National Transport Model Research Articles

The climate and environmental effects of policies for moving freight transport from road to other modes: The case of Sweden

The objective of this paper is to determine how policy instruments which aim to achieve a modal shift of long-haul freight transport from road to rail or sea affect the direct emissions to air of greenhouse gases, nitrogen oxides, sulfur dioxides, volatile organic compounds, and particulate matter. The analysis is conducted in two stages. First, a range of forecasts reflecting different assumptions are applied using the Swedish national freight transport model (SAMGODS) to derive a range of possible future developments of emissions levels up to 2030 and 2040. This has involved determining emission factors per tonne-km for each of the SAMGODS model's six road freight vehicle types, eleven freight train variants and 22 ship types. The model outcomes are then compared to those of the base year of 2017. Second, the effects of two hypothetical modal shift policy instruments are analyzed with respect to their potential impact on emissions to air. The effects of these two policy instruments are evaluated for the base-year of 2017 and for the 2030 and 2040 forecasts. The paper also analyzes whether the Swedish climate objective for domestic transport in 2030 can be expected to be fulfilled given different forecasts and policy instruments.Within the context of a predicted large increase in total freight tonne-km (between 31 and 53%), emissions of greenhouse gases are calculated to decrease by 50 to 60% by 2040. This means that the Swedish freight transport sector will not achieve its share of greenhouse gas emissions reductions necessary to attain the national climate objective of a 70% reduction by 2030. Emissions of nitrogen oxides (NOx) are forecast to reduce by between 60 and 75%, emissions of sulfur dioxides to reduce by between 41 and 50%, emissions of volatile organic compounds to increase by 8 to 30% and emissions of particulate matter (from exhaust and tyre/road wear) are calculated to increase by between 13 and 33%. Using modal shift policy instruments to achieve greenhouse gas reductions is calculated to attain worse results over time, by 2040 it might even be counterproductive.

Use of comprehensive datasets to estimate the Slovak National Transportation Model

Developing accurate and reliable transportation models capable of simulating real traffic behaviour is essential to making sound engineering and investment decisions. However, the construction of transport models is typically data- and time-intensive, opaque, and the results are not reproducible. This paper describes the development of the passenger component of the new macroscopic four-step National Transport Model (NTM) for Slovakia. Efforts were made to limit the number of dimensions of the model as far as possible, beginning with one demand stratum and increasing complexity only with improved model fit. Traditional data sets, such as national registries, censuses, and travel use surveys, were compared to novel data, such as public transport ticket sales, mobile phone GPS signals (call detail records), and open-access data sets for cross-border travel. Finally, validation against several existing models was conducted. The result is a passenger traffic model with 25 demand strata, 109 domestic zones, and 68 international zones. This is a significant simplification relative to the previous National Model. The aim of the new NTM and this paper is to increase transparency and reproducibility of transport models.

A stochastic logistics model for Indonesia's national freight transport model: Transport chain choice from the shipper perspective

This paper presents research towards the development of a stochastic approach for estimating the transport chain choice for domestic shipments in Indonesia. This stochastic model aims to improve the logistics choices within Indonesia's national freight transport model (INTRAMOD), which currently handles such choice deterministically. The INTRAMOD logistics model presents five distinct transport chain possibilities involving four main modes: truck, rail, ship, and plane. To acquire the necessary data, revealed preference (RP) and stated preference (SP) survey work has been undertaken. Using the obtained RP/SP data, multinomial logit (MNL) models have been used to estimate the transport choice model. The model with a single time coefficient was found to be superior to the other models. Additionally, this preferred MNL model was extended by segmenting according to shipment characteristics, particularly for high and low value of goods. The results indicate that shipments with a high value of goods are more sensitive to transport time.

The cost impacts of Fit for 55 on shipping and their implications for Swedish freight transport

This paper analyzes the European Commission’s (EC) climate policy package ‘Fit for 55′ and its potential impact on cargo ships calling at Swedish ports. Three scenarios are evaluated using the Swedish national freight transport model. The Main Scenario is based on the EC’sproposal to i) include ships weighing at least 5,000 gross tons (GT) in the European Emissions Trading System (EU ETS), and to ii) introduce a tax on marine fuel. The Low Scenario includes the EC’s EU ETS proposal but assumes no tax on marine fuel, whilst the High Scenario includes the fuel tax but assumes that ships weighing at least 400 GT are included in the EU ETS. Results show that the EC’s EU ETS proposal excludes 44% of the cargo ships and 22% of the CO2 emissions, and even more so given the use of smaller ships to avoid the EU ETS, as indicated by the freight transport model. The High Scenario implies shifts to larger ships since the increase in fuel costs provides incentives to make useeconomies of scale. Shifts to other transport modes are estimated to be marginal. A policy conclusion is therefore to include smaller ships than 5,000 GT in the EU ETS to further internalize the CO2 costs from cargo ships.

Optimal kilometre tax for electric vehicles

We approximate the spatial and temporal distribution of the Pigouvian kilometre tax for road traffic in the most urbanized part of Sweden, with four million inhabitants and a similar “degree of urbanization” to the Netherlands and the UK, in a future scenario where most vehicles are electric. We apply the national transport model and include all links and four time-of-day periods. We find that roughly half of the vehicle kilometres travelled in Mälardalen has a marginal external cost (congestion and other external costs included) below 0.04 €/km which is below the fuel tax in 2019). The mean marginal external cost is higher, at 0.09 €/km. Our focus is not the exact numbers but the magnitudes and the vast variation across links in a country-like region: 90 percent of the revenue is collected on 10 percent of the road network. Hence, a nation-wide kilometre tax, implying high enforcement cost, is likely not the best option. Instead, the marginal external cost could probably be internalized fairly accurate by a congestion tax in the big cities in combination with for instance an ownership tax. We find that the Pigouvian tax would cover the public costs for our target road system. We relate our findings to the mainstem fiscal tax literature.

Analysis in the field of volume-delay function research

One of the key parts of the travel demand model is the correct determination of the performance of the road system. Performance is partially determined by the correct choice of volume-delay functions (VDFs) and their input parameters. Worldwide, transport modellers have a wide range of VDFs to choose from, but research is needed to select the one that provides the most accurate estimates for specific conditions. The problem is that road conditions and traffic conditions are specific to each country, so assuming that we can use VDFs and their parameters based on traffic data from another country can lead to errors. For this reason, there is a need for research on VDFs in the context of a particular country's traffic conditions.The main objective of this paper is to provide knowledge and practices that can be used as a basis for future research. This paper discusses the most commonly used VDFs for links, their parameterisation and the types of data required for calibration, and approaches to their collection. This paper also aims to introduce crowdsourced real-time data as a possible new source of traffic data, with a focus on traffic data from the Google Maps Directions API. The results of this paper will be used in future research to select and parameterise the best-fitting VDFs for specific types of road facilities and traffic conditions within the national transport model of the Slovak Republic.

Implementing a Ride-sharing Algorithm in the German National Transport Model (DEMO)

As macroscopic travel demand models are widespread among regional modelers, it is crucial to develop extensions for new mobility services such as ride-sharing services to assess their effects on the transport system. In a feasible approach that was introduced with previous research, origin-destination network-based paths are transformed to a series of crossed traffic analysis zones (TAZs). These series are compared to find overlapping trajectories to derive ride-sharing potential. However, in national transport models the TAZ sizes are larger than in regional models, which can lead to unrealistically high matching rates because of imprecise path trajectories. This paper presents a modified approach for implementation in the German national transport model, DEMO. The main adjustment is to use population centers as pick-up locations instead of TAZs. Each TAZ includes multiple points. This way, the trajectories of paths can be better differentiated. Another advantage of this method is that intrazonal trips can be distributed between the centers and thus be included in the algorithm. With the successful implementation in DEMO, the effects of new mobility tools in German cities can be evaluated within the same model.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data.

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Impacts of large-scale driverless truck adoption on the freight transport system

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.

The implications of ambitious decarbonisation of heat and road transport for Britain’s net zero carbon energy systems

Decarbonisation of heating and road transport are regarded as necessary but very challenging steps on the pathway to net zero carbon emissions. Assessing the most efficient routes to decarbonise these sectors requires an integrated view of energy and road transport systems. Here we describe how a national gas and electricity transmission network model was extended to represent multiple local energy systems and coupled with a national energy demand and road transport model. The integrated models were applied to assess a range of technologies and policies for heating and transport where the UK’s 2050 net zero carbon emissions target is met. Overall, annual primary energy use is projected to reduce by between 25% and 50% by 2050 compared to 2015, due to ambitious efficiency improvements within homes and vehicles. However, both annual and peak electricity demands in 2050 are more than double compared with 2015. Managed electric vehicle charging could save 14TWh/year in gas-fired power generation at peak times, and associated emissions, whilst vehicle-to-grid services could provide 10GW of electricity supply during peak hours. Together, managed vehicle charging, and vehicle-to-grid supplies could result in a 16% reduction in total annual energy costs. The provision of fast public charging facilities could reduce peak electricity demand by 17GW and save an estimated £650 million annually. Although using hydrogen for heating and transport spreads the hydrogen network costs between homeowners and motorists, it is still estimated to be more costly overall compared to an all-electric scenario. Bio-energy electricity generation plants with carbon capture and storage are required to drive overall energy system emissions to net zero, utilisation of which is lowest when heating is electrified, and road transport consists of a mix of electric and hydrogen fuel-cell vehicles. The analysis demonstrates the need for an integrated systems approach to energy and transport policies and for coordination between national and local governments.

Impacts of Large-Scale Driverless Truck Adoption on the Freight Transport System

Driverless trucks could potentially lead to a significant reduction in road freight transport costs and thereby change the freight transport system. In this paper, a method to study the impacts of large-scale adoption of driverless trucks on transport systems is presented and applied to the Swedish freight transport system. An analysis of the impacts on transport patterns and system costs, considering all transport modes, is performed. The method is based on extending the application domain of the Swedish national freight transport model Samgods to two types of driverless truck scenarios. The first scenario represents a full adoption of driverless trucks that can operate the complete road network. The results show that there is an increase in road transport tonne-kilometers on Swedish territory by 22% and vehicle kilometers traveled by trucks increase by 35%, compared to a baseline scenario without driverless trucks. The annual total transport system costs decrease by 1.7 B€. In the second scenario, the current fleet of manually driven trucks is complemented by driverless trucks that can operate 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 increase by 15%, and annual total transport system costs decrease by 1.2 B€ compared to the baseline. For both scenarios, the impacts of driverless trucks vary significantly across commodity types and transport distances which suggest heterogeneity of benefits of automated driving between different types of freight flows. A sensitivity analysis is performed for the level of cost reduction of driverless trucks compared to manually driven trucks for both scenarios. For the second scenario, also which sections of the road network that driverless trucks can operate are varied. The magnitude of system impacts are for both scenarios highly dependent on the cost level of driverless trucks and it is crucial to enable driverless trucks for international, cross-border transport to achieve economic benefits on a system-level. Driverless trucks may also have significant societal costs due to potential infrastructure investments and from negative externalities such as increasing CO2 emissions and congestion. These are important topics for future research. Finally, the even more road-dominant transport system resulting from an introduction of driverless trucks strengthens the need to decarbonize road transport to meet non-negotiable climate targets.

Incorporating Passenger Load in Public Transport Systems and its Implementation in Nationwide Models

Abstract Reducing CO2 emissions is crucial to climate change mitigation, but there has been little progress made in the transport sector. Public transport could make a significant contribution, which would require substantial changes in travel demand. In various national transport models proportionally increasing capacities have been assumed; this must be scrutinized in the light of urgency, long-term planning and construction processes and limited financial resources. This paper presents an approach that addresses previous obstacles to the integration of unchanged capacities, namely the lack of a detailed public transport service model and rapidly increasing computation times even in small networks. Based on widely available data on public transport supply and the consideration of previous studies on the relationship between variable vehicle load and the resulting costs for passengers, an extension for a nationwide transport model is developed. In two scenarios – a substantial reduction in local public transport fares and the introduction of a charge for motorized private transport – it is shown that significantly different demand effects occur when passenger load is considered.

Impact of political measures on passenger and freight transport demand in Germany

In this paper we present the findings from the “Transport and Environment” (VEU) project related to the development of transport demand in Germany in three scenarios up to the year 2040. The results were obtained using the German national transport model DEMO, a complex model landscape covering all areas of transport (both passenger and freight), trip purposes and modes. The main findings are expressed in terms of number of trips, mode shares and distance travelled by mode.The results show that it is possible to push the transport sector toward a more sustainable state if comprehensive policy measures are established to reduce road transport and make other modes of transport more attractive. In the Regulated Shift scenario, the distance travelled on the road is 20% lower for passenger transport and 4% lower for freight transport compared to the Reference scenario. However, our results also indicate that road transport will still be the backbone of the future transport system in all scenarios, meaning that the mitigation of the environmental effects is only possible in combination with technological developments of road-based transport systems.

VMÖ – A new strategic transport model for Austria

For the preparation of a new traffic forecast for several time horizons up to the year 2040 and beyond (Verkehrsprognose Österreich, VPÖ 2040+) an up-to-date transport model is necessary. Currently this new national transport model Austria (Verkehrsmodell Österreich, VMÖ) is developed.The passenger model will be disaggregated tour-based model with 5 basic steps and some extensions for special applications, like for instance tourist traffic. The number of zones will be approximately 6000. In the model a special focus is on incorporating recent trends in mode choice like “park and ride” and other multimodal chains. The freight part of the model will be an Aggregated - Disaggregated - Aggregated (ADA) model with three steps: (1) the results of an input-output-model are transformed into firm-to-firm flows, (2) the choice of shipment size and transport chain is modelled and (3) the OD relations are aggregated for the individual modes and assignment to the networks.For individual transport with passenger cars and road freight, a quasi-dynamic road transport assignment will be developed. Public transport assignment is based on timetables. For the forecasts of travel demand for future years a pivot-point approach (with the changes) on the base matrices will be applied.

It was twenty years ago today: revisiting time-of-day choice in The Netherlands

Time-of-day (TOD) choice can be considered as a fifth stage in the modelling of transport behaviour, additional to the conventional four stages. Twenty years ago in The Netherlands, a stated preference (SP) study was designed for investigating the choice of time-of-day (departure time) and transport mode. A nested logit time period and mode choice model, largely based on this SP data set, was included as one of the components of The Netherlands national transport model (LMS). A new TOD SP survey has now been developed to obtain up-to-date information for the next re-estimation round of the LMS. The fieldwork was carried out in in 2019, followed by the re-estimation of the nested logit model of period and mode choice on the new SP data. The context for the SP is that of a tour (round trip) carried out by the respondent as car driver or by train, also distinguishing by travel purpose (commuting, business, education and other). This means that we are asking questions both about the outward leg of the tour and the inward leg. Both car drivers and train users are asked to participate in two SP experiments on TOD and mode choice: the first focussing on the trade-off between congestion or crowding and the departure/arrival times; the second also with differentiation in costs between peak and off-peak. Our tentative conclusion is that TOD choice seems to have become (relatively to mode choice) more flexible in the past two decades, in line with the trends towards more flexibility in scheduling activities over the day and a 24 hours economy. Moreover, we now estimate nest coefficients for both car drivers and train users (until now the assumption that had to be made in the LMS was that the nest coefficients for train followed those for car).

COMPARATIVE ANALYSIS OF METHODOLOGY ROAD TRANSPORT STATISTICS OF EUROSTAT AND UKRAINE

Methodological approaches to European Union statistical observations of road transport industry are described and comparative analysis of the methodology of statistical observations in Ukraine are performed. The significance of statistical analysis and research of publications on the results of statistical observations for the economy of the country, business environment, scientists and other users is established. The importance database for the National Transport Model is discussed. It is a powerful tool for analyzing, forecasting, feasibility studies of investment projects. The main indicators of statistical reporting for road transport in Ukraine are analyzed. The gaps in the statistical data road transport have been identified. The table provides a clear demonstration of the comparison of Eurostat and Ukraine’s road transport methodology by type of indicators. Differences in methodology with Eurostat in reporting areas were identified: rolling stock; enterprises, economic indicators and employment; road traffic; statistics of passenger road transport; freight road transport; national road freight transport; international road freight transport; cabotage by road. Conclusions and suggestions on converting positions and methodology of national statistics to Eurostat methodology have been provided. It is proposed to create a single electronic platform, accumulating all the various statistics on: carriers, routes, volumes and destinations of transportation, transport work, rolling stock, runs, fuel costs, financial results of activity, investment activity, cross-border traffic, export-import, road accidents, etc. that will be accessible to all potential users of statistics. Keywords: statistics, data base, methodology, reporting forms, sample surveys, road transport.

The relationship between accessibility and price – The case of Swedish food stores

Food accessibility has been an important issue for as long as there has been human civilisation. Today, food, at least in the wealthier parts of the world, is accessible to most people. However, there are concerns regarding the market power of major retailers and the availability of affordable food among certain population groups. In this paper, I investigate how well accessibility, the ease with which individuals can reach various destinations, explains food prices in Swedish food stores. I have done this by combining price information on a large number of Swedish food stores from 2010 to 2014 with a state-of-the-art accessibility measure from the Swedish national transport model. I have found a quadratic relationship between accessibility and price, in which prices in the least and most accessible parts of Sweden are 2–3 per cent higher than in the least expensive parts, which are towns and villages around larger cities. The relationship remains significant when adding controls and accessibility is better at explaining price than measures of market structure previously used in the literature.

How to achieve less emissions from freight transport in Sweden

Purpose For the case of Sweden, this paper aims to determine how a range of different infrastructure fees and taxes influences modal split, port throughputs, air emissions, societal costs of greenhouse gas (GHG) emissions and air pollution, as well as logistics costs. Design/methodology/approach The Swedish national freight model is used to simulate a range of different proposed infrastructure fees, one by one and in combination. The volume of emissions of CO2-equivalents, NOx, SOx and PM under the different scenarios is calculated in both volume and monetary terms, by applying national emission factors and EU values for external costs. Findings Road user fees are calculated to have the largest impact on the modal split, GHG emissions and air pollution. The impact increases slightly when road user fees are combined with higher fees for sea and rail and/or gate fees in all Swedish ports. The imposition of gate fees over €30 per truck in all ports leads to shifts in cargo to land-based modes and to ports outside Sweden. The logistics costs in Sweden are found to be three to ten times higher than the benefits of reduced GHG emissions and air pollution, although other benefits to society need to be considered as well. Research limitations/implications Methods which attempt to evaluate alternative approaches to the internalisation of the external costs caused by transport need to be further developed. In particular, they need to encompass a more holistic perspective on “benefits to society”, other than merely reductions in GHG emissions and air pollution. To facilitate international acceptance and adoption, such methods require agreements to be reached on common definitions and routines. Practical implications The results can be used as basis for policy-making. They illustrate the environmental impacts of the fees and taxes one by one and in combination and to what extent these reinforce each other and should be co-ordinated. Social implications The outcomes are relevant to national and international policymakers and authorities, as well as port authorities, shippers and transport companies who need to determine unilateral strategies on how to reduce GHG emissions and air pollution, without undermining their wider business objectives. Originality/value The approach is original in facilitating the testing of policies which impact on the transport system and the environment across different dimensions. The work has additional value in informing policy because of its use of Sweden’s national freight transport model.

Predict or prophesy? Issues and trade-offs in modelling long-term transport infrastructure demand and capacity

Effective planning and investment for transport infrastructure systems is seen as key for economic development in both advanced and developing economies. However, planning for such strategic transport investments is fraught with difficulties, due to their high costs and public profile, long asset life, and uncertainty over future transport demand patterns and technologies. Given that only a finite quantity of funding is available for transport investment, it is important that this funding is spent in the right places and on the right schemes in order to ensure that the best return is obtained from limited public resources. There is therefore a need for a model which is capable of assessing network demand and performance in a wide range of possible futures, in order that robust decisions can be taken with regard to which schemes are given the go ahead. This paper discusses a range of issues associated with the development of a strategic national transport model for Great Britain as part of a wider interdependent infrastructure systems modelling framework (NISMOD). It considers the compromises which have to be made in order to develop a model which can examine a wide range of potential futures in a reasonable timescale, outlines how such futures can be captured in the model, and finally assesses the continued role of planners and policy-makers in determining both how the model is applied and how the future of transport systems might play out in reality. While the paper is based on a case study example from Great Britain, most of the general issues discussed are of relevance to transport and infrastructure policy making in almost any national or international context.

Large-scale spatial population synthesis for Denmark

The recent development in micro-based transport models is a major step towards an improved understanding of transport demand and its underlying drivers. By adapting a detailed geographical resolution level and a fine-grained social description of individuals it becomes possible to investigate distribution effects across social classes and geographical spaces, elements which were not possible to take into account until recently. However, the increasing amount of details comes at a cost. As the prediction-space is enlarged, models become increasingly dependent on the quality of inputs and exogenous model assumptions of which the formation of synthetic population forecasts is by far the most important one. The paper presents a coherent description of a large-scale population synthesis framework involving all relevant steps in the synthesis stages from target harmonisation, matrix fitting, post simulation of households and agents and reweighting of the final population. The model is implemented in the Danish National Transport Model and is aimed at predicting the entire Danish population at a very detailed spatial and social level. In the paper we offer some insight with respect to the propagation of sampling noise caused by the household simulation stage and a brief validation of the model when comparing a modelled 2015 population with observed data.