The logsum as an evaluation measure - review of the literature and new results

Transport infrastructure projects in The Netherlands are appraised ex ante by using cost-benefit analysis (CBA) procedures following the so-called ‘OEI-guidelines’. The project benefits for travellers are incorporated in the form of changes in demand (e.g. from the Dutch national model system, LMS, or the regional models, NRM) and changes in the generalised travel costs (using values of time from Stated Preference studies to monetise travel time savings), and applying the rule of half. While a number of short-term improvements to the current procedures have been improved, it is also interesting to consider a more radical approach using explicit measures of consumer surplus, obtained by integrating the demand models directly. These measures are called logsums, from their functional form. The advantages that the logsums would give to the appraisal procedure would be that logsums can incorporate a degree of heterogeneity in the population, while also being theoretically more correct and in many cases easier to calculate. In this context, the Transport Research Centre (AVV) of the Dutch Ministry of Transport, Public Works and Water Management has commissioned RAND Europe to undertake a study comparing the conventional approach to the use of the logsum change as a measure of the change in consumer surplus that would result from a transport infrastructure project. The paper is based on the work conducted in the study. The paper opens with a review of the literature on the use of logsums as a measure of consumer surplus change in project appraisal and evaluation. It then goes on to describe a case study with the Dutch National Model System (LMS) for transport in which three methods are compared for a specific project (a high speed magnetic hover train that would connect the four main cities in the Randstad: Amsterdam, The Hague, Rotterdam and Utrecht): a.the ‘classical’ CBA approach as described above, b.the improved ‘classical’ CBA approach (introducing a number of short-term improvements) and c.the logsum approach (as a long term improvement). The direct effects of a particular policy on the travellers can be measured as the change in consumer surplus that results from that policy (there can also be indirect and external effects that may not be covered in the consumer surplus change). The consumer surplus associated with a set of alternatives is, under the logit assumptions, relatively easy to calculate. By definition, a person’s consumer surplus is the utility, in money terms, that a person receives in the choice situation. If the unobserved component of utility is independently and identically distributed extreme value and utility is linear in income, then the expected utility becomes the log of the denominator of a logit choice probability, divided by the marginal utility of income, plus arbitrary constants. This if often called the ‘logsum’. Total consumer surplus in the population can be calculated as a weighted sum of logsums over a sample of decision-makers, with the weights reflecting the number of people in the population who face the same representative utilities as the sampled person. The change in consumer surplus is calculated as the difference between the logsum under the conditions before the change and after the change (e.g. introduction of a policy). The arbitrary constants drop out. However, to calculate this change in consumer surplus, the researcher must know the marginal utility of income. Usually a price or cost variable enters the representative utility and, in case that happens in a consistent linear additive fashion, the negative of its coefficient is the marginal utility of income by definition. If the marginal utility of income is not constant with respect to income, as is the case in the LMS and NRM, a far more complex formula is needed, or an indirect approach has to be taken. This paper will review the theoretical literature on the use of the logsum as an evaluation measure, including both the original papers on this from the seventies and the work on the income effect in the nineties. Also recent application studies that used the logsum for evaluation purposes will be reviewed. Finally outcomes of runs with the LMS will be reported for the three different approaches (including the logsum approach) mentioned above for evaluating direct effect of transport policies and projects. Different methods for monetising the logsum change will be compared.

Cross-border Car Traffic in Dutch Mobility Models

Cross-border travel generates a substantial amount of mobility near the borders, but is not a large percentage of total Dutch mobility. However in the border regions of the country, these flows are important. For the Dutch national transport model LMS, O-D matrices are required that include cross-border car travel. This is a challenging task, due to scarcity of data. First, a production model (by travel purpose) is used to calculate the total production of car journeys. Next, these journeys are distributed over domestic and foreign destinations using a simplified destination choice model. From the resulting matrix, domestic journeys are removed and only the border crossing journeys are kept. Domestic journeys are then replaced by the results of the existing much more detailed mode-and destination choice models. The new models are estimated on the Dutch national mobility survey (MON) and are of reasonable quality. The predicted numbers of border crossing journeys to Belgium and Germany are lower than the numbers from traffic counts, and therefore an additional calibration to count data totals is carried out. The results indicate that for commuting the resistance to cross the border is equivalent to 35 (Belgium) or 46 (Germany) minutes extra travel time. Also for all other travel purposes in the model, it is found that the border resistance for journeys to Belgium is smaller than that for journeys to Germany, which can be explained by the additional factor of language difference. The smallest border resistance for both countries is found for shopping journeys

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions)

A model for time of day and mode choice using error components logit.

The severity of road congestion not only depends on the relation between traffic volumes and network capacity, but also on the distribution of car traffic among different time periods during the day. A new error components logit model for the joint choice of time of day and mode is presented, estimated on stated preference data for car and train travellers in The Netherlands. The results indicate that time of day choice in The Netherlands is sensitive to changes in peak travel time and cost and that policies that increase these peak attributes will lead to peak spreading

A model for time of day and mode choice using error components logit

The severity of road congestion not only depends on the relation between traffic volumes and network capacity, but also on the distribution of car traffic among different time periods during the day. A new error components logit model for the joint choice of time of day and mode is presented, estimated on stated preference data for car and train travellers in The Netherlands. The results indicate that time of day choice in The Netherlands is sensitive to changes in peak travel time and cost and that policies that increase these peak attributes will lead to peak spreading.Time of day Peak spreading Error components model Mixed multinomial logit model

A comparison of car ownership models

In this paper, car ownership models that can be found in the literature (with a focus on the recent literature and on models developed for transport planning) are classified into a number of model types. The different model types are compared on a number of criteria: inclusion of demand and supply side of the car market, level of aggregation, dynamic or static model, long-run or short-run forecasts, theoretical background, inclusion of car use, data requirements, treatment of business cars, car type segmentation, inclusion of income, of fixed and/or variable car cost, of car quality aspects, of licence holding, of socio-demographic variables and of attitudinal variables, and treatment of scrappage

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

This paper provides a review of transport model applications that not only provide a central traffic forecast (or forecasts for a few scenarios), but also quantify the uncertainty in the traffic forecasts in the form of a confidence interval or related measures. Both uncertainty that results from using uncertain inputs (e.g. on income) and uncertainty in the model itself are treated. The paper goes on to describe the methods used and the results obtained for a case study in quantifying uncertainty in traffic forecasts in The Netherlands. Copyright Springer Science+Business Media, LLC 2007Traffic forecasts, Travel demand, Uncertainty, Confidence interval, Simulation,