be calculated differently for usually differently sized and structuredintersections.
Figure 2.2: Map of the Bundesstraße 2 R in Munich [15]
Therefore, the Mittlerer Ring was selected as the area to be looked at (see Figure 2.2). Theofficial name is Bundestraße 2 R (R stands for Ringstraße, in English: Federal Highway 2 RingRoad), the abbreviation used in this work is B2R. The B2R is 28 kilometers long, has at leastfour but up to six lanes and the speed limit is mostly 60 km/h. A stretch that makes up over80% is crossing-free and has separated lanes. It also has 12 tunnels, whose lengths differ fromonly under one intersection to more than 1.5 kilometers. With up to 150,000 vehicles a day onsome sections it is, as stated before, one of the most frequented roads in Munich. Problems
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that come with this are for example that the B2R has very high levels of noise and air pollution,while going through densely populated areas. [16]
The western part of the B2R between the Federal Highway B304 and the Autobahn A96was furthermore the most congested street in Munich in 2017, where drivers were stuck for anaverage of 27 hours. [17]
The B2R also encircles the Umweltzone of Munich (Low Emission Zone), where only carswith a certain emission standard (EURO class), that must have a green particulate matter sticker,are allowed to enter. This does not play a role in this work though, as only air pollutants likeCO, NOx or particulate matter and not CO2 emissions are regulated within that zone. [18]
2.3 Data Collection
In order to be able to estimate the road traffic CO2 emissions of the B2R, the necessary data—the number of cars on the B2R and data about the car fleet in Munich, to calculate the averageCO2 emission of an average Munich car—had to be obtained. Because this kind of data isnot publicly accessible, it was requested from different offices and departments of the city ofMunich. The most important sources are stated below.
2.3.1 Department of Public Order
The first of the two main data providers for this work is the subject area KVR-III/124 Verkehrszen-trale München und Verkehrssysteme (in English: traffic control center of Munich and traffic sys-tems) within the Kreisverwaltungsreferat (KVR, Department of Public Order) of Munich. Thissubject area is allocated to the main department III for road traffic, and there to the divisionof traffic management and the subdivision traffic control systems. Therefore, they can accessdata from induction loop traffic sensors positioned throughout the city. Mr. Ralf Träger whoworks at the KVR-III/124, is the contact from whom the traffic sensor data was received andthe main cooperation partner for this bachelor’s thesis. He was able to provide hourly inductionloop traffic detector data (traffic volume data) of the year 2017 up to the date of the request fordifferent measuring cross-sections [19]. Moreover, he sent a map containing marks for all lightsignal systems and measurement points [20], another one showing the operative light signalsystems with their respective number [21] and one with all the measurement points [22]. Withthese maps, it was possible to determine the measurement point IDs of the necessary measuringcross-sections on the B2R, which are all four-digit numbers and start with a “4”. After furtherconsultation with Mr. Träger, more cross-section areas and data from the light signal system256 (German: Lichtsignalanlage, LSA) at the intersection of the B2R with Rosenheimerstraßewere added to cancel out too large distances between adjacent measuring cross-sections [23].
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With the traffic sensor data, he provided site plans of the measurement points to allow moreprecise locating [24].
2.3.2 Statistical Office of Munich
To be able to calculate the average CO2 emission of a car in Munich in g/km, informationabout the cars registered in Munich was necessary. It was possible to obtain this data from theStatistical Office of Munich through Mr. Sven Nöldner, who works at the information officethere. After a personal appointment for explanatory purposes, a defined request was sent. Thereceived data included car inventory data for all the districts separately and the whole city[25], and also car inventory data classified by CO2 emission value [26]. Due to personal dataprotection guidelines, all values where it would be possible to connect it to a person due toa small number were erased. Furthermore, the definition of CO2 classification intervals wasrequested by Mr. Nöldner for the same reason.
2.3.3 Department of Traffic Planning at the Department of Urban Planningand Building Regulation
This additional contact was obtained from the Statistical Office of Munich during the personalappointment there, as it is their contact for traffic count inquiries. The civil servants at theDepartment of Traffic Planning which is the Subdivision 3 under the Main Division I UrbanDevelopment Planning at the Department of Urban Planning and Building Regulation (HAI/3), finally provided a map with the most frequently used roads of Munich (see Figure 2.1)[14]. According to the "Traffic Data Team Munich" (Team Verkehrsdaten München, personalcommunication, November 8, 2017) the map depicts the traffic volume for street sections onan average workday extrapolated from measurements of two times four hours on workdays(Tuesdays through Thursdays) outside of school holidays. As this work does not focus on themaximum total of CO2 emissions on a busy workday, but on estimating the yearly value whileconsidering changes during the hours of the day, the different days of the week and the seasons,this data was not entirely suitable for the given purpose. However, it was still an interesting andreliable and also the only acquired source for comparison. Furthermore, it helps to justify theselection of the road that is looked at in this work, as is becomes clear by inspection, that theB2R is the most frequented road in Munich as stated in Section 2.2.
2.3.4 Other important sources
The other important sources for this work were mainly found online, like a document by theDeutsche Automobil Treuhand GmbH commissioned by the German car manufacturers aboutthe fuel consumption values for all new cars on offer in Germany in the fourth quarter of 2017.Besides that, data about the CO2 emission value of the German car fleet was received upon re-quest from the Federal Statistical Office of Germany. Other data e.g. about population numbers
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or about the B2R were obtained from the website of the administration of the city of Munich.Google Maps and Google My Maps were useful tools and sources to localize the measurementpoints and calculate the distances. Last but not least the bachelor’s thesis and data previouslyreceived by Dora Bali was looked at.
2.4 Working with Traffic Detector Data
The working principle of the traffic detectors was already explained by Dora Bali in the pre-ceding bachelor’s thesis and Mr. Träger at the KVR was not able to provide further specificinformation about the detectors installed at the measuring cross-sections. Moreover, this workfocuses on the usage of the data and the results that can be derived from it. This chapter istherefore only a short general introduction to induction loop traffic detectors, gives some infor-mation about the difficulties with this technology and furthermore indicate problems that arosewhile working with this data.2
2.4.1 Induction Loop Traffic Detectors
According to the Traffic Detector Handbook by the Federal Highway Administration (FHWA)of the United States, an inductive loop detector can be described in the following way: Aninsulated, electrically conducting loop, often in a rectangular shape in the dimensions of thevehicles it should detect, is installed in the pavement in the middle of a street lane (see Figure2.3) [27]. The connected electronics unit transmits alternating current at frequencies between10 kHz and 200 kHz into the wire loop. This setup behaves as a tuned electrical circuit, with theloop wire and the lead-in cable being the inductive elements. If a vehicle stops or passes overthe loop, the alternating magnetic field induces eddy currents in the undercarriage of the vehicleand these influence in turn the field. Therefore, the inductivity of the loop decreases, and theresulting rise of the oscillator frequency actuates the output and the vehicle is registered. [28]
Some of the traffic detectors, of which data is used in this work, are called “8+1” detectors.This means that they are able to differentiate between 8 different classes of vehicles (“+1” refersto the not classifiable vehicles) [29, p. 152].3 There are different ways to distinguish betweenseveral vehicle classes: For example, it is possible to use axle load registration devices (pressuresensors) [29, p. 43]. Without additional sensors, vehicles can be classified by means of therelative inductivity change and therefore the unique frequency change signature that differentlysized vehicles generate [27].
2 For further information, graphic illustrations and deeper explanation, both on the functional principle andpossible strengths and weaknesses, the given sources, especially the Traffic Detector Handbook (pp. 37-44, 61-116) by the Federal Highway Administration (FHWA) of the US, are recommended.
3 The eight classes are: motorbikes, passenger cars, vans until 3.5 tons, passenger cars with trailer, trucks,trucks with trailer, articulated trucks and busses.
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Figure 2.3: Cuts in the pavement from induction loop traffic detectors at measurement point4010 [30]
2.4.2 General Difficulties with Induction Loop Traffic Detectors
Mr. Ralf Träger from the KVR explained several difficulties that come with the use of thesekind of traffic detectors (R. Träger, personal communication, November 23, 2017): In general,the measurement should be the same for all parts of the road section around the measurementpoint. The error from drivers parking in between, is according to his knowledge negligiblecompared to others. These include having the measurement point directly before or after anexit or two neighboring detectors on a two-lane road registering the same vehicle, because itis driving in the middle of the road. Moreover, the oscillating circuits can be out of tune orjust break over time and the setup itself is in danger of intruding water or deformation of theasphalt due to vehicle movements. All of this can lead to the controllers registering too less ortoo many vehicles, until eventually the quality control algorithms act and signal the detectionerrors. The most exact result is reached under conditions that are seldom fulfilled in innercity traffic, namely that the vehicles do not accelerate or brake, there are no traffic jams, andeveryone is driving precisely in their lane. Therefore, the city administration has to live with themany detection errors if it wants to make use of traffic detection, which is mainly applied formanaging traffic at intersections. According to Mr. Träger, he and other experts are constantlyworking together on local and broader levels to resolve the existing problems. But despite alltheir weaknesses, the inductive loop traffic detectors still lead to the best results compared toother technologies like over-roadway video detectors, in his opinion.
To show the influence of these more or less small, inevitable errors, the hourly values of twomeasurement points that should have equal values were compared. Figure A.2 shows a detailedcomparison for the week in January and can be found in Appendix A.
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2.4.3 Difficulties with Traffic Detector Data within the Scope of this Work
During the data processing for this work, several difficulties connected to using data from trafficdetectors arose: First of all sometimes the data was missing for certain dates, so in one caseanother week had to be chosen for one measurement point. Other measurement points haddefective detectors on one lane for example, so the data was not usable.
The most challenging difficulty was connected to the “8+1” detectors: The original files fromMr. Träger showed vehicle number values for the eight different categories and the sum of allvehicles (named “QKFZ”), which was the needed value, but also another value. This value,which was not named, was mostly the same as the sum, but sometimes smaller and other timeswhen the “QKFZ” value was (erroneously) zero, it had a value. At first Mr. Träger also didnot know why this was the case, but finally he was able to provide an explanation (R. Träger,personal communication, December 5 and 7, 2017): The “8+1” traffic detectors always sendminute values to the data collection devices, but because the data set would have been signif-icantly larger, he only sent the hourly values that he can also read out. Furthermore there aretwo databases, one managed by another company, which he called the “Conduct database” andtheir own database. These two databases handle data losses during transmission differently.The first one, which corresponds to the “QKFZ” value, has a threshold for the minute values, ifnot enough are received, a zero is put as the hourly value. Mr. Träger noted that this of coursealso leads to the further problem, that with only this database it is not possible to know if therewere no cars or just not enough minute values. If the threshold is reached, the hourly value isprojected based on an algorithm not known to Mr. Träger. He sent an inquiry to the companybut finally could not find out and provide information about the exact threshold or the algo-rithm. The second database, of the KVR itself, always gives out the sum of the minute valuesas the hourly value, irrespective of whether for example only 18 or all 60 minute values weretransmitted and received correctly.
This explains the two different values in the original files, but leads to problems for peoplewho want to work with this data, as they have to find a way to manage this by themselvesmanually or with their own projections. How it was handled for this work is explained in thenext chapter.
Regarding future works, these problems will hopefully become less, as newer “8+1” detectorsare able to store more minute values and deliver them after a longer connection interruption,while the old ones are only able to store one or two. The new ones are already installed in thetunnels in the south west corner of the B2R and the data from these “8+1” detectors shows onlysome very minor irregularities (in this work this applies for the measurement points 4122 &4123 and 4174, as there the new technology is already installed). And of course the increasingdeployment of this type of detector also opens up new possibilities, as different vehicle typesbut also other measured data like vehicle speed could be considered.
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Chapter 3
Calculation Methods, Data Processing and IntermediateResults
In this chapter the estimation approach is described. After that, the employed calculation anddata processing methods for the two main data sets, the car fleet data from the Statistical Of-fice of Munich and the traffic detector data from the KVR are explained. Both data sets werereceived as Microsoft Excel files and Microsoft Excel and self-written Visual Basics for Ap-plications (VBA) macros were used to process them. Moreover, the intermediate results forthe different variables of the estimation approach are given, compared with other sources andcommented.
3.1 The Estimation Approach
The following formula describes the estimation approach which is used in this work, and beforethat has already been used in Dora Bali’s bachelor’s thesis:
mCO2= d× eavg,car × ncars (3.1)
The mass of CO2 (mCO2) emitted by traffic on a certain road section can be calculated by mul-tiplying the distance the vehicles are driving (d, in km), the average emission of a car (eavg,car,in gCO2/km) and the number of cars driving at that road section (ncars).
The advantages of this approach are that the necessary data was known to be available withouttoo much effort and according to Dora Bali’s thesis it is suitable for estimating CO2 emissionsfrom traffic [13]. The drawbacks are on the one hand that no other vehicle types like trucks ormotorbikes are considered. Since cars have the highest share (this can be easily observed on theroads in Munich), it was regarded as an at the current state unavoidable but acceptable fact. Onthe other hand, there are possible errors that come with this calculation, for example if the datais inaccurate/incomplete for eavg,car and ncars.
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3.2 Obtaining the Distances
Figure 3.1: Google My Maps map of the B2R used for the calculation of the distances
The first variable of the estimation approach is the distance between two measurement points,more precisely half the distance to the two adjacent points. A suitable tool to obtain these dis-tances was Google My Maps1, as routes in Google Maps represent the distances a car woulddrive. The procedure was as following: marking all measurement points on the outer lane go-ing counterclockwise around the center of Munich by consulting the LZA&MST map from theKVR [20], and if available, the site plans provided by the KVR for more accurate positioning[24]. Then car routes were created to connect these markers. By choosing “step-by-step direc-tions” the distance between the measurement points could be read off directly. Figure 3.1 showsa screenshot of the map in Google My Maps, with the red markers showing the positions of themeasurement points or detectors and the black dots showing half the distance to the next mea-surement point. The two routes are depicted in blue. The distances in kilometers were roundedto one decimal place and add up exactly to the total length of 28 kilometers of the B2R. To getthe distances necessary for calculating the emissions, for each measurement point half of eachof the distances to the neighboring points were added.
1 https://www.google.com/maps/about/mymaps/
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3.3 Car Fleet Data
3.3.1 Calculation of the Average CO2 Emission
Figure 3.2: Visualization of the calculation method for the average CO2 emission of a car inMunich
For the calculation of eavg,car, the average CO2 emission of an average car in Munich (the wholecalculation is visualized in Figure 3.2), data from various sources was used: The starting pointwas the car inventory data from the Statistical Office of Munich, which was given as a tablewith all the cars registered in Munich as of August 2017, sorted by vehicle class. This datawould also have been available separately for all the districts in Munich. The B2R is used toget into and around the city and encircles, crosses, or runs on the border of 18 of the 25 districtsthough. Only the city districts 15 and 19-24 are not close to the B2R [31]. Nevertheless it islikely that car owners from those parts of Munich also use the B2R often when they drive into
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the city or to the start of a highway. Therefore, cars from all 25 districts were considered, andthe car fleet data for the whole city was used. In the first step, this table with the car inventorydata was simplified by assuming that the most common car in each vehicle class represents allthe cars in the respective class. For example, the Volkswagen (VW) Golf is the most commoncar in the compact class, so it was assumed that all cars in Munich in the compact class are VWGolfs. [25]
In the second step the fuel consumption values for inner city traffic (in l/100km) for each ofthese cars were taken from the “Guide to the fuel consumption, CO2 emissions and electricityconsumption” for all new passenger cars in Germany on sale in the fourth quarter of 2017, sincethere was no data available to group the cars according to registration year. This documentis commissioned by the German Association of the Automotive Industry and the Associationof International Motor Vehicle Manufacturers and put together by the Deutsche AutomobilTreuhand GmbH (DAT) [32]. Because various models with different engine sizes etc. existfor each car, both the lowest and the highest value was taken, as it was not possible to find outwhich exact models are registered or sold most in Munich. Usually the lowest value was the onefrom a diesel model, the highest from a gasoline model. For some cars though, like the SmartFortwo, no diesel model exists. Electric or natural gas powered models were not considered.
This was done for all vehicle classes, but there were some special cases, where it was moredifficult to find usable emission values: For vehicles classified under “other”, where the entrywith the highest number of cars was “BMW without type specification”, the latest availablevalue for the whole European BMW fleet was taken, without a range therefore [33]. The rela-tively small amount of cars listed under “not classifiable” and the ones that could not be listedin the table for data protection reasons were accounted for with values of the average CO2 emis-sions from new passenger cars in Europe in 2015 according to an EEA report. Since there was avalue for diesel and petrol, these two values also produced a range [34]. The last group of cars,which are not classified because they were first registered before 1990, was included by takingthe mean of the values for vehicle CO2 emissions in Germany in 1973 and 1992 from a paperpublished in 1996 about the international trends in CO2 emissions from passenger transportfrom 1973 to 1992 [35].
The differentiation between diesel and gasoline models indicated above becomes relevant inthe next step of the calculation. The fuel consumption values are multiplied with the conversionfactors for diesel and gasoline (for diesel: 2.32 kgCO2/lD and for gasoline: 2.65 kgCO2/lG), whichresults in the CO2 emission in g/km for the respective car model. These conversion factors weretaken from a document of the Federal Motor Transport Authority in Germany. [36]
With these emission values the CO2 emission “range” in g/km for an average car in Munichwas calculated by considering the share of the number of cars in a vehicle class of the number ofall cars in Munich: The emission value for the cars in the respective vehicle class was multipliedwith the number of cars in the vehicle class. Then the resulting total CO2 emission values for allvehicle classes were summed up and divided by the total number of cars registered in Munich.
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Figure 3.3: Average CO2 emissions range for a new car in 2007, first quarter of 2012 andfourth quarter of 2017 (calculated with car fleet figures from 2017)
The calculation was furthermore carried out with the fuel consumption values for new cars in2007 and 2012 from the DAT document for these years, but with the car fleet figures, meaningthe most common car and vehicle class shares, of 2017 [32]. The values used for the specialcases were not changed.
3.3.2 Results and Comparison with other Sources
According to the calculation method described above, the CO2 emission “range” of an averagecar in Munich is 124.05 to 229.89 gCO2/km and the average is 176.97 gCO2/km. The rangedisplays where the emission value most likely lies, with the higher end corresponding to moremodels with higher fuel consumption on the streets and vice versa. The limits of the range areapproximately 30% higher or lower than the average value.
The calculated average CO2 emissions range for all vehicle classes for a new car dependingon the car models in the three respective years for which the calculation was carried out can beseen in Figure 3.3. It is clear that according to the official values given out by the manufacturers,there is a tendency towards lower CO2 emissions in newer cars and the range seems to becomemore and more narrow, which could imply that really high fuel consumption is reduced, but itmay be difficult to reduce the lower consumption values even more.
In comparison with the first other considered source, namely the table from the StatisticalOffice of Munich with the aggregation of the cars in Munich into CO2 emission groups2, the
2 e.g. 121-130 gCO2/km. These intervals had to be selected before the inquiry, as the Statistical Office cannot
give out the detailed data. They then provided the information how many cars are in each “emission group” thatwas defined.
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calculated average value is quite high. The average value from the Statistical Office data is151.92 gCO2/km. Even when always taking the upper limit value of each emission group, thevalue stays significantly lower (160,69 gCO2/km). The most likely explanation for the big differ-ence is, that they have the combined inner and outer city CO2 emission value of the registeredcars in their database, not the inner city one. In the combined value, only about 36.8% comefrom inner city consumption [32]. The value was finally considered very likely to be too lowand was thus not utilized for this work. [26]
This decision is supported by the fact that the value from another source, the Federal Statis-tical Office (FSO), for the current German car fleet (February 2017), calculated with the totaldriven kilometers and fuel consumption is almost the same as the value calculated in this work:177.70 gCO2/km. The value calculated from the FSO data is furthermore interesting as it repre-sents the value for the actual German car fleet, and does not assume that all cars are new cars,like the one calculated for this work mostly does. Thus, the real value should be close to theFSO data value, and therefore the mean from the range in the calculation for this work wasconsidered as satisfactory and sufficient for the estimation. [37]
To show the significant impact which the fuel consumption and the actual car model (meaninge.g. which type of VW Golf is actually on the streets in the city) has on the total CO2 emissionsfrom traffic, the range was also used and visualized in the final results.
3.4 Traffic Sensor Data
3.4.1 Data Processing
The last missing variable ncars, the number of cars, had to be extracted from traffic sensor dataprovided by the KVR. The received data was “raw” data from January 1 until October 23, 2017for many different measurement points in Munich. As described in Section 2.3.1, measuringcross-sections on the B2R that were not too far from each other were chosen [19], as well asdata from the LSA 256 [23]. Table 3.1 shows the IDs of the 14 measurement points which werefinally used and the name of the street or tunnel where they are positioned (a more detailedoverview can be found in the Appendix A, Table A.1, and for a map with the positions seeFigure A.1). The ones colored in red and not counted in the “own numbering” column werenot usable. For 4101 and 4103 to 4105 it was not possible to determine in which lanes thedetectors were actually installed and which data would have had to be used for the calculation.Measurement point 4004 had a malfunctioning detector and was scheduled for replacement andin the course of that for an update to a new “8+1” system in spring 2018.
Since it was not possible to process data of such a long time period for 14 measurementpoints, the final estimation is only based on 4 weeks of data, one in each season of the year,to be able to look for and represent seasonal differences. This was one of the requirements for
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Table 3.1: Overview of measurement points and their positions
the selection of the weeks, another one was that it should be weeks without public holidays andschool holidays, as the traffic on those days usually differs from normal daily traffic. Also theyshould be as equally distributed over the year as possible. Finally, the following four weekswere selected:
16.-22.01.2017 3
24.-30.04.2017 (one week later because of the Easter holidays in 2017)17.-23.07.201716.-22.10.2017
In the next step, the data for these weeks was extracted and processed in Microsoft Excel.As already stated, this was done with self-written Visual Basics for Applications macros and
3 Since for the measurement point 14 (4035 & 4041) there was only data available from January 17, 2017onwards, it was finally decided to choose the week from 30.01.-05.02.2017 for this point, so two weeks later,which should still be a good representation for the winter season.
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other Excel functions that made the processing easier. This procedure could be greatly im-proved in the future though, for example by programming the extraction and processing to runautomatically.
The basic principle that was applied for all measurement points and the four weeks was firstto extract the data for the respective measurement point from the original document with thetraffic detector data for all the measurement points into a new Excel file [19]. Then the datafor each of the four weeks was copied onto separate spreadsheets in the same file. After that,for each of the weeks, for each day and hour separately, all hourly traffic volumes of all lanesrespectively all induction loop detectors that were relevant to the calculation (for example twotimes three lanes/detectors for both driving directions, if available verified using the site plans[24]) were summed up. That resulted in hourly values of the number of cars for the wholemeasurement point for each day.
For the measurement point LSA 256 the procedure was slightly different: There it was firstnecessary to allocate the right lanes on the intersection with the corresponding detector IDs.Then the different values had to be added and subtracted (for example cars turning off) in orderto get the traffic volume of just the B2R [23]. This was done consulting a document provided byMr. Träger about the detector allocation at LSA 256 [38] and the site plan for this intersection[24]. As one of the lanes at the intersection is both for cars making a turn onto the B2R andgoing straight, the average between counting all and none of the cars on this lane was taken.
The hourly values were then plotted, to make the diurnal variation of the number of cars in oneweek visible (see Figure 3.4). This made it easily possible to see whether the data was plausibleand directly usable, because: Both the workday and the weekend day diurnal variation have aquite distinct shape with the morning and afternoon/evening rush hour on weekdays respectivelyonly the latter on weekend days (as already stated and discussed in detail by Dora Bali in herbachelor’s thesis [13]). That is also why the separation was kept in the next step, the averagingover the workdays (Monday to Friday) and the weekend days. The graph in Figure 3.5 showsthe average diurnal variation of the number of cars on workdays and on the weekend for theexemplary measurement point 4005 from the week in January.
As indicated in Section 2.4.3, there were problems with the “8+1” detectors (to see whichmeasurement point is equipped with “8+1” detectors see Table A.1 in Appendix A). Because itwas not possible to make own projections for the real values within the scope of this work, itwas decided to always take the higher value, if the two databases did not show the same valuefor one hour. This worked mostly fine, but there were still sets of “unusable” data from theolder “8+1” detectors, which had to be adjusted first. Apparently there were problems withthe data transmission at theses measurement points and neither of the two databases provided aplausible value. Figure 3.6 shows one example of this: the diurnal variation at the measurementpoint 4010 from the week in April with implausibly low values e.g. on Friday at 1 p.m.
The adjustment was done by hand—also something that could be automatized in the future—by observing the plausible data to find the “allowed” values for the hours of the workdays
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Figure 3.4: Diurnal variation of number of cars at measurement point 4005, 16.01.-22.01.2017
Figure 3.5: Diurnal variation of number of cars at measurement point 4005, 16.01.-22.01.2017, average from workdays and weekend
(mostly together) and Saturday and Sunday (separately). A lower and sometimes also a higherlimit was chosen: the lower one approximately half the value of the usual, plausible numberof cars at the respective hour and the higher one close above the highest value from all fourweeks of plausible data, as more than a certain number of cars per lane and hour is not possible.These lower and higher limit values were different for different measurement points, as thetraffic volume varies on the sections of the ring. The hourly values below or above the lower
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Figure 3.6: Diurnal variation of number of cars at measurement point 4010, 24.04.-30.04.2017, before adjustment
Figure 3.7: Diurnal variation of number of cars at measurement point 4010, 24.04.-30.04.2017, adjusted, and table showing the lower limits
and higher limits were erased and the now missing points where included again by simplelinear interpolation. Figure 3.7 shows the adjusted diurnal variation of the number of cars atthe measurement point 4010 from the week in April and a table showing the minimum allowedvalues. All values below these were erased in this plot and therefore some hourly values aremissing. For the averaging all hourly values were needed, so new hourly values were added bysimple linear interpolation, this is shown in Figure 3.8. When the averaging over workdays andweekend days was done for these weeks, the graphs were mostly looking similar as the onesfrom plausible data (see Figure 3.9).
The final steps in the processing were also carried out for each measurement point separately:
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Figure 3.8: Diurnal variation of number of cars at measurement point 4010, 24.04.-30.04.2017, adjusted, missing points included with simple linear interpolation
Figure 3.9: Diurnal variation of number of cars at measurement point 4010, 24.04.-30.04.2017, average from workdays and weekend
The 24 hourly values of the workdays respectively weekend average (see Figure 3.5 and Figure3.9) were summed up. Figure 3.10 shows an example of a result from this step for measurementpoint 4174 and all four seasons. The average daily sums of the workdays and the weekenddays of the season were then multiplied with the number of workdays respectively weekenddays of this season and then the two resulting values were added up. This calculation led to
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Figure 3.10: Workdays and weekend average daily sums for all seasons at measurement point4174, with approximate possible range
the estimated sum of cars in the season. Then the four seasonal sums were added up, whichresulted in the total estimated number of cars per year at the measurement point.
To have an insight into how much the differences in the daily values influence the final result,this calculation was also done for the highest and lowest daily sum of the respective weeks(separately for the workdays and weekend days), which gives us an approximate range wherethe real number of cars is likely to find. So for example if Monday had the lowest daily sumand Thursday had the highest, these two values were used to calculate the lowest and highestseasonal workday sum etc. In Figure 3.10 the highest and lowest daily sums are indicated bythe thin black lines. For the special case of LSA 256 the highest and lowest value was not takenfrom the average of the straight and at the same time right-hand turn lane but with all cars andwith none from this lane.
3.4.2 Results and Comparison with Traffic Volume Map Values
Figure 3.11 shows the results from traffic sensor data processing for all measurement points:The total number of cars per year derived from the average daily sums in the four seasons.The thin black lines indicate the approximate possible range described above, which is not verybroad though, according to the calculations carried out for this work. It does not have equalvalues in the plus and minus direction, because the average daily sum of five workdays is notthe mean of the highest and lowest daily sum (compare Figure 3.10) and this was included intothe final car sum results.
The measurement points in the figure from left to right are sorted according to their orderwhen driving counterclockwise on the B2R starting at the northwestern corner. The figure
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Figure 3.11: Total number of cars per year at all measurement points—estimated from the av-erage daily sums of all seasons, with approximate possible range
clearly shows, that the western part of the B2R has the highest traffic volume (especially aroundthe Donnersbergerbrücke, measurement point 4174, with about 42 million cars per year), whilethe southeastern corner has the lowest (LSA 256, with approximately 17 million cars per year).The big difference between LSA 256 and its neighboring measurement points is probably dueto the high amount of cars coming from the west and turning off onto the Autobahn 995 (A995)or the reverse and the ones coming from the northeast and turning off onto the A8 or the re-verse. The same most likely applies for the northwestern corner, where many cars coming frommeasurement point 4035 & 4041 in the Petueltunnel probably leave the B2R towards the A8and do not reach measurement point 4027 etc.
The furthermore conducted seasonal comparison showed that the seasons do not have equalbut quite similar diurnal variation averages, so there is not a very big differences between theseasons. The season with the highest sum of traffic (the highest number of cars per average day)is usually July, sometimes April. The lowest was almost always January. If the assumption thatthese 4 weeks represent the seasons they lie in can be made, this means that usually in springand summer the traffic volume is higher. It could also be due to specific weather in the Januaryweek though, for example if there was a lot of snow, maybe less people went to work by caretc. One example of a seasonal comparison, more precisely the diurnal variation of the averageworkdays from measurement point 4013 can be seen in Figure 3.12. Figure 3.10 shows thedifferences between the daily sums.
The accuracy of these results is of course largely dependent on several factors, for examplethe adjusting that was done for the “8+1” detectors. Moreover even the directly “usable” trafficdetector data may not always be correct, as indicated in Section 2.4.2 about the general difficul-
24
3 – Calculation Methods, Data Processing and Intermediate Results
Figure 3.12: Seasonal comparison of the average number of cars on workdays at measurementpoint 4013
ties with induction loop traffic detectors. Furthermore the results are derived from only 4 weeksof data, due to limited time and processing possibilities, so it depends on whether these weekswere representative enough.
The only source that could be obtained to compare the results of the car numbers, was thetraffic volume map (TVM) from the Department of Traffic Planning at the Department of UrbanPlanning and Building Regulation. This map shows the traffic volume for an average workday(Tuesday to Thursday) and the values are “snapshots from junction and cross-section countings”according to the description. The comparison with the yearly average daily sum on workdays(the average of the daily sums from the four different weeks) and also the highest value of theupper limit of range of the daily sums (so for example if the week in April had the highestupper limit in the range for the average daily sum on workdays, this value was taken) showedthe following: for 11 measurement points the TVM showed higher values (up to around 31.000cars more per day), for one measurement point the TVM value was higher or lower dependingon which of the two values from this work described above it was compared with, and for twomeasurement points the values calculated in this work were higher. The detailed comparisonfor all the measurement points can be found in Table A.1 in Appendix A. The colors indicatewhether two values from this work were higher (orange), higher and lower (yellow) or lower(green) than the TVM value they were compared with. The reason behind the differences couldfor example be, that the weeks when the data for for the TVM was measured had generally ahigher traffic volume than the four weeks selected for this work. Also the other possible errorsources described in the last paragraph could be an explanation for this. [14]
25
Chapter 4
CO2 Emissions
After calculating half the distance to each respective neighboring measurement point, the CO2
emission of an average car in Munich and the total estimated number of cars per year at themeasurement point, all the variables from the estimation approach formula were known. In thischapter the last step, the calculation of the total estimated CO2 emissions from road traffic peryear for each route segment and also for the whole B2R is described. After that the final resultsof the whole bottom-up estimation are given and visualized.
4.1 Calculation of the CO2 Emissions
Figure 4.1: Visualization of the calculation process for the total estimated CO2 emissions fromroad traffic for one route segment
Since all variables were known now, this step was straightforward: For each measurementpoint half the distance to each neighboring measurement point d, the calculated average CO2
emissions of an average car in Munich eavg,car and the total estimated number of cars per year atthe measurement point ncars were multiplied. This results in the total estimated CO2 emissionsfrom road traffic per year for the respective route segment. The same calculation was carriedout for all 14 measurement points and by summing up all of these values, the final result, theyearly total estimated CO2 emissions from road traffic on the whole B2R, was obtained.
26
4 – CO2 Emissions
However, these values for the individual route segments show a distorted picture regarding towhich route segment actually has the highest traffic volume and are not comparable because thedistances are not equal. Therefore the CO2 emission values for each segment of the B2R werealso normalized to 1 km to make them comparable, therefore there are values in tCO2/km givenin the results.
In order to show the influence of the actual car model and therefore the average CO2 emissionof a car, eavg,car, and on the other hand the number of cars, ncars, the two ranges described inChapter 3 were incorporated. This means the calculation was also done for the higher and lowerranges and the combination of both higher and lower ranges to get the approximate possiblerange for the final result.
4.2 Results
The final result of the bottom-up estimation of the CO2 emissions on the B2R in Munich is thatper year approximately 164,000 tons of this greenhouse gas are emitted by the traffic on thisroad. This total value is displayed on the left side of Figure 4.2 and to show the difference tothe number Dora Bali estimated for the Altstadtring, the yearly value from her thesis (about9,000 tons of CO2) is depicted next to it [13]. The big difference is of course mainly explainedby the 5.6 times longer road length, but that leaves a factor of about 3.3 unexplained. This isprobably due to the higher traffic volume on the B2R but could also be a result of differencesin the calculation, as Dora Bali only had one week of data for her bachelor’s thesis and forexample also had a slightly different calculation method for the average CO2 emission of a carin Munich.
Figure 4.2: Total yearly CO2 emissions on the B2R compared to the Altstadtring (*calculatedby Dora Bali in her bachelor’s thesis: “Bottom-up estimation of traffic CO2 emis-sions in Munich city center (Altstadt)”) [13]
27
4 – CO2 Emissions
Figure 4.3: Estimated average CO2 emissions per year for all route segments (with range de-pending on car models)
Figure 4.4: Estimated average CO2 emissions per year for all route segments (with range de-pending on car models & car number)
The diagrams depicted in Figure 4.3 and 4.4 show the yearly emissions for all route segmentsseparately, which are ordered from left to right according to their position on the B2R whendriving counterclockwise from the northwestern corner. The range is indicated in red. In Figure4.3, only the range depending on the car models is depicted (the range from the average CO2
emission value calculation), in Figure 4.4 the range from the car numbers, meaning from the
28
4 – CO2 Emissions
highest and lowest daily sums, is included. It is clearly visible that this second range has muchless impact than the average CO2 emission value per kilometer of a car, depending on what caris actually on the road.
Figure 4.5: Estimated average CO2 emissions per year for all measurement points, normalizedto 1 km (with range depending on car models)
Figure 4.6: Estimated average CO2 emissions per year for all measurement points, normalizedto 1 km (with range depending on car models & car number)
29
4 – CO2 Emissions
Since these two diagrams (Figure 4.3 and 4.4) show the estimated yearly values for the wholeroad segment, the data does not really show the true picture. Because the longer one road seg-ment is, the higher is the total value, hence the values were also normalized to one kilometer tomake them comparable, as stated in Section 4.1. This is shown in Figure 4.5 and 4.6. Conse-quently, in these diagrams, the measurement points with the highest values actually correspondto the section of the B2R with the highest traffic volume. Again, the first diagram includes onlythe range depending on the car models, meaning the different consumption values and thereforeCO2 emission values for one car. The second one also includes the car number range again.
As stated before, when looking at the two ranges, it is obvious that the one depending on theaverage CO2 emission from different car models has much more influence on the final possiblerange, than the car number range. The first one is, due to the method of calculating, always thesame and about 30% in both directions from the estimated average CO2 emissions. The latteraffects the final range much less, with 3-10% divergence from the estimated value, dependingon the different highest and lowest daily sums from different road segments. For the final yearlyvalue for the whole B2R, the end of the higher approximate possible range (both included) isabout 36% over the estimated value (about 223,000 tons of CO2) and the lower end is about34% under it (about 109,000 tons of CO2).
Figure 4.7: Estimated diurnal variation of CO2 emissions at measurement point 4013 for theyearly average of a workday, normalized to 1 km (with range depending on carmodels)
Figure 4.7 does not show any additional information but is just another visualization of themore influential, car model depending range. Since this range is equal for all the hours of theday, it is possible to show it in the daily variation, unlike the range coming from the highest andlowest daily sums. The diagram shows the estimated diurnal variation of CO2 emissions in tons
30
4 – CO2 Emissions
per kilometer at the measurement point 4013, for the yearly average of a workday (the red line)and the stated range (in grey). It becomes clear how broad the possible range of the de factotraffic CO2 emissions becomes due to this factor, meaning which car models are actually on thestreets. Since the average emission value for one car is multiplied with the number of cars, thepossible range of course increases with the traffic volume.
Finally, Figure 4.8 gives an overview of the situation on the B2R according to the calculationscarried out for this thesis. The road segments used for the calculations are highlighted in colorsfrom red to orange, yellow and finally green, which have the following meaning: The deeperthe red, the higher is the traffic volume and the CO2 emission value normalized to one kilome-ter. The road segment where this applies is found in the west of Munich, at the measurementpoint 4174 close to the Donnersbergerbrücke, which was the most frequented bridge in Europefor a long time according to the statement of a Munich district committee member [39]. Thegreenest part in the southeast has the lowest traffic volume and emission value according to theestimation. All yearly CO2 emission values, normalized to one kilometer to allow comparing,with both ranges for each road segment are listed in Table A.3 in Appendix A.
If the reader is interested in more details from the final results as well as the intermediate re-sults and calculation methods: All the findings in figures and all graphs that were not includedin the thesis itself as well as the Excel spreadsheets used for the calculations, the VBA macrocodes, etc. can be found in the Network Attached Storage (NAS) of the Professorship of En-vironmental Sensing and Modeling at TUM. A more detailed explanation about this is given inSection A.1 in Appendix A.
31
4 – CO2 Emissions
Figure 4.8: Visualization of the CO2 emissions per kilometer, respectively the traffic volume(red = highest, green = lowest) for all measurement points
32
Chapter 5
Current Situation, Outlook and Discussion
This chapter provides some more in-depth information on the current situation and the outlookfor the transport sector regarding CO2 emissions. More specifically the main current and futureissues are presented and commented. Furthermore an outlook specifically for the case of thecity of Munich is given. After that the final results of this work are discussed with reference tothese aspects and background information.
5.1 Current Situation and Outlook Regarding the Transport Sector
5.1.1 Misinformation and Insufficient Legislation
One of the main difficulties regarding the estimation approach utilized in this work was, that itis hard to calculate the average CO2 emission of an average car in Munich, if it is only knownhow many there are of each car (e.g. a VW Golf). Because usually there exist at least a fewdifferent models or editions of each car (e.g. VW Golf GTI), which can have very different fuelconsumption and emission values and it is not possible to tell how many cars of which modelare actually on the streets in the city. In this work almost all the cars were associated with the(inner city) CO2 emission value stated by the manufacturer for the models that were sold in thefourth quarter of 2017, like it is explained in Section 3.3.1.
But even if all the data was available, meaning the official CO2 emission value for each carin Munich, for calculating the value for the average car in the city, one factor would still leadto errors: The misinformation by the manufacturers. According to the International Council onClean Transportation (ICCT), the real-world fuel consumption and CO2 emission values of newpassenger cars in Europe were 42% higher than at type-approval in 2016. This discrepancy,between the values that the car companies present to the public and the real-world value, hasmore than quadrupled since 2001 according to the ICCT analysis. This leads to more costs forfuel for the car owners, but another consequence from this is, that only a third of the supposedCO2 emission reduction since 2001 really took place. [40]
Up to now this misinformation and lack of action was supported by the law in the EU, morespecifically by the test cycle used in Europe. The ICCT data shows that the so called New Eu-
33
5 – Current Situation, Outlook and Discussion
ropean Driving Cycle (NEDC) does not really represent the real-world values very well, unlikefor example the test cycle in the United States, where the divergence is a lot smaller[40]. FromSeptember 2018 onwards, the Worldwide harmonized Light vehicles Test Procedure (WLTP)will be mandatory for all new cars in the EU, which is expected to cut the discrepancy in half,but experts say it will still have possible loopholes [41]. Also, the new legal thresholds for CO2
emissions, which are not even ambitious enough according to environmental lobbyists (and tooambitious according to the car industry) [42], will still be checked based on the NEDC in thenext years [43]. Therefore, it is questionable if the WLTP will really lead to the necessaryreductions of real-world CO2 emissions, and not only on paper.
All of this does not seem like the automotive industry and the responsible politicians are verycommitted to the goal of reducing emissions from traffic. This impression may be strengthenedif one looks at the slow approach of especially the European car companies to electric mobilityrespectively the lack of statutory quota in the EU until today. On the other hand, there arepositive examples like Norway, which has managed to have a 25-30% share of electric cars innew approvals within only three years [44]. So if the EU and especially the German governmentare able to push real reduction and electric mobility quota laws through against the car industrylobby, perhaps starting with the EU Commission’s proposal from March 2018 to measure thefuel consumption on-board additionally [45], real changes should be possible.
5.1.2 Outlook for the Case of Munich
Figure 5.1: Population and passenger car fleet growth in Munich from 2006 to 2016
When looking specifically at (the future of) traffic in the city of Munich, one crucial factor isthe continuously growing population and passenger car fleet. Figure 5.1 shows the total values
34
5 – Current Situation, Outlook and Discussion
for the years 2006 to 2016 [46] [47], and it is very likely that both values will keep rising:The outlook on the population growth shows, that especially outer districts will see a significantpopulation increase of up to 89,6% (about 19% increase per district when averaging the increasefor all 25 districts) until 2035 [48]. Thus, also a rise in the volume of traffic is programmed,and if no improvements concerning the emissions of vehicles are achieved, likewise a rise in theemissions from traffic. A further question is how the already overloaded road network will copewith that. As of 2017, Munich was the city in Germany where drivers were stuck in traffic jamsfor the longest time—for 51 hours on average [17]. Hence, individual traffic will most likelyhave to be reduced somehow.
The public transport system can currently not provide much relief, as it is also already workingto capacity every day especially during rush hour. On top of that, it is quite old and has to berenovated and expanded with operations running. It remains to be seen whether this will happenfast enough. [49]
Furthermore, the rising share of vehicles with higher engine power and emissions, like SportsUtility Vehicles (SUVs), has to be taken into account, which at the moment increases one per-cent per year [25]. This is of course more a societal problem, as a change of mind would benecessary, meaning not seeing big cars as status symbols anymore and buying small and lightcars which are appropriate for inner city traffic instead.
5.2 Discussion of Final Results
As stated before, the total value of CO2 emissions from road traffic on the B2R is about 164,000tons. According to data from the expert report “Klimaschutzziel und -strategie München 2050”(English: Climate Protection Goals and Strategy Munich 2050) this is about 13% of the totalCO2 emissions from traffic and 2% of the total CO2 emissions from all sectors in the city ofMunich in 2014. The higher end of the combined ranges value would be already about 18%of the total CO2 emissions from traffic. As the methods of estimation probably differ quite alot, this statement of course has to be viewed with caution, and it is hard to say whether thesepercentage values could be true. The length of the B2R covers only 1.2% of the whole roadnetwork of Munich (2330 km) [50]. However, it always has at least 2 lanes and by far thehighest traffic volume, so it might be possible that the CO2 emission share is that much higher.[51]
As no other really comparable work could be found and the city of Munich does not have morespecific data on CO2 emissions from traffic except for the whole city from 2014 as mentionedabove, it was not really possible to thoroughly validate the final results. But since the rangeswere included for both the uncertain variables, it is possible to say that the real value is verylikely within the given final range, if the data used to calculate the variables of the estimationapproach was not too faulty.
35
5 – Current Situation, Outlook and Discussion
If one includes the problems in the current situation and in the outlook for the traffic sectorintroduced in the previous chapter, there is of course a clear tendency. In that case it is verylikely that the real value is to be found at the top end of the possible higher range or even beyondit, as it is only 36% in the plus direction from the estimated yearly value of 164,000 tons of CO2
on the B2R. With 42% higher real-world emissions [40], the new “middle” value would alreadybe about 233,000 tons, with the new higher range value then climbing over 315,000 tons of CO2
(this corresponds to 4% of all the city’s CO2 emissions respectively over 25% of the emissionsfrom traffic) [51].
Especially from the view of a more informed person it will be very interesting to see how andif the city of Munich will reach the goal to become almost climate neutral by 2050. This wouldmean an emission of 0,2tCO2 per inhabitant and year in 2050 [51]. Even if one assumes thatuntil then every other aspect of our daily lives does not result in any CO2 emissions anymore(which is not very likely), this would mean the following for our mobility: With the currentvalue for the CO2 emissions per kilometer of an average car in Munich calculated in this work,and assuming that everyone would be driving their own car, the maximum allowed distancewould be just above 20 km per week and person, which for many people maybe only coverstheir daily way to work or university. And since this thought experiment even excluded all otheremissions, for example from heating, it is possible to say that it will at least be a considerablechallenge for the city to reach this goal. Nevertheless, especially in the traffic sector, there aremany different possibilities, like making public transport free of charge on the one hand andcharging a city toll on the other hand, to try to reduce individual traffic. Another way could beto invest in and induce a general change in the way of our daily mobility, e.g. with the increaseduse of car sharing or rental bikes. However, it is of course not exactly predictable which effectthese measures would have or if they would be accepted by the population. Furthermore, otherpossibly helping factors have to be looked at with caution, like autonomous driving, whichcould for example also lead to a big increase in mileage.
36
Chapter 6
Conclusion and Suggestions for Future Work
6.1 Conclusion
Climate change as the outcome of the high anthropogenic greenhouse gas emissions will prob-ably lead to drastic changes for most inhabitants of the earth. Urban areas and the transportsector contribute significantly to these emissions, whether globally, in the European Union, inGermany or just in the city of Munich. On the road towards reaching the climate goals, in thecase of Munich extensive climate neutrality by 2050, research on CO2 emissions from trafficwill hopefully help to get a deeper understanding of the topic in order to take the appropriateactions. In continuation of Dora Bali’s bachelor’s thesis, this work aimed to estimate emissionsfrom road traffic through a bottom-up approach and further look into the uncertainties that comewith this.
During the preparatory phase, one of the most frequented roads of Munich, the Mittlerer Ringor B2R, was selected as the area to be looked at. The necessary data regarding the vehicle fleetand from the traffic detectors was obtained through the cooperation with civil servants at cityoffices like the Statistical Office of Munich and the Department of Public Order. Working withtraffic sensor data from induction loop traffic detectors implies having to deal with inevitableerrors within this data, but according to the responsible contact, it is the best available technol-ogy.
With the estimation approach, which was already used by Dora Bali before, the CO2 emis-sions from traffic were calculated by multiplying half the distances to the adjacent measurementpoints d with the CO2 emissions per kilometer of an average car in Munich eavg,car and the num-ber of cars on the road segment ncars. Applying this estimation approach implied the advantagethat the necessary data was known to be available, while the drawbacks, for example consider-ing only cars and no other vehicle type, were considered inevitable and acceptable.
The distances for the calculation were obtained using Google My Maps, while for the othertwo variables the received data had to be processed first. This was done in Microsoft Excel forboth data sets. In the calculation process for eavg,car, the car fleet data from the Statistical Officeof Munich was simplified and combined with official fuel consumption values and conversionfactors for diesel and gasoline to finally get the resulting average value of 176.95 gCO2 /km.As it is not possible to tell which models of a car are actually on the streets, the potential
37
6 – Conclusion and Suggestions for Future Work
range from the calculation was carried on to show the uncertainties. The traffic sensor datafrom 14 different measurement points around the B2R, more specifically the data from fourselected weeks in January, April, July and October in order to get a seasonal representation,was averaged for the workdays and weekend days separately. For some measurement pointsadjusting the values was necessary before that, as the diurnal variation of the number of carsdisplayed implausibly low or high values. Then the daily sums of the number of cars fromthese averages were used to calculate the number of cars in a season and through this the valuefor the whole year. To include the uncertainty as good as possible, the calculation was alsodone for the highest and lowest daily sum values, which like for eavg,car resulted in a range.The intermediate results from the traffic detector data processing showed that the traffic volumediffers significantly on different segments of the B2R and that the western north-south axisaround the Donnersbergerbrücke has the highest traffic volume. The seasonal differences fromthe comparison of the four weeks for each measurement point were rather small, but in general,the traffic volume is higher in summer than in winter if the respective weeks represent theirseasons well enough. A comparison to the traffic volume map obtained from the Departmentof Traffic Planning showed relatively significant differences to the average daily car sum onworkdays calculated in this work, which could be due to different measurement times or perhapsalso the conducted adjustment of unusable data.
With all three variables known, the final result of around 164,000 tons of CO2 emissionsfrom traffic on the whole B2R could be calculated, which means the objective of this workwas achieved. This final calculation step was done by first multiplying the variables for eachmeasurement point separately and then adding up all the results from all road segments. Thevalues from the separate measurement points were moreover normalized to 1 km to make themcomparable. The influence of the two ranges differs considerably, with the range depending onthe exact car model and its emission value on the street, making up 30 percentage points of the36% higher and 34% lower limit of the combined ranges of the final result value.
More in-depth information provided about the current situation and the outlook regardingthe transport sector emphasized how the misinformation on real-world emission values by themanufacturers makes explicit statements about the validity of the results even more difficult. Itremains to be seen if this kind of practice can be prevented by law in the future. Concerning thecity of Munich itself, the rising population and car fleet will likely lead to rising traffic volumeand emissions, so in order to reach the climate protection goals, the need for change is evenmore urgent. It was discussed that 164,000 tons of CO2 make up about 13% of all emissionsfrom traffic in Munich—however, with the additional background information included, thisvalue would be even a lot higher. The future evolution of the transport sector, hopefully towardsclimate neutrality, will be interesting to see, but the different possible “solutions” have to beviewed with caution, as they could also backfire.
Regarding the future of traffic, especially in cities with ambitious climate reduction goals likeMunich, the final conclusion can be summed up in the following statement: The transport sector
38
6 – Conclusion and Suggestions for Future Work
will be one of the keys to achieving the climate action goals, whether in the city of Munich, inGermany, Europe or globally, and more in-depth research will hopefully allow all stakeholdersto make more sustainable decisions on this matter. Because a change in politics and in theapproach and commitment of the automotive industry but also in the actions of each individualis urgently needed.
6.2 Suggestions for Future Work
Further studies regarding the validation of these bottom-up estimation results should be carriedout. A suggestion is to compare them to numbers that are obtained by a measurement approach,like differential column measurements [52]. Apart from trying to validate the results, severalother topics could be covered in future works. For example, one could try to estimate the CO2
emissions from traffic of the whole city. Additionally, it would be good to improve or findbetter methods to process the data and handle the difficulties with the different data sources.For this, a continued exchange with the KVR will be necessary, as more and more measurementpoints will be updated to the “8+1” detector system, which is the system that provided themost “unusable” data. Nevertheless, if these problems with the detector systems can be solvedor worked around, this also implies more possibilities. More information like vehicle typeand speed will be available for all the measurement points eventually, allowing more accuratecalculations and estimations.
39
APPENDIX
40
Appendix A
Additional Data
A.1 Information about Access to Detailed Data and Graphs
As described in Section 4.2, it is possible to access more detailed data if interested. The datacan be found in the Network Attached Storage of the Professorship of Environmental Sensingand Modeling at the Technical University of Munich. There it is archived in the “Thesis” folderunder “2018 BA Helena Hahne”, and there under “Data”. Next to many literature sources, alsothe KML and KMZ files of the Google My Maps maps can be found there (folder “MyMaps-Map”), which can easily be imported into a new My Maps map. The data sets were put intofolders named after the data source: KVR etc. Regarding the Excel files that were used for thecalculations, the most recent files are either named “current” or in the case of the KVR data canbe found in the folder “processedExcelFiles” under “most advanced”.
The diurnal variation graphs like in Section 3.4.1, as well as the information about whichthreshold values were exactly chosen for the measurement points and all other informationregarding this can be found in the respective Excel sheet: For example for measurement point4027, the graphs for January can be found in the “Jan” spreadsheet of the “4017”-Excel file andthe seasonal comparison graphs in the “seasonal comparison” spreadsheet of the same file. Thecalculation and results of the yearly car sums and also of the CO2 emissions can be found in thefile named “CalculationCarSumsandCO2” and the overview of all measurement points under“Overview”.
A.2 Supplementary Tables and Figure
On the next pages the most important supplementary tables and another figure with more detailscan be found.
41
A – Additional Data
Tabl
eA
.1:D
etai
led
over
view
ofm
easu
rem
entp
oint
sw
ithco
mpa
riso
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Traf
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apda
ta
42
A – Additional Data
Figu
reA
.1:G
oogl
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yM
aps
map
ofth
eB
2Rw
ithal
lthe
mea
sure
men
tpoi
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(not
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tpoi
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incl
uded
)
43
A – Additional Data
Tabl
eA
.2:C
ompa
riso
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betw
een
two
mea
sure
men
tpo
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that
shou
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show
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avoi
dabl
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loop
traf
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dark
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abi
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44
A – Additional Data
Table A.3: Yearly CO2 emissions per kilometer at each measurement point and total yearlyvalue for the B2R with higher and lower end of the combined ranges
45
Appendix B
Contact Information
B.1 Department of Public Order
Ralf TrägerLandeshauptstadt München, KreisverwaltungsreferatHauptabteilung III Straßenverkehr, Abteilung 1 - Verkehrsmanagement,Unterabteilung 2 - Verkehrssteuerung, Sachgebiet 4 - VZM und Verkehrssysteme (KVR-III/124)Address: Ruppertstr. 19, 80337 MünchenEmail address: [email protected]
B.2 Statistical Office of Munich
Sven NöldnerStatistisches Amt München, AuskunftsbüroAddress: Schwanthalerstraße 68, 80336 MünchenEmail address: [email protected]
B.3 Department of Traffic Planning at the Department of Urban Planningand Building Regulation
Team Verkehrsdaten MünchenLandeshauptstadt MünchenReferat für Stadtplanung und BauordnungStadtentwicklungsplanung HA IAbteilung Verkehrsplanung HA I/3Address: Blumenstraße 31, 80331 MünchenEmail address: [email protected]
46
B – Contact Information
B.4 Federal Statistical Office of Germany
Statistisches BundesamtContact form: https://www.destatis.de/kontaktService number: +49 611 75 2405
47
List of Figures
Figure Page
2.1 Map of the average traffic volume on workdays in Munich (TVM) . . . . . . . . . 5
2.2 Map of the Bundesstraße 2 R in Munich . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Cuts in the pavement from induction loop traffic detectors . . . . . . . . . . . . . 10
3.1 Google My Maps map of the B2R used for the calculation of the distances . . . . . 13
3.2 Calculation process for the average CO2 emission . . . . . . . . . . . . . . . . . . 14
3.3 Average CO2 emissions range for a new car . . . . . . . . . . . . . . . . . . . . . 16
3.4 Diurnal variation of number of cars at measurement point 4005 . . . . . . . . . . 20
3.5 Diurnal variation of number of cars at measurement point 4005, average fromworkdays and weekend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.6 Diurnal variation of number of cars at measurement point 4010, before adjustment 21
3.7 Diurnal variation of number of cars at measurement point 4010, adjusted, and tableshowing the lower limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.8 Diurnal variation of number of cars at measurement point 4010, adjusted, missingpoints included with simple linear interpolation . . . . . . . . . . . . . . . . . . . 22
3.9 Diurnal variation of number of cars at measurement point 4010, average fromworkdays and weekend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.10 Workdays and weekend average daily sums for all seasons at measurement point4174, with approximate possible range . . . . . . . . . . . . . . . . . . . . . . . 23
3.11 Total number of cars per year at all measurement points, with approximate possiblerange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
48
List of Figures
3.12 Seasonal comparison of the average number of cars on workdays at measurementpoint 4013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1 Visualization of the calculation process for the total estimated CO2 emissions . . . 26
4.2 Total yearly CO2 emissions on the B2R compared to the Altstadtring . . . . . . . 27
4.3 Estimated average CO2 emissions per year for all route segments (with range de-pending on car models) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4 Estimated average CO2 emissions per year for all route segments (with range de-pending on car models & car number) . . . . . . . . . . . . . . . . . . . . . . . . 28
4.5 Estimated average CO2 emissions per year for all measurement points, normalizedto 1 km (with range depending on car models) . . . . . . . . . . . . . . . . . . . . 29
4.6 Estimated average CO2 emissions per year for all measurement points, normalizedto 1 km (with range depending on car models & car number) . . . . . . . . . . . . 29
4.7 Estimated diurnal variation of CO2 emissions at measurement point 4013 . . . . . 30
4.8 Visualization of the CO2 emissions per kilometer for all measurement points . . . 32
5.1 Population and passenger car fleet growth in Munich from 2006 to 2016 . . . . . . 34
A.1 Google My Maps map of the B2R with all the measurement points . . . . . . . . . 43
49
List of Tables
Table Page
1.1 CO2 emissions from traffic in Germany and share of total emissions . . . . . . . . 2
3.1 Overview of measurement points and their positions . . . . . . . . . . . . . . . . 18
A.1 Detailed overview of measurement points with comparison to Traffic Volume Mapdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
A.2 Comparison table between two measurement points that should have equal values . 44
A.3 Yearly CO2 emissions per kilometer at each measurement point and total yearlyvalue for the B2R with higher and lower end of the combined ranges . . . . . . . . 45
50
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