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.

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.

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.

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.

Recent revisions to the road traffic statistics appear to show that there has been a substantial growth of motor vehicle traffic on GB minor roads in recent years, from 108 to 136 billion vehicle miles between 2010 and 2019, an increase of 26%. Traffic on major roads rose from 197 to 221 bvm over the same period, an increase of 12%.  (DfT Road Traffic Statistics TRA0102).

Road traffic statistics are based on a combination of automatic and manual traffic counts. Major roads are well covered in that traffic in all links is counted on typical days, although not every link in every year. Given the vast number of minor roads, however, it is only possible to count traffic at a representative sample of locations every year, and the observed growth is applied to minor road traffic overall. Estimates from a fixed sample may drift over time such that the sample becomes less representative of the changing minor road network. To account for any errors incurred in the fixed sample, the sample is revised through a benchmarking exercise every decade, involving a much larger sample of locations.

The most recent minor roads benchmarking exercise was published in 2020, based on 10,000 representative locations. Overall, the benchmark adjustment for 2010-2019 was 1.19, which is the factor to be applied to 2019 data from the original sample to bring this to the observed traffic level. Data for minor roads traffic for intermediate years are adjusted pro rata, to avoid a step change in the reported traffic data. There is significant regional variation in the adjustment factor, from 1.35 for Yorkshire to 1.09 for East of England, with London at 1.32. For B roads the factor is 1.25, for C roads 1.17; while for urban roads, 1.22, and for rural roads, 1.15. Applying the regional weightings yields an increase in traffic on minor roads of 26%, as noted above, whereas the increase based on the original sample would have been 6%.

The previous benchmarking exercise published in 2009 found a smaller overall adjustment factor of 0.95, with a regional range of 0.81 to 1.08.

The substantially greater adjustment required following the recent benchmarking, compared with the earlier exercise, suggests that there has been a real change in use of minor roads, beyond errors arising from drift in the sample. Importantly, had the increase in minor road use been spread evenly across the national road network, the traffic estimation based on the sample would have been close to that from the benchmark exercise. Hence the major difference between sample and benchmark indicates considerable heterogeneity of minor road traffic growth. Moreover, the fact that the sample failed to detect the traffic growth suggests either that the process for establishing the sample was deficient in some way, or that significant changes occurred in use of minor roads over a decade.

DfT statisticians have created a revised minor roads representative sample (4,400 locations) from the latest benchmark data, which will be used for the coming decade. It would be desirable to have comparative analysis of the previous and the new samples, to gain insight into what has been happening on the minor road network. Regrettably, the statisticians only report findings, and do not attempt to explain them, which leaves uncertainty as to the nature and cause of the reported changes to traffic volumes. The representative nature of the new sample must be questionable if the reasons for the failure of the previous sample to reflect reality are not understood and addressed.

Transport for London has recognised this uncertainty. The recent Travel in London Report 13 discusses the implications of the minor roads traffic correction (p92). The revisions mean that, for 2018, the DfT estimate of vehicle kilometres was 20% higher than previously reported last year (and included in Travel in London Report 12). The previous estimate suggested a fall of 1.8% in vehicle kilometres in London between 2009 and 2018, whereas the revised series now suggests an increase of 17.9% over the same time period, this change wholly arising from revisions to the minor road estimates. TfL notes that it is currently working through how the DfT have made this assessment, and also what this could mean for London data sets. For the moment, TfL is relying on its own traffic monitoring data, although it does not report traffic on minor roads separately.

The National Travel Survey could provide a cross-check on the traffic data. Average distance travelled by car/van driver decreased from 3388 miles per year in 2010 to 3198 miles in 2019, a decline of 5.6% (NTS0303). The GB population grew from 60.95m in 2010 to 64.90m in 2019, an increase of 6.5%. The net increase in car use of about one percent is inconsistent with the new road traffic statistics which show an increase in traffic for all roads of 17% over the same period. The NTS employs a fresh sample of respondents each year, so sample drift should not be a concern. However, it is not clear that the travel diary technique would pick up rerouting to minor roads, given that respondents are asked to provide their own estimates of distance travelled for each trip.

Possible causes of increase in traffic on minor roads

One factor contributing to the growth of traffic on minor roads is the increase in van traffic, including that arising from the growth of online shopping with home deliveries. The number of vans (light commercial vehicles) registered in Britain increased by 28% between 2010 and 2019. Total van traffic increased by 34% over this period, with an increase of 49% on urban minor roads compared with 10% on urban ‘A’ roads, although ‘delivery/collection of goods’ was less important in respect of journey purpose than ‘carrying equipment, tools or materials’. However, in 2019 van traffic amounted to 15% of traffic on urban minor roads, and 19% on rural minor roads, cars being responsible for 82% and 78% of traffic respectively. So, the growth of van traffic on minor roads is responsible for only part of the overall traffic growth on these roads.

Another possible explanation of the apparent large growth of traffic on minor roads is the widespread use of digital navigation (satnav) that offers routes that take account of traffic conditions and estimated journey times. Such devices make possible the general use of minor roads that previously were largely confined to those with local knowledge. This is likely to occur when major roads are congested and represents an effective increase in the capacity of the road network, so generating additional traffic – the converse of the ‘disappearance’ of traffic when carriageway is reduced. Increased use of minor roads is problematic when policy is concerned to decarbonise the transport system and to promote active travel, which these roads facilitate.

The possible role of digital navigation might be investigated by an analysis of the correlation of the upward adjustment factor for each minor road sample location with traffic volumes on nearby major roads – to test the hypothesis that there would be more use of minor roads in areas where major roads were most congested. If so, this factor should be taken into account when setting up the new minor roads sample for the coming decade.

The use of digital navigation has been growing and may continue to grow in the future. A better understanding of the phenomenon would be important for forecasting road traffic growth by means of the National Transport Model and models at regional level and below.

A further possible cause of the changed distribution of traffic on minor roads arises from intentional interventions aimed at reducing such traffic. It has long been the practice to discourage ‘rat running’ on urban minor roads by means of suitable physical control measures, as are used in low-traffic neighbourhoods (LTN). Such measures would reduce traffic in certain locations while possibly increasing it in others through diversion. Some locations in the minor roads sample may be so affected. If LTNs and similar measures increase over time, the sample may become increasingly unrepresentative, a factor that should be taken into account in setting up the new sample. However, the net effect of intentional interventions would be to reduce traffic overall, so this cannot account for the reported growth of traffic on minor roads.

The growth of minor road use by through traffic apparently facilitated by digital navigation would strengthen the case for implementing LTN measures. Alternatively, or additionally, the providers of digital navigation might be encouraged to omit routes that direct through traffic along minor roads.

More generally, the impact of digital navigation on the functioning of the whole road network seems likely to be significant and therefore worthy of investigation.

The above considerations prompt a number of questions:

  1. How reliable are the statistics for motor vehicle use of minor roads, given the apparent sensitivity to the sampling of locations?
  2. How reliable are the NTS findings for car use?
  3. What information is available on the likely causes of the increase of traffic on minor roads?
  4. What is known of the impact of digital navigation on the road network?
  5. What are the implications of digital navigation for transport and traffic modelling?

Summary

The reported increase in motor vehicle traffic on minor roads over the past ten years is substantial and locationally heterogenous, for reasons that are unclear. This lack of understanding raises methodological questions about the sampling of minor roads. The reported increase in traffic is not consistent with the findings of the National Travel Survey, as well as being of concern to Transport for London. While interventions to reduce traffic on urban minor roads may increase the heterogeneity of the sample, they would not increase the volume of traffic. Hence this increase is most likely due to the growing use of digital navigation devices that allow minor roads to be used by those without local knowledge. This has implication for transport modelling as well as for policies to decarbonise the transport system and encourage active travel.

This blog post is the text of an article published in Local Transport Today 19 March 2021

I have a new paper on how time constraints affect our travel behaviour. The link to the journal is here. Some copies are free to download here. The manuscript is here. The abstract is below.

Considerable observational evidence indicates that travel time, averaged across a population, is stable at about an hour a day. This implies both an upper and a lower bound to time that can be expended on travel. The upper bound explains the self-limiting nature of road traffic congestion, as well as the difficulty experienced in attempting mitigation: the prospect of delays deters some road users, who are attracted back following interventions aimed at relieving congestion. The lower bound implies that time savings cannot be the main economic benefit of transport investment, which means that conventional transport economic appraisal is misleading. In reality, the main benefit for users is increased access to desired destinations, made possible by faster travel, which is the origin of induced traffic. Access is subject to saturation, consistent with evidence of travel demand saturation. However, access is difficult to monetise for inclusion in cost-benefit analysis. Consequential uplift in real estate values may be a more practical way of estimating access benefits, which is relevant to the possibility of capturing part of such uplift to help fund transport investment that enhances such access.

The Department for Transport has initiated an exercise to assess how the transport system could be decarbonised, in line with the Government’s commitment to a net zero carbon target for the whole economy by 2050.

I have submitted some thoughts on behavioural aspects, including the scope for increasing active travel, decreasing motorised road travel and air travel, and the need to improve modelling to accomodate such behavioural changes.

The Secretary of State for Transport has announced £2 billion of investment to boost cycling and walking in response to the Covid pandemic. The Mayor of London has plans that he hopes will increase cycling ten-fold and walking five-fold post-lockdown. These intentions take advantage of current public health constraints on use of public transport and are certainly praiseworthy, but there are serious questions about feasibility.

Increasing cycling in London ten-fold would take its share of trips to that found in Copenhagen, where there are segregated cycling lanes on all roads with significant traffic. Moreover, the city is relatively small, such that you can cycle from the centre to the edge in an hour or so, as I have done but would not attempt in London. However, car use in Copenhagen is only slightly less than in London, while public transport mode share is half that in London. This indicates that you can get people off buses onto bikes, which are cheaper, healthier, better environmentally, and no slower in traffic than public transport. But it seems more difficult to get people out of their cars, even in a city where virtually all motorists have current or past experience of cycling. And a decrease in users of public transport, while helpful while the pandemic lasts, would lead in the longer term to loss of fare revenue and of level of service.

Transport for London (TfL) analysed cycling potential in 2017, considering which trips by motorised modes could reasonably be cycled, mainly taking into account trip length. If achieved in full, cycling would be responsible for 40% of all trips, which would be consistent with the Mayor’s ambition. However, a large proportion of potential cycling trips are currently done with at least one other person, which would limit switching. Beyond that observation, considerations of behaviour change and public acceptability were not taken into account.

Boosting walking five-fold is even more problematic, to say the least, given that its mode share of trips in London has long been stable at 25%. Some modest increase in the near term is likely as people avoid short bus journeys. Perhaps the Mayor has in mind to increase the distance walked per trip, which again is likely to some extent in the near term. Yet walking is the slowest mode of travel and time available for travelling is always a constraint. A TfL analysis of walking potential in 2017 estimated that there are more than two million potentially walking trips in London per day, compared with just under one million at present, but most potential walking trips could also be cycled. So it is very hard to see how a five-fold increase in walking could be achieved, even before we consider behavioural factors, in particular the reasons why so many people prefer to drive rather than walk short journeys.

To see if the Mayor’s ambition is realistic, we need a full analysis from TfL, taking into account behavioural aspects, including travel time constraints, and avoiding double counting. It also needs to show that the cost of new cycling infrastructure to meet objectives is affordable, given the current loss of revenue, and to recognise the implications for public transport of the longer-term loss of fare income.

This blog post also appeared as a letter in Local Transport Today 29 May 2020.

The Covid-19 epidemic has prompted discussion of the implications for travel behaviour once it is over. Will the recent downward trend in trips to work and for shopping accentuate? Might people prefer their cars and bikes to travel on crowded public transport? Will there be a bounce back in leisure air travel? All is speculation at present.

More immediately, we see that the new highly infectious virus has prompted huge and rapid global efforts of technological development: tests for the virus and antibody, and novel vaccines. The epidemic has also stimulated extensive efforts to model the consequences, modelling that is open, transparent and collaborative, and that has proved crucial in informing government decisions, in particular to see how new technologies can lessen the need for social distancing.

There are lessons for the transport sector, for which the main priority must be decarbonisation to achieve the government’s net zero carbon target by 2050. Technological development should be stepped up in the areas of batteries for surface transport, and new propulsion technologies for aviation and marine. Electric charging infrastructure needs to be made generally available to stimulate the purchase of electric vehicles.

The models employed in the transport sector are neither open nor transparent. They are obsolete in that they were developed well before current concerns with carbon emissions and are deficient in predicting observed outcomes. We need a new generation of travel/transport models that are open, transparent, and possibly crowdsourced, to inform decisions on policies for decarbonisation, including how new technology can complement behavioural change.

Transport decarbonisation is the top priority. In contrast, automation is not important. As I argued in my recent book, Driving Change, autonomous vehicles will be difficult to deploy on the existing road network, and the benefits are quite limited. It is for the car industry to develop automated features if it thinks that customers might purchase such vehicles. It is for governments to put in place suitable regulatory regimes. But it should not be a priority for governments to support the development of the technology, which would be a distraction from decarbonisation efforts.

This blog post appeared also as a letter in Local Transport Today 17 April 2020