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The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effe, QC 20240719

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

Traditionella metoder som används för insamling av resdata har en del nackdelar och samtidigt kräver de modeller som används för att analysera transportsystemet allt mer detaljerad data av hög kvalitet. Det finns således ett stort behov av nya metoder för insamling av resdata och mest framgångsrikt har det visat sig vara att samla rådata från enheter som kan använda olika positioneringsteknologier (t.ex. GPS, WiFi-positionering, GSM, etc.) och omvandla denna information till användbar resdata. Eftersom de flesta smartphones är utrustade med olika positioneringsteknologier och eftersom dessa enheter är integrerade i det dagliga livet för de flesta människor, ger de ett unikt tillfälle för insamling av resdata i stor skala. Syftet med forskningsprojektet som presenteras i denna rapport är att studera om en applikation som installeras på en smartphone kan användas som ersättare eller komplement till en traditionell resvaneundersökning. Detta görs genom att samla in data och studera fördelar och nackdelar med respektive metod. Data har samlats i samma stad, vid samma tidpunkt och med samma svarande med båda metoderna för att göra resultaten så jämförbara som möjligt. För att uppfylla projektets mål har ett system, MEILI, bestående av en smartphone-applikation som loggar telefonens rörelser samt ett webbaserat gränssnitt där användaren kan se, korrigera och komplettera insamlad data utvecklats. Systemet har, i olika utvecklingsstadier, testats och utvärderats i både ett litet pilotförsök och ett större fältförsök. Som väntat varierade användarnas åsikter och upplevelse av systemet en hel del både i piloten och i det stora fältförsöket. Detta beror förmodligen främst på grund av att graden av IT-vana varierar, men också på grund av att systemet fungerar bättre för vissa telefonmodeller än andra. Förutom vissa sällsynta problem med installationen och registrering har funktionaliteten hos appen fungerat smidigt för majoriteten av deltagarna. Den algoritm som utvecklats för, Försök med smartphone-baserad insamling av resdata

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed, Funding agencies: Swedish Transport Administration

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed, Funding agencies: Swedish Transport Administration

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed, Funding agencies: Swedish Transport Administration

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed, Funding agencies: Swedish Transport Administration

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed, Funding agencies: Swedish Transport Administration

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes ahead in time to allow the journey to be completed., Mobile Millenium Stockholm

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show t

Syftet med denna rapport är att presentera förutsättningarna för att använda mobilnätsdata som indata samt för kalibrering och validering av framtidens prognosmodeller. Prognosmodellerna begränsas till stor del av vilken data de baseras på och det är därför viktigt att så snart som möjligt få en bild av hur nästa generations data om människors resvanor ser ut, så att utvecklingen av morgondagens prognosmodeller kan fortgå. 2 (33) Vidare så är syftet att presentera konkreta exempel på hur mobilnätsdata kan komplettera traditionella insamlingsmetoder och ge rekommendationer för fortsatt arbete., Mobilnätsdata som indata till prognosmodeller

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.

Real-time estimations of current and future traffic states are an essential part of traffic management and traffic information systems. Within the Mobile Millennium project considerable effort has been invested in the research and development of a real-time estimation system that can fuse several sources of data collected in California. During the past year this system has been adapted to also handle traffic data collected in Stockholm. This paper provides an overview of the model used for highways and presents results from an initial evaluation of the system. As part of the evaluation process, GPS data collected in an earlier field-test and estimations generated by the existing system used by the TMC in Stockholm, are compared with the estimations generated by the Mobile Millennium system. Given that the Mobile Millennium Stockholm system has not undergone any calibration, the results from the evaluation are considered promising. The estimated travel times correspond well to those measured in the field test. Furthermore, the estimations generated by the Mobile Millennium system can be regarded as superior to those of existing traffic management system in Stockholm. The highway model was found to perform well even with a reduction in the number of sensors providing data. The findings of this study indicate the robustness of the Mobile Millennium system and demonstrate how the system can be migrated to other geographical areas with similar sources of available data., Mobile Millennium Stockholm

TSC import 847 2012-02-06. QC 20120413

Mobile Millennium Stockholm

Roundabouts have become a more common type of intersection in Sweden over the last 30 years. In order to evaluate the roundabout level-of-service both analytical models and simulation models are being used. Analytical traffic models for intersections, such as the Swedish capacity model Capcal, has difficulties estimating the level-of-service of a roundabout if there are pedestrians and cyclists at crossings located close to the roundabout. It is well known that a crossing located after a roundabout exit can cause an up-stream blocking effect that affects the performance of the roundabout. But how the upstream blocking effect depends on the different flows of vehicles and pedestrians is not known. In this paper an existing analytical model by Rodegerdts and Blackwelder has been investigated and compared to simulations in VISSIM and measurements from Swedish roundabouts. The purpose of this investigation is to examine if the model by Rodegerdts and Blackwelder is suitable for implementing into existing analytical models such as Capcal. The results show that the model by Rodegerdts and Blackwelder can estimate if a capacity loss will occur, but the magnitude of this loss is more difficult to evaluate. The conclusion and recommendation is that the model by Rodegerdts and Blackwelder should be implemented into the Swedish capacity model Capcal. The model by Rodegerdts and Blackwelder is to be used as a warning system if the results in Capcal are too uncertain to use for analysis of the roundabout performance.

Trafiken på våra vägar ökar stadigt och det finns inga tecken på att denna trend kommer att brytas. Denna utveckling innebär en mängd problem såsom ökat buller, mer utsläpp och ökad trängsel. En lösning på trafikproblemen är att utnyttja de befintliga vägarnas kapacitet på ett bättre sätt. Genom att få bilisterna att välja alternativa vägar, resa på andra tidpunkter eller välja alternativa transportmedel kan trängseln på våra vägar minska och därmed minskar också utsläpp och buller. Ett sätt att påverka bilisterna är genom att förmedla information om, exempelvis, rådande trafikförhållande och alternativa resvägar. Om trafikledningscentralerna hade tillgång till tillförlitliga predikteringar av restider skulle de ha än större möjligheter att dirigera om trafiken så att de värsta problemen kan undvikas. Behovet av tillförlitliga prediktioner av restider är som störst när något oförutsett händer, men det är också då som det är svårast att göra prediktioner. Syftet med detta examensarbete är att ge en inblick i ett antal olika metoder för prediktering av restider, vidare har en av dessa metoder, Holt-Winters metod, testats på tillgänglig trafikdata och resultatet av detta test har analyseras. Prediktering av restider är ett komplext problem och en hel del forskning har utförts i ämnet, med blandad framgång. Metoderna och modellerna som det har forskats kring är många och de flesta av dem kan sorteras in i någon av följande kategorier: enkla statistiska modeller, neurala nätverk, regressions- och tidsserieanalys och trafiksimulering. Vilken metod som fungerar bäst beror till stor del på vad resultatet av prediktionen ska användas till, vilken data som finns tillgänglig samt hur trafikförhållandena ser ut på den aktuella platsen. Resultaten av predikteringar av restider på trafikdata som utförts med Holt-Winters metod visar tydligt att Holt-Winters metod i vissa fall visserligen är likvärdig med och ibland bättre än de enkla naiva prediktorerna som den har jämförts med, m, The number of vehicles on our roads is constantly increasing, which results in a lot of problems such as increased noise, pollution and congestion. One solution is to make better use of the capacity of the existing roads. If you can make people use alternative roads, travel at alternative times or use alternative ways of travelling the congestion would decrease, which also would decrease the noise and pollution. One way to influence the drivers is to supply information about, for example, alternative roads or the current traffic situation. If the traffic management centrals had access to reliable predictions of travel times their possibilities to influence the traffic situation would increase, and maybe the worst problems could be avoided. It is when something unexpected occurs that reliable travel time predictions are really needed, but it is also at that time it is most difficult to calculate reliable predictions. The purpose with this thesis is to examine a number of methods that are used to predict travel times. Furthermore, one of these methods, Holt-Winters method, has been tested and analysed. Prediction of travel times is a complex problem and a reasonable amount of research has been conducted in the subject, with varying success. There are a lot of different methods and models that has been tested and analysed and most of them can be sorted in under one of the following categories: simple statistic analysis, neural networks, regression- and time series analysis and traffic simulation. Which method that is most suitable for predicting travel times depends on how the traffic conditions looks like at the specific road, which data that is available and what the predictions are to be used for. The results of the predictions made with Holt-Winters method clearly shows that Holt- Winters method in some cases are just as good as the simple naive predictors it has been compared with, but in most cases the simple naive predictors are better. The results of the predic

Trafiken på våra vägar ökar stadigt och det finns inga tecken på att denna trend kommer att brytas. Denna utveckling innebär en mängd problem såsom ökat buller, mer utsläpp och ökad trängsel. En lösning på trafikproblemen är att utnyttja de befintliga vägarnas kapacitet på ett bättre sätt. Genom att få bilisterna att välja alternativa vägar, resa på andra tidpunkter eller välja alternativa transportmedel kan trängseln på våra vägar minska och därmed minskar också utsläpp och buller. Ett sätt att påverka bilisterna är genom att förmedla information om, exempelvis, rådande trafikförhållande och alternativa resvägar. Om trafikledningscentralerna hade tillgång till tillförlitliga predikteringar av restider skulle de ha än större möjligheter att dirigera om trafiken så att de värsta problemen kan undvikas. Behovet av tillförlitliga prediktioner av restider är som störst när något oförutsett händer, men det är också då som det är svårast att göra prediktioner. Syftet med detta examensarbete är att ge en inblick i ett antal olika metoder för prediktering av restider, vidare har en av dessa metoder, Holt-Winters metod, testats på tillgänglig trafikdata och resultatet av detta test har analyseras. Prediktering av restider är ett komplext problem och en hel del forskning har utförts i ämnet, med blandad framgång. Metoderna och modellerna som det har forskats kring är många och de flesta av dem kan sorteras in i någon av följande kategorier: enkla statistiska modeller, neurala nätverk, regressions- och tidsserieanalys och trafiksimulering. Vilken metod som fungerar bäst beror till stor del på vad resultatet av prediktionen ska användas till, vilken data som finns tillgänglig samt hur trafikförhållandena ser ut på den aktuella platsen. Resultaten av predikteringar av restider på trafikdata som utförts med Holt-Winters metod visar tydligt att Holt-Winters metod i vissa fall visserligen är likvärdig med och ibland bättre än de enkla naiva prediktorerna som den har jämförts med, m, The number of vehicles on our roads is constantly increasing, which results in a lot of problems such as increased noise, pollution and congestion. One solution is to make better use of the capacity of the existing roads. If you can make people use alternative roads, travel at alternative times or use alternative ways of travelling the congestion would decrease, which also would decrease the noise and pollution. One way to influence the drivers is to supply information about, for example, alternative roads or the current traffic situation. If the traffic management centrals had access to reliable predictions of travel times their possibilities to influence the traffic situation would increase, and maybe the worst problems could be avoided. It is when something unexpected occurs that reliable travel time predictions are really needed, but it is also at that time it is most difficult to calculate reliable predictions. The purpose with this thesis is to examine a number of methods that are used to predict travel times. Furthermore, one of these methods, Holt-Winters method, has been tested and analysed. Prediction of travel times is a complex problem and a reasonable amount of research has been conducted in the subject, with varying success. There are a lot of different methods and models that has been tested and analysed and most of them can be sorted in under one of the following categories: simple statistic analysis, neural networks, regression- and time series analysis and traffic simulation. Which method that is most suitable for predicting travel times depends on how the traffic conditions looks like at the specific road, which data that is available and what the predictions are to be used for. The results of the predictions made with Holt-Winters method clearly shows that Holt- Winters method in some cases are just as good as the simple naive predictors it has been compared with, but in most cases the simple naive predictors are better. The results of the predic

This paper describes the lessons learned from the trial of MEILI, a smartphone based semi-automatic activity-travel diarycollection system, in Stockholm city, Sweden. The design of the system, together with state-of-the-art improvements of different elements of the tool, are presented before and after the trial to better illustrate the improvements based on the lessons learned. During the trial, both MEILI and a paper-based diary captured about 65% of the total number of detected trips, but only about half of the trips were captured by both systems. The unmatchable trips are partly due to different activity declaration and system specific destination specification, i.e., a verbose specification of address in the paper-and-pencil survey and a point of interest selection / declaration in MEILI. In terms of subjective appreciation, the user experiences vary greatly between the different participants in the pilot. Presumably, this is mainly due to different level of IT-knowledge of the respondents, but also due to the occasionally non-uniform behaviour of the location collection service caused by hardware and / or software difficulties. Based on these inputs, further web and support system improvements have been implemented for future trials., QC 20160525, TRV 2014/10422