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Nya system för att kombinera transportsätt, till exempel Mobility as a Service (MaaS), ger nya möjligheter för trafikanter att växla mellan olika färdmedel. Samtidigt ger stora mängder data från såväl kollektivtrafiknätet som vägtrafiknätet samt multimodala data från mobilnäten i kombination med nya metoder för att uppskatta resmönster uppdelat på färdmedel möjligheter till en helt ny förståelse av multimodala resmönster i en stad. Att förstå hur multimodala resmönster utvecklas över tid ger nya möjligheter att utveckla effektiva verktyg för multimodal trafikledning. Det övergripande målet med projektet är att möjliggöra förbättrad tillgänglighet i transportsystemen genom effektivare trafikledning. Mer specifikt syftar projektet till att utveckla nya metoder för att uppskatta multimodal efterfrågan samt färdmedelsval och ruttval för multimodal trafikledning. Vidare har potentiella effekter av multimodal trafikledning analyserats. Projektet omfattar en litteraturstudie för analys av möjligheter och utmaningar med multimodal trafikledning. En explorativ analys baserad på oövervakat lärande har utförts för att identifiera typiska nätverksövergripande mobilitetsmönster. Val av rutt och färdmedel har predikterats med hjälp av statistiska modeller. Ett multimodalt dataset för fem veckor i Stockholm med storskalig mobilitetsdata för vägnätet och biljettdata för kollektivtrafiknätet har sammanställs för den explorativa analysen samt utvärderingen av rutt- och transportsättsmodellerna i samband med trafikledning. Baserat på litteraturstudien kan vi dra slutsatsen att koordinerad ledning av väg och kollektivtrafik har potential att minska trängseln och säkerställa effektiv förflyttning av resenärer i ett storstadsområde. Det finns flera motiv för multimodal trafikledning, där de viktigaste är potentiellt ökad efterfrågan för kollektivtrafik, förbättrad robusthet för transportsystemet och bättre prioritering av trafikledningsåtgärder. De största utmaningarna är samarbete mella, New systems for combining modes of transport, for example Mobility as a Service (MaaS), provide new opportunities for road users to switch between different means of transport. At the same time, large amounts of data from both the public transport network and the road traffic network as well as multimodal data from mobile networks in combination with new methods for estimating travel patterns divided by means of transport provide opportunities for a completely new understanding of multimodal travel patterns in a city. Understanding how multimodal travel patterns develop over time provides new opportunities to develop effective tools for multimodal traffic management. The overall goal of the project is to enable improved accessibility in the transport systems through more efficient traffic management. More specifically, the project aims to develop new methods for estimating multimodal demand as well as mode of transport and route selection for multimodal traffic management. Furthermore, potential effects of multimodal traffic management should be analysed. The project includes a literature survey for analysis of potential and challenges of multimodal traffic management. An explorative analysis based on unsupervised learning is performed for identification of typical network-wide mobility patterns. Route and mode choice is predicted using statistical models. A five-week multimodal dataset for Stockholm including large-scale mobility data for the road network and smartcard data for the public transport network is compiled for the explorative analysis as well as evaluation of the route and mode choice models in the context of traffic management. Based on the literature survey, we can conclude that simultaneous management of road and public transport has the potential to reduce congestion and ensure efficient movement of travelers in an urban area. There are several motives for integrated management of multiple modes, where the most important are potential demand shifts, QC 20240425

Characterizing link-types and day-types in road networks is vital for understanding recurring traffic state patterns. Link-types and day-types in road networks describe road segments and days based on their specific characteristics.For long-term planning, clustering can be used to categorize links and days with similar characteristics and patterns that may indicate degraded performance in the road network in the future. In this paper, we apply cluster analysis to automate this process and identify similarities among links and days to find potential infrastructure deficiencies and recurring traffic states. Our study uses k-means on descriptive statistics to reveal link-types and day-types. Applying our method to high-resolution travel speed data from a road in Sweden reveals distinct characteristics based on the link and day. The results indicate that the relative difference between the measured travel speed and the free-flow travel speed is negative on links with higher free-flow travel speeds. Additionally, the variability in travel speeds is greater on links with lower free-flow travel speeds.

Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context., Funding: This work was supported by the Swedish Transport Administration (Trafikverket) via the Centre for Traffic Research (CTR) [grant number TRV 2020/118663].

Avgörande för ökat hållbart resande är tillgängligheten till hållbara färdmedel. Planering för cykeltrafik blir därför ett allt viktigare område. Därmed behöver våra transportmodeller förbättras i hur de modellerar cykelresor och specifikt hur cyklisters ruttval bör modelleras. Under 2019 har rapporten ”Behovsanalys för cykeltrafikmodeller” levererats till Trafikverket och Sveriges Kommuner och Regioner. Rapporten sammanfattar behov av att vidareutveckla makroskopiska trafikmodeller bland annat för att utvärdera effekter av cykelinvesteringar/åtgärder. I projektet RUCY adresserar vi denna fråga. Många investeringsåtgärder handlar om att bredda befintliga cykelbanor vilket inte kan utvärderas med det befintliga samhällsekonomiska analysverktyget. Även internationellt har nästan inga studier fångat upp hur cykelbanans bredd har påverkat ruttvalet. Det finns därmed ett värde i att öka kunskapsmängden inom området och jämföra resultat mellan olika länder/städer. RUCY använder GPS-data från ca 13 000 cykelresor från projektet ”Bike data – Crowd sourced Big Data för cykling” för att skatta en ruttvalsmodell för cykel för Göteborg. En modell baserad på HIdden-Markovs kartmatchningsalgoritm används för att identifiera rutter utifrån GPS-spår. Möjliga alternativa rutter genereras via upprepade ruttsökningsmetoder samt observerade rutter med samma start- och målpunkt. En Path-size-logit-modell har skattats med relativa effekter för exempelvis cykelbana med olika breddkategorier, belysning och lutning. Resultaten visar att osäkerhet i kartmatchning, som inte har fått uppmärksamhet i de flesta tidigare studierna, har en stor påverkan på modellskattningsresultaten. Studien lyckas fånga resenärernas skillnad i upplevd resuppoffring mellan cykelbana med bredd mindre än 2 meter och de med bredd större än 2 meter. Studien fångar också effekter av lutning, korsningsfördröjning, belysning samt typ av beläggning. Effekterna jämförs med internationella studier. Fångade effekter av bredd, It is critical to maintain good accessibility for active travel modes in order to increase and encourage sustainable travel. Therefore, planning for cycling infrastructure is becoming an increasingly important area. This also necessitates improvements in our transportation models in how they model bicycle journeys. In 2019, the report "Research needs for Bicycle Traffic Models" was delivered to Swedish Transport Administration and SKR. The report summarizes the needs for further development of macroscopic traffic models, among other things, to facilitate evaluation of effects of bicycle investments/measures. Moreover, many infrastructure investments are about widening existing bike lanes, which cannot be evaluated with existing Cost-Benefit analysis tools, GC-kalk. Internationally, almost no studies have evaluated the effect of bike lane width. Therefore, there is a need to increase the knowledge in this area and compare results between different countries/cities. The above mentioned issues are addressed in the project RUCY, which is presented in this report. RUCY employs GPS data from approx. 13,000 bicycle trips from the project "Bike data - Crowd sourced Big Data for cycling" to estimate a route choice model for cycling for Gothenburg. A model based on a Hidden-Markov map matching algorithm is used to identify routes based on GPS tracks. Possible alternative routes are generated via repeated path search methods as well as observed routes with the same origin and destination points. A Path-size logit model is estimated. The results illustrate that uncertainty in map-matching, which has not received much attention in most previous studies, has a large impact on model estimation results. The study captures the travelers' difference in perceived travel time between cycle lanes with width less than 2 meters and those with width larger than 2 meters. The study also captures effects of grade, intersection delay, lighting, and type of pavement. The effects are compared w, Skattning av ruttvalsmodell för cykel för svenska förhållanden (RUCY) / Bicycle route choice modelling for the Swedish context

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35% for perception errors of approximate to 10% which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic., Funding Agencies|Swedish Transport Administration via the Centre for Traffic Research (Trafikverket) [TRV 2022/8287]

Automatisering av trafiksystemet innebär ett paradigmskifte med en förändrad förarroll och integration av fordon och infrastruktur. Automatisering kan därför förväntas bidra till ökad trafiksäkerhet genom att förarens del i det operativa framförandet av fordonet minskar. Effekter i form av minskad miljöpåverkan och förbättrad energieffektivitet kan också åstadkommas genom mera effektivt framförande av fordonen. Bättre framkomlighet och smidigare trafikflöde är också möjliga effekter av automatiseringen. När det gäller kollektiva färdmedel är det främst den kostnadsbesparande effekt som finns av automatisering som möjliggör användning av mindre och därmed flexiblare fordon. För infrastrukturansvariga är automatiseringens potential att bidra till en effektivisering av vägtransportsystemet viktig. Genom att via automatisering ge fordon mera exakt styrning och minskad reaktionstid kan kanske körfältsbredder reduceras och avståndet mellan fordon minskas. Det finns många hypoteser om automatiseringens betydelse för framtidens vägtrafiksystem. Som stöd för utvecklingen av automatiserade trafiksystem krävs kunskap om dessa effekters relevans och storlek. Här finns ett stort forskningsbehov. Projektets övergripande syfte var att vidareutveckla dagens trafikmodeller för att möjliggöra analys av framtidens automatiserade trafiksystem. Projektet har undersökt denna potential genom utveckling och tillämpning av trafiksimulering för analys av effekter av automatisering av trafiksystemet. Projektet har bestått av två doktorandprojekt, det ena med inriktning mot mikrosimulering och interaktion mellan manuellt körda och självkörande fordon och det andra mot systemeffekter med mesosimulering inklusive färdmedels- och ruttval och alternativa trafikeringsformer i framtida trafiknät. Upplägget för SMART2 har baserats på de forskningsplaner som togs fram för de båda doktorandprojekten under SMART1.

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

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.

The introduction of automated vehicles (AVs) is commonly expected to improve different aspects of transportation. A long transition period is expected until AVs become prevalent on roads. During this period, different types of AVs with different driving logics will coexist along human driven vehicles. Using microscopic traffic simulation, this study investigates the range of potential impacts on traffic performance in terms of throughput and travel delays for different types of AVs and human driven vehicles on motorways. The simulation experiment includes scenarios with combinations of three different driving logics for AVs together with human driven vehicles at increasing penetration rates. The utilized AV driving logics represent the evolution of AVs, they were defined in the microscopic simulation tool Vissim and were created by modifying and extending the human driver behaviour models. The results of the simulation experiment show a decrease in vehicle throughput and significant effects on delay times when AVs with a more cautious driving logic are predominant. Overall, results show higher vehicle throughput and lower travel delays as AVs evolve to more advanced driving logics., Funding Agencies|Swedish Transport Administration (Trafikverket) [TRV 2016/20608, TRV 2019/27044]; European UnionEuropean Commission [H2020-ART-2016-2017, 723201]

Ökad andel resor med hållbara färdmedel är en förutsättning för att kombinera fortsatt tillväxt med minskad resursförbrukning och miljöpåverkan. I många europeiska städer har cykel blivit ett alltmer populärt färdmedel under de senaste decennierna. Dagens storskaliga transportmodeller, som utgör viktiga verktyg för utvärderingar och samhällsekonomiska analyser, är dock oftast fokuserade på modellering av resor med bil eller kollektivtrafik. Den här rapporten presenterar en tur-baserad transportmodell med syfte att bättre modellera cykelresor. Nyheterna i denna modell är bland annat ett detaljerat cykelnät som innehåller mer än 200 000 länkar och att modellen nyttjar en mer detaljerad zonindelning. Jämfört med nuvarande verktyget för samhällsekonomisk analys av cykelåtgärder, GCkalk, beskriver modellen ett fullständigt utbud och efterfrågan för cykel på detaljerad geografisk nivå. Modellen har skattats på data från den senaste resvaneundersökningen i Stockholms län från 2015 och representerar därmed observerat resebeteende. Modellen beaktar även cykel som anslutningsfärdmedel till resor med kollektivtrafik. Därigenom behandlar modellen cykel- och kollektivtrafik både som konkurrerande och som komplementära färdmedel och modellen kan utvärdera effekten av en förbättring av cykelinfrastrukturen på både enbart cykelresande och på cykel som anslutningsfärdmedel till kollektivtrafikstationer. Modellen är validerad mot cykelräkningar i Stockholm stad från september och oktober 2015. Modellen har testats på sex scenarier valda från Stockholms stads investeringsplan. Resultaten visar att investeringarna har en begränsad effekt på överflyttning mellan färdmedel och en måttlig effekt på befintliga cyklisters ruttval, restid och generaliserad kostnad., Encouraging the use of active travel modes such as walking and cycling is vital for ensuring a sustainable urban development. In many European metropolitan areas, cycling is becoming increasingly popular within the recent decades. On the other hand, large-scale transport models, which serve as the main tools for policy evaluation and cost-benefit analysis, are often designed for modelling motorised travel modes such as private car and public transport. This study presents a tourbased transport model to better model cycling demand and supply. First, it uses a detailed bicycle network containing more than 200,000 links, covering the whole Greater Stockholm Area. Second, the model utilizes a refined zoning system with totally 5 808 zones each of the size of 250 m×250 m covering the entire Greater Stockholm Area. Third, the model is estimated on the newest Stockholm travel survey 2015, and therefore the model can represent travel behaviour that is up-to-date. In addition to the improvements mentioned above, the model also considers cycling as an access travel mode to a trip by public transport. Therefore, the model treats cycling and public transport as both competing and complementary modes, and the model is capable of evaluating the impact of an improvement in bicycle infrastructure on cycling, as well as on cycling to public transport stations. The model is then validated against bicycle count data from the City of Stockholm. Six scenarios are tested according to the investment plan from the City of Stockholm. The results show that the investments have a limited effect on modal shift but a moderate effect on the existing cyclists' route choice, travel time and generalised cost.

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained., Funding agencies: National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT)

The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows., Funding agencies: Swedish Governmental Agency for Innovation Systems (VINNOVA)

To get a better understanding of people’s mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low., MOFT

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 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 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