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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.

Car useTravel

Inrix, a firm that analyses road traffic, recently reported average delays in London due to congestion in 2021 of 148 hours, twice the national average, but virtually the same as in 2019, before the pandemic. This prompted debate about the impact of the increase in cycle lanes put in place in London in response to Covid-19.

To make sense of what is happening, we need to recognise that our availability of time always constrains the amount we can travel. There are many activities that we need to fit into the 24 hours of the day, and on average we spend just an hour on the move. This limits the build-up of congestion.

Road traffic congestion arises in areas of high population density and high car ownership where is not enough road space for all the car trips that might be made. If traffic volumes grow for any reason, delays increase and some potential car users make other choices. We may change the timing or route of a car journey, or the travel mode where there are alternatives available, or a different destination such as an alternative shopping centre, or not to travel at all, for instance by shopping online.

So, if road space is taken away from cars in order to create cycle or bus lanes, then initially congestion will increase. But the additional delays will induce some car drivers to make alternative choices and congestion will revert to what it had been. The overall impact is to reduce the share of trips by car, which is what has been happening in London for many years as the population has grown and as there has been large investment in public transport, with less road space for cars: private transport use fell from 48% in 2000 to 37% in 2019, while public transport use grew from 27% to 36% over the same period. Cycling increased from 1.2% to 2.4% while walking held steady at 25%.

The London Mayor’s transport strategy ambitiously aims to cut private transport use to 20% of all trips by 2041. This would be expected to diminish the total amount of traffic congestion, although not necessarily its intensity at peak times in the busiest areas.

Creating cycles lanes reduces the space available for cars but in itself it does not get people out of their cars. Copenhagen is a city famous for cycling, with 28% of journeys made by bike. Yet car traffic is only slightly less than in London. Aside from cycling, the other big difference is that public transport accounts for only half the proportion of trips compared with London.

The experience of Copenhagen indicates that we can get people off buses onto bikes, which are cheaper, healthier, better for the environment and no slower in congested traffic. Yet buses are an efficient way of using road space to move people in urban areas, with diesel engines being replaced by electric or hydrogen propulsion to cut carbon emissions. We would like to get drivers out of their cars onto bicycles, yet this has proved difficult, even in Copenhagen, a small flat city with excellent cycling infrastructure and a strong cycling culture.

Looking across a range of European cities, we find very diverse patterns of journeys by the different travel modes, reflecting, history, geography, size and population density. But we do not find cities with high levels of both cycling and public transport. So, the prospects for a substantial increase in cycling in London are far from certain, given the relatively high level of past public transport use.

The pandemic has had a major impact on public transport use in London, with bus and Tube journeys currently at only 70-75% of pre-pandemic levels. The financial consequences have been severe. Transport for London may have to embark upon a ‘managed decline’ scenario unless more support from the government is forthcoming.

In such circumstances, further investment in new rail routes would not be possible and existing services would be reduced. Investment in cycling would then be the most attractive way of implementing the strategy of reducing car use in London, both by encouraging cycling as an alternative and by lessening the scope for people to drive.

The above blog post was the basis for an article in The Conversation on 9 December 2021.

TransportTravel

The Department for Transport’s Decarbonisation Plan projects the decline of domestic transport greenhouse gas emissions from the present 120 MtCO2e a year to approaching zero by 2050 (see figure above). There is considerable initial uncertainty about the pathway but the range of projected outcomes narrows over time as the proportion of zero emission vehicles increases. The modelling is largely based on the Department’s long-established National Transport Model. Although little detail is provided, there are headline numbers for cumulative emission reductions over the period 2020 to 2050.

For cycling and walking, the projected savings from investments and policy initiatives are put at 1-6 MtCO2e, a notably wide range. For cars and vans, the savings are estimated to be 620-850 MtCO2e, a proportionately narrower range, reflecting greater confidence in the impact of policy to phase out the internal combustion engine. What is striking is the relatively tiny decarbonisation contribution expected from increases in active travel, at best one percent of that from car decarbonisation. This is surprising, given the prominence in the Plan of the intention to promote active travel, including expenditure of £2 billion over five years. This looks like a case of virtue signalling by DfT, wanting to be on the side of the angels.

Counting on minimal decarbonisation benefit from more cycling is consistent with the evidence from Copenhagen and other European cities that you can get people off the buses onto bikes but that it is difficult to get them out of their cars. In any event, 80% of car carbon emissions arise from trips of more than five miles, implying limited scope for savings from mode switching from car to active travel.

More generally, the DfT’s Plan relies very largely on technological innovation to achieve net zero for domestic transport by 2050. Others see a need for significant reduction in travel demand, including the Climate Change Committee (CCC) in its Sixth Carbon Budget report of December 2020, which envisages 20% of transport emission reductions from reduced demand. The CCC recommended commitment to 78% reduction in overall emissions by 2035 compared with 1990 levels. This was accepted by the government and is to be implemented through sector plans, of which the Transport Decarbonisation Plan is the first. 

The question of the need for travel demand reduction is crucial, given this could be both unpopular and difficult to achieve. One can see why the DfT might shy away from measures to reduce demand, such as significant increases in the cost of travel. But aside from the political sensitivities, could such measures be justified on the basis of existing models that generate conflicting conclusions and whose validity is unproven?

The National Transport Model is an elaborate model of the surface transport sector. Other relevant models are essentially energy models of the whole economy, including the transport sector. All these models are complex and opaque, with many parameters whose magnitude requires professional judgement. Given the timescale to 2050, it is not possible to validate models by comparing forecast with outturn. Models are therefore prone to optimism bias, whether unconsciously because modellers want to please their clients, or consciously in aid of achieving some higher purpose.

Greater transparency of the National Transport Model would allow us to understand whether there has been undue optimism about the prospects for decarbonisation by technology. However, such transparency seems unlikely. The DfT has always resisted allowing others to use its model on the grounds that its components employ proprietary software developed by consultants. Not that this is unique. Most transport modelling utilises proprietary models owned by consultants. This contrasts with practice elsewhere.

The Treasury’s model of the UK economy has been available for external use for many years. The Department for Business, Energy and Industrial Strategy has developed its energy modelling in close collaboration with academia and plans to increase transparency. Climate modelling is an international, open, collaborative effort that feeds into the findings of the Intergovernmental Panel on Climate Change. The epidemiological modelling of the coronavirus pandemic, which has informed decisions on lockdowns and vaccine deployment, has been carried out not within government, but by university modellers, collaboratively and transparently.

Transport modelling needs to move on, to become transparent and collaborative rather than opaque and proprietary. More effort needs to be devoted to validating models by comparing forecast with outturn where that is possible, for instance over the initial years following the opening of a new element of infrastructure. For the period through to 2050, the best that can be done is for modellers to run their models on common assumptions, to understand why forecasts differ, and then to vary assumptions to test the sensitivity of forecasts to bias, both optimism and pessimism, whether concerning technological innovations or behavioural change.

We need an informed consensus from the modellers of transport decarbonisation to inform the development of policy.

The text above was published in Local Transport Today edition of 18 October 2021. Since this piece was drafted, two further relevant publications have become available.

CREDS, the Centre for Research into Energy Demand Solutions, a consortium of university groups, published a substantial report on the role of energy demand reduction in achieving net zero, including the energy associated with the transport system. The report concluded that the UK could halve its demand for energy by 2050, which would substantially ease the task of meeting that demand with zero carbon emissions. For transport, an ambitious set of assumptions were made, including that single occupancy car use becomes socially unacceptable and that the car fleet is reduced substantially. The model employed represents the whole energy system and is known as UK-TIMES. The model and assumptions are set out in some detail and the code has been published, which is very creditable, although it would take a professional modeller to fully appreciate the content.

The government has just published its Net Zero Strategy. This covers the whole economy including transport, although little is added to the previously published Transport Decarbonisation Plan, which placed minimal reliance on changes in travel behaviour. A technical annex sets out the modelling and assumptions used to justify the pathway to net zero by 2050, again employing the UK-TIMES model. For transport, the only behavioural change assumed is that the share of journeys in towns by active travel increases from 42% in 2019 to 55% in 2035. The trajectory of emission reduction for domestic transport on page 154 is similar to that shown in the Department for Transport decarbonisation plan, although the modelling framework used is different.

It is evident that the emission consequences of a wide range of travel behavioural change possibilities are being projected using different kinds of model. The recent publications reinforce the case for transparency and collaboration amongst the modellers.

A further addendum, November 2021

DfT has recently published papers about its new version of the National Transport Model, which is based on standard industry software and is intended to be available for use outside the Department. This is a welcome development that will make the model more transparent, although its complexity means that it is still quite opaque. However, the carbon modelling discussed above was derived form the older version of the NTM.

Travel

The Department for Transport has published its 218-page detailed Decarbonisation Plan. Boosting cycling and walking features first amongst the measures for reducing the carbon emissions of individual modes of travel, with £2 billion to be invested over five years to make active modes the first choice for many journeys. The aim is to have half of all journeys in towns and cities cycled or walked by 2030.

However, the expected carbon reductions are only 1-6 MtCO2e over the period 2020 to 2050 (page 53 of the Plan), which is tiny in relation to UK domestic transport carbon emissions of 122 MtCO2e in the single year 2019 (page 15).

In contrast, the emission reductions expected from electrification of cars and vans amounts to 620-850 MtCO2e over the period 2020-2050 (page 87).

The DfT’s very small expectations of carbon reduction from increased investment in cycling is consistent with the evidence that you can attract people off the buses onto bikes, but it is much harder to get them out of their cars, as seen in Copenhagen and elsewhere.

Altogether, it seems that the DfT’s apparent enthusiam for cycling is virtue signalling, with low expectations of real carbon reduction benefits.

Car useTransportTravel

This blog post is the text of an article in Local Transport Today.

Cycling is widely advocated as a desirable means of travel – healthier, cheaper, more environmentally friendly and barely slower than the car for short-to-medium length trips. The Government seeks a step-change increase in cycling with £2 billion new funding, as a cost-effective way of reducing transport carbon emissions.

Certainly, there is substantial scope to increase cycling by investment in better infrastructure, witness Copenhagen with dedicated cycle lanes on all major roads, where 28% of all trips are by bike, compared with 2.5% in London. So when, at the outset of the pandemic, the Mayor of London announced his ambition to increase cycling by tenfold, you could see that this should be possible with the requisite investment. However, when you’re in Copenhagen, you are aware of the considerable amount of general traffic (and viewing Scandi noir crime dramas set in that city, you see very few of the characters using a bike). In fact, with 32% of all trips by car, Copenhagen is only slightly less car-dependent than London with 35%.

Aside from cycling, the other big difference between these two capital cities is that public transport use in Copenhagen is only half that of London, 19% versus 36% of trips. This indicates that you can get people off the buses onto bikes, but that it is much harder to get them out of their cars, even in a small, flat city with excellent cycling facilities where almost everyone has experience of safe cycling. Yet we don’t want to diminish the use of buses, which are an efficient means of moving people in urban areas, the diesel engines of which can be replaced by electric or hydrogen propulsion. Fewer bus passengers mean less fare revenue and less frequent services.

Data for other European cities indicate that Amsterdam is similar to Copenhagen, with 32% of trips by bike and 17% by public transport. In marked contrast, both Zurich and Vienna have excellent public transport responsible for 40% of trips, with cycling accounting for only 7-8%. More generally, while the pattern of urban travel reflects both local geography and history, we don’t find cities in developed economies with high mode shares of both cycling and public transport.

In seeking to reduce transport carbon emissions, we should be careful not to underestimate the attractions of the motorcar, which is useful for longer journeys and for shorter trips with less sweat, for carrying people and goods, including child seats and the stuff left permanently in the boot. The car is well-suited for meeting our needs for access to people and places, for door-to-door travel where there is road space to drive without unacceptable congestion delays and the ability to park at both ends of the trip.

But there is more to car ownership than the ability to go from A to B. The growth in popularity of SUVs suggest that there are feel-good factors that motivate purchase of these costly vehicles (it would be interesting to see the findings of the market research carried out by the car manufacturers, regrettably proprietary). The fact that cars are generally parked for 95% of the time is a good economic argument for car sharing. But conversely, this also indicates the value we place on individual ownership, to have vehicle available when we want it, a vehicle that reflects our personal consumer preferences. Cars are not unique in this respect. My washing machine sits unused more than 95% of the time. I could share with others at the laundrette, but it’s more convenient to have my own.

Car sharing in its various forms is advocated as a means to reduce car use, road traffic congestion and carbon emissions. Sharing has been facilitated by online digital platforms, which have been transformative of many aspects of the economy. For travel, we have the disruptive impact of ride-hailing as exemplified by Uber, and of online booking of trips by rail and air. By contrast, the growth of car sharing has been relatively slow, indicating the development of niche markets, with substantial replacement of private ownership looking unlikely.

Where road capacity limits car use in city centres, both public transport and active travel are attractive alternative modes. Agglomeration economics have led to increased population density in successful cities, which shifts travel away from the car. The growth of higher education in urban centres has contributed to reduced car use by young adults.  However, these trends may weaken post-Brexit and post-Covid. And while car use can be impeded in low traffic neighbourhoods in favour of cycling and walking, the aggregate impact may not be great.

We need to be careful to avoid optimism bias when projecting the impact of measures to reduce transport carbon emissions. The models that are used for this purpose are complex and opaque, with many input assumptions and parameters to be specified. Optimism bias arises when modellers make choices, consciously or unconsciously, that tend towards achieving a strategic purpose. Yet optimism bias leads to outcomes that fall short of those that are forecast. 

It is now part of the culture of transport planning to place emphasis on the opportunities for promoting cycling. But caution is needed. When addressing the impact of changing mode share, attention should be paid to the modes from which the shift to cycling is expected. For instance, the well-established Propensity to Cycle Tool, which assesses the potential to increase the amount of cycling, assumes that commuters are equally likely to shift to cycling from any prior mode. However, the evidence from Copenhagen and elsewhere indicates that a shift to cycling from public transport is much more likely than from car use, which would substantially reduce the carbon reduction benefits assumed from boosting cycling.

If optimism bias informs assumptions about mode shift from cars to bikes, or about the scope for car sharing, then disappointment is likely to ensue.