Tag Archives: modelling

Transport Data – not just for modelling geeks

Firstly, a massive apology for not posting in some time. Life has been a bit full on over the last year or so, so the blog has taken a back seat. My apologies especially go to those comments that have remained unmoderated for 6 months. My bad. ūüė¶

Also, just to let you know that while I will touch on the Streetgeekery stuff every now and then, this blog will take on a more transport technology and futures focus. I will still remain true to its original intent, just less about street lamps and tactile paving. Besides, there are loads of great blogs covering that. Anyhow, on with the post.

Pedestrian Modelling

A pretty pedestrian model. Source: Wikipedia

If you think about the UK’s contribution to the transport world you will no doubt think of the likes of Brunel, George Stephenson, maybe even maritime (the whole Britannia rules the waves thing). But one area where the UK has made an undervalued contribution is transport data. While Detroit may have led the way with four-stage modelling, and¬†Lowry’s model of Metropolis form the basis of modern transport modelling, the early work of the Transport Research Laboratory set the standard for data collection, and standards required of its quality.

Now, this blog has been critical of models in the past (and boy has it been), but they are and are likely to remain a useful tool on which to inform transport decisions. But the data collection game to inform these models, and the data collection requirements of transport professionals, are changing in a digital world.

The sheer amount of devices that can collect data on transport (there are over 83 million mobile phones in the UK alone) but tracking movements isn’t enough. There is an ever-expanding sea of personal information willingly shared out there, with an exploding number of things to collect it, and even more opportunities that it brings. Businesses like transportAPI are finding new ways of manipulating this data, and generating new business models from it.¬†The shift is taking place where simply collecting and modelling transport data isn’t enough. Transport professionals need to understand that data creates value outside of major scheme business cases or informing a purely transport decision.

Let me put it this way, and this is something that may be uncomfortable to many. The best users of transport data are not transport professionals.

That sounds nuts doesn’t it?

Ok, perhaps i’ll clarify a bit. If you want someone to use transport data for a specific transport purpose, to a modeller you must surely go. But there are many more companies using open transport data that, frankly, none of us could have thought of. Want proof? Try CityMapper for one – transport data repackaged into something usable. Or how about how Transport for London’s open data now powers over 200 apps?

Transport data courses need to be so much more than building spreadsheet models and regression analyses. It needs to be something more fundamental – how transport data can create value outside of transport, and our responsibilities as professionals to not just rigorous data quality for our clients but to making this data available for the common good. That means going outside our comfort zone, and facing challenge from outside the industry on how this data is used.

A report from the Transport Systems Catapult¬†(yeah, its a work plug, so sue me) is intended to be the first step on this journey. It’s not the entire story but I’ll say one thing: the days of only collecting data from manual counts, and putting into simple spreadsheet models to form the basis of transport decisions are numbered.


Using driverless cars to help create liveable cities

My first blog post in this series contained a hell of a lot of subtle hints towards the topic of this. For all the knowns and unknowns about how driverless vehicles will affect travel patterns (and there are a hell of a lot of unknowns), the infrastructure.

As highlighted in¬†this blog’s very first post, key to delivering livable cities is recognising that the needs of vulnerable highway users – particularly pedestrians and cyclists – need to take priority.¬†Driverless cars being tested to date have some impressive claims made against their safety. A common quote is how one Google car¬†has driven 300,000 miles without a single crash. But this is without¬†independent¬†verification of not just these statistics, but also things such as number of near misses with vulnerable road users. Indeed the only thing we can be sure of at present is that driverless cars will react quicker (nanoseconds) to a situation than a driver (at least 0.7 seconds), and¬†research shows that where reaction times are slowed the¬†likelihood and severity of a collision¬†is likely to increase. So this technology offers the¬†potential¬†for reduced pedestrian and cyclist fatalities.

Whilst objectively there may be some scope for driverless vehicles to reduce the incidence of collisions with vulnerable road users, safety is also a subjective matter. The impacts of driverless cars on the subjective safety of vulnerable road users is even less well researched. What we are able to do, however, is make inferences from existing research into vehicle / vulnerable road user interactions, for which there is plenty.

Many vulnerable road user groups feel unsafe in vehicle-dominated spaces, something that is extensively backed by research. Research into shared space by the MVA Consultancy shows that high traffic flows discourages pedestrians from sharing highway space. Understanding Walking and Cycling shows the biggest barrier to take-up of cycling is safe cycle infrastructure. Making the Connections reveals traffic as a major barrier to social equality and access to services for the most vulnerable members of society. The elderly have difficulty adjusting walking pace, judging gaps in traffic, and generally interacting in more complex traffic environments. I could go on, but you get the point.

New Road in Brighton

New Road in Brighton (Image Source: Google)

To deal with this, vulnerable road users Рas human beings Рadopt coping mechanisms, the most notable of which is to not visit places at all, which is not condusive to good placemaking. Clearly physical seperation and priority of vulnerable road users is critical in many areas to creating quality places. But what is interesting in the case of driverless vehicles is the value vulnerable road users place on interaction with the driver, particularly in low speed environments.

It seems counter-intuituve, but current evidence shows that interaction with drivers is uncommon. Again,¬†the¬†MVA Consultancy¬†shared space research¬†is important as it records no instances of eye contact negotiation between pedestrians and drivers, with vehicle speeds, pedestrian flows, and demarkation of the carriageway influencing the propensity of drivers to give way to pedestrians. Whilst there is an inherent human value in such contact (which may boost it’s credentials as a subjective safety tool, I don’t know), it’s use as a negotiating tool appears overstated.

The current research indicates that in a driverless future, the technology may not significantly influence how vulnerable road users interact with vehicles and their subjective feelings of safety, even if actual safety is improved. This may change as the technology becomes more widespread and proven, particularly on vulnerable road user safety. But more research is needed for that.

The elephant in the room in all of this, which you caught a quick peek of earlier, is how driverless vehicles are able to support liveable cities through facilitating infrastructure change to favour people-friendly modes. Looking into the research on this, much has focussed upon how driverless vehicles interact between themselves, and interact with Intelligent Traffic Management Systems to produce optimal vehicle flows and reduce delays. Examples include space-time reservation systems at junctions, and the well-known road train experiment undertaken by Volvo. But there is no data on how much extra capacity driverless vehicles may deliver. What we can assume is that there is potential for greater highway vehicle capacity through elimination of driver mistakes, and more efficient movement of vehicles along links and through junctions. In theory.

But as we know by now, what is beneficial for vehicle capacity is not always condusive to livable cities. Lets look at the main constraint on highway capacity – junctions.

Signal controlled junction

Signal controlled junction with pedestrian crossing at Finsbury Park, London

My posts are long enough without going into the details of junction modelling. But to give a quick overview of the process

  • Count the number of vehicles on approach links and turning movements;
  • Get all of the relevant details on the junction – geometry, signal timings, topography to name a few – that affect the capacity of the junction;
  • Whack it all into a modelling package.

This then produces reams of numbers (see a Modelling Report to see how bad this can be) of which 3 are of most importance for understanding the operation of the junction: total delay minutes, mean delay minutes, and ratio of flow to capacity or RFC Рsimply how well the junction flows. This Ignores debates of this approach focusing on vehicles and not person movements, and the relative weighting of modes in calculations (covered excellently by Rachael Aldred), which I will cover at some point.

Needless to say that the impact of driverless cars on junctions is not well understood currently. But there is a reason for my basic introduction to junction modelling. Bearing in mind the huge variety of factors that influence junction capacity, it is unlikely that driverless cars will have a significant effect in increasing junction vehicle theoretical capacity. But driverless vehicles are likely to lead to more efficient vehicle movements through junctions. Consequently, busy junctions are more likely to operate closer to their theoretical capacity in such a way the reduces mean and total delays to vehicles.

But this is an extremely vehicle-focused way of looking at things – as is junction modelling generally. We are interested in people here, as such another way of looking at this is that if junctions do not operate at their current theoretical vehicle capacity, could the roll-out of driverless cars mean we actually reduce this theoretical vehicle capacity to its current actual use? This gives rise to all sorts of other options for creating livable cities without huge impacts on current traffic flow – longer pedestrian green phases, advanced green phases for cyclists are a couple of good examples.

I should stress here that these are just my thoughts based upon my professional intuition. Research into the impacts of driverless technology on junction operations is hardly abundant, and I expect our understanding to improve immeasurably in the near future.

The other aspect of highways are highway links. Driverless vehicles are lauded as offering potential gains in terms of highway vehicle capacity, but this assumes that the existing infrastructure remains relatively unchanged Рparticularly in terms of lane capacity. But as this extremely important diagram from Manual for Streets 2 shows us, that just looks at streets in terms of their movement status, and not how they should function as a place.

Again, due to the newness of the technology means there is little research on driverless vehicles and place, and this certainly needs to be researched. But it is my feeling that the principles of good street design will be the same even in a driverless future, because what we value in our streets and public spaces –¬†spaces on the personal level that encourage social interaction (or ideal for walking and cycling in the transport planning sense) –¬†has remained unchanged for hundreds of years. This value has even survived the rise of the motor vehicle.

On a practical note, there is the opportunity that driverless vehicles may support the reallocation of space on links by increasing capacity in the remaining running lanes, Again, the research into this is at an early stage, with varying reports of the effects of platooning (where numerous vehicles follow each other in close proximity) on highway capacities. Some initial research indicates if just vehicle sensors were used, capacity could increase by 43%, and when you add in vehicle to vehicle communications this goes up to a 273% increase.

Let’s apply these initial estimates to an example two lane single direction link near me, Bedford High Street.

Bedford High Street

As you can see, this is a two-lane single direction carriageway, 30mph with mixed frontage (including loading) with a total width of just under 7 metres.  Under the Design Manual for Road and Bridges, this gives the highway a theoretical capacity of 1110 vehicles per hour.

Applying the capacity increases cited previously, delivery driverless vehicles would increase theoretical capacity here to between 1587 and 3030 vehicles per hour. If a single traffic lane was removed for expanded footways or cycle tracks, capacity would change again from a loss of just under 200 vehicles per hour (that could easily be absorbed by the town centre network generally), to or a gain of over 500 vehicles per hour.

(I should probably state now that it has been a long-held ambition to fully pedestrianise Bedford High Street)

The implication from these back of the envelope calculations is that the roll out of driverless technology in vehicles could maintain highway capacity for vehicles, yet at the same time dramatically increase capacity for walking and cycling and give greater potential for creating people-friendly cities.

Personally I have doubts as to whether such capacity enhancements can be realised. It’s very well increasing link capacity, but if junction capacity increases are negligible then no more vehicles can be stuffed through your highway network. Additionally, movement will still¬†need to be managed by design so as to actively contribute to the streets’ place function, attractive town centres will still need to be walkable and easy to cycle through, and highly valued public spaces will still encourage social interaction and positively contribute to city life. The only thing that will change is that machines may drive through them, not people.

Overall, driverless vehicles offer opportunities for reinventing the infrastructure of our cities in a more livable manner, and supporting livable city strategies. This can be of benefit to our most vulnerable road users, improve the overall environment of our streets. They also offer the opportunity to maintain the status quo by simply making existing operation more efficient.

My view is that the decisions that we as professionals need to make our cities more livable have not changed, and will not changed with driverless cars. They offer potential to support this approach. We just need to get away from thinking about what driverless cars can do for vehicles, and start thinking about what they can do for people.