To Örebro University

The purpose of the paper is to analyze how sensitive the Swedish national freight model system Samgods is to uncertainties in its production-consumption matrices (PC-matrices). This is done by studying how sensitive outputs from one of its key component, the logistics model, are to changes in the PC-matrices. This paper is, to our knowledge, the first attempt to analyze the sensitivity and economies of scale of a national freight transport model using Monte Carlo simulation. The results indicate that the logistics model is able to find new logistics solutions when larger demand volumes are assumed. Freight volumes are calculated to shift to sea transport. If the transport volume increases with one percent, the logistics cost per tonne is on average reduced by about 0.5 percent. Part of the cost reduction comes from increased consolidation of shipments due to larger transport volumes. There is also a positive correlation between total transport demand and the load factor for heavier lorries, trains and larger ships. Without empirical data and further analysis it is difficult to assess the estimated strength of the effect. Furthermore, the analysis indicates that it might be possible to reduce runtimes by removing small transport flows from the PC-matrices without affecting aggregate results too much.

Methods: We develop on a conceptual level an estimation approach for the value of reduced transportation time variability (VTTV) related to the cargo component based on precautionary and operative delay costs. This approach is inspired by the safety stock approach but includes more general precautionary measures that firms take to avoid stock-out costs. This paper presents the analysis of a Swedish grocery company’s transports by shuttle train as a case study. First, the distribution of the arrival times of the shuttle train is analyzed in order to estimate the transportation time variability for the firm. Second, precautionary costs for measures undertaken to manage and mitigate the transportation time variability are estimated and the additional operational costs that occur in case of major delays are calculated.

Results: It is found that the 10 % worst delays contribute to more than half of the total train delays, showing that actual transportation times exhibit a heavily skewed distribution with fat tails, indicating that the standard deviation might not be a sufficient measure of transport time variability. The calculated VTTV related to the cargo component based on the precautionary costs is around €4 per delay-tonne-hour and around €2.2 per standard deviation of transportation time.

Conclusions: We show that by conducting a case study it is possible to get VTTV estimates for the cargo component valid for a specific company. In conclusion, assuming a high degree of transport market concentration with regard to shippers, a limited number of case studies for key companies in the market might be sufficient to get a representative VTTV measure. We therefore advocate further case studies and research aimed at getting more inputs from firms that send and receive goods. More research should also be done on how to incorporate risks for delays and the extremeness of empirical delays in transport models and VTTV definitions.

Purpose: There is limited up-to-date knowledge about the monetary valuation of improved reliability for freight transports. This means that the benefits of reduced variability in transport time are not properly taken into account in cost-benefit analysis relating to, for example, infrastructure investments.

There is limited knowledge about the valuation of reduced transport time variability for freight transports. This paper analyses a Swedish grocery company’s transports by shuttle train, as a case study. The distribution of the train arrival time is analyzed; it is shown that the 10 per cent worst delays contribute to more than half of the total train delays. Type and amount of the firm’s precautionary and operative costs are identified and calculated. It is shown that it is possible to get estimates for the cargo component of the VTTV (Value of Transport Time Variability) valid for the specific company based on the precautionary costs, the operative costs for delays and for the costs for cancellations separately or in combination. Further case studies are advocated in order to cover the whole freight transport market and study the differences between different segments of the market.    

We modify a method recently suggested by Weitzman (2012, 2013) for determining a risk-adjusted social discount rate (SDR) term structure consistent with both the (augmented) Ramsey rule and the consumption-based CAPM. Using this approach we estimate SDR for transportation infrastructure investments based on an analysis of correlations between transportation, split between road and rail, and between passenger travel and freight transport, and GDP in Sweden 1950–2011. We show that this can be estimated from two time-series following a random walk with drift, even if the variables are not co-integrated. Based on current estimates of the risk-free rate and the equity risk premium, we estimate the relevant SDR to be 5–6 per cent, possibly somewhat lower for investment in railroads for passenger travel, and only slowly declining within the investment horizon. This is higher than the current rates used in, for instance, Sweden, Germany and the UK.

Purpose: This paper examines the value of fuel choice flexibility derived from a flex-fuel engine.

Method: Based on the stochastic properties of fuel prices, we use Monte-Carlo simulation in order to value the option to switch fuel.

Results: Our findings indicate a considerable value of fuel choice flexibility, ranging between 7,500 and 37,800 SEK, depending on the underlying stochastic process we assume that fuel prices follow. This can be compared to the state subsidy of 10,000 SEK provided until recently for buying a flex-fuel car.

Conclusion: Compared to an environmentally friendly pure ethanol strategy, the switching strategy is considerably less costly, about 2,000-19,000 SEK depending on the assumed underlying stochastic process, a fact that is important to take into consideration with environmental policy.

Problem: In recent years a considerable number of papers have examined socioeconomic factors influencing the number and the outcome of traffic accidents. There is however more research needed to confirm the previous results in order to generalize them and a need to examine additional factors that might have an impact. Method: This paper uses both regional panel data and national time series data combined with filtering techniques to determine what factors influence the number of accidents, the accident outcome and detected drunk driving. Results: Using time series data, it is found that the number of traffic fatalities increases for both per capita and per person kilometer travelled during economic booms. This indicates that the death risk rises not only because of increased mileage or motorization during booms. Using panel data, it is found that traffic fatalities decrease with unemployment, whereas personal injuries increase on a per capita basis with youth and the number of cars. In contrast to property crimes and other types of crime, drunk driving in Sweden decreases during economic contractions. Discussion: The main policy conclusion from our results is that resources for safety measures should not be spend uniformly across time and space. Instead, safety measures should be concentrated to areas with a high share of young people and to periods with low unemployment. The results of the time series analysis suggest that factors other than increased mileage during booms contribute to the higher rate of fatalities during good times. Increased risk taking, such as drunk driving, might be an explanatory factor. Impact on Industry: The results might be interesting for safety-oriented car and truck producers as well for developers of traffic safety products, since the results indicate in what regional markets and under what market conditions their products are most needed.

This paper addresses the lack of reliability within the Swedish rail network by identifying passenger train delay distributions. Arrival delays are analyzed in detail using data provided by the Swedish Transport Administration, covering all train departures and arrivals during 2008 and 2009. The paper identifies vulnerabilities by size, space and time in the network.

Our results show that the delay distribution seems to be plagued by low probability high impact events. A major share of all delay time is associated with the tail of the delay distribution, indicating that extreme delays cannot be neglected when prioritizing between measures improving rail infrastructure. Delays are not only concentrated in size, but also concentrated in space and time and seem to follow a precise power law with respect to days and an exponential distribution with regard to stations. Moreover, we also examine the link between capacity usage and expected delay over different time scales.

This paper analyzes how freight train delays are distributed with respect to size, location and time of their occurrence. Arrival delays are analyzed in detail using data covering all freight train departures and arrivals during 2008 and 2009 in Sweden. Moreover, the link between capacity usage and expected delay is analyzed using the fact that demand fluctuates on different time scales, especially due to the economic chock in 2009. Since the distribution of delays on different scales describe reliability and vulnerability in the rail transport system, the results have potentially important policy implications for rail investment appraisal.

Unreliable rail transports are the main contributor to the transport time variability. The Swedish Transport Administration registered around 40 000 delay hours in 2012, which corresponds roughly to 20 million tonne-delay-hours, given that 500 tonnes per train is assumed to be the average load. This figure is 10 times higher than the corresponding figure for road transports. Information about the reliability of the rail transports and how different policy measures influence i.e. the number and length of delays and or the risks for delays has been limited. The former has been improved in recent years but the latter is still a problem. The lack of underlying data is one reason why the recommended Value of reduced transport time variability (VTTV) have only been applied in a few rail infrastructure projects. Nevertheless, the industry complains about the extensive problems caused by train delays or cancellations and that the reduction of these problems is not taken into account in a proper way in the cost benefit analyses (CBA). Another question that is debated is how much more high value products would be transported by rail if the rail transports would be more reliable. The value of the outgoing goods transported by road (SEK 9.5 per kg) is about three times the value of the outgoing goods transported by rail or rail in combination with other modes (SEK 3.7 per kg). The Transport Administration are aware of the shortcomings and has among others funded this pilot study that studies different approaches to calculate the VTTV for the cargo transported. The following parts were carried out in the pilot study: • Part One presents the Stated preference studies (SP-studies) to calculate VTTS (Value of Transport Time Savings) and VTTV that have been carried out recently in The Netherlands and Norway. Part One also discusses to what extent the VTTV calculated in the Dutch and Norwegian SP-studies can be transferred to Sweden. It is shown that the VTTV calculated in the Dutch and Norwegian SP-studies in principle can be transferred to Sweden. However, empirical studies that are adjust with respect to the commodity mix, the transport distances, level of congestion etc. in Sweden are needed. • In Part Two three alternative approaches to calculate VTTV for Swedish rail freight transports are demonstrated, that to the best of our knowledge have not been implemented before o Precautionary costs approach, o COOP case study, o Stock market approach. The report includes a table that summarizes VTTV for goods carried by rail, calculated using the different approaches.

Otillförlitliga järnvägstransporter bidrar mest till transporttidens variabilitet. Trafikverket registrerade 2012 cirka 40 000 förseningstimmar, vilket motsvarar ungefär 20 miljoner tontimmar försening om man antar att det fraktas 500 ton per tåg. Denna siffra är 10 gånger större än motsvarande siffra för vägtransporter. Informationen om tillförlitligheten av järnvägstransporter och hur olika åtgärder påverkar till exempel antalet av och längden på förseningar har varit begränsad. Mer och bättre information om förseningar har tagits fram under de senaste åren, men bristen på så kallat effektsamband är fortfarande ett problem. Bristen på förseningsdata är en anledning till varför de rekommenderade Value of reduced transport time variability (VTTV) endast har tillämpats i ett fåtal kalkyler för järnvägsinfrastrukturåtgärder. Industrin klagar över omfattande problem som förorsakas av sena eller inställda tåg och att minskningen av dessa problem inte beaktas på ett korrekt sätt i kostnadsnyttoanalyserna. En annan fråga som diskuteras är att mer högvärdiga produkter skulle transporteras på järnväg om järnvägstransporter skulle vara mer tillförlitliga. Värdet för avgående gods som transporteras på väg (9,5 kronor per kg) är ungefär tre gånger så stor som värdet för avgående gods som transporteras på järnväg eller på järnväg i kombination med andra trafikslag (3,7 kronor per kg). Trafikverket är medveten om bristerna och har bland annat finansierat detta pilotprojekt som studerar olika ansatser för att beräkna VTTV för det transporterade godset. Följande delar genomfördes i denna studie:• Den holländska VOTVOR-studien och de norska studierna GUNVORoch PUSAMhar analyserats. Alla dessa studier är SP-studier (Stated preference studies). Resultat: VTTV har störst betydelse för varuägare och speditörer och varierar över varugrupper. Överföring av värden är i princip möjligt. Sverige kan lära av erfarenheterna i Nederländerna och Norge. • Tre alternativa metoder som inte använts tidigare för att beräkna VTTV för svenska godstransporter på järnväg redovisas: o Försiktighetsprincipen, o COOP fallstudie, o Aktiemarknadsbaserad modell. I rapporten finns en tabell som sammanfattar VTTV för gods som transporteras på järnväg, beräknade med hjälp av de olika metoderna.

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the timescale decomposition of risk with Swedish traffic demand data for 1950–2005. In general, rail transport shows a stronger co-movement with GDP than road transport. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice between public investment alternatives, they are deemed important for public policy in general.