The value of travel time: random utility versus random valuation

This paper identifies, relates and compares two popular modelling approaches to estimate the value of travel time changes. The first (random utility [RU]) assumes that the random component of the model relates to the difference between the utilities of travel options; the second (random valuation [RV]) assumes that it relates to the difference between the value of travel time and a suggested valuation threshold. This paper gives details of the theoretical relationship between the two approaches and compares them empirically at several levels of model sophistication. Datasets from two national studies (the UK and Denmark) are employed. The results show a consistent superiority of the RV approach and a systematic gap in the value of travel time between approaches. A similar pattern across models is found in both countries. This raises questions about the validity of results using the RU approach. The analysis has direct implications for both researchers and policy-makers

The use of recovery time in timetables: rail passengers' preferences and valuation relative to travel time and delays

Recovery time in the rail industry is the additional time that is included in train timetables over and above the minimum journey time necessary often with the explicit aim of improving punctuality. Recovery time is widely used in railways in a number of countries but prior to this study there has been no investigation of the rail users’ point of view. Perceived recovery time, such as being held outside stations and prolonged stops at stations, might have some premium valuation due to the frustration caused. If perceived recovery time in train timetables does carry a premium, then the benefits of improved punctuality achieved by it will be reduced. This paper is the first to investigate passengers’ views and preferences on the use of recovery time. We summarise the findings of a large study and provide estimates of passengers’ valuations of recovery time, both relative to in-vehicle time and late time, that can be used for economic appraisal purposes. Overall, we find most passengers support the use of recovery time but the context is important. Only 13% of users disapprove of its use as a tool to reduce lateness. The estimated premia vary by demand characteristics and are significant in some contexts, although on average are of a small magnitude. The applicability of the estimates is demonstrated through the appraisal of an actual scheme in the UK. We observe that the introduction of more recovery time along with the subsequent improvement in reliability can lead to significant reductions in generalised journey time, even when recovery time carries a valuation premium. We must however strike a word of caution since we note that there were higher than expected proportions of non-traders in the survey which may have affected the results; future studies into the topic should look to minimise the proportion of non-traders. This study provides valuable and necessary first steps in this challenging topic

The impact of planned disruptions on rail passenger demand

Disruptions to rail journeys are experienced by rail passengers on a daily basis throughout the world, with the impacts on passengers ranging from minimal to major. Such disruptions can be categorised as unplanned (e.g. extreme weather, vandalism, accidental damage to lines and power supplies etc.) or planned engineering-based disruptions. This paper focuses upon the latter, providing a valuable contribution to an area which is largely under researched, particularly in comparison to unplanned disruptions. Emphasis is placed upon understanding how passengers react to planned engineering-based disruptions: do they continue their journey (using the modified service); use other stations or routes that are not affected; make the journey on another day; travel to another destination; or simply not make that journey. Consideration is also given to how being aware or unaware may impact on passenger behaviour and whether disruptions of this type have any long run impacts over and above the short run. Ultimately, passenger behaviour translates into what can be substantial financial impacts for rail operators. The paper considers this, with the development of choice models based on both revealed preference (RP) and stated intentions (SI) data from a large scale face-to-face survey of rail users (7000+) and a smaller online panel of rail and non-rail users (500). These are used to estimate demand impacts resulting from planned engineering-based disruptions. Some of the key findings to emerge include: (1) Bus replacement services for disrupted rail services are inferior to rail diversions, with around three times more rail demand lost with bus replacement than with rail diversion; (2) The level of awareness prior to arriving at the station does not seem to have a large impact on the pattern of behavioural response, this may reflect the increased information available from mobile devices; (3) There is some evidence to suggest that rail travellers see planned disruptions as a ‘fixed cost’; and (4) Guaranteed connections have a benefit, to the tune of around 9 min, whilst rail travellers have higher disutility from longer periods of disruption to the extent of around 22 min

Who Blows the Whistle on Cartels? Finding the Leniency Applicant at the European Commission

Competition authorities need a better understanding of the determinants of cartel self-reporting in order to increase cartel members’ incentives to apply for the benefit from leniency programs and thus improve the effectiveness of anti-cartel policy. Using information on 683 firm groups that participated in 132 cartels that were penalized by the European Commission between 1996 and 2020, we estimate which type of cartel member is most likely to be the first or subsequent leniency applicant. Our results emphasize the role of firm groups as a driver to self-report: The higher is the proportion of firms that are part of the same group (relative to the size of the cartel), the greater is the likelihood of applying for leniency. Fines also incentivize cartelists—with the exceptions of ringleaders—to self-report. While ringleaders or instigators tend to avoid being first confessors, they appear to be more likely to self-report than are others only after someone else has revealed the cartel. Finally, cartels that do bid-rigging are less likely to be uncovered by a leniency application

Judicious selection of available rail steels to reduce life-cycle costs

The rate of rail degradation and hence its expected life is not uniform throughout any railway network and is governed by a combination of track, traffic and operating characteristics in addition to the metallurgical attributes of the rail steel. Consequently, it is suggested that any route or network is not a single linear asset but is a compilation of individual segments with different track characteristics, degradation rates and expected life spans. Thus, the choice of rail steel grade to maximise life (and minimise life-cycle costs) needs to combine knowledge of the metallurgical attributes of the available rail steels with the conditions prevailing at the wheel–rail and vehicle–track interfaces, whilst also considering the economic costs and benefits of the different options. This paper focuses on the classification of the susceptibility to rail degradation in various parts of a mixed-traffic network using vehicle dynamics simulation. The metallurgical attributes of the currently available rail steels are summarised along with an assessment of the life-cycle costs and wider economic implications associated with selection of a rail steel which provides improved resistance to the key degradation mechanisms of rolling contact fatigue and wear. Overall, the proposed methodology, which incorporates engineering, metallurgical and economic assessments, provides guidance on the circumstances in which the introduction of alternative rail steels make sense (or not) from an economic perspective

Transport Accessibility and Land Value – A Commercial property price model for Northern England

The central aim of this study was to use newly-developed models to develop the evidence base on the relationship between transport accessibility and commercial property prices. Understanding commercial property impacts help understand the benefits following transport interventions and potential revenues from land value capture. In developing a set of hedonic models of commercial property value for the TfN area, we allowed for a detailed analysis of the role of accessibility, by multiple modes. Our models include business-to-business (B2B) accessibility, as well as business-to-labour (B2L) accessibility, to take into account important linkages between businesses as well as access to labour/customers. Our modelling results found significant impacts for floorspace, nearby tram stops, local area employment density, income and deprivation measures. For accessibility, the picture is more nuanced with different accessibility measures emerging as important for different property types

Valuing travel time changes: a case of short-term or long-term choices?

The valuation of travel time is of crucial importance in many transport decisions. Most studies make use of data framed around short-term decisions such as route choice. However, people may have a greater ability to trade time and money in a longer term setting, such as when considering changes in residential or employment locations. We study the value of travel time in both the short and long-term, finding differences in the valuations. Given the importance of these valuations for policy making, our results call for more research into how time-cost trade-offs should be represented with stated preference

Understanding valuation of travel time changes: are preferences different under different stated choice design settings?

Stated choice (SC) experiments are the most popular method to estimate the value of travel time changes (VTTC) of a population. In the simplest VTTC experiment, the SC design variables are time changes and cost changes. The levels of these variables create a particular setting from which preferences are inferred. This paper tries to answer the question “do preferences vary with SC settings?”. For this, we investigate the role of the variables used in the SC experiment on the estimation of the set of VTTC (i.e. mean and covariates). Ideally, one would like to observe the same individuals completing different SC experiments. Since that option is not available, an alternative approach is to use a large dataset of responses, and split it according to different levels of the variable of interest. We refer to this as partial data analysis. The estimation of the same model on each sub-sample provides insights into potential effects of the variable of interest. This approach is applied in relation to three design variables on the data for the last national VTTC study in the UK, using state-of-the-art model specifications. The results show several ways in which the estimated set of VTTC can be affected by the levels of SC design variables. We conclude that model estimates (including the VTTC and covariates) are different in different settings. Hence by focussing the survey on specific settings, sample level results will be affected accordingly. Our findings have implications for appraisal and can inform the construction of future SC experiments

A disaggregate freight transport chain choice model for Europe

This paper presents the estimation of a discrete freight transport chain choice model for Europe, which was developed for the European Union as part of the Transtools 3 project. The model describes nine different multi- and single mode chain alternatives of which three can be either container or non-containerised, and it segments freight into dry bulk, liquid bulk, containers and general cargo. The model was estimated on the basis of disaggregate data at the shipment level (Swedish CFS and French ECHO data). Several transport costs specifications and nesting structures were tested and elasticities compared with reference literature. It was found that freight models are characterised by heterogeneity, non-linearity in transport costs and hence Value of Times and non-constant rates of substitution. Not taking these elements into account will have consequences for the evaluation of transport policies using the freight transport model