Travel

Travel

Behaviour change is a topic of continuing interest to transport professionals, with current focus on getting people out of their cars, to use instead public transport, walking or cycling. The main policy motivations are to reduce the harmful consequences of car use, in particular tailpipe carbon emissions and air pollutants, as well as to reduce road traffic congestion, enhance the sense of urban place, increase healthy exercise and improve the economic viability of public transport. This was the topic of contributions to a recent Transport Thinking Forum Round Table discussion on achieving behavioural change, organised in association with the TAPAS network. Here I offer some further reflections on the interesting contributions and on the practical possibilities.

A common approach to effecting behaviour change is conceptualised by the simple idea of ‘sticks and carrots’ – punishments for undesirable behaviour and rewards for better behaviour, judged against policy objectives. However, this was criticised as an unhelpful metaphor by Pete Dyson, a behavioural scientist who has worked at the Department for Transport, now at the University of Bath. He regards the concept as too simplistic and also problematic because the ‘stick’ element prompts pushback from those who see themselves as adversely affected, as well as from the politicians who aim to represent them, car users in particular (see also the book he co-authored with Rory Sutherland: Transport for Humans – Are we nearly there yet?).

As alternatives to sticks and carrots, there are other concepts available including: Avoid-Shift-Improve, a general approach to environmental sustainability; the Upstream-Downstream model of behavioural change developed by the Behavioural Insights Team; and the COM-B model developed at UCL, which posits that behavioural change can only come about if there are in place all three elements of Capability, Opportunity and Motivation. The COM-B approach has been widely used in the public health context, for instance to encourage smoking cessation, and is one of a number of techniques that have been applied with success to improving road safety. The Scottish Government has stated that it has considered interventions to reduce car use in the context of the COM‐B model, although no detail has been provided.

Reducing smoking and improving road safety are generally seen as desirable objectives, so behavioural change interventions go with the grain of public opinion. But the objectives of reducing car ownership and use are very different, given that three-quarters of households in Britain own cars, which are seen both as useful for their utility in getting from A to B, as well as engendering good feelings associated with ownership (see my paper about Car Dependence). The M of COM-B is therefore largely lacking, so attempting to reduce car use by measures based on behavioural insights alone would be pushing uphill. On the other hand, interventions to foster electric vehicle purchase could be effective since the Capability to drive is as for internal combustion engine vehicles, there is Opportunity to purchase EVs, albeit only a limited used car market at present, and Motivation takes the form of lower operating costs and environmental virtue.

Measure to promote EV uptake, such as investment in public charging  points, can be seen as examples of the well-known approach known as ‘nudging’, a concept from behavioural economics that proposes adaptive designs of the decision environment as ways to influence the behaviour and decision-making of groups or individuals. Nudging is not a stick, in that compliance with the intention of the nudge is optional, nor is it straightforwardly a carrot in that the benefits of compliance may not always be self-evident. Richard Thaler, Nobel laureate in economics and a populariser of the concept, has cited satnav technology as an example: you decide where you want to go, the app offers possible routes, and you are free to decline the advice if you decide to take a detour. While this is a neat example, its ambition is quite limited. Nudging people to make significantly less use of cars by means of such ‘soft’ measures would seem likely to have only small impact, in the absence of complementary ‘hard’ unavoidable interventions.

One challenge to the usual view of behaviour change in the transport sector was put by Tom Cohen of the University of Westminster, at the TAPAS meeting, who argued that the overwhelming majority of interventions can be expected to lead to changes in behaviour, so that ‘behaviour change’ is simply the right way of doing transport planning, and not a subset of interventions such as those based on marketing or by appealing to our better nature. In short: transport is movement; movement is behaviour; so transport planning is travel behaviour planning. The implication is that we should look to packages of complementary interventions, of the kind envisaged by transport planners, to achieve behaviour change.

Also at the TAPAS meeting, Lisa Martin, of consultancy Steer, reviewed UK experience of packages of interventions aimed at changing travel behaviour, including those supported by the Sustainable Travel Towns demonstration projects and the Local Sustainable Travel Fund. Evaluation indicated that while high value for money could be obtained, as judged by the benefit-cost ratio, yet the impact on car use and/or traffic levels was quite small, of the order of 2%. This has not been sufficient to ensure that such packages are in the mainstream of approaches to transport decarbonisation, where typical aspirations of local and regional authorities are to achieve 20% reductions in car use by as early as 2030. Moreover, the ‘cost’ represented in the benefit-cost ratio is the economic cost. What is disregarded is the political cost that may be experienced by politicians, local and national, in advocating measures that engender pushback from those who feel they would be adversely affected, drivers and others.

There is certainly merit taking a broad view to identify packages of measures that might persuade, enforce or nudge travel behaviour change in directions helpful to wider policy purposes. Yet in practice much of the debate in the transport sector is about the consequences of investment in new road and rail capacity, the behavioural consequences of which tend to be multifaceted, yet which is supported by analysis and modelling that narrowly focuses on estimates of economic benefits that make simplifying assumptions about changes in travel behaviour. There are opportunities for behavioural scientists to question these simplifying assumptions, in particular that the main benefit of investment that results in faster travel is the saving of travel time that is used for more work or leisure activities. On the contrary, there is much evidence that, over timescales relevant for investment appraisal, the main benefits take the form of enhanced access – to people and places, family and friends, employment, services and activities, with ensuing increases in opportunities and choices. The travel-time-saving simplification is computationally convenient but does not capture the real behaviour of users. This means that conventional transport models whose outputs are traffic speeds and volumes, comparing with- and with-out investment cases, are behaviourally misleading. Models that project changes in access would be a better basis for investment decisions, as behavioural analysis would reveal.

Nevertheless, given that the conventional methodology for investment appraisal is so well established, a consequence is a search for approaches to effecting behavioural changes that can be pursued outside this orthodoxy, hence the continued popularity of ‘sticks and carrots’ as well as interest in alternative approaches that avoid direct confrontation with the economists.

The general problem we face is that the system of travel/transport is particularly complex, has evolved over time in parallel with our built environment, and where remedies to problems tend to be specific to particular locations. This makes it difficult to gain a sufficiently deep understanding of what is going on and what options are likely to succeed in ameliorating detrimental aspects and/or enhancing positive features. Einstein said: ‘Everything should be made as simple as possible, but not simpler’. While perhaps more straightforwardly relevant for physical systems, this maxim begs the question of how simple descriptions of complex systems can be before the charge of oversimplification could be legitimately laid. My own view is that there is scope for simplification through the application of heuristics – rules of thumb, mental shortcuts – that are good enough accounts of what’s going on and what our options are, and that will promote common understanding – a topic on which I will write at greater length on a future occasion.

In the end, the test is ‘what works’ – the observed outcome in the real world of behavioural interventions. Reasonably clear outcomes may be observable in the case of sticks, such as adjustments to taxation, implementation of changes in road layouts or of charging schemes aimed at reducing vehicle emissions or traffic congestion. Similarly, the impact of carrots in the form of cash incentives can often be measured. But more nuanced interventions such as nudges may be harder to document.

Outcomes of behavioural interventions may be observed from experience in other countries. Yet local conditions and behavioural responses may be significantly different from those in the UK. For instance, Paris was one of the first cities to allow rental electric scooters, but then was one of the first to ban them on grounds of safety, following a referendum. But that is not to say that other cities would follow the example of Paris. Nevertheless, the idea of trialling an intervention for a defined period and then having a vote may be a practical way of testing public acceptability. Stockholm’s congestion charge was trialled for a seven-month period, then turned off prior to a referendum, the outcome of which led to permanent implementation.

The substantial scope for travel behaviour change was illustrated by the coronavirus pandemic, which had a major impact on travel behaviour largely through unavoidable constraints imposed by governments, an example of a big stick, deployable in extraordinary times. But once these constrains were removed, people reverted largely to their previous travel behaviour, which reflected prior choices about where they lived in relation to where they worked, to where family and friends lived, where their children were educated, as well as activities and services they had become accustomed to access. Such reversion implies that large changes in travel behaviour could not realistically be delivered in a democratic society in normal times.

While there is undoubted scope for the application of behavioural science to investigate how travel behaviour might usefully be changed incrementally, in practice on/off trials plus referenda might provide both a practical test of acceptability as well as a justification for initiatives by sufficiently bold politicians. And after a decision to implement permanently, evaluation on short-, medium- and longer-term timescales would help inform subsequent decisions.

Yet when contemplating the possibilities for travel behaviour change, we should not fail to recognise that the car has provided valued access benefits to a majority of households, notwithstanding its problems. Persuading people to use their cars less would therefore require provision of alternative modes that feel to them at least as good, reinforced by effective, probably incremental constraints on car use. Whether we use the shorthand of ‘carrots and sticks’ to describe such complementary measures is less important than finding the funding to invest in attractive alternatives to the car, particularly rail-based travel in urban areas where the economic viability of public transport is most feasible. In the absence of good alternatives, getting people out of their cars would be difficult, regardless of the sophistication of our behavioural analysis.

 
 







					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

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The coronavirus pandemic caused major dislocation in society, not least to the amount and modes of travel, with many similarities across countries, albeit differing in detail depending on local constraints imposed on work and travel. This amounted to a ‘natural experiment’ in that an exogenous event led to large changes in travel behaviour over a two-year period, 2020-2021, before the cause faded away and normal life largely resumed, yet with some possible permanent long-term consequences. The findings of the National Travel Survey for 2022 are ambiguous as to whether we are on the path to pre-pandemic normality, or whether some permanent changes have arisen. Transport for London has recently published its annual Travel in London report for 2023 that includes relevant data for the capital. So it is worth considering the evidence for pointers to the future.





The pandemic led to two main changes in how we live and in the related demand for travel: more working from home and more shopping online.





Working from home





While some of those who do not need face-to-face contact with customers, clients or colleagues have always worked from home, the pandemic resulted in a step-change in the numbers adopting this practice. In some cases, this was a sub-optimal response to an emergency, for instance in the education sector. In other cases, this reflected advantages of not travelling to a workplace for at least part of the week, avoiding both the time and discomfort of commuting, flexibility of when to work, and perhaps benefiting from the avoidance of interruptions in the privacy of the home environment.  





For some organisations, it has been found that the workplace office could be dispensed with entirely. For many others, some form of hybrid working has emerged, with employees spending part of the week in the office, although the long-term stability of this outcome is yet to be seen. The extent of hybrid working reflects a balance between the preference of many employees for working at home and the preference of many of their managers for having people in the office – for oversight, to stimulate creative interactions and to induct new staff into the culture and practices of the organisation. This balance is affected by the state of the employment market – the demand and supply of employees with appropriate skills. The market was tight following the pandemic, with low levels of unemployment as many older workers decided not to return. But over time, this balance could shift, particularly if the benefits of agglomeration are as significant as had previously been supposed, so that businesses that have more staff on site prove to be more successful and profitable. On the other hand, businesses that commit to hybrid working may be able to attract staff from a wider area, as well as reducing the expense of maintaining office space for the full complement of staff.





Surveys by the Office for National Statistics of working adults in Britain found that while 50% reported working from home at some point in the previous seven days in the first half of 2020, early in the pandemic, this had fallen to 40% in early 2023; throughout 2022, when the restrictions of the pandemic had been lifted, the percentage of working adults reporting having worked from home varied between 25% and 40%, without a clear upward or downward trend, indicating that homeworking was resilient to the end of travel restrictions. Professionals and those in higher income bands were more likely to work from home, whereas those who require face to face contact with clients or personal engagement with facilities resumed travelling to their workplace – in education, healthcare, hospitality, retail, manufacturing and laboratories.





The emergence of a new normal involving both fully remote and hybrid working raises a question about the value of agglomeration benefits from learning, sharing, and matching in city centres. Estimation of the economic value of agglomeration has been based on econometric analysis addressing the change in productivity in relation to the change in effective economic density, with the biggest benefits accruing to knowledge-focussed businesses, despite remote or hybrid working being most feasible for such businesses. The observed movement of businesses to central locations in recent decades reflects net agglomeration benefits, the positive benefits being offset by the negative, the balance being affected by technological developments. But this may be changing.





Fleet Street, for instance, was once the physical location of the national newspapers in central London, with printing presses in the basements, print workers on floors above and editorial staff on the upper floors. This was a classic cluster, with benefits from shared facilities and staff, allowing news to travel faster and gossip to flourish. But there were offsetting disbenefits: newsprint had to be brought into central London, from where newspapers were distributed across the country overnight, and there were restrictive labour practices reflecting trade union power when the product had to be made anew each day. But then the advent of digital typesetting allowed newspapers to be printed at remote printworks with better access to transport networks, so that the editorial offices could disperse to scattered locations around London. Nowadays, ‘Fleet Steet’ is a metaphor for the newspaper industry, no longer the actual location. With hindsight, the agglomeration benefits and disbenefits were more finely balanced than had been supposed, so that new technology could tilt the balance in favour of dispersion of the cluster.





A question, then, is whether something similar may be happening more generally to knowledge-based businesses that had been benefiting from clustering in city centres. It has long been suggested that modern information and telecommunications would lead to the ‘death of distance’, yet the benefits of agglomeration seemed to trump those associated with dispersal. But then the shock of the pandemic both enforced working from home where possible and brought forward technologies to facilitate online meetings and collaboration based on broadband telecommunications that had steadily been improving. The disbenefits of agglomeration to employees in the form of the time, cost and discomfort of commuting became immediately apparent, with a consequential reluctance to return full time to the workplace. The balance of benefits and disbenefits may have shifted in favour of dispersal, although it may take time to reach a settled outcome.





For employers, increased working from home could lead to a decrease in demand for office space in the centres of cities, although this would depend on how workspace is managed to accommodate staff who are there for only part of the week. Shrinkage of space to save rental costs could make the office a less attractive destination. High quality premises with good facilities within and nearby would be preferred, to attract high quality staff. Older, lower quality buildings are becoming redundant, particularly on account of regulatory requirements to improve the energy efficiency of rented buildings. This presents opportunities to repurpose such redundant workplaces, as has long been the case by creating loft apartments from historic warehouses. The scope for repurposing more recent office accommodation can be limited by the depth of floor plan, since windows would be expected by residents of flats, and by the core location of services. Creation of laboratory space, hotels and student accommodation are being considered. Perhaps the simplest repurposing would be a reversion to residential use of inner city eighteenth and nineteenth century houses built for families with servants but subsequently converted to offices. Such repurposing would fit the concept of the 15-minute city or 20-minute neighbourhood where most needs can be met by active travel within a short distance. However, with many tenants and landlords bound by long term leases, it will take time for the extent of the full changes to occupancy to emerge





While reduced use of public transport for commuting means less crowding at peak times, it also results in less revenue for the operators and so either more subsidy is required, or the outcome is poorer service and/or higher fares. This raise the question of the role of bus and rail travel in sustaining the economic and social vibrancy of towns and cities, particularly those whose density is such the general use of the car is not viable. The scope for raising fares is limited by use made by those who cannot afford a car, which means that some external source of funding support is required. Support from government was increased substantially during the pandemic as an emergency measure, but the longer-term position remains to be seen. Transport for London (TfL) has been more dependent on operating income from passenger revenue than other major cities, hence it was hit harder by the loss of fare income during the pandemic so that tortuous negotiations with central government were required to avoid serious loss of services. The case for increased external subsidy to sustain high quality public transport fits well with the need to decarbonise the transport sector by offering alternatives to car use, given that internal combustion engine vehicles will be dominant for some years to come.





It is possible that the time saved by commuting less will be used for other travel, given the long run invariant hour a day of average travel time. If this other travel is local active travel, cycling or walking, that would be helpful for reducing the environmental impact; if by car, less so, particularly if commuting had been by public transport. Working from home also allows living more remotely from the workplace if travel to work is less frequent; this leads to changes in residential property prices as between urban and rural locations, and new construction where land with planning consent is available for development, with consequential changes for travel behaviour, particularly increased car use.





Online shopping





The other shift prompted by the pandemic was to online retail, growth of which was accentuated markedly. Yet shopping is also a social activity, and the suitability of many goods are best judged first hand, whether the feel and look of fashion items or the bulk of furnishings. Data for internet sales as a proportion of total retail sales had been on a steadily increasing trend before the pandemic, rising from around 3% in 2007 to 19% immediately before the pandemic. It spiked to reach 38% in early 2021 before falling back to 25% in mid-2022, broadly returning to trend, although for how long the upward trend will continue is as yet unclear.





The main impact of this shift to online shopping has been to reduce the attractiveness of city centre department stores, some chains of which have closed entirely while others have shut some branches and repurposed floor space in continuing locations. Stronger city centres that relied on a wide catchment area were most affected by the pandemic, while highstreets in economically weaker cities and towns were less affected, although many were already experiencing difficulty in attracting shoppers and shops on account both of general economic conditions in towns that had lost major industries and the shift to online retail. Over time, rents will adjust to a lower demand for retail floor space, either allowing new entrants or repurposing for other uses.





Implications for travel demand





Department for Transport monitoring data showed that, by April 2022, motor vehicle use nationally had returned to just over 100% of pre-pandemic levels. Public transport use grew back at slower rates and some components have tended to remain below pre-pandemic levels: by late 2023, national rail use was 85% of that observed in the same period in 2019, London Underground use a little higher, and bus use was about 90%, although there have been significant fluctuations due to school holidays, weather events, tourist flows and industrial action. Use of the Underground to central destinations bounced back more quickly at weekends than in the week.





There was a burst of recreational cycling during the first lockdown, reaching a peak of 63% above a 2013 baseline in mid-2021, falling back to a 24% increase above 2013 in late 2022, consistent with a modest rate of long-term growth. Although there were many adaptations to urban roads at the outset of the pandemic to facilitate cycling as an alternative to crowded public transport, the ultimate impact of this will not be clear until the extent of return to the office becomes evident.





The findings for 2022 as a whole, from the National Travel Survey, show only partial return to pre-pandemic levels, which may reflect the emergence of the Omicron variant in late 2021, even though travel restrictions were lifted by February 2022. Thus, average travel time prior to the pandemic was close to 60 minutes a day; during 2020 and 2021 it fell to about 45 minutes, but rose in 2022 to 53 minutes. It would not be surprising if average travel time returned to an hour a day in 2023, although it remains too early to rule out some longer term change in travel behaviour, for instance from increased working from home. Thus, the average number of commuting trips in 2022 was 85% of that in 2019, whereas the average number of education trips (including escorting) was 94% of the earlier year, indicating the greater opportunity for working from home in contrast to studying at home. Average car mileage in 2022 was 89% of that in 2019.





Data published by Transport for London provide a more granular account of the position as of late 2023 (see Figure). Overall public transport demand reached 90% of the pre-pandemic baseline. There has been a consolidation of weekday travel on Tuesdays to Thursdays, where demand is typically higher than on Mondays and Fridays (particularly on rail modes), although only 26 per cent of all London residents have the option to work from home, reflecting a ‘blue collar’ versus ‘white collar’ difference. There is also more travel on weekends than on some weekdays, and slightly longer average journey lengths, all of which appear to be becoming established features of post-pandemic demand.





Conclusions





A key question is whether the travel changes triggered by the pandemic will have long term impacts that will help achieve transport decarbonisation. The evidence is that car use rebounded towards pre-pandemic levels faster than public transport use, where full recovery has yet to occur, and may not do so if working from home persists as an alternative to the full week in the workplace. Active travel at best shows a slow growth trend.





The pandemic has shown that we could make major changes to lifestyle and travel behaviour under the impetus of concerns about personal health. Coming out of the pandemic, some analysts saw indications of a long-term shift to travelling less, notably those working from home making less use of the car. It is possible that working from home will prove to be a long term feature for those for whom it is practicable and where employers are amenable, resulting in more agreeable and less crowded and congested commuting. Yet this leaves open whether and how the saving in commuting time might be used, whether for nontravel activities or for other kinds of journey purpose, and by what mode.





The full impact of the pandemic on travel behaviour therefore remains to be seen, yet the emerging evidence suggests that we largely reverted to pre-pandemic travel behaviour, particularly by car, once the threat to health had receded. The impetus of the climate emergency is less immediately pressing, and so we persist in travel behaviour that meets our needs for access to people, places, activities and services, with the opportunities that ensue, hoping that advances in technology would avoid having to make hard choices about travelling less. Those seeking substantial reductions in car use to mitigate climate change can take but little comfort from the pandemic experience.





This blog was the basis for an article in Local Transport Today 23 January 2024.


					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

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The National Infrastructure Commission published its Second National Infrastructure Assessment on 18 October. The Commission’s objectives, set by the Government when it was established in 2015, are to support sustainable economic growth across all regions of the UK, improve competitiveness, improve quality of life, support climate resilience and transition to net zero carbon emissions by 2050, all within a specified long-term funding envelope for its recommendations.





The NIC’s remit is to issue a comprehensive analysis of the UK’s infrastructure requirements once every five years. This covers all economic infrastructure sectors, setting out recommendations for transport, energy, water and wastewater, flood resilience, digital connectivity and solid waste. The Assessment takes a 30-year view of the infrastructure needs within UK government competence and identifies the policies and funding to meet them.





First, I will look at some of the key conclusions and recommendations concerning transport from the NIC’s analysis, which should be fairly uncontentious, at least for transport planners and practitioners:





  • The public transport networks of England’s largest cities under-perform relative to comparable European cities. Initial priorities for investment should be in Birmingham, Bristol, Leeds and Manchester and their wider city regions, to prevent growth being constrained. The scale of capacity increases required justifies investment in rail- or tram-based projects. The government should make financial support conditional on cities committing to introduce demand management measures to reduce car journeys in city centres, and cities should provide a contribution of at least 15-25% to the funding of large projects, whether from fiscal devolution or transport user charging.





  • Transport budgets should be devolved to all local authorities responsible for strategic transport so that all places are able to maintain existing infrastructure – for example improving the condition of road surfaces – and invest for local growth. This will also help places develop locally led infrastructure strategies through which transport investment can be considered against long term goals and planned alongside housing and land use development.





  • For the national road and rail networks, the government’s first priority should be to maintain existing networks by investing adequately in maintenance and renewal, including ensuring resilience to climate change impacts.





  • In order to align the processes of road and rail capital investment, the government should set a long-term investment pipeline across road and rail around an indicative total budget envelope and with clear common strategic objectives. This should incorporate a strategic vision for the main transport corridors that includes both road and rail, ensuring that they are considered together and not separately.





The NIC goes on to say that the cancellation of HS2 beyond Birmingham, which happened only at the beginning of October, after the Assessment had been completed, leaves a major gap in the UK’s rail strategy around which a number of cities have based their economic growth plans. A new comprehensive, long term and fully costed plan is needed, says the Commission, to set out how rail improvements will address the capacity and connectivity challenges facing city regions in the North and Midlands. Who could argue with that?





More problematic, in my view, is the NIC ‘s proposition that the government should plan and invest in enhancements to the road network, targeting under-performing sections that can facilitate trade in goods, and provide better connections between cities to facilitate trade in services, observing that it is not clear that this happens at present. Accordingly, the NIC has developed a portfolio of road enhancement options, based on a connectivity metric developed by consultants, that gives each place in Britain a score to denote how well connected it is to other places, calculated by taking the average travel time between a given place and other places in Britain, and weighting them by population and distance, which are useful indicators of likely demand for travel between places. This approach is used to identify the worst performing routes on the network with substantial demand potential between key cities and towns (see map in illustration). The portfolio has been developed within a proposed budget for road investment to cover the next 20 to 30 years.





In support of its proposals for road investments, the Commission states that better connectivity will help improve trade efficiency, making it easier for businesses to move freight and trade goods and services. However, the evidence for this is problematical. For instance, one source cited by the NIC concludes that for an inter-regional transport investment, economic activity may shift either to the lower productivity region (the periphery) or to the higher productivity region (the core), the outcome depending on the underlying economic conditions and the type and scope of the investment. This is known as the Two-way Road Effect.





The emphasis of the NIC ‘s analysis is on trade in goods and services, only indirectly on non-business travel. Yet adding capacity to road and rail routes accommodates and generates more use of all kinds. On motorways, for instance, there is evidence that the increased capacity arising from converting the hard shoulder to a running lane results in local users, commuters and others, diverting to take advantage of a faster journey, pre-empting capacity intended for longer distance business users. A low connectivity metric score may well arise from delays due to morning and evening traffic congestion, indicating the existence of substantial car-based commuting. This suggests that enhancement of capacity could be expected to further increase in use by commuters, with little benefit to trade in goods and services. So, I would contend, the NIC’s approach to connectivity is too simplistic.





A further problem with the NIC’s analysis is that although it recognises that road investment will need to be compatible with plans to decarbonise transport, it concludes that the additional emissions from its proposals will not themselves substantially alter the scale of the challenge (which must therefore be borne by the plan to achieve widespread vehicle electrification by 2035). This conclusion is based on embracing the Department for Transport’s projections of road traffic demand growth of 10-28% by 2035 (as indicated in the DfT Decarbonisation Plan), while a road enhancement programme over that period would be expected to increase demand by only around 0.6 to 1.3% (based on historic evidence from a number of studies).





However, the rule of thumb, based on general experience, is that we cannot build our way out of congestion, so any increase in capacity will result in more journeys (good for trade), it will also mean more traffic, resulting in more carbon emissions – at least until fossil fuels are eliminated from road transport – and restoring congestion to what it had been (not good for trade). The Commission’s analysis, suggesting that the additional carbon emissions from its road investment proposals are relatively small, is unconvincing. What is missing is an estimate of the total additional carbon emissions from its programme of road investment, to be compared with the DfT Decarbonisation Plan projection of 620-850 MtCO2 savings from vehicle electrification between 2020 and 2050. If the total additional carbon emissions from the proposed road investments turns out to be relatively small, this implies relatively little benefits to trade; if they are large relative to the impact of vehicle electrification, then the pathway to net zero is put at risk.





A lacuna in the Commission’s analysis of transport infrastructure investment more generally is the failure to consider the application of digital technologies, both to the highway network and the vehicles using it, to enhance the performance of the system overall. The exemplar for this is the application of digital signalling on the railways that allows shorter headways between trains at peak times, thus increasing the capacity of existing track.





Conclusion





I had high hopes for the NIC as an alternative source of policy advice and appraisal methodology when it was set up in 2015. Its analysis of rail investments for the Midlands and North of England offered fresh thinking and was influential in shaping the Government’s plans published in 2021. But the Commission’s proposals for road investment are disappointing, both as regards methodology and conclusions. I suspect at least part of the problem is that its efforts are spread across the whole range of infrastructure investment it is required to cover, so that there is too little capability for deep thinking about how the road network functions and how additional capacity impacts on performance. The NIC needs to develop better models, methodologies and data sources if it to offer fresh thinking for road investment and challenge conventional wisdom and assumptions. If not to provide fresh thinking to that hitherto applied by the DfT, what is the purpose and benefit of the Commission?





Moreover, the Commission was badly unsighted by the Prime Minister’s announcement of the truncation of HS2. The failure of the Government to engage with it on such a major decision prompts a question about the purpose and status of the Commission. The politically-driven redistribution of the funds allocated to HS2 to local transport schemes is quite contradictory to the long term analytically-driven approach that is the remit of the NIC. So, while in principle, analysis of long term requirements for infrastructure investment must be right, in practice short term budgetary constraints and political priorities can render the long view nugatory. One has to ask whether there is a future for the NIC.





A Labour government might well be more sympathetic to the NIC’s role, given that the party in opposition in 2012 established a review of infrastructure planning under Sir John Armitt, now chair of the NIC. That review indeed proposed a National Infrastructure Commission be established. Labour has plans for major capital investment to support the transition to net zero, so having a source of independent advice on such expenditure may be continue to be attractive.





Indeed, there may be a case for merging the NIC with the Climate Change Committee, given the overlap of functions and their cross-departmental approach to future demand and supply. Yet as long as individual departments and their ministers retain responsibility for their budgets and spending plans, with the associated tendency to take a short term view, the strategic may continue to be subordinate to the politically pragmatic.





This blog post was the basis for an article in Local Transport Today of 28 November 2023


					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

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The Department for Transport has recently issued a new set of National Road Traffic Projections (known as ‘forecasts’ in the past, ‘projections’ perhaps indicating rather less commitment to the findings). These apply to England and Wales and look ahead as far as 2060. The new projections are derived from the DfT’s National Transport Model (NTM), which has been developed and updated since the 2018 forecasts –  so it is claimed, but see below.





The projections relate to a set of Common Analytical Scenarios, developed by DfT with the aims of better assessing uncertainty in scheme appraisal. There is a Core Scenario plus seven variants illustrating differences in economic growth, regional distribution of population, behavioural change, new technology and decarbonisation. Except for the Behavioural Change scenario, the other variants were created by changing some of the Core Scenario assumptions. For the Core Scenario, relationships between the key drivers of demand and road traffic are broadly assumed to continue in line with historical trends.





A noteworthy feature of the new projections is that traffic is expected to grow in all scenarios, by between 8% and 54% to 2060. This contrasts with the widely held view that car use needs to be reduced to meet the Government’s commitment to Net Zero by 2050, although this is not the DfT’s view. Projections of CO2 reductions to 2060 range from 38% to 98%, depending on scenario.





The Core Scenario is based on ‘existing firm and funded policies only’ and projects a 22% increase in traffic to 2060 and a 42% decrease in carbon emissions. Yet Net Zero by 2050 is surely a firm government commitment. The DfT published its Transport Decarbonisation Plan in 2021 which projected that this would be achievable, implying that future funding and policy development would need to constrain carbon emissions from road traffic to that indicated by the low carbon scenario projections. In which case, one might wonder why publish high carbon projections that go less than half way to achieving Net Zero.





This notion of ‘existing firm and funded policies only’ is stated as encompassing published plans or funded policies. So perhaps the civil servants are drawing attention to the shortcomings of the Transport Decarbonisation Plan, which was pretty vague about the details, particularly about the achievement of behavioural change. Carbon emissions under the Core Scenario are projected to fall initially quite rapidly, but then level off, apparently because ‘the details of future car and LGV regulations to reduce CO2e emissions beyond this point have yet to be finalised.’ (para 4.15 of the NRTP). Certainly, the details of the ‘ZEV Mandate’ remain to be settled – this is intended to oblige manufacturers to sell a specified increasing proportion of zero emission vehicles during the transition – a relevant factor may be the availability of battery production in the UK, which may require government financial support. Perhaps ministers are having a wobble about this Mandate, leading the civil servants to point up the implications for Net Zero of a weakening of policy intent. Nevertheless, it all seems very odd. If no new cars and vans propelled solely internal combustion engines are to be sold after 2030 (hybrids after 2035), then the normal turnover of vehicles would mean continuing decarbonisation until all internal combustion engine vehicles are scrapped (the average age of car at scrappage is around 14 years).





Congestion delays are projected to increase by between 4% and 59% by 2060, depending on scenario, which would provide a justification for creating more capacity. So another possible explanation of the ‘firm and funded’ qualifier is that no account has been taken of a future road investment programme, in particular RIS3 for the period 2025-2030, currently being planned.





In contrast to the Core Scenario, there are three scenarios that project carbon emissions reducing to near zero by 2050. The Vehicle-led and Mode-balanced Decarbonisation Scenarios assume high and fast uptake of EVs and other zero emission vehicles ‘in line with the government’s stated ambitions to end the sale of diesel and petrol cars, vans, HGVs,
and buses/coaches.’ (para 4.48). The Technology Scenario adds a high uptake of connected and autonomous vehicles.





A further scenario of interest is a Behavioural Change Scenario, involving new ways of working, shopping and travelling. This reflects past falling trends in driving licence holding by young people and in trip rates for most purposes, as well as coronavirus pandemic-induced changes in behaviour that are assumed to continue. Some of the latter assumptions are quite striking: 40% reduction in education trips by 2041, 39% in commuting, 41% in personal business, and 55% reduction in visiting friends and relatives (para 3.37). While it is welcome that the DfT is addressing the scope for changes in travel behaviour, these particular assumptions seem on the high side. Nevertheless, the impact of these behaviour changes is surprisingly small – only to level off the growth of car traffic, with van traffic increasing by 45% to 2060 (para 4.39), to compensate for car trips not taken. Car carbon emissions are similar to those of the Core Scenario, indicating that behavioural change in itself is expected to make minimal impact.





The new traffic projections adhere to the traditional practice of predicting demand for road travel driven predominantly by changes in travel costs, population and economic growth, 90% of demand growth being attributed to these factors. A large number of other factors are considered, which is appropriate since they are relevant. The outcome is an extremely detailed formulation of a set of scenarios, which therefore avoids criticism that potentially important factors have been disregarded, but it is then hard to see the wood for the trees.





Big picture





So, let’s stand back from the detail, to see the big picture, which in my view is this: the National Travel Survey has found a gradual decline in per capita travel since the turn of the century, including decline in the average number of trips taken and in distance travelled by car (prior to the pandemic) (see Chart above). This followed rapid growth in car use in the last century, mainly the result of increasing car ownership. But household car ownership has stabilised, with some three-quarters of households owning one or more cars or vans. There was a clear break in trend at the turn of the century, which implies a change in relationships between the determinants of demand and growth of car use. Accordingly, for modelling purposes, such relationships (known as elasticities) need to be forward-looking; assuming continuity of historic trends, as does the Core Scenario, is inappropriate.





The average distance travelled per capita by car depends mainly on three factors: speed of travel, time available for travel, and level of car ownership. The first two seem unlikely to change in the future, and while there has been growth of the number of cars owned within car-owning households, the second or third car tends to be used less than the first. The impact of economic growth and income growth on per capita car use is a second order effect, seen mainly as the purchase of larger, more expensive and fuel-consuming vehicles, notably SUVs. The unvarying travel time constraint, of about an hour a day on average, limits the distance that can be travelled, irrespective of income. The DfT Projections assume that three-quarters of the projected traffic growth is driven by increases in GDP and reduced costs of driving (para 4.7), which seems improbable.





That leaves population growth, which the new traffic projections take from the Office of Budget Responsibility as an overall 4% increase by 2060, and which therefore would have a very small impact on travel demand. The consequences for road traffic growth would depend on the extent to which the additional inhabitants were accommodated in new homes on greenfield sites where car use would be the norm; or at higher density within existing urban areas, where public transport would be relevant. Unfortunately if understandably, the Regional Scenario considers accommodating the population growth in regions beyond the Wider South East, which may be relevant to the Levelling Up agenda but has little impact on overall traffic or carbon emissions.





So, I would not expect much future change in either per capita car use or total road traffic, based on recent trends. This conclusion is at odds with the DfT modelling and so raises questions about the validity of the NTM, which has been in use for over twenty years in a series of versions. The version cited in the new projections is NTM version 2 Rebased (NTMv2R), which is unexpected since a new version, NTMv5, was announced in 2019.





Modelling regressed





NTMv5 was developed as a spatially detailed model to complement NTMv2R by providing additional capabilities for assessing the impact of major new road schemes, packages of transport improvements or spatially based charging arrangements. One particular purpose was to develop scenario-based traffic forecasts arising from changes in population, travel trends, GDP, car ownership, fuel price and road tax. Accordingly, it is surprising that NTMv5 does not appear to have been used to generate the new traffic projections.





One possible explanation is that a peer review of NTMv5 by experienced practitioners made a considerable number of criticisms. The reviewers advised caution in application of the model, primarily due to the focus of the NTMv5 being on the more strategic highway network, whereas many of the potential applications relate to urban travel policy and public transport interventions. In particular, the reviewers were critical of the treatment of urban traffic, observing that the assumed relation between traffic speed and demand growth lacked validity, and that the range of policies aimed at reducing urban car use were not taken into account. Besides, it was noted that the DfT’s car ownership model has not recognised that ownership in dense urban areas has been declining for many years in response to increasing population density, notwithstanding rising incomes. The reviewers concluded that the model could not be safely used to examine policies that relate specifically to London, and queried whether this might apply more generally to rapidly growing dense urban areas across England. They took the view that the model should be suitable for use in forecasting the growth of road traffic in most areas other than those adjacent to or within major urban areas, which is a pretty major qualification.





So perhaps the DfT was unable to rectify NTMv5 to respond to these criticisms, and hence reverted to NTMv2R, which had previously been used to prepare the 2018 National Road Traffic Forecasts. This version was also the subject of peer review, the reviewers noting problems with modelling traffic in London and other conurbations where non-car modes are most competitive. The 2018 Forecasts predicted substantial traffic growth in London, but admitted that this was likely to be over-forecasting because travel behaviour in London and relationships between key variables and road traffic demand can be different to the rest of the country, due to a high use of public transport and significantly higher congestion on roads. This was recognised as known issue with the NTM, which it was intended would be addressed in the future (para 4.33 of 2018 forecasts). However, it is not clear whether this has been done, prior to preparation of the new projections.





There is bound to be feedback from congested road capacity to travel demand. In the ‘vision and validate’ approach, nowadays effectively adopted by cities, the vision of the balance between the twin functions of roads – movement and place – means that travel demand must be managed. This contrasts with the earlier ‘predict and provide’ perspective, where forecasts of traffic growth led to proposals to increase road capacity. If our vision now includes Net Zero, the presumption of increased road capacity is problematic, and the modelling should take account of capacity constraints on demand for road travel. Given that over 80% of the UK population live in urban areas, models need to be responsive to urban traffic conditions.





The validity of the NTM is therefore questionable. The modelling suite used to prepare the present projections is complex and opaque, hence it is not possible for those other than DfT modellers and their consultants to understand what has been achieved and what has not. The peer reviews provide an exceptional opportunity to look under the bonnet, and what was found make one doubt whether the NTM in its various versions is reliably roadworthy. And that’s before the problems associated with specifying scenarios to reflect policy uncertainties.





One intention in creating NTMv5 was to make this important model transparent to external stakeholders, which NTMv2R is not. Lack of transparency and accessibility contrasts unfavourably with the online Carbon Calculator of the Department for Business, Energy and Industrial Strategy, open to all.





Projecting future road traffic volumes is not an end in itself, rather it serves policy purposes. Projections of growth of both traffic and congestion delays would help justify a further major road investment programme. Projecting carbon reductions of 98% meets the Net Zero objective. Arguably, the modellers have struggled to reconcile both policy requirements, but have fallen short.





This blog was the basis for an article in Local Transport Today 24 January 2023.


					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

					Car useTravel				

						
		


				
				
		

			

The British government has long been keen on encouraging the prospect of what were initially known as ‘connected and autonomous vehicles’ (CAV). In 2015 it set up the Centre for Connected and Autonomous Vehicles to stimulate the development of the technology and steered funds into the sector via the Transport Systems Catapult. Typical of recent bullish statements by DfT ministers is this from Grant Shapps, when Transport Secretary, earlier this year:





‘The benefits of self-driving vehicles have the potential to be huge. Not only can they improve people’s access to education and other vital services, but the industry itself can create tens of thousands of job opportunities throughout the country. Most importantly, they’re expected to make our roads safer by reducing the dangers of driver error in road collisions. We want the UK to be at the forefront of developing and using this fantastic technology, and that is why we are investing millions in vital research into safety and setting the legislation to ensure we gain the full benefits that this technology promises.’ 





There’s no doubting the enthusiasm, but is this just hype, or is there a reality underpinning the vision? And what exactly is the vision anyway? It seems to be principally focussed on re-imagining the existing private car as a vehicle that no longer needs a driver behind the wheel, and which can whizz its occupants around in robotic mode, avoiding collisions and improving safety, whilst allowing the passengers to get on with other tasks, or simply enjoying the ride and the view – or maybe having a nap.





Trials of such driverless cars on public roads are taking place in a number of countries, mostly with a human ‘safety driver’ still on board, but this requirement has been dropped in some US and Chinese locations where traffic conditions are deemed favourable. Yet the prospects remain uncertain, so it is timely that the House of Commons Transport Committee has initiated an inquiry into self-driving vehicles. 





I am specifically exploring here the prospects for such driverless cars ever becoming widespread, acceptable and effective in the foreseeable future. I’m not considering the possibilities of driverless multi-user pods or rapid transit buses on at least semi-segregated roads, where some deployment has already taken place. Nor do I discuss the scope for freight vehicles to operate in platoons on inter-urban motorways, where a recent UK trial found the hoped-for improvements in fuel use to be disappointing. 





The government published a substantial policy document in August of this year entitled Connected and Automated Mobility 2025, jointly presented to Parliament by the Secretary of State for Transport and the Secretary of State for Business, Energy and Industrial Strategy. 





Note that Automated has replaced Autonomous, perhaps reflecting a scaling down of expectation. Note also that Mobility has replaced Vehicles, hence CAM rather than CAV, a sensible but small generalisation. But note also the absence of reference to Connected within the new document: the whole emphasis is on automation of individual vehicles, with little mention of the possibilities of connectedness between vehicles. In this, the government is following the prevailing mindset of the automotive industry, tech start-ups as well as established vehicle manufacturers, who see autonomy easier to achieve than connectedness between vehicles of different manufacture and type.





The self-driving vehicle concept is seen as the application of CAM technologies with greatest commercial potential. But for deployment to happen, safety must be assured. So in 2018 the government asked the Law Commission to develop the legislative framework for a safety regime. The Commission’s final report, also published earlier this year, will be the basis of legislation that the DfT will bring forward. 





Legal consequences





Because, to be useful, a self-driving vehicle will need to be able to drive itself for at least part of a journey, there are profound legal consequences. The human driver can no longer be the principal focus of accountability for road safety. Instead, new systems of safety assurance are needed, both before and after vehicles are allowed to drive themselves on roads. The legal framework has to cover both self-driving functions, where a user must be able to take over control when the vehicle cannot cope – for instance in poor visibility-  as well as more advanced automation when the vehicle is able to cope under all conditions.





The Law Commission proposes three new legal actors:





  • Authorised Self-Driving Entity (ASDE) – the vehicle manufacturer or software developer who puts a self-driving vehicle forward for authorisation to the regulator.
  • User-in-charge (UiC) – the human in the driving seat while the vehicle is driving itself, who can take charge if the self-driving function cannot cope.
  • NUiC operator, a supervisory system responsible when there is no user in charge, to navigate obstructions and deal with incidents.





There will need to be regulators for new functions:





  • Vehicle type approval.
  • Authorisation to self-drive.
  • In-use safety regulation.
  • NUiC operator licencing.





The proposals of the Law Commission will need to be enacted in legislation, to make possible the government’s intention to make the UK an attractive place for the deployment of self-driving vehicles, even though the main development of the technology is taking place in the US, where there is considerable diversity of regulatory regimes across the states. However, the comprehensive legal framework may deter some prospective developers.





Ride-hailing operators such as Uber, who have aspirations for a driverless future, might hope to save the costs of the driver. But under the proposed regime they would need to own the vehicles and accept responsibility as the NUiC operator for oversight of operations, and possibly also to be the ASDE. This would be a large and costly change from the company’s current capital-light business model, where ownership of the vehicles lies elsewhere.





Electric vehicle maker Tesla’s approach to automation has meanwhile been to incorporate the hardware into existing privately-owned vehicles and to progressively improve the software over time to get to the point of implementing what is being marketed as ‘Full Self-Driving’ capability. Taken literally, this would also require Tesla to be licenced as a NUiC operator, with oversight of its whole fleet of vehicles on the road, which is hardly conceivable. So if they use the automated mode, Tesla drivers would need always to stay alert and be prepared to take charge in the event of the unexpected.





Safety





A key question in this discussion is the appropriate level of safety required for a self-driving vehicle. The Law Commission took the view that this is best decided by ministers, in the light of judgement of the public’s acceptance of risk. The view of the government, set out in the recent policy document, is that self-driving vehicles should be held to the same standard of behaviour as that expected in road traffic legislation for human drivers: ‘competent and careful’. This standard is higher than that generally enforceable now on the average human driver – who include, for example, drivers who are fatigued, distracted or under the influence of drink or drugs- despite this not being the behaviour that is expected of them.





The ‘competent and careful’ standard appears to be the minimum required politically. Anything less would hardly be publicly acceptable. But does it do the trick? On occasion, self-driving vehicles will inevitably be involved in crashes with fatalities. ‘Competent and careful’ implies that the self-driving vehicle could not be blamed. The fault would be attributed to the other drivers (‘human error’) or to vehicle- or road-related defects. Yet would it be possible to demonstrate such blamelessness in practice, when the other parties to the crash would be seeking to shift responsibility?





In Britain there is one fatality per 140 million miles driven, so deaths involving self-driving vehicles could be expected to be exceedingly rare events. But when they do occur, they will surely  attract much public attention. As a result, there may well be pressure to tighten safety standards further, which, done in response to public anxiety, may undermine the general acceptability of self-driving vehicles. There is therefore a good case for a higher initial safety standard than ‘competent and careful’ to be set.





Benefits





The practical development of vehicle automation for general deployment is proving more difficult than the pioneering technology optimists had hoped. Apart from getting the technology to the point of being publicly acceptable, there is an evident need to develop viable business models consistent with the expected regulatory regime. And that means asking who will buy the vehicles? This in turn will depend on how purchasers perceive the benefits. In this regard, the practical benefits of vehicle automation remain unclear.





Proponents point up the safety benefits, especially in the United States where almost 39,000 people were killed in motor vehicle crashes in 2020, a fatality rate of 12 per 100,00 population, compared with 2.3 for the UK. Given that human error and risky behaviour is said to be responsible for 90% or more of fatalities, it would seem a reasonable expectation that the ‘robot driver’ of a self-driving vehicle could do better than fallible humans. On the other hand, robots suffer from their own shortcomings, tending to be less effective at perceptions involving high variability or alternative interpretations. In particular, robots would find it difficult to engage in the kind of visual negotiation that occurs between human drivers to settle which gives way when space is tight. Besides, the driving performance of a robot would need to be very similar- or better- than that of a human driver to ensure public acceptability. A self-driving vehicle that proceeded particularly cautiously to meet safety requirements could be unattractive to the purchasers, and an irritation to other road users . So the robot driver would effectively need to learn how to drive like a human.





Another claimed benefit is that automation might increase the capacity of existing roads by allowing these safety-conscious vehicles to move with shorter headways, that is, with a smaller distance between them than the recommended two-second gap on fast roads. The more precise control exercised by a robot might also smooth traffic flows and allow the use of narrower lanes. However, such increases in capacity would seem likely to be possible only on roads dedicated to self-driving vehicles, since the presence of conventional vehicles, not to mention cyclists, motorcycles and pedestrians, would require standard spacing to be maintained. In any event, in line with current experience, any increase in capacity would be expected to attract additional traffic, so that long term congestion relief would not be expected.





Automation that allowed an increase in road capacity might anyway be of interest to a road authority, as a public benefit, but not to individual vehicle owners if they had to bear the cost of the necessary technology. Because self-driving vehicles would be capable of operating empty, for example when returning to base after dropping off the occupant, and thus not needing to pay to park, they could add to the traffic miles generated and hence to congestion. Conventional taxis operate without a passenger while seeking a fare, of course, but privately owned vehicles without occupants would be a new source of traffic. All this is before any consideration of how they would be licenced and the fee, and/or any road user charges they must pay- whether the same as, or different to, conventionally driven vehicles.





Prospects





One obvious problem of driverless vehicles is how they can happily enter the vehicle population alongside conventionally-driven vehicles The prospects for widespread vehicle automation on existing roads with mixed traffic seem very uncertain. The driving task on motorways might be lessened in good visibility and in the absence of road works, but the driver would need to be immediately available to take control in adverse situations – and when going into general traffic. There are some low-speed environments that might accommodate driverless vehicles, including campuses, business parks and other new developments with extensive road space, and some such deployment has taken place; in these circumstances driverless shared-use pods could be a cost-effective transport technology. Yet it is hard to see driverless vehicles successfully negotiating historic towns and cities with complex road layouts, often narrow streets, extensive kerbside parked cars, cyclists and randomly moving pedestrians.





This impediment to automation in general traffic in urban areas is particularly relevant to the idea of driverless taxis that could be attractive to ride-hailing operators such as Uber, to avoid the cost of the human driver and spread the extra capital cost of the vehicle through intensive use. On the other hand, there would be the potential cost of ownership of fleets of automated taxis, rather than the current model of ride-hailing operators where drivers own their own vehicles. Besides, a driverless taxi system operator would need to exercise oversight of the activity of each vehicle in their fleet, to deal remotely with incidents and navigate obstructions, which would add to costs. It is certainly possible that the relatively unskilled human taxi driver in their own car may remain a lower cost option than the robot driver in a specially provided one.





The way forward thus remains unclear. Indeed, after more than a decade of development of autonomous vehicles, the early excitement and optimism have been followed by some disillusion as the problems of achieving and implementing an acceptably safe product have been recognised. The recent decline in the value of technology stocks and venture capital investments suggests that finance to support further development of vehicle automation may be harder to come by. Indeed, Argo, an autonomous vehicle tech start up funded by Ford and VW, is shutting down, evidence of uncertain prospects for the technology. Accordingly, it would be premature to predict the eventual outcome, both in timing and extent of deployment. Who can judge whether, as enthusiasts for the technology assert, children born today will not need to learn to drive a car?





The major transport innovations of the past have been those that made possible step-change increases in the speed of travel – the railway and the modern bicycle in the nineteenth century, the internal combustion engine for road vehicles and the jet engine for aircraft in the twentieth century. These step-change increases in speed, and thus reductions in journey times, permitted increased access to people and places, opportunities and choices, which were the benefits of harnessing the energy of fossil fuels for the motive power of mobility. In contrast, it is unlikely that automation would increase the average speed of travel on the existing road network, which is constrained by safety and congestion. Accordingly, the benefits of vehicle automation are much more likely to take the form of improved journey quality, and hopefully safety, and the possibility of doing other things whilst on the move – particularly those that have come with the digital information and communications era . It remains to be seen to what extent purchasers of driverless vehicles will be willing to pay for these benefits – or if they can achieve them by adapting to wider new mobility thinking in other ways, particularly at a time of changing inter-generational attitudes to the ownership of vehicles personally, and their use.





The UK government has been very supportive of vehicle automation, in particular aiming to put in place a comprehensive legislative regime based on the thorough analysis by the Law Commission. Yet there is only limited commercial development of the technology underway in Britain, so the benefits for both industrial and transport policy do not seem to be that great. A more important technology priority for road transport policy has surely to be the switch to electric propulsion and the quest for transport decarbonisation.





This blog is the basis for an article in Local Transport Today dated 14 November 2022.







					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

					Car useEconomicsTravel				

						
		


				
				
		

			

I made a presentation to the Highways UK Conference held at Birmingham 2-3 November 2022. These are the main points.





The widespread use of Digital Navigation (DN) (generally known as satnav) is changing travel behaviour (see my recent paper). One impact is to divert local users to major roads to take advantage of increased capacity. I have analysed two Smart Motorway investments in detail: M25 Junctions 23-27 and M1 Junctions 10-13. The Smart Motorway concept involves converting the hard shoulder to a running lane, originally during the periods of morning and evening peak demand, or, as has been recent practice, throughout the day. The advantage is that capacity can be increased without the cost of additional land take or rebuilding bridges.





Monitoring the traffic flows and speeds 3-5 years after opening of the two schemes showed that the forecast increases in speed had not occurred, and hence the economic benefits, which largely depend on the value of time savings, were not obtained. Something had gone badly wrong with the traffic modelling that informed the investment decisions. Accordingly, I sought and obtained copies of the relevant reports.





The traffic modelling in both cases employed regional variable demand models that utilised the long established SATURN software. Traffic flows and speeds for the with- and without-investment cases were compared, and the outputs fed into the economic model, the Department for Transport’s TUBA model, which forecasts the economic benefits. In both cases, substantial travel time saving benefits were projected for business users, offset by a small increase in vehicle operating costs (VOC). There were also substantial time savings for non-business users (commuters and others) but these were very largely or entirely offset by increased VOC. Hence there was no net economic benefit to non-business users. It seems likely that increased number such users, above that forecast, pre-empted the increased capacity intended for longer-distance business users.





Examination of the routing information offered by Google Maps, for a journey between two locations in the neighbourhood of a Smart Motorway scheme, shows that diversion to the motorway can save time, but at the cost of increased distance and hence fuel cost (see screenshot at top). This is consistent with the modelling, on the basis that road users are likely to take the faster route and be less concerned about VOC.





Those making local trips have a variety of options, using the motorway as well as local roads, while long-distance users are likely to stay on the motorway. In the past, local users would have made routing decisions based on recent experience of congestion and  broadcast traffic information. But with the widespread use of DN, the choice of the fastest route is clear. The impact of DN is to increase local use of motorway capacity, to the disadvantage of longer-distance users. This seems likely to be an important contributory factor to the failure of the M25 and M1 Smart Motorway investments to deliver the expected travel time savings.





Although detailed information is available only for two Smart Motorway schemes thus far, it is likely that these may not be unrepresentative. The Strategic Road Network (SRN) is under greatest stress in or near areas of population density, where local users and longer-distance users compete for road space. Remote from such areas, the traffic generally flows fairly freely. So opportunities for investment appear to be where local users are best placed to take advantage of new capacity.





If the motorway system operated as a toll road, as in France or Italy, tolls would deter use by locals. The one example in the UK is the M6 Toll road in the West Midlands, built and operated with private finance, where daily traffic is half that on the adjacent M6 proper, doubtless due in part to the toll that local users do not choose to pay. But this is the exception that proves the rule: which is that attempts to alleviate congestion by increasing the capacity of major roads experiencing marked peaks of traffic at commuting times, as with Smart Motorways, must be expected to result in increased use for local trips, to the disadvantage of longer-distance users.





To better appreciate the benefits of road investment, it would be important to understand the impact of DN on road user behaviour, so that this can be incorporated into the traffic modelling that informs investment decisions. It would also be important to get a more granular evaluation of outcomes of investment. Traffic and economic modelling of prospective investments distinguishes between business and non-business users, the former split between cars, light goods and heavy goods vehicles, and the latter between commuter and other journey purposes. In contrast, monitoring of outcomes only tracks total traffic, volume and speed. However, it is now possible to employ DN to distinguish between local and longest-distance traffic, as exemplified by the TomTom Origin/Destination analytical service. Making such a distinction is important for evaluating the economic benefits of investment since the total volume of traffic might be close to that forecast, but if the share of local users is greater than forecast, the economic benefit will be less than expected.





We are at present midway through the second five-year road investment programme, known as RIS2, worth £27bn over the period 2020-2025. RIS3 is now being planned. But there are headwinds:





  • The potential economic benefits are likely to be overstated, as discussed above.
  • Any increase in road capacity is inconsistent with the Net Zero climate change objective since both tailpipe and embedded carbon would be increased.
  • There are public anxieties about the safety of Smart Motorways in the absence of the hard shoulder, reflected in a critical report from the House of Commons Transport Committee, to which the government responded by halting new schemes until five years of safety data is available.
  • The government’s Levelling Up White Paper, published in early 2022, identified a dozen ‘missions’ across departments. The single mission for the Department for Transport is aimed at improving public transport in regional cities towards that achieved in London, a sensible political and social objective. There was no reference to road investment, which is sensible given that congestion delays on the SRN are less in the Midlands and North than in the South East.
  • Current pressures on medium term public expenditure.





Given these impediments, there is a good case for treating the SRN as a mature network, with a focus on operational efficiency. This is the case for urban roads, which in the past were enlarged to accommodate more traffic, but nowadays the trend is to reduce capacity allocated to general traffic, to encourage active travel and facilitate public transport. Similarly, the aviation sector focuses on operational efficiency – airlines maximising use of aircraft, allocated routes and passenger load factors; airports (struggling recently) optimising throughput of passengers and baggage; and air traffic managment making best use of crowded airspace. The underlying discipline is operations research, not civil engineering, plus the modelling and economic analysis of operations, not long-lived investment.





A focus on operational efficiency of the SRN would naturally prompt consideration of how best to take advantage of the huge investment in DN that has been made, both by providers of the service and by road users. Here a very odd phenomenon is the apparent disregard of DN by road authorities, at least a judged by their publications – no reference to satnav in those of National highways, the Department for Transport, or local authorities (with the one exception known to me, Transport for London’s collaboration with Waze). Why is this? Possibly because of the preoccupation of highways engineers with civil engineering works, the need to spend the large budget allocated to the SRN, the lack of professional background to cope with digital technologies, and that fact that road authorities are monopolies, so not subject to competitive pressures?





The one constituent of road users that is highly competitive is road freight, particularly that forming part of integrated logistics businesses, which makes extensive use of digital technologies to manage fleets on the SRN and delivery vehicles on local roads. We are well aware of this when we order goods online, with a specified delivery date and often a time slot, the ability to track packages, delivery confirmed on the doorstep, and our feedback sought on the experience – all done by algorithm. This kind of operational efficiency needs to be brought to bear on the totality of traffic on the road network.





Experienced network operators would naturally want to take advantage of DN, which is vehicle-to-infrastructure connectivity that is changing travel behaviour on a massive scale. One aim would be better to cope at times of stress – major incidents, bad weather, peak holiday flows. A second would be to optimise use of the network in normal times, including avoiding routing traffic through unsuitable minor roads.





There is a maxim that you can’t build your way out of congestion, which we know from experience to be generally true. The Smart Motorway case studies exemplify this truth and provide an explanation: increased capacity is taken up by local users, pre-empting capacity intended for longer-distance business users, with no overall economic benefit, and restoring congestion to what it had been before. However, when road users are asked why congestion is a problem, their main concern is the uncertainty of journey time. Digital Navigation provides estimates of journey time in advance, so those who need to be at their destination at a particular time can decide when best to set out; those who are more flexible can avoid the worst of congestion; and all can choose the fastest route.





Digital Navigation is vastly more cost-effective as a means to mitigate the impact of road traffic congestion than costly civil engineering investment.


					

		
				
		

	




			
				



			
				



			
				



		
			
						

		
	

		
		

										

					Car useTravel				

						
		


				
				
		

			

I have previously drawn attention to the impact of Digital Navigation (DN) of travel behaviour. The providers of this service, commonly known as satnav, respond to requests from users by providing routing options and journey times that take account of prevailing traffic conditions. Here I want to consider how this is achieved.





There is only fragmentary published information on how the routing algorithms function. It appears that a model of travel behaviour on the road network is constructed from trip data derived from users of the navigation service: trip origins, destinations, routes through the network, time of day/week, prevailing traffic conditions, journey times. Such a model may be analogous to a microsimulation model, but using observed trip data rather than synthetic data. It could also be viewed as combining the trip generation,  distribution and assignment stages of the standard four-stage transport model (the mode split stage not being relevant for committed road users seeking routing advice).





Providers of DN offer predictions of journey time in advance of setting out. Comparison of predicted and outturn journey times provides a check on the validity of the model. Machine Learning has been employed to improve the accuracy of journey time predictions of Google Maps.





The type of model developed by DN providers is novel and powerful in that it can utilise huge amounts of trip data, both real time and historic. A question is whether such models could be used to inform decisions on road investments and other interventions aimed at improving experience on the road network. That is, could the DN models replace conventional transport models for planning purposes?





The DN models already exist. Their cost of construction and operation is met by the income generated from sales, whether of direction services to business premises (e.g. Google Maps) or to vehicle manufacturers that fit DN as standard equipment (e.g. TomTom). So, the cost of using these models for planning purposes could be less than for building and using conventional models.





TomTom offers Origin Destination Analysis as a service and may therefore be open to suggestions for use of the underlying model for planning purposes.





Another possibility would be to create an open-source, crowd-funded DN model – a kind of not-for-profit version of Waze, a provider that encourages user input. The funders might be road authorities that would gain access the the underlying model for planning purposes.





A further possibility arises from the likelihood that some form of electronic road user charging will be introduced, as electric propulsion replaces the internal combustion engine, to replace revenue from fuel duty. This is likely to involve technology similar to DN, and might therefore be the basis of traffic modelling for other purposes.





DN is both changing travel behaviour and generating new travel models to inform public policy. We may be at the beginning of a new era of travel and transport analysis.


					

		
				
		

	




			
				



		
			
						

		
	

		
		

										

					Car useEconomicsTravel				

						
		


				
				
		

			

Induced traffic is the additional traffic that arises from investment to increase road capacity. The usual reason to increase capacity is to relieve congestion. The intended outcome is that journeys are faster and easier. Yet this can lead to more frequent or longer car trips, changes to route or destination, or mode switching from public transport. All these changes lead to more traffic on the network.





The problem with induced traffic is that the more of it there is, the less the savings in travel time, which are treated as the main economic benefit of investment. So, the magnitude of induced traffic is of interest, prompting the Department of Transport to commission a study by consultants WSP and RAND Europe of options to improve its measurement. Two broad approaches were identified: econometric analysis that quantifies the relation between road capacity changes and observed traffic levels over time; and Before and After (B&A) studies that compare traffic before and after particular interventions.





The disadvantage of the econometric approach is that it generates an aggregate measure that does not indicate the components of induced traffic. B&A studies are more illuminating and could be improved by use of mobile phone network data (MND) to quantify changes to travel behaviour. MND allows an understanding of origins and destinations of trips, before and after an intervention. Large samples of road users are available, which would enable distinction to be made between the various kinds of change in travel behaviour. Transport for London has developed a multi-modal strategic transport model that estimates demand from MND.





One possibility not considered in the WSP/RAND study would be to carry out a sample survey of users of the road network, before and after an intervention, identifying changes in travel behaviour over time. This could employ seven-day travel diaries as for the National Travel Survey, or GPS to track travel patterns via a smartphone app. Studies of this kind, known as longitudinal studies, are well established in medicine and the social sciences. Much current research into the impact of Covid-19 is longitudinal, for instance following the immune response to vaccination over time. However, longitudinal studies of travel behaviour are rare, although they have the potential to understand the impact of investments in far more illuminating detail than is possible with conventional before and after traffic counts. 





The WSP/RAND study concludes that all components of induced travel can be represented in the standard four-stage transport model, except that arising from changes to land use, which may have a substantial impact. However, the study did not consider the implications of induced traffic for the economic analysis of road investments, which routinely employs the output of a traffic model (including induced traffic effects) as input to an economic model. This is usually the DfT’s TUBA model, which generates monetary values of the time savings and other benefits/disbenefits. The net present value of the benefits is then compared with the investment costs to yield a benefit-cost ratio, important for investment decisions.





The phenomenon of induced traffic was recognised in a landmark 1994 report by the Standing Advisory Committee on Trunk Road Assessment (SACTRA). It is remarkable how little progress has been made in understanding its origins and incorporating this into modelling and economic appraisal. A cynic might say that this is because induced traffic undercuts the economic case for a road investment where the main benefit is supposed to be travel time savings, and so is yet a further headwind for the DfT’s £27 billion road investment programme. My own analysis of the widening of the M25 J23-27 showed that induced traffic, largely arising from rerouted local trips, was substantially greater than forecast and wiped out the economic benefits expected to accrue to longer distance business users. This is likely to be typical of investment to add capacity near densely populated urban areas where local commuters and others compete for road space with long distance business users. Standard traffic models are biased against fully recognising induced traffic.





The concept of induced traffic as an aggregate measure is now obsolete. Instead, we need to focus on how travel behaviour actually changes as the result of an intervention, and then work out how to value those behaviour changes. If an investment allows travel time to be saved, then monetary value can be ascribed according to established methods. However, we lack methodology for valuing longer trips to more distant destinations, motivated by the greater value of access to goods or services. Increased access is the real benefit of transport investment.





The above blog post was the basis for an article in Local Transport Today 836, 16 December 2021.