| 1. |
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| 2. |
- Gundlegård, David, et al.
(författare)
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Probe-data för kontinuerlig skattning av OD-matriser och länkflöden (CODE PROBE)
- 2024
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Ingår i: Sammanställning av referat från Transportforum 2024. - Linköping : Statens väg- och transportforskningsinstitut. ; , s. 86-87
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Nya storskaliga datakällor för mobilitet, som GPS-baserad probe-data och mobilnätdata, har på senare tid fått ökat intresse inom trafikplaneringsapplikationer. Dessa datakällor är baserade på kontinuerliga positionsobservationer av ett urval av enheter som möjliggör en helt ny förståelse av spatiotemporala mobilitetsmönster både inom och mellan städer. En storskalig mobilitetskälla av särskilt intresse för trafikplanering är GPS-baserad probe-data, som inkluderar högupplösta mobilitetsdata för några procent av fordonsparken. Datakällan har potential att automatisera trafikstatistik på nätverksnivå för tillämpning inom både trafikprognoser och nationell ekonomisk analys. I tidigare forskning har egenskaperna hos detaljerad GPS-data och möjliga applikationsområden analyserats för svenska förhållanden och länkflödesskattningar har identifierats som en prioriterad datatyp som är användbar för trafik- och infrastrukturplanering. Nyligen har forskning också använt probe-data som indata till skattning av efterfrågan i form av OD-matriser. Projektet ”Probe-data för kontinuerlig skattning av OD-matriser och länkflöden” (CODE PROBE) syftar till att jämföra och kombinera lokala probe-baserade länkflödesuppskattningar med konceptet datadriven nätverksutläggning och skattning av OD-matriser för kontinuerlig (24/7) och konsistent skattning av efterfrågan och länkflöde. Uppskalning av probe-data för direkt skattning av länk- och OD-flöden baseras på statistiska modeller, som exempelvis multipel linjär regression och trädbaserad regression, där modellen tränas med historiska data för att sedan kunna användas för skattning av länkflöden. Konceptet datadriven nätutläggning, som utvecklats vid LiU de senaste åren, har använts för att samtidigt skatta både OD- och länkflöden, för att möjliggöra en konsistent skattning av dessa. Metoden ger möjlighet att kombinera probe-baserade skattningar av OD- och länkflöden med länkflödesmätningar, mobilnätsdata och tidigare skattningar av OD-matriser från SAMPERS. Den datadrivna nätutläggningen består av två huvudkomponenter, en del som skattar ruttval och en del som propagerar trafik i tid och rum, givet skattat ruttval för en viss tidsperiod. Resultatet från den datadrivna nätutläggningen är en tidsuppdelad fördelningsmatris som fördelar efterfrågan i vägnätet med hjälp av observationer av restid och ruttval som input. Inom ramen för projektet har även ny metod för att skatta avvikelser i resmönster baserat på GPS-baserad probe-data tagits fram.Projektets resultat visar på goda möjligheter att utnyttja storskaliga mobilitetsdata i form av detaljerad GPS-data och mobilnätsdata för att skatta OD- och länkflöden över tid.
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| 3. |
- Tsanakas, Nikolaos, 1987-
(författare)
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Data-Driven Approaches for Traffic State and Emission Estimation
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Traffic congestion is one of the most severe problems in modern urban areas. Besides the amplified travel times, traffic congestion intensifies the amount of emitted pollutants impacting human health and the environment. By making the appropriate interventions in traffic, transportation planners can mitigate congestion and enhance the performance of a traffic system. One crucial step in traffic planning and management is the estimation of the current or historical traffic state of a network. The estimation of the traffic state variables (traffic flow, density and speed) reveals the problematic parts of a network, namely, the parts associated with severe congestion and high emission rates. Traffic-related observations and traffic models constitute two core elements of a traffic state estimation approach. While the available observation data explicitly or implicitly provide partial information on the traffic state, traffic models define the traffic behaviour and contribute to estimating the variables when they are not directly observable. The estimated traffic state variables form the input to the so-called emission models, which estimate the mass of the emitted pollutants.The type and availability level of the observation data play a key role in traffic state and emission estimation. Traditionally, the primary source of traffic-related field data are stationary detectors (loop detectors, radar sensors or cameras). Today, following the late advances in communication systems, a vast amount of traffic-related data from mobile sources (GPS or cellular networks) is available. Such high data availability may give transportation planners new insights into understanding traffic behaviour. Appropriate exploitation of data coming from mobile sources can improve the existing approaches for estimating the traffic state and emissions.The broad aim of this thesis is to enhance the quality of traffic state and emission estimation. A special focus is given to the development of methods for exploiting the growing availability of traffic-related field data. By combining traffic data and models, the thesis proposes data-driven approaches for traffic state and emission estimation.The first part of the thesis (Paper I and Paper II) focuses on improving the current approaches for network-wide emission estimation. Traditionally, network-wide emission estimations rely on a static traffic-modelling framework. In Paper I, we suggest an alternative emission estimation approach, which is based on a quasi-dynamic traffic model. To evaluate our approach, we perform field experiments on a 19 km long highway stretch in Stockholm. The results show that our method can improve the spatiotemporal distribution of the estimated emissions. In Paper II, the approach suggested in Paper I is applied to a more extensive network covering the city of Norrköping. The results indicate that our approach yields a realistic spatial layout of emissions.The second part of the thesis (Paper III and Paper IV) suggests novel data-driven approaches for estimating network-wide traffic flows and demand. More specifically, in Paper III, we develop a data-driven traffic-flow propagation approach by utilising traveltime observations. Our method is based on a piecewise linear approximation of the travel time function, which allows the use of an efficient event-based structure for propagating the traffic flow. We evaluate our approach through simulation-based experiments, and the results provide proof of the concept. In Paper IV, we exploit the approach suggested in Paper III to develop an efficient data-driven scheme for estimating the traffic demand. The results of the simulation-based experiments indicate that our approach might lead to more accurate estimations compared to other data-driven estimation approaches suggested in the literature.Finally, the last part of the thesis (Paper V) focuses on the estimation of fuel consumption and emissions at a vehicle level. In paper V, we propose a novel method for generating virtual vehicle trajectories by fusing data from different sources. Our approach provides a detailed description of vehicle kinematics, and thus, it permits the use of the underlying virtual vehicle trajectories to vehicle dynamics-sensitive applications, such as emission modelling. The results of our experiments show that the advanced modelling of vehicle kinematics can enhance the accuracy of the estimated emissions.
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| 4. |
- Tsanakas, Nikolaos, et al.
(författare)
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Data-driven network loading
- 2021
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Ingår i: Transportmetrica B. - : TAYLOR & FRANCIS LTD. - 2168-0566. ; 9:1, s. 237-265
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level
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| 5. |
- Tsanakas, Nikolaos, 1987-, et al.
(författare)
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Emission estimation based on cross-sectional traffic data
- 2017
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Ingår i: Prceedings of TAP 2017 22nd International Transportation and Air Pollution Conference. - : EMPA. ; , s. 1-15
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Konferensbidrag (refereegranskat)abstract
- The continuous traffic growth has led to highly congested cities, with negative environmental effects, both related to air quality and climate change. According to the European Environment Agency, transportation remains a significant contributor to the total emissions of the main air pollutants, (EEA, 2016). Specifically, Nitrogen Oxides (NOx), Carbon Oxide (CO) and fine particulate matter (PM2.5) make up 32%, 23% and 8% of the total emissions, respectively. This vigorous impact of vehicular emissions to the urban environmental air quality, raises concerns over the impact of traffic on human health. Therefore, the effective implementation of emission reducing policies, such as traffic control measures or congestion pricing, becomes crucial for many European cities in order to meet the air quality standards and mitigate the human exposure to pollution. To quantify the environmental effects of these measures and demonstrate their effectiveness, a reliable estimation of pollutants concentrations through emission and dispersion modelling is needed....
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| 6. |
- Tsanakas, Nikolaos, et al.
(författare)
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Emission estimation based on cross-sectional traffic data
- 2017
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Ingår i: Proceedings of the 22nd International Transportation and Air Pollution Conference, 2017.
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Konferensbidrag (refereegranskat)abstract
- The paper outlines as follows: Section 2 consists a literature review on methods for emission estimations based on sensors measurements. Section 3 provides a description of the methodology of estimating emissions from cross-sectional data by either using AADT estimation techniques or more sophisticated traffic estimators. Section 4 presents the case study that is a part of the E4 motorway in Stockholm, and provides details about the data collection. The results are presented in Section 5 and finally Section 6 concludes the study and discusses future work.
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| 7. |
- Tsanakas, Nikolaos, 1987-
(författare)
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Emission estimation based on traffic models and measurements
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements.In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect.Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.
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| 8. |
- Tsanakas, Nikolaos, et al.
(författare)
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Estimating Emissions from Static Traffic Models : Problems and Solutions
- 2020
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Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- In large urban areas, the estimation of vehicular traffic emissions is commonly based on the outputs of transport planning models, such as Static Traffic Assignment (STA) models. However, such models, being used in a strategic context, imply some important simplifications regarding the variation of traffic conditions, and their outputs are heavily aggregated in time. In addition, dynamic traffic flow phenomena, such as queue spillback, cannot be captured, leading to inaccurate modelling of congestion. As congestion is strongly correlated with increased emission rates, using STA may lead to unreliable emission estimations. The first objective of this paper is to identify the errors that STA models introduce into an emission estimation. Then, considering the type and the nature of the errors, our aim is to suggest potential solutions. According to our findings, the main errors are related to STA inability of accurately modelling the level and the location of congestion. For this reason, we suggest and evaluate the postprocessing of STA outputs through quasidynamic network loading. Then, we evaluate our suggested approach using the HBEFA emission factors and a 19 km long motorway segment in Stockholm as a case study. Although, in terms of total emissions, the differences compared to the simple static case are not so vital, the postprocessor performs better regarding the spatial distribution of emissions. Considering the location-specific effects of traffic emissions, the latter may lead to substantial improvements in applications of emission modelling such as dispersion, air quality, and exposure modelling. © 2020 Nikolaos Tsanakas et al.
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| 9. |
- Tsanakas, Nikolaos, et al.
(författare)
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Generating virtual vehicle trajectories for the estimation of emissions and fuel consumption
- 2022
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Ingår i: Transportation Research Part C. - : Elsevier. - 0968-090X .- 1879-2359. ; 138
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Tidskriftsartikel (refereegranskat)abstract
- Microscopic emission models estimate second-by-second emissions and fuel consumption for individual vehicles based on vehicle trajectories. A vehicle trajectory describes how the position, speed and acceleration of a vehicle evolves over time. In practice, collecting a complete trajectory data set on a road stretch is not always feasible due to economic and privacy constraints. Therefore, several researchers suggest approaches for generating Virtual Vehicle Trajectories (VVT) given some partially observed traffic data. However, the traditional VVT generation approaches, being originally developed for travel time estimations, usually consider a simplified description of vehicle kinematics, hindering their applicability in emission modelling. In this paper, we suggest a novel approach for generating VVT, which facilitates their use in emission modelling. We empirically evaluate our method by comparing it to the traditional approaches. The results are promising, showing that, under certain experimental settings, our method can enhance the accuracy of emission estimations.
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| 10. |
- Tsanakas, Nikolaos, et al.
(författare)
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Generating virtual vehicle trajectories for the estimation of emissions and fuel consumption
- 2022
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Ingår i: Transportation Research Part C. - : Pergamon-Elsevier Science Ltd. - 0968-090X .- 1879-2359. ; 138
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Tidskriftsartikel (refereegranskat)abstract
- Microscopic emission models estimate second-by-second emissions and fuel consumption for individual vehicles based on vehicle trajectories. A vehicle trajectory describes how the position, speed and acceleration of a vehicle evolves over time. In practice, collecting a complete trajectory data set on a road stretch is not always feasible due to economic and privacy constraints. Therefore, several researchers suggest approaches for generating Virtual Vehicle Trajectories (VVT) given some partially observed traffic data. However, the traditional VVT generation approaches, being originally developed for travel time estimations, usually consider a simplified description of vehicle kinematics, hindering their applicability in emission modelling. In this paper, we suggest a novel approach for generating VVT, which facilitates their use in emission modelling. We empirically evaluate our method by comparing it to the traditional approaches. The results are promising, showing that, under certain experimental settings, our method can enhance the accuracy of emission estimations.
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