Currently, there is a great need for new methods to collect travel data. Traditional methods have considerable drawbacks and, at the same time, the models used to analyse the transport system require more and more detailed and high-quality data. Since most smartphones are equipped with various sensors that can be used to determine the location of the smartphone, and since smartphones are integrated in the daily life of most people, they provide an unprecedented opportunity for large-scale travel data collection. This method has a great potential to solve the problems related to the estimation of distance/travel time, geographic coding of departure/destination locations and forgotten trips and it will also provide a more detailed and extensive data set.

In a recently completed research project the feasibility of replacing or complementing the traditional travel diary, with a suite of tools that make use of smartphone collected travel data has been evaluated. The advantages and disadvantages of the traditional method and the proposed method were studied. For a fair comparison, both methods have been tested in the same city, at the same time, and with the same respondents. To achieve the objectives of the project, MEILI, a system that consists of a smartphone application for capturing the movement of users and a web application for allowing the users to annotate their movement, has been deployed. In total 2142 trips were collected and annotated by 171 users. 51 of the users annotated trips covering more than a week. The main findings of the paper are that smartphone based data collection is feasible, that the algorithms to save battery work well and that trips of the same respondent vary considerably depending on day of the week.

In this paper we use a nested latent class logit specification to define and estimate a large-scale mode choice demand forecasting model. We estimate this model based on mobile phone network data translated to roughly 100 000 long-distance trips within Sweden, achieving convergence of the model and credible parameter estimates. We develop methods to address two problems stemming from the nature of this data: the difficulties of distinguishing bus trips from car trips (since they share the same infrastructure) and distinguishing business from private trips (since trip purpose is unknown). To address the first issue, we estimate a nested logit model with an artificial nest that accounts for the differences in utility between bus and car. To address the latter issue, we estimate a latent class model, identifying classes of trips interpreted as private and business trips. Addressing these two issues substantially improves model fit. 

In this paper we develop two methods for the use of mobile phone data to support the estimation of long-distance mode choice models. Both methods are based on logit formulations in which we define likelihood functions and use maximum likelihood estimation. Mobile phone data consists of information about a sequence of antennae that have detected each phone, so the mode choice is not actually observed. In the first trip-based method, the mode of each trip is inferred by a separate procedure, and the estimation process is then straightforward. However, since it is not always possible to determine the mode choice with certainty (although it is possible in the majority of cases), this method might give biased results. In our second antenna-based method we therefore base the likelihood function on the sequences of antennae that have detected the phones. The estimation aims at finding a parameter vector in the mode choice model that would explain the observed sequences best. The main challenge with the antenna-based method is the need for detailed resolution of the available data. In this paper we show the derivation of the two methods, that they coincide in case of certainty about the chosen mode and discuss the validity of assumptions and their advantages and disadvantages. Furthermore, we apply the first trip-based method to empirical data and compare the results of two different ways of implementing it. © 2021 The Authors

Abstract Automated vehicles are likely to have significant impacts on the transport system such as increased road capacity, more productive/enjoyable time spent travelling in a car, and increased vehicle kilometres travelled. However, there is a great risk that automated driving may negatively impact the environment if adequate policies are not put in place. This chapter examines the effects of driverless vehicles and the types of policies required to attain sustainable implementation of the technology. To understand the effects on a systemic level, and to understand the needs and impacts of policies, the dynamics must be understood. Therefore, a causal loop diagram (CLD) is developed and analysed. One important insight is that the effects of driverless vehicles are mainly on the vehicular level (e.g., the reduced number of accidents per vehicle). These effects can be cancelled out on a systemic level (e.g., due to increased vehicle-kilometre travelled (VKT) that increases total number of accidents). The marginal costs of road transport are central to both freight and passenger transport. Automation will reduce marginal costs and shift the equilibrium in the transport system towards a state with higher VKT. This will lead to greater energy consumption and higher emissions. To attain sustainability goals, there might be a need to balance this reduction of marginal costs by using policy instruments. In the work, CLDs is experienced to be a useful tool to support the collaboration between experts from different fields in the dialogue about policies.

Den här rapporten är en del i ett forskningsprojekt där vi studerar det svenska anslutningsresandet. Här beskrivs anslutningsresor med data från svenska nationella resvaneundersökningen. Ungefär 35 procent av alla långväga (mer än 10 mil) resor sker med ett färdmedel som förutsätter en resa till en station eller flygplats. De flesta långväga resor har en mycket enkel struktur med korta anslutningsresor till huvudfärdmedlet. Det är ovanligt med fler färdmedel under en resa förutom huvudfärdmedel, anslutande och avslutande färdmedel. Anslutningsfärdmedel och huvudfärdmedel är enkelt identifierbara i data. Det råder också en stor symmetri mellan utresa och återresa avseende både huvudfärdmedel och färdmedel för anslutningsresa. För flygresor utgör anslutningsresan ofta under 10 % av resans totala längd men 30% till över 50 % av reseuppoffringen. Anslutningsresan utgör således en signifikant del av den totala reseuppoffringen både i tid och pengar för flyg medan restiden och biljettkostnaden för tåg är en mer komplett beskrivning av hela resan. Resor med tåg startar och slutar ofta i stadsmiljö med närhet till många målpunkter och relativt stort utbud av kollektivtrafik. Det råder därmed en asymmetri i beskrivningen av reseuppoffringen mellan flyg och tåg när man inte tar hänsyn till anslutningsresan.

The standard textbook analysis shows that drivers as a group lose from congestion charges. However, it omits taste heterogeneity, shorter travel times far out in the larger network arising from less blocking back of upstream links and the possibility for drivers to reschedule. Taking account of these factors, using a dynamic scheduling model with heterogeneous users we find that all three add significantly to the benefit of the Stockholm congestion charges and that drivers as a group benefit from these charges even without recycling of revenues. This paper also provides an update on the consumer benefits of the Stockholm charges.

According to the standard textbook analysis, drivers as a group will be worse off with congestion charging if not compensated by revenues. This result is confirmed by an analysis of the Stockholm congestion charging scheme using a static model with homogeneous users. However, both this static model and the standard textbook analysis omit three important factors: taste heterogeneity, effects of charges on the larger network arising from less blocking back of upstream links and the possibility for drivers to reschedule. Taking account of these factors, using a dynamic scheduling model with heterogeneous users estimated and calibrated for Stockholm, we find that drivers as a group benefit from the charging scheme in Stockholm without recycling of revenues. This paper further investigates the importance of the three mentioned factors. We find that all three factors add significantly to the benefit of the charges and that the most important is heterogeneity in the value of travel time savings. This paper also provides an update on the consumer benefits of the Stockholm charges.

This paper summarizes the traffic effects of the Gothenburg congestion charges introduced in 2013. The system is similar to the system introduced in Stockholm in 2006; both are designed as time-of-day dependent cordon pricing systems. We find that many effects and adaptation strategies are similar to those found in Stockholm, indicating a high transferability between smaller and larger cities with substantial differences in public transport use. However, there are also important differences regarding some of the effects, the accuracy of the model forecasts and public support arising from different topologies, public transport use, congestion levels and main objectives communicated to the public. Finally, the Gothenburg case suggests that whether congestion charges are introduced or not depends on the support among the political parties, and that this is determined primarily by the prevailing institutional setting and power over revenues, and to a lower extent by the public support, and benefits from congestion reduction.

Time-of-day dependent cordon-based congestion charging systems were introduced in Stockholm in 2006, and in Gothenburg in 2013. The Stockholm system was significantly extended in 2016, and the peak charge has been increased in the two cities. This paper analyses the effects of the first decade with the Swedish congestion charges, specifically effects of the system updates, and draws policy lessons for the years to come. Should we introduce congestion charges in more cities? Should we extend the systems that we have? We synthesize previous research findings and focus on the long-term effects that have varied over time including the recent years: the price elasticities on the traffic volume across the cordon, the revenue and system operating cost, the public and political support, and consequences for the transport planning process. We also explore the effects on peak and off-peak, and different types of traffic (trucks, company cars and private passenger cars), because of access to novel data that make this analysis possible. We find that the price elasticities have increased over time in Stockholm, but decreased in Gothenburg. We find that the public support increased in the two cities after their introduction until the systems were revised; since then, the public support has declined in both cities. We find that the price elasticity was substantially lower when the charging levels were increased, and when the Stockholm system was extended, than when the charges were first introduced, a likely reason being that the most price-sensitive traffic was already priced off-the road at the introduction.

According to the standard textbook analysis, drivers as a group will be worse off with congestion charging if not compensated by revenues. This result is confirmed by an analysis of the Stockholm congestion charging scheme using a static model with homogenous users. However, both this static model and the standard textbook analysis omit three important factors: taste heterogeneity, effects of charges on the larger network arising from less blocking back of upstream links and behavioural adjustments in the temporal dimensions. Taking account of these factors, using a dynamic model with heterogeneous users in a large-scale network, we find that drivers as a group benefit directly from the charging scheme in Stockholm. This paper investigates the importance of the three factors omitted in the standard textbook and the static model analysis in the Stockholm case, finding that all three add significantly to the benefit of the charges.

According to the standard textbook analysis, drivers as a group will be worse off with congestion charging if not compensated by revenues. This result is confirmed by an analysis of the Stockholm congestion charging scheme using a static model with homogenous users. However, both this static model and the standard textbook analysis omit three important factors: taste heterogeneity, effects of charges on the larger network arising from less blocking back of upstream links and behavioural adjustments in the temporal dimensions. Taking account of these factors, using a dynamic model with heterogeneous users in a large-scale network, we find that drivers as a group benefit directly from the charging scheme in Stockholm. This paper investigates the importance of the three factors omitted in the standard textbook and the static model analysis in the Stockholm case, finding that all three add significantly to the benefit of the charges.

According to the standard textbook analysis, drivers as a group will be worse off with congestion charging if not compensated by revenues. This result is confirmed by an analysis of the Stockholm congestion charging scheme using a static model with homogenous users. However, both this static model and the standard textbook analysis omit three important factors: taste heterogeneity, effects of charges on the larger network arising from less blocking back of upstream links and behavioural adjustments in the temporal dimensions. Taking account of these factors, using a dynamic model with heterogeneous users in a large-scale network, we find that drivers as a group benefit directly from the charging scheme in Stockholm. This paper investigates the importance of the three factors omitted in the standard textbook and the static model analysis in the Stockholm case, finding that all three add significantly to the benefit of the charges.

The benefit, in terms of social surplus, from introducing congestion charging schemes in urban networks is depending on the design of the charging scheme. The literature on optimal design of congestion pricing schemes is to a large extent based on static traffic assignment, which is known for its deficiency in correctly predict travel times in networks with severe congestion. Dynamic traffic assignment can better predict travel times in a road network, but are more computational expensive. Thus, previously developed methods for the static case cannot be applied straightforward. Surrogate-based optimization is commonly used for optimization problems with expensive-to-evaluate objective functions. In this paper, we evaluate the performance of a surrogate-based optimization method, when the number of pricing schemes, which we can afford to evaluate (because of the computational time), are limited to between 20 and 40. A static traffic assignment model of Stockholm is used for evaluating a large number of different configurations of the surrogate-based optimization method. Final evaluation is performed with the dynamic traffic assignment tool VisumDUE, coupled with the demand model Regent, for a Stockholm network including 1240 demand zones and 17000 links. Our results show that the surrogate-based optimization method can indeed be used for designing a congestion charging scheme, which return a high social surplus.

Omstridd skatt fyller sex. Trängselskattens påverkan på trafiken är till och med större i dag än när den infördes. Att många ändå upplever att köerna blivit längre beror på flera stora byggprojekt som påverkar kapaciteten på vägarna. Trängselskatten bör därför bli mer flexibel och anpassas efter vägarbeten, årstider etc. Essingeleden bör också snarast avgiftsbeläggas. Det skulle enkelt minska trafiken där med 13 procent och göra Stockholm effektivare, renare och trevligare, skriver fyra transportforskare.

Driving automation technology is attractive for the road freight transport sector since driverless trucks (DL-trucks) may drastically reduce driver costs, increase truck utilization and improve road safety. Although DL-trucks may bring significant impacts to the transport system, research on the future diffusion and impacts of DL-trucks is scarce compared to passenger transport. In this paper the sociotechnical innovation system developing, diffusing and utilizing DL-trucks in Sweden is analyzed based on the technological innovation systems (TIS) framework. The analysis is based on 20 expert interviews with a total of 23 representatives from 16 actors in the DL-truck TIS in Sweden. The TIS analysis shows that there are significant uncertainties in the timeline, operational capabilities, infrastructure requirements and regulative landscape for a widespread DL-truck deployment. There is a general view among the interviewees that DL-trucks is an important opportunity for Swedish industry and the economy. From a transport system perspective, DL-trucks are expected to bring sustainability benefits but it remains uncertain whether these benefits will be realized and what the negative side effects might be. The development of DL-trucks is heavily influenced by incumbent firms in the truck manufacturing industry but new actors from the telecom sector, energy sector and emerging truck technology companies are entering the area and shaping the development. The current relatively rigid institutions for truck manufacturing and road freight transport will require significant alignment to adapt to DL-truck operations in areas such as laws and regulations, business models and operational practices. The value chain of road freight transport may be disrupted as some of the current key actors, for instance traditional road carriers, could become less relevant in future DL-truck value chains. A critical uncertainty is how and by which actors the setting of requirements, deployment and financing of digital infrastructure for DL-trucks will be done.

This paper presents an analysis of the potential impacts of large-scale adoption of driverless trucks on transport patterns and system costs for a national freight transport system with Sweden as a case study. The analysis is performed by extending the application domain of the Swedish national freight transport model Samgods to analyze two types of driverless truck scenarios. The first scenario represents a full adoption of driverless trucks that can operate the complete road network and thereby substitute manually driven trucks. In this scenario, road transport tonne-kilometers on Swedish territory increase by 22%, vehicle kilometers traveled by trucks increase by 35% and annual total system costs decrease by 1.7 B€ compared to a baseline scenario without driverless trucks. In the second scenario, the current fleet of manually driven trucks is complemented by driverless trucks that can operate on major roads between logistics hubs, but not in complex traffic environments like urban areas due to a limited operational design domain. This may be an initial use-case for driverless trucks operating on public roads. In this scenario, road tonne-kilometers increase by 11%, truck vehicle kilometers traveled increase by 15%, and annual total system costs decrease by 1.2 B€ compared to the baseline. For both scenarios, the impacts of driverless trucks vary significantly between commodity types and transport distances which suggests heterogeneity of benefits of automated driving between different types of freight flows. A sensitivity analysis is performed in which the costs for driverless truck operations is varied, and for the second scenario, also which parts of the road network that driverless trucks can operate are varied. This analysis indicates that the magnitude of impacts is highly dependent on the cost level of driverless trucks and that the ability for DL-trucks to perform international, cross-border transport is crucial for achieving reductions in system costs. An overarching conclusion of the study is that driverless trucks may lead to a significant increase in road transport demand due to modal shifts from rail and sea as a result of the improved cost performance of road transport. This would further strengthen the need to decarbonize road transport to meet non-negotiable climate targets. Important topics for future research include assessing potential societal costs related to driverless trucks due to infrastructure investments and negative externalities such as increasing CO2 emissions and congestion. © 2021 The Authors

Road freight transport is believed by many to be the first transport domain in which driverless (DL) vehicles will have a significant impact. However, in current literature almost no attention has been given to how the diffusion of DL trucks might occur and how it might affect the transport system. To make predictions on the market uptake and to model impacts of DL truck deployment, valid cost estimates of DL truck operations are crucial. In this paper, an analysis of costs and cost structures for DL truck operations, including indicative numerical cost estimates, is presented. The total cost of ownership for DL trucks compared with that for manually driven (MD) trucks has been analyzed for four different truck types (16-, 24-, 40-, and 64-ton trucks), for three scenarios reflecting pessimistic, intermediate, and optimistic assumptions on economic impacts of driving automation based on current literature. The results indicate that DL trucks may enable substantial cost savings compared with the MD truck baseline. In the base (intermediate) scenario, costs per 1,000 ton-kilometer decrease by 45%, 37%, 33%, and 29% for 16-, 24-, 40-, and 60-ton trucks, respectively. The findings confirm the established view in the literature that freight transport is a highly attractive area for DL vehicles because of the potential economic benefits.

Urban traffic simulation models could benefit significantly from new validation methods with potential to reduce the time-consuming calibration and validation work needed before application of the model to evaluate city infrastructure or policy implementations. Current practice is to validate simulation models locally through comparison with point flow measurements and travel times on some important routes. However, for many applications, the level of congestion in an entire area is important. During the last decade, several studies have found empirical evidence of a relation between flow and density on city district level, the existence of a so-called macroscopic fundamental diagram (MFD). This paper shows how the MFD can be used to validate results from a traffic simulation model for a city district. Furthermore, the paper shows empirical results for Stockholm, Sweden. © 2017 The Authors. Published by Elsevier B.V.

This paper uses a simulation model to compare traffic and welfare effects of changes to the charging schedule currently in use in Stockholm. In particular, a step toll is compared to its flat counterpart at two charging levels. The increments between steps are also increased in a peaked step toll scenario. Furthermore, results from simulation of the current toll ring are compared to real-world measurements in a first attempt to validate model predictions regarding impacts of a time-varying congestion charging scheme. In the model, car users have the possibility to respond to congestion charging by changing departure time, route or switch to public transport and travel times are calculated using mesoscopic traffic simulation. Validation shows that departure time choice adjustments because of congestion charging are overestimated by the model that is based on stated preference data. This warrants further research on discrepancies between stated and revealed adjustments to congestion charging.

The current step toll reaches the highest social benefit estimate in model predictions, but differences in traffic effects between the current step toll and its flat counterpart are rather small. Furthermore, results show that demand changes occur in the model to a considerably greater extent for trips with low value of time. The differences in welfare effects is for that reason large for different trip purposes, indicating the importance of accounting for heterogeneous trips when modelling effects of congestion charges

Punktlighet är en mycket viktig fråga för järnvägen för att öka passagerares och transportköpares nöjdhet och för att järnvägen ska vara ett konkurrenskraftigt färdmedel i förhållande till andra transportsätt. Att uppnå hög punktlighet är emellertid en komplex uppgift som innefattar samarbete mellan olika organisationer så som infrastrukturförvaltare och tågoperatörer. Ett sådant samarbete har byggts upp i Sverige och kallas TTT (Tillsammans för tåg i tid). TTT har delat upp arbetet för ökad punktlighet i åtta så kallade effektområden: Infrastruktur, Fordon, Obehöriga i spår, Trafik- och resursplanering, Operativ trafikering, Banarbete, Avgångstid och noder, och Från utland. I denna rapport genomförs en analys av arbetet i TTT vilken identifierar synergier mellan effektområdena samt rapporterar om aktuell status för punktlighetsarbetet inom varje effektområde. Vidare har tolv huvudindikatorer för förbättrad järnvägspunktlighet valts ut i denna rapport. Syftet med dessa indikatorer är att hjälpa TTT att analysera och förbättra punktlighetsarbetet. De utvalda indikatorerna täcker både de viktigaste frågorna från effektområdena och aspekter av särskild vikt för resenärer, såsom mycket långa förseningar och inställda tåg.

Sampers är det nationella modellsystemet för att analysera persontransporter i Sverige och används vid trafikslagsövergripande analyser (Trafikverket 2018). Sampers består av två hittills separata modeller – en för regionala resor och en för långväga resor över 100 km. Regionala och långväga modellerna har olika ingående färdmedel (cykel och gång finns inte i långväga och flyg finns inte i regionala modellen), olika zonsystem, olika ingående ärenden etcetera. Att på detta sätt ta fram separata modeller för regionala och långväga resor är vanligt och görs t. ex. även i Norge och Storbritannien. Geografiskt mindre länder så som Nederländerna och Danmark analyserar regionala och långväga resor i samma modell, men behöver då ta särskild hänsyn till skalningseffekter (Rich and Hansen 2016). I modeller så som Sampers där resor delas in i två segment – regionala och långväga – kommer frågan upp hur anslutningsresor till långväga resor bör hanteras.

Att få en bättre modellering av anslutningsresande i Sampers vore önskvärt av flera anledningar. Efter en bakgrund och motivering till varför bättre modellering av anslutningsresande behövs, fortsätter rapporten genom att i Kapitel 2 beskriva rapportens syfte. Kapitel 3 ger sedan en beskrivning av den typologi som vi utvecklat för klassificering av anslutningsrese-modeller. I Kapitel 4 redovisas resultaten från genomförd litteratursökning och Kapitel 5 avslutar med att diskutera lärdomar och överväganden inför vidareutveckling av Sampers långväga modell.

Connection trips is often an important part of long-distance travel, especially for air travel. Models of long-distance travel would therefore benefit from a more detailed representation of the connection part. In this paper it is however shown that most models of connection trips are stand-alone models not integrated with the model for main mode. Only a handful models that integrate connection trip modelling into a large-scale transport model for long-distance travel are found. The connection trip models are classified into different types using a typology developed within the paper. The typology identifies nine model types that differ in how access/egress mode choice and terminal choice are handled. The scarce literature on connection trip modelling within large-scale transport modelling systems call for more research regarding detailed representation of access/egress mode choice and terminal choice, especially regarding the trade-off between model complexity and detailed representation, as well as whether the detailed representation of connection trips should primarily be conducted within the public transport network assignment or on the demand modelling side.

Connection trips is often an important part of long-distance travel, especially for air travel. Models of long-distance travel would therefore benefit from a more detailed representation of the connection part. In this paper it is however shown that most models of connection trips are stand-alone models not integrated with the model for main mode. A handful models that integrate connection trip modelling into a large-scale transport model for long-distance travel are found and classified into different types using a typology developed within the paper. The scarce literature on connection trip modelling within large-scale systems call for more research regarding detailed representation of access/egress mode choice and terminal choice, especially regarding the trade-off between model complexity and detailed representation.

Tour generation is conventionally modelled separately according to tour purpose. Tours with different purposes are, however, in reality not generated independently of each other. For example, few travellers conduct more than three tours per day. In this paper, the conventional tour generation model is extended into estimation of a model that takes travellers’ daily tour patterns into account. Results show that access to a car and a drivers’ licence, having a job and presence of children in the household increase the probability of making many tours in one day. Furthermore, results show that accessibility is an important factor for the generation of non-mandatory tours, that weekends and holiday seasons are important determinants of when tour purposes are generated, that high income increases the probability of conducting business tours as well as tour patterns that include expensive activities, and that high income reduces the probability of conducting inexpensive activities such as visiting friends and family. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Tour generation is conventionally modelled separately per tour purpose. Tours with different purposes are however not generated independently of each other in reality. For example, few travellers conduct more than three tours per day. In this paper, the conventional tour generation model is extended into estimation of a model that takes travellers’ daily tour pattern into account. Results show that access to car and drivers’ licence, having a job and presence of children in the household increase the probability of making many tours in one day. Furthermore, results show that accessibility is an important factor for generation of non-mandatory tours, that weekend and holiday season are important determinants of when tour purposes are generated, that high income increases the probability of conducting business tours as well as tour patterns that include expensive activities and that high income reduces the probability of conducting cheap activities such as visiting friends and family.

Denna rapport har tagits fram som slutrapportering av projektet ”Sampers4 -fortsättning” finansierat av Trafikverket, som genomförts under hösten 2017 och våren 2018. Detta projekt har tagit vid där tidigare omskattningsprojekt för Sampers regionala efterfrågemodeller slutat. De största förändringarna och förbättringarna i denna senaste omskattning är:

• att även de som inte angivit någon inkomst i RVU har fått en inkomst påkodad,
• att hushållsvariabler kunnat inkluderas i skattningen på grund av framsteg inom framtagning av syntetisk befolkning med hushållsdata som kan användas vid implementering,
• att konsistensen mellan utbud och efterfrågan förbättrats genom att impedansen från kollektivtrafikutläggningen använts i skattning av efterfrågemodeller för färdmedelsval.

Projektet ”Sampers4 –fortsättning” har avgränsats till att gälla skattning av regionala efterfrågemodeller. Implementering av modellerna har påbörjats i tidigare omskattningsprojekt, men har inte ingått i detta projekt. Vi inser att mycket av vårt arbete i detta projekt inte kunnat göras utan det arbete som gjorts i tidigare omskattningsprojekt.

The charging systems in Sweden show that congestion charges can be an efficient (socio-economically beneficial) and effective policy measure for combating urban congestion. Furthermore, the technology of the Swedish charging systems has proven to work well, with high accuracy of correctly identified vehicles using the video technique with ANPR. The case of Gothenburg demonstrates this measure is not only less efficient if initial congestion levels are low, but also less efficient in the long run: the effects are declining in the long run. In Stockholm, the effects have increased over the years. The difference between the cities in this respect could be a result of the lower density city structure and high car dependence in Gothenburg. From this perspective, congestion charges are likely most successful in cities where congestion levels are high and where there exist good alternatives to driving.

We explain the rationale for congestion charges in congested cities. We review the existing congestion charging systems in the world and their design. We show that congestion charges have proven to be efficient in reducing congestion in the long run. Hence, it works. However, we also show that congestion reduction cannot be taken for granted, it demands a good design of the system. A poor system design can easily deteriorate the traffic situation, by causing second-best problems. To develop an efficient system design, using a good, calibrated state-of-practice transport model is key. We end the chapter by discussing why are congestion charges so rare in the world. This has mainly to do with difficulties of building long-term public and political support. These depend to a large extent on the power over the revenues.

Development of major shopping centres continues even though online shopping is increasing. This has implications for mode and destination choice for shopping travel and therefore also for sustainability, which need to be considered in planning policy. In this paper, we estimate models for shopping travel using an unusually rich data set of shopping attractions. We find that shopping travel is best represented in three separate models: consumables in short and long activity segments and durables. In all of these models, we show that representing nearby attractions outside the destination zone adds to the measured attraction. For long activity consumables and for durables, the addition of secondary attractions within 2 km of the main destination gives the best models. For short activity consumables, both 2 km and 5 km add to the model, but 5 km is slightly better. Furthermore, we find significant within-zone correlation in the consumables models but are unable to find significant between-zone correlation, indicating that zone boundaries have some behavioural meaning for shopping travellers, but larger areas are not viewed in this way. Shopping attractions with a specifically Swedish impact, Systembolaget (official alcohol outlet in Sweden) and IKEA, proved to be important in all the models. These attractors work better as part of the size than as part of the utility, indicating that they appear to be separate attractors of trips, rather than as adding to the utility of other attractors. The models are also applied in two policy scenario analyses in which the impacts of new IKEA establishments and availability of Systembolaget in all zones on destination and mode choice are assessed.

In this paper we show that travel cost variation for long-distance travel is often substantial, even within a given mode, and we discuss why it is likely to increase further in the future. Thus, the current praxis in large-scale models to set one single travel cost for a combination of origin, destination, mode, and purpose, has potential for improvement. To tackle this issue, we develop ways of accounting for cost variation in model estimation and forecasting. For public transport, two methods are developed, where the first method focuses on improving the average fare, whereas the second method incorporates a submodel for choice of fare alternative within a demand model structure. Only the second method is consistent with random utility theory. For car, cost variation is related to long run decisions such as car type choice and employment location. Handling car cost variation therefore implies considering car type choice and workplace choice rather than different options related to a specific trip. These long-term choices can be considered using a car fleet model.

In this paper, we show that cost variation for long-distance travel is often substantial and we discuss why it is likely to increase further in the future. Thus, the current practice in large-scale models, to set one single travel cost for a combination of origin, destination, mode, and purpose, has potential for improvement. To tackle this issue, we develop ways of accounting for cost variation in model estimation and forecasting. For public transport, two approaches are proposed. The first method focusses on improving the average fare, whereas the second approach incorporates a submodel for choice of fare alternative within a demand model structure. Only the second method is consistent with random utility theory. For car, cost variation is related to long run decisions such as car type choice. Handling car cost variation therefore implies considering car type choice. This long-term choice can be considered using a car fleet model. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Road traffic congestion is an increasing problem in urban areas. Building new roads often attracts latent demand and turns parts of the city into building sites for several years. Policy measures that stimulate more effective use of the existing network, such as variable road pricing, are therefore becoming increasingly popular among policy makers and citizens. These measures are often aimed at changing the temporal distribution of traffic. Yet transportation models taking departure time choice into account are rare. This paper describes the implementation of an urban transportation application for Stockholm, which includes departure time choice, mode choice and time dependent network assignment. Through iterations between demand and supply the objective of the transportation model is to forecast effects of congestion charges, intelligent transport systems and infrastructure investments on departure time choice. The complexity of large-scale departure time choice modelling and dynamic traffic assignment is high, which results in very long run times. Therefore, research on how to increase model efficiency is needed. This paper describes choices made in the implementation for a more efficient model.

In order to reliably predict and assess effects of congestion charges and other congestion mitigating measures, a transportation model including dynamic assignment and departure time choice is important.

This paper presents a transport model that incorporates departure time choice for analysis of road users’ temporal adjustments and uses a mesoscopic traffic simulation model to capture the dynamic nature of congestion.Departure time choice modelling relies heavily on car users’ preferred times of travel and without knowledge of these no meaningful conclusions can be drawn from application of the model.

This paper shows how preferred times of travel can be consistently derived from field observations and conditional probabilities of departure times using a reverse engineering approach. It is also shown how aggregation of origin–destination pairs with similar preferred departure time profiles can solve the problem of negative solutions resulting from the reverse engineering equation. The method is shown to work well for large-scale applications and results are given for the network of Stockholm.

Stockholmsförsöket med trängselavgifter genomfördes våren 2006 och i augusti 2007 blev trängselavgifterna permanenta. När avgifterna blev permanenta skedde en del ändringar av systemet (t ex. att taxibilar inte var undantagna längre), men betalstationernas placering, avgiftens belopp och tiderna då de olika beloppen tas ut förblev desamma. Eftersom trängselavgiften i Stockholm är en skatt som regleras i svensk lag, måste ändringar av avgiftssystemen antas av riksdagen. Förändringar kan därmed inte ske särskilt ofta. Det är därför extra viktigt att använda trafikmodeller för att simulera effekter av olika avgiftssystem innan en ändring sker.

This paper analyses the trade-off between equity and efficiency in the design of the Stockholm congestion charging systems. Comparing different designs for Stockholm, the paper shows that the most efficient system is the least equitable. Indeed, we show that moving towards a more efficient system design favours high-income-users most. The reason is the uneven distribution of workplaces and residential areas, combined with richer socio-economic groups living in areas with more workplaces. Hence, the conflict between efficiency and equity of this policy arises from the spatial mismatch of residential areas and locations of employment, and the spatial separation between low-income and high-income groups that characterise most cities. This paper shows that these spatial patterns have a large effect on the distribution effects of the congestion charges and that the system design can have a major impact on equity.

This paper extends previous research by developing future scenarios for self-driving vehicles and their societal impacts in freight transport using Sweden as a case study. Freight experts from vehicle manufacturers, agencies, universities and hauliers were recruited for a workshop where they assessed the benefits, costs, possibilities and barriers for self-driving vehicles in freight transport. The paper shows that reduction in driver and vehicle costs, reduced number of incidents and more fuel-efficient driving are seen as the main benefits of self-driving vehicles in freight transport, and increased vehicle costs, lost jobs, reduced degree of filling and more transport as the main costs. Furthermore, reduced drivers' costs, more hours-of-service and new business models are identified as the main drivers of the development and traffic management, small hauliers, loading and unloading and cross-border transport as the main barriers. The paper also integrates the description of possible developments of self-driving vehicles in freight transport into the four future scenarios developed for passenger transport in Sweden.

Utvecklingen inom tekniken för självkörande fordon går snabbt och många fordonstillverkare (GM, Ford, Toyota, BMW, Audi, VW med flera) anger att de kommer lansera ett fullt ut självkörande fordon på marknaden kring år 2020. Även om teknikutvecklingen har gått och kommer gå snabbt de närmaste åren finns stora frågetecken kvar kring hur de självkörande fordonen kommer tas emot av samhället, var de kommer få köra, om de kommer användas främst som privata eller delade fordon, hur trafik-, integritets- och cyber-säkra de kommer vara och upplevas som av användarna, och i vilken utsträckning de kommer påverka accepterad pendlingstid, färdmedelsval och inducerat bilresande.

Samtidigt påverkas de långsiktiga samhällsnyttorna med självkörande fordon inte främst av teknologiska framsteg utan mestadels av vilken roll de självkörande fordonen kommer få i vårt samhälle, det vill säga vilka effekter de får på trafiksystemet och samhällsplaneringen i stort. Det är därför viktigt att tidigt uppskatta möjliga framtidsscenarier för självkörande fordon. Utifrån dessa scenarier kan man sedan föra en diskussion kring hur regler och styrmedel bör användas för att största möjliga samhällsnytta ska uppnås.

Detta notat beskriver det arbete med framtidsscenarier för självkörande fordon som gjorts under vintern 2016/2017. En analysgrupp på fem personer har, med stöd av en expertgrupp för persontransporter som samlats för tre heldagsworkshops, arbetat fram både en säker utveckling mot 2030 och två osäkra axlar som lett fram till fyra möjliga scenarier för framtiden med självkörande fordon i Sverige. Med medverkan från 40 experter från 23 organisationer inom transportområdet är denna studie unik jämfört med tidigare scenario-arbeten kring utvecklingen för självkörande fordon, vilka byggt antingen på litteraturstudier eller expertworkshops med ett fåtal forskare.

This study presents a transport model to better model cycling demand. The model improves modelling of cycling in several ways compared to a conventional transport model. First, it uses a detailed bicycle network containing information about existing bicycle infrastructure. Second, generalised cost measures based on different bicycle route choice models are calculated and compared to evaluate how to best represent the impact of bicycle infrastructure in the model. Third, the model utilizes a refined zone system with smaller zones of size 250 m × 250 m. Using these smaller zones, more short-distance tours are included in the model, and these are predominantly walking and cycling trips. Fourth, the model considers cycling also as an access mode choice to public transport. Therefore, the model treats cycling and public transport as both competing and complementary modes. Results show that the model captures detailed individual heterogeneity in cycling demand for different trip purposes. Impacts of bicycle infrastructure, land use characteristics and individual/household socio-demographics are investigated. Detailed individual level travel time and generalised cost are derived for cyclists of different socio-demographics. The result highlights the importance of choosing a good measure of generalised cost, given that different bicycle route choice models result in different effects of bicycle infrastructure. In future applications, the model can be used to evaluate proposed bicycle investments regarding their impact on link flow, bicycle route choice, modal shift and generation of completely new tours. The model can also be a powerful tool in a cost-benefit analysis of bicycle investments.

Cost-benefit analysis (CBA) for cycling infrastructure investments are less sophistically developed compared to the ones for private cars and public transport, and one of main reasons is the lack of “well-developed” transport models for cycling. In this study, a dedicated transport model for cycling is used to appraise cycling infrastructure investments in Stockholm, Sweden. The model captures the impact of a change in cycling infrastructure on cycling route choice, mode choice, destination choice and trip generation and calculates cycling flow on link level. the generalised cost measure defined in the route choice model captures the impact of cycling infrastructure. Results suggest that although cycling flow on the links with investment may increase substantially, only a small share comes from modal shift and thus the external effects such as reducing car congestion and emissions are marginal. For all three scenarios investigated, over 97% of the benefits measured in the unit of generalised cost belong to the existing cyclists. The route choice model does not minimize travel time but generalised cost which also measures health, safety benefits and other possible benefits that may be considered by the cyclists when they choose to cycle. In fact, travel time saving benefits of the investments evaluated in this paper are all negative. The existing effect evaluation models therefore need to be adjusted to be more consistent with the transport model.