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.

I post a blog a few months ago about the Office of Rail and Road (ORR), which was consulting on its involvement in the third road investment strategy (RIS3). The outcome of this consultation was recently announced.

I had responded to the consultation, suggesting that the ORR should take an interest in the benefits of road investment as experienced by road users, since there was doubt about who mainly benefited, local users (commuters and others) or business users. My contribution was published by the ORR, anonymously, as ‘Response from member of the public D’ (I would have been happy to be named, but was not asked).

In its detailed response to the consultation, the ORR responded to my suggestion at paragraph 3.7, saying that it is not within its remit. It is unusual for an industry regulator to take no interest in the consumer experience. This is a central concern for the regulators of other industries – energy, water, telecoms, financial services.

The ORR also ducked out of accepting other suggestions for extending its responsibilities, including in relation to other important government objectives such as Net Zero and Levelling Up. The detailed critique of the Transport Action Network (page 78 of the ORR response document) is well worth reading.

Altogether, the ORR is a pretty feeble regulator in respect of roads.

I have a new article published in a peer reviewed journal:

Metz D. The impact of digital navigation on travel behaviour. UCL Open: Environment. 2022;(4):05. https://doi.org/10.14324/111.444/ucloe.000034


Digital navigation – the combined use of satellite positioning, digital mapping and route guidance – is in wide use for road travel yet its impact is little understood. Evidence is emerging of significant changes in use of the road network, including diversion of local trips to take advantage of new capacity on strategic roads, and increased use of minor roads. These have problematic implications for investment decisions and for the management of the network. However, the ability of digital navigation to predict estimated time of arrival under expected traffic conditions is a welcome means of mitigating journey time uncertainty, which is one of the undesirable consequences of road traffic congestion. There is very little available information about the impact of digital navigation on travel behaviour, a situation that needs to be remedied to enhance the efficiency of road network operation.

An article in Local Transport Today of 13 June broadens the consideration.

The Department for Transport (DfT) has started planning its third Road Investment Strategy (RIS3), a five-year investment programme for the Strategic Road Network (SRN) for the period 2025-2030. The approach is conventional – a programme of projects, with little overview of how societal objectives will be advanced by the likely substantial expenditure. Yet there are five major issues that need to be addressed for the programme as a whole.

First, there is a need to reconcile the government’s Net Zero objective with the carbon emissions from both the tailpipes of the additional traffic arising from increased road capacity and the embedded carbon in the cement, steel and asphalt used in construction. Recent presentations by the DfT’s Transport Appraisal and Strategic Modelling (TASM) division indicated an intention to tackle this issue at scheme level, but this is misconceived. What matters is the overall contribution of RIS3 to carbon emissions and how this is to be offset or otherwise justified.

Second is the question of how RIS3 advances the government’s Levelling Up agenda, where the recent, well-received White Paper identified twelve medium-term ‘missions’ to be pursued across all departments. The one specific to transport states: ‘By 2030, local public transport connectivity across the country will be significantly closer to the standards of London, with improved services, simpler fares and integrated ticketing.’ Although the rate of progress implicit in ‘significantly closer’ is vague, the direction of travel is clear and the objective is not in dispute.

There is no mention of investment in the SRN in the Levelling Up White Paper. This is appropriate since there is, if anything, an inverse relation between the performance of the road network and economic prosperity across the nation, given that delays on the SRN due to congestion are greater in London and the South East than in other regions of England.

The implication of the White Paper approach is that there should be a substantial switch of DfT funds from road investment to improve public transport beyond London, if the Department is to play a full role in supporting the government’s the Levelling Up agenda. Yet the Department’s recently issued Levelling Up Toolkit is essentially a pro forma for a box-ticking exercise aimed at justifying investments already forming part of agreed expenditure programmes. There is palpable inconsistency here.

Third, we have the problem of the safety of smart motorways. These require conversion of the hard shoulder to a running lane as an economical means of increasing capacity without the expense of rebuilding bridges. Generally, new roads are safer than older roads, which meant that adding road capacity yields a modest safety benefit. But this is not obviously the case for smart motorways, and there has been considerable pushback from the public and the House of Commons Transport Committee. As a result, the DfT has paused the roll out of new smart motorways until five years of safety data is available for schemes introduced before 2020. A decision on the generic safety of smart motorways will be an important factor in developing RIS3.

Fourth, and less recognised, there is a question about the economic benefits from additional road capacity. There are two published evaluations of smart motorway schemes where the traffic flows after opening were very different from those that had been forecast. For the M25 Junctions 23-27 scheme, the traffic flowed faster one year after opening but subsequently delays reverted to what they had been before opening on account of greater traffic volumes than forecast. For the M1 J10-13 scheme, traffic speeds five years after opening were lower than before opening. Since the main economic benefit of road widening is the saving of travel time, both schemes had negative benefit-cost ratios (BCR) at outturn.

Examination of the reports of the traffic and economic modelling of these two schemes showed substantial time-saving benefits expected for business users, offset by a small amount of increased vehicle operating costs (VOC) arising from additional traffic volumes. There were also time savings to non-business users (for commuting and other local travel) but these were entirely offset by increased VOC – because these were local trips that rerouted to the motorway to save a few minutes of time, at the expense of additional fuel costs.

The scope for rerouting local trips to take advantage of increased motorway capacity is likely to be underestimated in modelling. Local users have the flexibility to vary routes whereas long distance business users will stay on the motorway unless there is a major holdup. Moreover, the general use of digital navigation in the form of Google Maps and similar offerings makes choice of minimum time options commonplace.

Even when the outturn total traffic flows are a reasonable match to those forecast, the scheme economics could be much worse than predicted if there is more local traffic, and hence less long distance business traffic, than projected. Traffic and economic modelling involve recognition of different classes of road user with different values of travel time: cars, LGVs, HGVs, business, local commuters, and other local users. However, the monitoring of outturn traffic flows does not distinguish between these classes of users. GPS tracking make such distinctions possible.

The DfT has emphasised the importance of evaluation of outturns of investments. Yet the failure to appreciate the need to break total traffic flows down into the segments that had been modelled reflects a serious professional shortcoming. As a result, we cannot be at all confident that investments to increase SRN capacity do more than facilitate rerouting of short trips by local users, of nil economic value. Likewise, we do not have the kind of detailed evaluation data that would allow traffic models to be better calibrated for future use.

The fifth issue for RIS3 is that the widespread use of digital navigation by drivers prompts questions about the continued focus of DfT and National Highways on major civil engineering expenditure. Contrast the aviation sector, where new runways or terminals are occasional efforts, not regular business. The main focus of airlines and air traffic control is to improve operational efficiency, to sweat the assets employing the techniques of operational research. We have a mature road network in Britain. It’s time to focus on operational efficiency. Yet it seems not to occur the National Highways that working with Google Maps, TomTom and other providers of digital navigation services would be a cost-effective means of improving the performance of the network.

More generally, the DfT is trapped in its box labelled Transport Analysis Guidance (TAG), a thousand pages of prescription to which more text is added when some new issue or policy arises, such as Net Zero, Levelling Up, inequalities or gender. The task for those promoting a scheme is to tick all the boxes and flex the modelling to generate BCRs that represent good value for money. Evaluation of outturns is inadequate to distinguish between success and failure.

Although the DfT pays lip service to the need to think at the strategic level, the TAG framework does not facilitate this in that the detailed analysis is at project level. Other interested parties do not challenge the Department’s approach. The consultants and local authorities do not bite the hand that feeds them. The professional societies, institutions and think-tanks do not engage. The National Audit Office carries out good analysis of road investments on occasion, but not systematically. The Office for Rail and Road scrutinises the management of the SRN, including how well new investments are delivered, but does not see its role as enquiring into how investments benefit road users. This is quite unlike the regulators of other infrastructure industries – electricity, gas, water, telecoms – that are focused on how consumers benefit from investment.

The DfT is stuck in its box and seems unlikely to break out. The best bet for a strategic view of RIS3 may come from the National Infrastructure Commission, which has begun the development of its second National Infrastructure Assessment. The Commission’s advice was the basis of the government’s £96 billion rail investment programme for the North and the Midlands. This required fresh thinking about the benefits of transport investment at the level of the whole programme, an approach clearly needed for RIS3.

This blog post formed the basis of an article in Local Transport Today of 25 March 2022.

The Department for Transport’s National Transport Model (NTM) was first constructed two decades ago and has subsequently undergone a number of phases of development. The main function of the model has been to provide projections of travel demand as the basis for justifying investment in the road network. The model has also been used to project future carbon emissions, to inform the Department’s Transport Decarbonisation Plan, as well as to explore the impact of technological developments such as electric vehicles.

An account of the latest version, effectively a new model known as NTMv5, was released recently in the form of a 250-page ‘Quality Report’, oddly, two years after completion. NTMv5 is a spatially detailed conventional four-stage transport model structure, iterated so that congestion feeds back into demand. The model has been implemented using the standard commercial software, PTV Visum. The intention is that the model should be transparent to external stakeholders, a very welcome development given the opacity of previous versions of the NTM. The complexity of the model means that a single run takes around ten hours, with a number of iterations needed to achieve convergence of outcomes.

However, there are some notable limitations to the model. There is no detailed treatment of public transport capacity. Car ownership data derives from a separate model, which has not been updated. And the primary source of growth of travel demand is the DfT’s National Trip End Model data set that projects expected changes in demography and land use, which are problematic of account of uncertainty of economic growth, population growth and distribution, and planning policy.

A number of potential applications of the model have been identified, of which the most immediate is the preparation of new national road traffic forecasts. Also recognised is a need to project future transport carbon emissions, and for the analysis of packages of road schemes at national level, including value for money.

The purpose of the succession of NTM versions has been to support the traditional ‘predict and provide’ approach to road investment. This viewpoint persists in the latest version where the stated rationale for analysis of packages of road schemes is to identify ‘gaps in the network… where the road capacity in future may be insufficient, leading to unacceptable rises in congestion and journey times.’ (section 2.4.2). Yet we do not adopt that approach when considering urban roads, and the scope for enlarging peri-urban motorways at acceptable cost by converting the hard shoulder to a running lane is now problematic on account of public concerns about safety. Besides, the scale of induced traffic has been persistently under-estimated in traffic modelling, so the aim of avoiding unacceptable congestion seems naïve, even before addressing the Net Zero objective.

The model builders struggled to treat the complexities of urban traffic. It was accepted that a full link-based modelling of urban road capacity and related journey time responses could not be achieved, and therefore a simplified approach had to be applied. This involves assuming general fixed speeds on urban networks for the Base Year, which were reduced over time based on assumed growth of demand. (sections 4.7 and 11.4). This simplification has implications for projections of traffic in London, as recognised by the peer reviewers.

Peer Review

The DfT has published a Peer Review and an Audit of NTMv5. The 120-page peer review, led by the seasoned practitioners John Bates and Ian Williams, drew attention to a number of apparent shortcomings in the methodology (too technical for me to appreciate sufficiently to offer comment). These led to counterintuitive results when sensitivity tests were run, notably for London.

The reviewers advise 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 focus on urban travel policy and public transport interventions. In particular, the reviewers are critical of the treatment of urban traffic, observing that the assumed relation between traffic speed and demand growth lack validity, and that the range of policies aimed at reducing urban car use are not taken into account. Besides, it is 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 find that for London, the model results are not convincing. The observed car (driver + passenger) trip mode share is 38% from the National Travel Survey in 2015/16, whereas that in the model in 2015 is 50%. Moreover, the model projects a future gain of car share, whereas over the period 2005-16 a major decline of 5.6% was observed (para 4.3.5). The reviewers concluded that the model could not be safely used to examine policies that relate specifically to London, and query whether this relates 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 (section 6.3.24), which is a pretty major qualification.


The 260-page audit of NTMv5, carried out by consultants Arup and AECOM, drew attention to a number of shortcomings in both documentation and substance, including that some of the model components and tools used to process the data are not owned by the Department, which limited access to some of the key processes and data used in model development – not consistent with the aim of transparency to external users. The auditors advised that users of model outputs should be cautious because of problems in reaching convergence to a stable outcome as the model is run through repeated iterations, a concern also of the peer reviewers.

What next?

The NTM documents recently published are two years old. No doubt, further development of the model has been taking place to respond to the issues raised in peer review and audit. In its Transport Decarbonisation Plan published last July, the DfT stated its intention to review the National Policy Statement on National Networks, the basis of strategic planning of road and rail investment, and to update the forecasts on which it is based. NTMv5 will presumably be used for this purpose. Yet the modellers will be stretched to meet the divergent needs of their client policy makers, between bullish forecasts of travel demand to justify continued infrastructure investment and bearish projections of transport carbon emissions. Given the uncertainties of the model illuminated by peer review and audit, it will be hard to be confident about the validity of carbon forecasts out to 2050 and 60-year investment appraisals.

While the DfT’s intention to make NTMv5 available for use beyond the Department is praiseworthy, this seems problematic in practice. Doubtless the large transport consultancies could master the software and data, but given their complexity, clients would need deep pockets to fund the work. That would rule out non-government bodies that might want to challenge particular schemes. Regional transport undertakings have their own bespoke models. I am not aware of any academics who would be likely to buy into the NTM, a situation unlike national energy modelling where government and a substantial group of university researchers work with the same model. The DfT would be well advised to support academic researchers and others wishing to use NTMv5 to explore a range of policy scenarios.   

This blog was the basis for an article in Local Transport Today of 11 March 2022.

The Law Commission has recently completed a review of the law relating to automated vehicles. The review had been requested by the Centre for Connected and Autonomous Vehicles, which reports to the Secretary of State for Transport. This was the first occasion in which the Law Commission had been asked to develop legal reforms in advance of future technological development. The government’s aim is to avoid regulatory barriers that might impede innovation while managing potential risks of harm.

The review process involved three rounds of consultation that fruitfully engaged a wide diversity of interested groups. It was a good example of how the need to capture policy intentions in legislation generates close scrutiny and clarification of the issues involved. The 300-page report is comprehensive, except that it does not consider V2V connectivity for AVs, a slight surprising omission given who commissioned it.

Because an automated vehicle (AV) will 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 self-driving functions where a user has to be able to take over control when the vehicle cannot cope, for instance in poor visibility, as well as for more advanced automation when the AV 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 an AV forward for authorisation to the regulator.
  • User-in-charge – the human in the driving seat while the vehicle is driving itself.
  • NUIC operator, 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 of an AV.
  • Authorisation of automated functions.
  • In-use safety regulation.
  • NUIC operator licencing.

The proposals of the Law Commission will need to be enacted in legislation to have effect. The government’s intention is to make the UK an attractive place for the deployment of AVs, 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 might hope to save the costs of the driver, but 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 current capital-lite business model.

Tesla’s approach to automation has been to incorporate the hardware into existing privately-owned vehicles and to 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 require Tesla to be licenced as a NUIC operatory, with oversight of its whole fleet of vehicles on the road.

Developing vehicle automation for general deployment is proving more difficult than the pioneering technology optimists had hoped. As well as 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.

I have previously discussed the widening of the M25 motorway between Junctions 23 and 27, where the economic benefits forecast did not materialise. Another example has now arisen.

Conversion of the hard shoulder of the J10-13 section of the M1 motorway to dynamic running was intended to reduce congestion by allowing the hard shoulder to be used as an additional running lane during busy periods. The scheme, one of the earlier ‘smart motorways’, opened in 2012 and a report of its first five years of operation, up to 2017, was published in 2021. The cost was £489m for 15 miles of widened motorway, adjacent to Luton, to the north of London.

Electronic signs tell drivers when it is safe to use the hard shoulder as a running lane, but then speeds are limited by the level of congestion, with a maximum of 60mph. This, together with some traffic growth on the route, meant that journey times were in fact longer than before the road was converted.

The main economic benefit of road investments is taken to be travel time savings. In the present case, the forecast had been for an average travel time savings of 1.5 min per vehicle in the opening year, increasing to 2.25 min by 2028. But time savings were not observed. The forecast benefit-cost ratio (BCR) had been 1.4, whereas the estimate based on the five-year outturn was negative, -0.8.

The stated conclusion, five years after opening, was: ‘In this case, the monetisation of journey time benefit is not a good measure of value for money and the qualitative evidence presented in the evaluation is considered a more robust measure.’ The failure of forecasting was attributed to limited prior experience of such smart motorway conversions.

In view of this marked discrepancy between forecast and outturn, I made a Freedom-of-Information request to see the detailed reports of the traffic modelling and economic analysis of the proposed investment. The original modelling had been for a widening from three to four standard lanes in each direction. The model was adapted for the use of the hard shoulder for the extra lane. The model drew upon the East of England Regional Model, a variable demand model, for data to input to a local traffic model for the section of the M1 involved. As elsewhere, the traffic modelling employed the established SATURN package.

The traffic modelling projected increased traffic volumes, comparing the investment case with the ‘do minimum’ case without the investment, as well as journey time reductions in the range 4-15% for the opening year, depending on section of the road and time of day. The economic appraisal used the output of the traffic model as input to the standard TUBA economic model to project the economic benefits. The main benefits were time savings to business users of £456m pv, offset by increased vehicle operating costs (VOC) of £61m. There were time savings to consumers of £170m, more than offset by increased VOC of £197m. This suggests that the increased road capacity is attracting local users, such as commuters, who save a few minutes of their journey by rerouting to the motorway, at the cost of more fuel use for a longer trip, as illustrated in the screenshot from Google Maps above. A similar situation arose in the M25 case.

The forecast BCR from the TUBA model was 3.5, which is different from the forecast of 1.4 provided in the year five report (above), apparently on account of a change in how increases in revenue from fuel taxation are required to be treated, whether as offsetting the scheme costs or as an element of the benefits from the investment.


Transport models are complex and opaque. Generally, little effort is made to valid forecast against outturn. The present M1 case demonstrates a marked failure of a model to forecast the observed traffic flows and speeds five years after opening.

More generally, monitoring traffic flows and speeds provides only limited information about the validity of a model that projects economic benefits for different classes of road user. The outturn of a widening scheme that matched projected flows might arise if all the increase in traffic volumes arose from more local users taking advantage of the increased capacity to save time on local trips, thereby pre-empting benefits to long distance business users. Effective monitoring needs to track the travel behaviour of different classes of road users.

It seems likely that there is often a bias in traffic modelling of road investments to underestimate the growth of local traffic and hence to overstate the economic benefits to business users.

The Office of Rail and Road has extensive responsibilities for regulating the largely private sector rail industry but quite limited oversight of public sectors roads. The Department for Transport is planning its third Road Investment Strategy investment programme (RIS3). The ORR has been consulting on its role in relation to RIS3. Essentially, the ORR sees its role as ensuring that National Highways (formerly Highways England) achieve value for money in implementing the DfT’s investment priorities.

The ORR consultation document states that it is not the role of the ORR to set roads policy or determine investment priorities. However, it is a shortcoming of the ORR’s approach that it does not consider to what extent the investments agreed by government achieve the benefits to road users that are expected. This is a major gap in public oversight.

The National Audit Office from time to time evaluates benefits to users of road investment, for instance its 2019 report on improvements to the A303. But NAO oversight is occasional, not systematic.

Detailed analysis of the outcomes of road investment may show major discrepancy between forecast and outturn, for instance for widening the M25 between junctions 23 and 27. One general explanation is the underestimation of the scale of induced traffic . Induced traffic reduces travel time savings, supposed main economic benefits of investment, which is why transport models tend to underestimate its magnitude.

One source of induced traffic is the rerouting of local trips, such as commuting, to take advantage of faster travel on widened motorways, pre-empting capacity intended for business users and so undermining the economic case for widening. This is likely to be a general phenomenon in or near areas of high population density, where the strategic road network comes under greatest stress, and where the case for additional capacity seems strongest.

More generally, average travel time, as determined in the National Travel Survey, has remained essentially unchanged for half a century, during which time huge sums have been invested in road infrastructure justified by the saving of travel time. Travel time savings are short-run. In the longer run, over the greater part of the life of the assets, the main benefit of investment that allows faster travel takes the form of increased access to people and places, opportunities and choices.

All in all, there is reason to suppose that the outcomes of road investments may be substantially different from that forecast by the traffic and economic models in use, and that road users are not benefiting from investment in new capacity to the extent intended. The ORR should take on the task of ensuring that road investment appraisal methodologies are fit for purpose.

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.

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.