The car traffic sector faces a major challenge in the coming decades as CO2 emissions must be drastically reduced. A significant part of this transition is likely to occur through the electrification of the car fleet, which implies that new car types will be introduced on the market. Various transport policies can be used to boost this change towards electric vehicles. A car type choice model is essential for understanding which transport policies will achieve the intended effects and which are cost-effective. Such models have been used in Sweden for many years. Until now, car type choice models have either relied on register data without socioeconomic information or on survey data including socioeconomic information. Relying on survey data has unfortunately become increasingly difficult because of low response rates. However, opportunities to estimate car type choice models using national register data that include socioeconomic information have recently become available in Sweden. In this paper, we estimate a car type choice model based on register data including socioeconomic variables and compare it with an existing survey-based model to examine the advantages and disadvantages of the two data sources. Our findings show that a car type choice model estimated on register data can match the results of a survey-based model, suggesting that register data including socioeconomic variables may well be the preferred data source for this kind of models in the future. 

Att planera och koordinera den stora mängd banarbeten som genomförs på det nationella järnvägssystemet är en stor utmaning. Trafikverket använder begreppet stråkplanering för arbetet med att planera banarbeten fyra till sex år före en tågplan startar. Denna stråkplanering ska bland annat beakta de trafikala effekterna på utpekade trafikflöden. Det arbetet bedrivs idag huvudsakligen manuellt och med begränsat stöd för att analysera samverkanseffekter, trafikpåverkan på hela linjenätet, och samhällsekonomiska konsekvenser.

Forskningsprojektet SATT-SP (Samplanering av trafikpåverkande åtgärder och trafikflöden –stråkplanering) har studerat hur en tidigare presenterad tvådelad optimeringsmodell för schemaläggning av banarbeten och trafikanpassningar kan vidareutvecklas för att kunna ge ett verktygsstöd för Trafikverkets stråkplanering. Detta inbegriper funktionalitet för att presentera och analysera olika planer, inklusive samhällsekonomisk värdering.

Denna rapport redovisar det arbete som genomförts och de resultat som uppnåtts, dels för modell- och verktygsutvecklingen, dels i de fallstudier som genomförts i nära samarbete med Trafikverkets experter.

Resultaten visar att verktygen med goda lösningsprestanda kan producera planer för ett till flera år på hela det svenska järnvägsnätet och med hög detaljupplösning beräkna de dagliga trafikanpassningarna på drygt 1 500 trafikrelationer för hela trafikåret.

Styrkan med dessa verktyg är att de kan hantera en stor mängd projekt och trafikrelationer på ett stort geografiskt nätverk, med en rimlig detaljeringsgrad för både schemaläggning och trafikflöden. Modellerna stödjer också uppdelning i olika tåg- eller trafiktyper, på ett sätt som stämmer väl med kommande europeiska processer för planering och tilldelning av kapacitet.

Planning and coordinating the large amount of track work carried out on a national railway system is a major challenge. At the Swedish Transport Administration, the process of planning track works four to six years before a particular traffic year is termed corridor planning. Corridor planning should considerthe effects on designated traffic flows. Today, this planning is mainly carried out manually and with limited support for analyzing interaction effects, traffic impact on the entire network, and socio-economic impact.

The research project SATT-SP (Joint planning of temporary capacity restrictions and traffic flows for long term corridor planning) has studied how a previously presented bi-level optimization model for scheduling track work and traffic adjustments can be further developed to provide a tool support for the Swedish Transport Administration's corridor planning. This includes functionality for presenting and analyzing different plans, including socio-economic valuation.

This report presents the conducted work and the results that have been achieved, both regarding model and tool development, and the case studies that have been done in close collaboration with the experts at the Swedish Transport Administration. 

The results show that these tools, with good solution performance, can produce plans for one to several years on the entire Swedish railway network, including detailed calculations of daily traffic adjustments on 1,500 train flow relations for the entire traffic year.

The strength of these tools is that they can handle numerous projects and traffic relationships on a large geographical network, with a reasonable level of detail for both project scheduling and traffic flows. The models also support segmentation into different train or traffic types, in a way that alignswith upcoming European processes for planning and allocation of railway capacity.

Long-term tactical infrastructure planning for a transportation network consists of deciding on renewals and major maintenance works. Such projects constitute large budget volumes and will impair the available traffic capacity during their execution, especially for railway systems. Quantitative methods that schedule and coordinate infrastructure projects together with traffic flow adaptations is however largely lacking today.

This paper addresses the joint planning of temporary capacity restrictions and traffic flow adaptions during track work closures, by proposing a bi-level optimization model which separates the problem into project scheduling (upper level) and traffic assignment (lower level). The latter model uses a novel traffic flow formulation for routing volumes of trains through the transportation network under the capacity restrictions given by the project scheduling. An aggregated network is used together with time discretized into uniform periods, which makes it possible to treat large national planning problems with a planning horizon of up to a year and a period length of a couple hours. The computational properties are evaluated, both for the individual models, and for their joint usage. Furthermore, results from applying the models on two case studies, concerning Northern and South-Western Sweden, are presented.

The main conclusion is that the model formulations are capable of solving realistic planning cases and to provide support for capacity planners at an infrastructure manager, even for a large national railway. The results show that a good overview over the collective traffic impact is obtained, but also that details of particular traffic relations or capacity usage over individual network links and their variation over time can be studied. One major deficiency has been identified in the flow-based traffic assignment model, which can lead to incoherent train flows over long traveling distances and many time periods.

Policy for supplying charging infrastructure is an important issue for acceleration of car electrification. In Sweden, most early buyers of chargeable vehicles have been residents in detached houses. Residents in apartment buildings have been assessed to be more dependent on public charging. This paper therefore examines how access to public charging affects the probability of buyers to choose a chargeable car. The main result suggests that both private and public charging close to home and work increase the propensity for buyers of private cars to choose a chargeable car, but private charging had a relatively larger impact than existing densities of public charging stations in 2019. The results therefore raises some doubt on the intense focus on providing public charging infrastructure to speed up car traffic electrification, and perhaps more focus could be put on regulating the provision of private charging possibilities for residents in apartment buildings.

För att undersöka effekten av tillgång till laddinfrastruktur på bilval skattar vi om den del av Bilparksmodellen som modellerar val av biltyp och inkluderar antal publika laddstationer inom 1km från individens hem respektive arbete. Vi använder registerdata om individer och privatägda bilar (från SCB) från 2019, eftersom 2019-data inte är påverkade av de långa förseningar i billeveranser som skedde under Covid-19-pandemin. Vidare kontrollerar vi för hushållsinkomst, bostadstyp, ha mer än en bil i hushållet och ha fått bidrag för att installera laddstation i bostadsrättsföreningen. Modellen är en multinomial logit modell med 597 biltyps-alternativ att välja mellan, som definieras av märke, bränsletyp och bränsleförbrukningsklass. Ett stort antal attribut inkluderas för att beskriva biltypsalternativet, dessa data kommer från Bilvision och inkluderar bilens inköpspris, driftskostnad, rostgaranti, storleksklass, säkerhetsnivå etcetera.

Resultaten från modellskattningen visar en signifikant men liten effekt av antal publika laddstationer inom 1 km från bostaden på benägenheten att köpa en elbil eller laddhybrid. Det samma gäller vid arbetsplatsen. Effekten av privat laddning vid småhus eller lägenhet är betydligt större. Hushållsinkomst påverkar i modellen genom att de lägre inkomstgrupperna är mer känsliga för inköpspris. Vidare visar resultaten av skattningen att de som har mer än en bil i hushållet har större benägenhet att köpa elbil. Benägenheten att köpa elbil och laddhybrid är mindre i småstäder jämfört med på landsbygden och i storstäder.

I denna studie har vi undersökt samband mellan individuella köpare av nya personbilar och deras benägenhet att välja elbil eller laddhybrid givet deras placering och typ av bostad och arbete i relation till utbudet av publik och privat laddinfrastruktur. Resultaten indikerar att de senaste årens tryck på att snabbt bygga ut publik laddinfrastruktur kan vara överdriven och att det kan vara bättre att lägga mer fokus på att stödja utbyggnad av privat laddinfrastruktur för boende i lägenhet.

Räddningstjänsten är en central del av samhällets motståndskraft och är nära sammankopplad med transportsystemet. Tjänsterna måste vara tillgängliga överallt och när som helst, vilket ställer höga krav på enheternas rörlighet och fordonsflottans kapacitet. Övergången från förbränningsmotorer till fossilfria lösningar påverkar denna kapacitet. Vi presenterar ett angreppssätt för att ge räddningstjänster kvantitativt, evidensbaserat beslutsstöd för en fossilfri framtida fordonsflotta.

Predicting the impacts of a transition to a decarbonized freight transport system is challenging due to the inherent uncertainty surrounding the development and deployment of electric and automated truck technologies. This paper presents an exploratory analysis of techno-economic uncertainties for the deployment of electric trucks and automated driving technology and their impacts on the Swedish freight transport system by 2045. A modified version of the Swedish national freight model, Samgods, extended to represent manual electric trucks (METs) and automated driverless electric trucks (AETs), is used to analyze over 300 scenarios. In these scenarios, assumptions about the development and performance of METs and AETs are varied relative to the Swedish reference forecast for freight transport. System-level impacts including mode splits, logistics costs, and energy demand are analyzed. Higher levels of electric truck technology maturity correlate with reduced transport costs, increased road freight demand, and decreased reliance on biofuels. AETs further amplify these effects although with significant variation by operating model and technology maturity. Even without full SAE Level 5 automation, AETs operating exclusively on highways could, in some scenarios, perform over 75 % of domestic road transport tonne-kilometers, provided their unit economics are favorable. In addition to contributing by exploring a plausible outcome space of electrification and automated driving technology, this paper demonstrates a tractable approach for exploring system-level impacts of MET and AET deployment on logistics, mode shifts, and energy consumption with national-level freight models under uncertainty.

The aim of this study is to improve the modeling of emerging road freight technologies in the Swedish national freight model Samgods and to develop methods for generating and analyzing large ensembles of scenarios that account for uncertainties related to the development and impacts of such technologies. The focus is on modeling of battery electric and driverless trucks. A case study is conducted to explore the impacts of input uncertainty related to the development of battery electric and driverless trucks on the current base forecast for 2045. In the base forecast, it is assumed that a decarbonization of the road freight transport system will be achieved through large-scale adoption of battery electric trucks complemented by conventional trucks operating with 100% biofuels.

A set of tools has been developed which includes the following: Extension of the logistics module in Samgods (Logmod) to include three parallel groups of trucks that can complement and compete with each other. Model representations of key aspects of truck electrification and automated driving such as: routing that accounts for driving range and charging infrastructure locations, impacts on vehicle characteristics and operating costs, and operational constraints for driverless trucks depending on their automation level.

The modeling of battery electric and automated trucks is achieved through a combination of input models that calculate scenario specific vehicle parameters, exogenous datasets of scenarios for public charging locations and routing tools to calculate route-specific charging patterns and their associated time and monetary costs and to account for route-specific feasibility of driverless truck operations. The results include impacts in terms of key system indicators including modal split, the share of transport work by each truck technology type, system costs, biofuel and electricity use. Tools for scenario discovery are used to identify scenario conditions that generate substantial deviations in outcomes compared to the base forecast.

I dagsläget vet vi inte så mycket om hur olika åtgärder inom sjöfarten, såsom farledsförbättringar eller regelförändringar,påverkar sjösäkerheten. I detta projekt tas en metod fram för att skatta olycksrisken på sjön med syftet att förbättra effektsambanden för sjösäkerhet.  

Modellen skattar med hjälp av AIS-data och expertbedömningar risken för kollisioner mellan fartyg och tar i dagsläget hänsyn till trafikintensitet, säkerhetsavstånd samt hur fartygen möts. 

There is a lack of standardized methods for socio-economic evaluations within the maritime transport sector. This paper presents a model for analysing and quantifying the intensity and severity of ship conflicts using AIS (Automatic Identification System) data and expert assessments. Also, a case study applying the proposed method to the Southern Gothenburg Archipelago is carried out. The goal is to contribute to cost-benefit analyses within the maritime transport sector by a better understanding of how different actions, such as the widening of fairways or new regulation, impact maritime safety. The importance of validating the model by comparing its results with independent sources of reported maritime accidents is emphasized, and the challenges of using existing accident statistics for this purpose is discussed. The basic model described in the paper can be built upon by differentiating parameters by region and vessel type, account for seasonality etc. Furthermore, a downstream consequence analysis model is needed to enable a monetary valuation of (the consequences of) identified conflicts. Finally, the same principles as laid out here for conflict analysis can also be adopted to the identification of groundings.