Modelling departure time and mode choice

As a result of increasing road congestion and road pricing, modelling the temporal response of travellers to transport policy interventions has rapidly emerged as a major issue in many practical transport planning studies. A substantial body of research is therefore being carried out to understand the complexities involved in modelling time of day choice. These models are contributing substantially to our understanding of how travellers make time-of-day decisions (Hess et al, 2004; de Jong et al, 2003). These models, however, tend to be far too complex and far too data intensive to be of use for application in large-scale modelling forecasting systems, where socio-economic detail is limited and detailed scheduling information is rarely available. Moreover, model systems making use of the some of the latest analytical structures, such as Mixed Logit, are generally inapplicable in practical planning, since they rely on computer-intensive simulation in application just as well as in estimation. The aim of this paper, therefore, is to describe the development of time-period choice models which are suitable for application in large-scale modelling forecasting systems. Large-scale practical planning models often rely on systems of nested logit models, which can incorporate many of the most important interactions that are present in the complex models but which have low enough run-times to allow them to be used for practical planning. In these systems, temporal choice is represented as the choice between a finite set of discrete alternatives, represented by mutually exclusive time-periods that are obtained by aggregation of the actual observed continuous time values. The issues that face modellers are then: -how should the time periods be defined, and in particular how long should they be? -how should the choices of time periods be related to each other, e.g. is the elasticity for shorter shifts greater than for longer shifts? -how should time period choice be placed in the model system relative to other choices, such as that of the mode of travel? These questions cannot be answered on a purely theoretical basis but require the analysis of empirical data. However, there is not a great deal of data available on the relevant choices. The time period models described in the paper are developed from three related stated preference (SP) studies undertaken over the past decade in the United Kingdom and the Netherlands. Because of the complications involved with using advanced models in large-scale modelling forecasting systems, the model structures are limited to nested logit models. Two different tree structures are explored in the analysis, nesting mode above time period choice or time period choice above mode. The analysis examines how these structures differ by data set, purpose of travel and time period specification. Three time period specifications were tested, dividing the 24-hour day into: -twenty-four 1-hour periods; -five coarse time-periods; -sixteen 15-minute morning-peak periods, and two coarse pre-peak and post-peak periods. In each case, the time periods are used to define both the outbound and the return trip timings. The analysis shows that, with a few exceptions, the nested models outperform the basic Multinomial Logit structures, which operate under the assumption of equal substitution patterns across alternatives. With a single exception, the nested models in turn show higher substitution between alternative time periods than between alternative modes, showing that, for all the time period lengths studied, travellers are more sensitive to transport levels of service in their choice of departure time than in choice of mode. The advantages of the nesting structures are especially pronounced in the 1-hour and 15-minute models, while, in the coarse time-period models, the MNL model often remains the preferred structure; this is a clear effect of the broader time-periods, and the consequently lower substitution between time-periods.

Appropriate Methodologies to Better Measure Consumer Preferences for Postal Services

This report summarises work undertaken testing the use of stated preference discrete choice experiments to measure consumer preferences for postal services. It discusses the importance of understanding and quantifying consumer priorities in the postal sector and presents different methods used for valuing non-market goods. We recommend the use of stated preference discrete choice experiments, and test the use of this approach in three member states. We provide the findings for these member states, as well as a “tool kit” for applying this methodology in other member states in future.Consumer preferences, postal services, discrete choice, two-sided market

Decarbonising UK transport - Final report and technology roadmaps

This report informs the UK Government’s Transport Decarbonisation Plan. It considers what needs to be achieved over the next 30 years, in terms of technological solutions, to reduce and remove direct emissions from the UK’s domestic transport sector across modes by 2050.In June 2019 the Government amended the Climate Change Act committing the UK to a net-zero contribution to global greenhouse gas emissions by 2050. Direct (tailpipe) emissions from domestic transport represent over a quarter of UK greenhouse gas emissions, making it the largest single source, 99% of which is comprised of CO2. In October 2019, the Government announced the development of the first Transport Decarbonisation Plan (TDP). To support the development of the TDP, the Department for Transport (DfT) asked Mott MacDonald and its partners, as part of the future resilience support they provide, to examine technological solutions for reducing and removing CO2 at point of use across all modes for domestic transport. In March 2020 the DfT published Decarbonising Transport: Setting the Challenge which confirmed the role of this study to “give advice on the support we need to provide in the near and medium term in order to de-risk, and have in place, the technologies which will help us deliver a decarbonised transport system by 2050”. The study’s purpose has not included consideration of the role of changing travel behaviour in reducing CO2 emissions from transport.This report sets out a series of seven roadmaps for decarbonising domestic transport in the UK. These roadmaps address: cars and light goods vehicles; buses; coaches; heavy goods vehicles; rail; domestic shipping; and domestic aviation. International aviation and shipping are not included within the scope of this study. These have been recognised by the Government as important to address through international co-operation and action, to which some of the solutions discussed in this report will contribute. It should be noted that in its Sixth Carbon Budget report published on 9 December 2020, the Committee on Climate Change (CCC) recommends that the legal limit for UK net emissions of greenhouse gases “should cover all greenhouse gas emissions, including those from international aviation and shipping”. The implications of this recommendation are not within scope of the roadmaps in this report.Each roadmap considers the progression of relevant candidate technology solutions. The roadmaps work backwards from an achievable 2050 end state aligned to the goal of decarbonisation, identifying developments and milestones over the period between 2050 and 2020 that would enable the 2050 end state to be reached. Developments are centred – especially for road transport - upon fleet turnover (the replacement of CO2-emitting vehicles with zero-emission vehicles) and the supporting infrastructure (for refuelling/recharging those zero-emission vehicles). Underpinning the developments, the roadmaps set out recommended research and innovation interventions that need to be progressed in the coming five to ten years. The roadmaps also consider the recommended role of policymaking and fiscal/regulatory measures in helping to enable progress

Scenario planning for transport practitioners

Scenario planning helps in contemplating how the future may develop and can be especially important when needing to make sense of uncertainty – something now pertinent to the transport sector. Accordingly, scenario planning is moving from the periphery of strategic transport planning towards becoming a more normalised and integral contribution. By examining rather than ignoring a range of uncertainties about the future, scenarios can be developed that enable an exploration of different futures, in turn improving transport planning. Scenarios can be narrative based, represented quantitatively, or combine ‘storytelling and number crunching’. Both the process of creating them and of representing the scenarios, deepen an appreciation of uncertainty about the future. In turn this allows planners and policymakers to better understand potential outcomes and challenges and determine how to address these. Scenarios can also be used to identify and assess candidate measures for influencing the transport system, testing these against a range of uncertain future conditions. This helps to identify measures that together can help form a strategy that is more robust. Drawing upon the combined experience of its authors, this paper provides insights into the development of scenarios and their use to improve decision making in transport planning. It offers advice on how to help ensure the scenario development process is credible, how to produce a coherent set of scenarios and how to ensure they are used to engage key stakeholders and to enable policymakers to confidently develop their strategic thinking and plans

On the development of time period and mode choice models for use in large scale modelling forecasting systems

A substantial amount of research is presently being carried out to understand the complexities involved in modelling the choice of departure time and mode of travel. Many of these models tend to be far too complex and far too data intensive to be of use for application in large scale model forecasting systems, where socio-economic detail is limited and detailed scheduling information is rarely available in the model implementation structure. Therefore, these models generally work on the basis of a set of mutually exclusive time periods, rather than making use of continuous departure time information. Two important questions need to be addressed in the use of such models, namely the specification used for the time periods (in terms of length), and the ordering of the levels of nesting, representing the difference in the sensitivities to shifts in departure time and changes in the mode of travel. This paper aims to provide some answers to these two questions on the basis of an extensive analysis making use of three separate Stated Preference (SP) datasets, collected in the United Kingdom and in the Netherlands. In the analysis, it has proved possible to develop models which allow reasonably sound predictions to be made of these choices. With a few exceptions, the results show higher substitution between alternative time periods than between alternative modes. Furthermore, the results show that the degree of substitution between time periods is reduced when making use of a more coarse specification of the time periods. These results are intended for use by practitioners, and form an important part of the evidence base supporting the UK Department for Transport’s advice for practical UK studies in the WebTAG system

Automobility in Brazil, Russia, India, and China: Quo Vadis?

This paper introduces an innovative methodology to answer the question: Toward which 2 levels of automobility are the BRIC countries headed? We apply this methodology to understand 3 why long-term saturation levels for car travel differ across the OECD study countries and what 4 this means for the saturation levels that the BRIC countries may attain. In our approach we factor 5 out the GDP development in order to focus on how other factors have influenced specific paths 6 of automobility in individual countries. That is, we focus on the question: Why are the long-term 7 automobility saturation levels so much higher for some countries than for others, even at similar 8 levels of GDP? Our methodology draws on quantitative analysis of historic developments in four 9 industrialized countries (USA, Australia, Germany, and Japan) that serve as case studies 10 representing prototypical paths of automobility with very different levels of per capita automo-11 bility, in combination with qualitative data derived from an expert-based qualitative approach. 12 We use the latter to transfer historic experiences about how automobility evolution was shaped in 13 industrialized countries and how these experiences may affect the future of automobility in the 14 BRIC countries. Based on our analysis, Brazil turns out to be the most car-oriented country 15 among the BRICs with a potential long-term level of automobility between Germany and Aus-16 tralia. Russia comes in second, also with a likely long-term level of automobility above that of 17 Germany. China and India, on the contrary, are heading towards lower levels of automobility 18 below Germany but higher than Japan

On the development of time period and mode choice models for use in large scale modelling forecasting systems

A substantial amount of research is presently being carried out to understand the complexities involved in modelling the choice of departure time and mode of travel. Many of these models tend to be far too complex and far too data intensive to be of use for application in large scale model forecasting systems, where socio-economic detail is limited and detailed scheduling information is rarely available in the model implementation structure. Therefore, these models generally work on the basis of a set of mutually exclusive time periods, rather than making use of continuous departure time information. Two important questions need to be addressed in the use of such models, namely the specification used for the time periods (in terms of length), and the ordering of the levels of nesting, representing the difference in the sensitivities to shifts in departure time and changes in the mode of travel. This paper aims to provide some answers to these two questions on the basis of an extensive analysis making use of three separate Stated Preference (SP) datasets, collected in the United Kingdom and in the Netherlands. In the analysis, it has proved possible to develop models which allow reasonably sound predictions to be made of these choices. With a few exceptions, the results show higher substitution between alternative time periods than between alternative modes. Furthermore, the results show that the degree of substitution between time periods is reduced when making use of a more coarse specification of the time periods. These results are intended for use by practitioners, and form an important part of the evidence base supporting the UK Department for Transport's advice for practical UK studies in the WebTAG system.1

Do Patients Always Prefer Quicker Treatment?: A Discrete Choice Analysis of Patients

The London Patient Choice Project (LPCP) was established to offer NHS patients more choice over where and when they receive treatment, and to reduce waiting times. The LPCP offered those patients waiting around 6 months for elective procedures a choice of treatment at an alternative NHS or private hospital, or treatment at an overseas hospital. The aim of this article is to investigate the following questions regarding patients PatientsHospitalisation, Patient-preference