Reliability of GPS based traffic data: an experimental evaluation

GPS tracking of mobile objects provides spatial and temporal data for a broad range of applications including traffic management and control, transportation routing and planning. Previous transport research has focused on GPS tracking data as an appealing alternative to travel diaries. Moreover, the GPS based data are gradually becoming a cornerstone for real-time traffic management. Tracking data of vehicles from GPS devices are however susceptible to measurement errors – a neglected issue in transport research. By conducting a randomized experiment, we assess the reliability of GPS based traffic data on geographical position, velocity, and altitude for three types of vehicles; bike, car, and bus. We find the geographical positioning reliable, but with an error greater than postulated by the manufacturer and a non-negligible risk for aberrant positioning. Velocity is slightly underestimated, whereas altitude measurements are unreliable.

Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

Emissions Characterisation of residential pellet boilers during start-up and stop periods

In this study, gaseous emissions and particles are measured during start-up and stop periods for an over-fed boiler and an under-fed boiler. Both gaseous and particulate matter emissions are continuously measured in the laboratory. The measurement of gaseous emissions includes oxygen (O2), carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxide and (NO). The emissions rates are calculated from measured emissions concentrations and flue gas flow. The behaviours of the boilers during start-up and stop periods are analysed and the emissions are characterised in terms of CO, NO, TOC and particles (PM2.5 mass and number). The duration of the characterised periods vary between two boilers due to the difference in type of ignition and combustion control. The under-fed boiler B produces higher emissions during start-up periods than the over-fed boiler A. More hydrocarbon and particles are emitted by the under-fed boiler during stop periods. Accumulated mass of CO and TOC during start-up and stop periods contribute a major portion of the total mass emitted during whole operation. However, accumulated mass of NO and PM during start-up and stop periods are not significant as the duration of emission peak is relatively short.

EMISSIONS CHARACTERISTICS OF A RESIDENTIAL PELLET BOILER AND A STOVE

Gaseous and particulate emissions from a residential pellet boiler and a stove are measured at a realistic 6-day operation sequence and during steady state operation. The aim is to characterize the emissions during each phase in order to identify when the major part of the emissions occur to enable actions for emission reduction where the savings can be highest. The characterized emissions comprised carbon monoxide (CO), nitrogen oxide (NO), total organic carbon (TOC) and particulate matter (PM 2.5). In this study, emissions were characterised by mass concentration and emissions during start-up and stop phases were also presented in accumulated mass. The influence of start-up and stop phases on the emissions, average emission factors for the boiler and stove were analysed using the measured data from a six-days test. The share of start-up and stop emissions are significant for CO and TOC contributing 95% and 89% respectively at the 20kW boiler and 82% and 89% respectively at the 12 kW stove. NO and particles emissions are shown to dominate during stationary operation.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

Emissions from realistic operation of residential wood pellets heating systems

Emissions from residential combustion appliances vary significantly depending on the firing behaviours and combustion conditions, in addition to combustion technologies and fuel quality. Although wood pellet combustion in residential heating boilers is efficient, the combustion conditions during start-up and stop phases are not optimal and produce significantly high emissions such as carbon monoxide and hydrocarbon from incomplete combustion. The emissions from the start-up and stop phases of the pellet boilers are not fully taken into account in test methods for ecolabels which primarily focus on emissions during operation on full load and part load. The objective of the thesis is to investigate the emission characteristics during realistic operation of residential wood pellet boilers in order to identify when the major part of the annual emissions occur. Emissions from four residential wood pellet boilers were measured and characterized for three operating phases (start-up, steady and stop). Emissions from realistic operation of combined solar and wood pellet heating systems was continuously measured to investigate the influence of start-up and stop phases on total annual emissions. Measured emission data from the pellet devices were used to build an emission model to predict the annual emission factors from the dynamic operation of the heating system using the simulation software TRNSYS. Start-up emissions are found to vary with ignition type, supply of air and fuel, and time to complete the phase. Stop emissions are influenced by fan operation characteristics and the cleaning routine. Start-up and stop phases under realistic operation conditions contribute 80 – 95% of annual carbon monoxide (CO) emission, 60 – 90% total hydrocarbon (TOC), 10 – 20% of nitrogen oxides (NO), and 30 – 40% particles emissions. Annual emission factors from realistic operation of tested residential heating system with a top fed wood pelt boiler can be between 190 and 400 mg/MJ for the CO emissions, between 60 and 95 mg/MJ for the NO, between 6 and 25 mg/MJ for the TOC, between 30 and 116 mg/MJ for the particulate matter and between 2x10-13 /MJ and 4x10-13 /MJ for the number of particles. If the boiler has the cleaning sequence with compressed air such as in boiler B2, annual CO emission factor can be up to 550 mg/MJ. Average CO, TOC and particles emissions under realistic annual condition were greater than the limits values of two eco labels. These results highlight the importance of start-up and stop phases in annual emission factors (especially CO and TOC). Since a large or dominating part of the annual emissions in real operation arise from the start-up and stop sequences, test methods required by the ecolabels should take these emissions into account. In this way it will encourage the boiler manufacturers to minimize annual emissions. The annual emissions of residential pellet heating system can be reduced by optimizing the number of start-ups of the pellet boiler. It is possible to reduce up to 85% of the number of start-ups by optimizing the system design and its controller such as switching of the boiler pump after it stops, using two temperature sensors for boiler ON/OFF control, optimizing of the positions of the connections to the storage tank, increasing the mixing valve temperature in the boiler circuit and decreasing the pump flow rate. For 85 % reduction of start-ups, 75 % of CO and TOC emission factors were reduced while 13% increase in NO and 15 % increase in particle emissions was observed.

To what extent are CO2 emissions from intra-urban shopping trips by cars affected by drivers’ travel behaviour and store location?

Transportation is seen as one of the major sources of CO2 pollutants nowadays. The impact of increased transport in retailing should not be underestimated. Most previous studies have focused on transportation and underlying trips, in general, while very few studies have addressed the specific affects that, for instance, intra-city shopping trips generate. Furthermore, most of the existing methods used to estimate emission are based on macro-data designed to generate national or regional inventory projections. There is a lack of studies using micro-data based methods that are able to distinguish between driver behaviour and the locational effects induced by shopping trips, which is an important precondition for energy efficient urban planning. The aim of this study is to implement a micro-data method to estimate and compare CO2 emission induced by intra-urban car travelling to a retail destination of durable goods (DG), and non-durable goods (NDG). We estimate the emissions from aspects of travel behaviour and store location. The study is conducted by means of a case study in the city of Borlänge, where GPS tracking data on intra-urban car travel is collected from 250 households. We find that a behavioural change during a trip towards a CO2 optimal travelling by car has the potential to decrease emission to 36% (DG), and to 25% (NDG) of the emissions induced by car-travelling shopping trips today. There is also a potential of reducing CO2 emissions induced by intra-urban shopping trips due to poor location by 54%, and if the consumer selected the closest of 8 existing stores, the CO2 emissions would be reduced by 37% of the current emission induced by NDG shopping trips.

THE CARBON EMISSIONS OF THE BOSTON RED SOX

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The Carbon Emissions of the Boston Red Sox

The Boston Red Sox emit a great deal of carbon throughout the regular baseball season because of flights to the home fields of their opponents. Knowing that air travel is one of the biggest transportation-based contributors to global climate change, the Boston Red Sox (and all major league teams) should be encouraged to offset their carbon emissions from regular season travel. Using ArcGIS to map the flight paths along great circle routes, the distance of flights to opponents’ cities was calculated to total the number of miles traveled in the 2008 season. The price of offsetting this carbon was estimated using the calculators of carbon offset retailers, such as Native Energy, a Vermont-based retailer. This project provides the potential costs of offsetting the carbon emitted from Red Sox air travel. To take the lead in the future of the Northeast, the Red Sox should begin to consider their contribution to climate change.

Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions

Most of water distribution systems (WDS) need rehabilitation due to aging infrastructure leading to decreasing capacity, increasing leakage and consequently low performance of the WDS. However an appropriate strategy including location and time of pipeline rehabilitation in a WDS with respect to a limited budget is the main challenge which has been addressed frequently by researchers and practitioners. On the other hand, selection of appropriate rehabilitation technique and material types is another main issue which has yet to address properly. The latter can affect the environmental impacts of a rehabilitation strategy meeting the challenges of global warming mitigation and consequent climate change. This paper presents a multi-objective optimization model for rehabilitation strategy in WDS addressing the abovementioned criteria mainly focused on greenhouse gas (GHG) emissions either directly from fossil fuel and electricity or indirectly from embodied energy of materials. Thus, the objective functions are to minimise: (1) the total cost of rehabilitation including capital and operational costs; (2) the leakage amount; (3) GHG emissions. The Pareto optimal front containing optimal solutions is determined using Non-dominated Sorting Genetic Algorithm NSGA-II. Decision variables in this optimisation problem are classified into a number of groups as: (1) percentage proportion of each rehabilitation technique each year; (2) material types of new pipeline for rehabilitation each year. Rehabilitation techniques used here includes replacement, rehabilitation and lining, cleaning, pipe duplication. The developed model is demonstrated through its application to a Mahalat WDS located in central part of Iran. The rehabilitation strategy is analysed for a 40 year planning horizon. A number of conventional techniques for selecting pipes for rehabilitation are analysed in this study. The results show that the optimal rehabilitation strategy considering GHG emissions is able to successfully save the total expenses, efficiently decrease the leakage amount from the WDS whilst meeting environmental criteria.

Dealing with greenhouse gas emissions (Dublado)

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Análise das emissões gasosas de um veículo flex funcionando com diferentes combustíveis

Efforts in research and development of new technologies to reduce emission levels of pollutant gases in the atmosphere has intensified in the last decades. In this context, it can be highlighted the modern systems of electronic engine management, new automotive catalysts and the use of renewable fuels which contributes to reduce the environmental impact. The purpose of this study was a comparative analysis of gas emissions from a automotive vehicle, operating with different fuels: natural gas, AEHC or gasoline. To execute the experimental tests, a flex vehicle was installed on a chassis dynamometer equipped with a gas analyzer and other complementary accessories according to the standard guidelines of emission and security procedures. Tests were performed according to NBR 6601 and NBR 7024, which define the urban and road driving cycle, respectively. Besides the analysis of exhaust gases in the discharge tube, before and after the catalyst, using the suction probe of the gas analyzer to simulate the vehicle in urban and road traffic, were performed tests of fuel characterization. Final results were conclusive in indicating leaded gasoline as the fuel which most contributed with pollutant emissions in atmosphere and the usual gasoline being the fuel which less contributed with pollutant emissions in atmosphere