Assess the importance of transport improvements to the UK economy.

Transport projects and schemes can substantially impact our day-to-day lives, as well as mid-term decisions like whether or not a buy a car or long-term decisions like where to live. They also have a profound impact on the economic growth of the country, its productivity and people’s well-being. Cost-benefit analyses (CBA) and feasibility assessment of potential transport projects are primarily based on the monetary savings from travel time reductions. It is estimated that savings on travel time are responsible for around 80% of the predicted benefits of a new transport project in the UK (Mackie et al., 2001, Fosgerau and Jensen, 2003, Daly et al., 2014). The Values of Travel Time (VTT) estimates, which are used to quantify the trade-offs that decision-makers are willing to make in terms of travel time and travel cost, are hence critical components of CBA. An accurate estimation of VTTs is thus important in order to properly evaluate the costs and the benefits of a new transport project and sufficiently forecast future demand for specific services, e.g. a new public transport route, leading to better informed decisions during the planning phase.Estimates of the trade-offs that travellers would be willing to make in terms of travel time and cost were first produced in the 1960s. For a long period of time, VTT estimates were derived as relative values to the average wage cost or as a percentage of it (wage cost method or cost savings approach — CSA) and that method is still in use in several countries (Daly et al., 2014). Another approach involved Contingent Valuation, where VTTs were derived from direct questions about how much a participant would be willing to spend for a particular service or an improvement of a current one. In recent decades, most types of VTT analysis are based on the work of Daly and Zachary (1975), who first estimated VTT values from behavioural models based on theoretical frameworks of time allocation (Becker, 1965, DeSerpa, 1971) and the Random Utility framework (Marschak, 1960, McFadden, 1973, Domencich and McFadden, 1975, Williams, 1977, Ben-Akiva and Lerman, 1985, Train, 2009).Revealed Preference (RP) data, usually coming from travel diaries, would at face value provide the natural data source for estimating VTTs, and indeed were used in early studies (Beesley, 1965, Daly and Zachary, 1975). Nonetheless, while RP data provide the ability to capture real-world choices, most of the parameters influencing them are outside of the analyst’s control. Furthermore, traditional RP data sources include recalled/reported data that are prone to issues like omitted trips (particularly short ones), perception and rounding errors, etc. — often leading to large measurement errors. During the 1980s, there was also an increasing desire to capture VTTs for non-work trips – largely ignored up to that point – in addition to commuting VTTs. RP data, however, were proved to be unsuitable for providing useful real-world observed choices on non-work trips with the available data collection methods of that era. The aforementioned limitations of RP data led to the growing popularity, over the last two to three decades, of Stated Preference (SP) data as the main input to models, with RP data often being used only in limited scale for verification of the SP results (Mackie et al., 2003). SP surveys present respondents with a number of hypothetical scenarios, where they are asked to choose among a set of alternatives. This approach has a long tradition for example in the United Kingdom (UK) with the first major SP survey conducted in 1984 (MVA et al., 1987) and follow-up studies in 1994 (Accent and Hague Consulting Group, 1996) with the same data then re-analysed by Mackie et al. (2003) before the most recent study involving primary data collection taking place in 2014–2015 (Batley et al., 2019, Department for Transport, 2015, Hess et al., 2017).SP surveys are generally seen to have the advantage of providing the analysts with an environment where they have control over a large number of parameters that could influence VTT estimates, such as the attributes of the alternatives. On the other hand, SP surveys are prone to behavioural incongruence and hypothetical bias and are often criticised for being too sensitive to the experimental design and the representation of the SP scenarios (Brownstone and Small, 2005, Daly et al., 2014, Haghani et al., 2021). Concern in a VTT context has also been raised in relation to the use of overly simplistic settings in some countries (Hess et al., 2020).In a recent study examining the impact of hypothetical bias in SP surveys within several domains including transport (Haghani et al., 2021), the authors concluded that although it is more sensible to assume that individuals would likely overstate their Willingness-to-pay (WTP) in a hypothetical scenario (Li et al., 2020), there are a number of transport studies showing the opposite (Nielsen, 2004, Brownstone and Small, 2005, Shires and de Jong, 2009, Krcal et al., 2019). That downward bias of SP has also been proven in two meta-analyses on VTT values across countries and time of Shires and de Jong (2009) and Wardman et al. (2016).

Assess the importance of managing transport as part of urban sustainability. [6 marks] (2020)

Which of the following London transport strategies is most sustainable?

The most important transportation improvement in the eighteenth century was the

Of the following, what benefit can 'transportation' contribute to an organization strategy?Select one:a.Faster movement means higher customer satisfactionb.Less expensive means higher profitsc.Various modes of transport can offer better evaluation of riskd.All of these