Moving the capital of New South Wales to the west

The capital of New South Wales is currently in Sydney, eastern Sydney, historic Sydney, tourist Sydney, or to speak the language the planners understand, the Harbour City. Parliament meets in a gorgeous building adjacent to the Domain, a large urban park. Government offices are scattered throughout the city and the metro area.

New South Wales Parliament Building
New South Wales Parliament Building

Policy in Sydney has recently engaged around the idea of a 30-minute city, the idea that people can get where they need to go on a daily basis (work, shop, school) in 30 minutes or less by walking, biking, or public transport. (Or that 70% of the people do so, depending on which definition.) This can be achieved through a combination of transport and land use strategies. On the transport side is the question of how fast and how direct the transport network is. On the land use side is the question of where desired activities are located relative to each other. The government of New South Wales is promoting the development of jobs in Western Sydney (and housing in Eastern Sydney) to reduce commuting times and encourage the 30-minute city. This is a noble goal, and the market may move in that direction.

The 30-Minute City by David M. Levinson
The 30-Minute City by David M. Levinson 

At one extreme we can imagine a completely functionally separated city, where all the homes are on one side of town, and all the jobs are on the other side of town. If the sides are more than 30 minutes apart, there is little that can be done to achieve the goal, though perhaps the connection between the two parts can be made faster or more direct. But since transport networks act to spread out cities physically, it might only induce more suburban development. This functionally separated city is equivalent to the classic monocentric city, with a single dominant downtown surrounded by residential suburbs.

At the other extreme we can imagine a completely functionally integrated city, probably relatively dispersed, where jobs and housing are completely integrated, so there are as many jobs in any suburb as there are workers. There is no guarantee that a worker will be able to find a job next door (or choose it), but the likelihood of finding a job nearby is higher than in the monocentric city

If everything else were equal, from a transport perspective, we would probably prefer an integrated city, as this would place the least strain on the transport network. Moving towards jobs/housing balance is a long held goal, if only weakly operationalized.

But all else is not equal. Employers have an affinity for each other. All the big banks want to be near each other, as do other big companies in various sectors. As does the government. This is what economists call economies of agglomeration.

The government is not just an employer, it is also a major player in real estate markets. It can catalyze development of western Sydney, its Aerotropolis/Parkland City, as it is called in the 2056 Three Cities plans, by moving itself there first.


Cities change with the pre-dominant transport technology. When the capital was established in Sydney in 1788, the dominant technology was animal and human powered, with wind and sails moving ships. Since then, much has changed, and the center of population has migrated inland.

The shape and form of the pedestrian city differs from the rail (trams and trains) city, and  differs from the automobile city. Retrofitting trams into the pedestrian city, and especially automobiles into the pedestrian and rail cities broke much earlier urban functionality, while creating new problems, new opportunities, and new designs. Technology played and plays out differently on greenfields, which could be designed to serve a new transport paradigm.

As we approach the transition from the traditional automobile to the autonomous electric and shared vehicle, with all of the ancillary changes, the opportunity for a new city of the future emerges. This technology will invade existing places, which will need to adapt, and new places which can more fully adopt the new technology. But we also need to keep an eye out for the next transition, whatever that may be (flying cars?), so that what we build now is not soon obsolete.

Transport is not the only shaper of cities, other technologies are also critical, from piped water and sewer, electricity, telephony, elevators, and air conditioning historically, to wireless high-speed internet most obviously today, and robotics coming up shortly.

The new capital will need to orient itself around these new technologies, as well as new extensions of well known technologies, like trains and Metros and light rails and bicycles and pedestrians. This is a huge opportunity, and while I won’t suggest a specific design, I will say it should be forward looking as well as reflective of the changes that have come before. Canberra was an opportunity, but by spreading itself out so much, it foreclose the possibility to effectively use slower modes.

If Daniel Burnham were designing the new capital for Sydney, it might look like this.
If Daniel Burnham were designing the new capital for Sydney, it might look like this.


A government campus for key departmental headquarters and Parliament at the end of the Mall, a now traditional design for capitals, with the vast majority of government offices scattered throughout the rest of New South Wales, could spark development. Access to the new airport and rail lines will provide connectivity to the rest of the state.

Ancillary businesses, not just those serving lunch to government workers, but those dealing with government on a daily basis, will migrate to deal with their public sector clients and customers. There are many sites on the axis between Parramatta and the Blue Mountains that could serve this purpose.

Sydney’s soon-to-be-abandoned historic Parliament House can have a variety of uses, from appropriately sized conventions to space for a museum. Other government offices in Sydney can be sold off, retrofitted for urban housing, or replaced as warranted. The Sydney CBD is thriving, and will continue to without a few thousand additional government workers. But that could be all the difference in success for a new city for Western Sydney.

In 1908, Australia, then with a population of 4.1 million, decided to relocate to Canberra. Today (2018) New South Wales has a population 7.8 million. As Australia has proven, the political capital need not be the largest city.  In the US, most state capitals are not the largest city: St. Paul not Minneapolis, Sacramento not Los Angeles, Albany not New York, Harrisburg not Philadelphia, Springfield not Chicago, Annapolis not Baltimore, and so on to name but a few.


Albany, New York, another planned state capital district
Albany, New York, another planned state capital district. Source: Flickr

It is time to plan and create a new government precinct, out west, to help spark the development the government seeks. It will bring the government to the people, de-center the government from its locational bubble, and juvenate new places with new ideas.


Rewinding the clock of techology

Last week, I tweeted

I am looking for examples of technologies that were deployed in a widespread way and reversed, so that the earlier technology resumed its pre-eminence (or nearly). (Like what if we abandoned mobiles and went back to landline phones). Can we wind the clock back?


I was thinking of transport cases, which a number of commenters suggested, like streetcars (trams, LRT) which were once dominant in cities, and then faded in importance, and are seeing some resurgence, but nowhere near original levels. Concomitantly autos in central cities, after decades of growth, are now losing mode share. But these have not gone all the way back to the status quo ante-auto.

Perhaps there were other situations we could point to.

This was a surprisingly popular tweet (110 comments to date, well above average). I have not linked to the original poster, though you can track it down through replies to the Twitter link, but to be clear, these are not my ideas. Since Twitter is a mess, I have distilled and organized them below.

These do not constitute endorsement, more as prospective cases to evaluate, in some cases I have comments. This is more than enough cases for someone to write a dissertation on.

I am not clear how many of them hold to the original request of being fully reversed and the technology before the technology being restored.   Also I would not say these reverted cases are necessarily failed technologies, in that they persisted in many cases for decades or centuries. And of course, technologies never really die, but they do fade away.

The ones I really like (in that I think they are really good fits to the question) are bolded.



  • Nuclear power [still a lot of it, and is replaced by renewables rather than fossil fuels]
  • Leaded gasoline

Food / Agriculture

  • Full fat products and real sugar vs low fat and sugar
  • Cholesterol
  • Butter vs. Margarine (But see link )
  • Slow Food movement
  • Organic Foods
  • Coke/New Coke
  • Ovens/microwaves/ovens [microwaves still seem really useful to me]
  • Baby formula
  • Frozen/Fresh juice,
  • Macro breweries
  • Driftnets
  • The return to Instant Coffee
  • High fructose corn syrup


  • Paper Ballots/ Electronic voting / Paper Ballots
  • Voter suppression (though this is extremely cynical, many places are reinventing the tools of suppression)



  • Vinyl records
  • Pre-lit Christmas trees
  • 3D Movies


  • Lobotomies  (not really widespread though)
  • Shock therapy (not really widespread though)
  • Withdrawn drugs (link)

Information and Communications Technologies

  • Writing/No Writing/Writing (e.g. Greeks)
  • Telegraph
  • MS Windows Vista vs. XP (etc.)
  • Laptops in the classroom
  • Ebooks vs. Physical books (link)
  • Browser plugins (Flash/no Flash / Web VR)
  • Over-the-air/Cable TV/Over-the-air (HDTV/Freeview)
  • Two-way radio (walkie-talkie) / Cell / Two-way radio (in select applications)
  • The rise of Emoticons/Emoji to replace words
  • Mainframe/Desktop/Cloud


Appliances and Household Goods

  • Electric Can-openers
  • Electric blankets
  • Dryers/Clothes lines
  • Wall-to-wall carpeting
  • Chamber pots / Roman plumbing /chamber pots again until 1800s
  • Paper bags/Plastic bags/Paper bags
  • Gas ovens (fire) / Electric ovens / Gas ovens
  • Analog watches/Digital watches/analog watches/smart watches


  • Copper/Aluminum/Copper for electrical wiring

Chemicals and Materials

  • DDT
  • CFCs (though replaced with different technology than went before)
  • Asbestos
  • Smoking (replaced by the technology of not smoking)
  • Lead paint



  • Coined money


  • The re-emergence of home deliveries, especially food.
  • The rise of EVs (but EVs were hardly a dominant technology c. 1900-1915) [link]
  • Trails / Roman road building / trails (until mid 1800s European roads were of lesser quality than those almost 2000 years previous)
  • No aqueducts/Aqueducts/No aqueducts/Aqueducts
  • Catamarans/Hyrdofoils/Hovercraft
  • Large ocean-going ships in China [Zheng He]
  • Double-hulled transoceanic vessels in Hawaii
  • Dirigibles
  • Single use rockets/Space shuttle/single use rockets
  • Concorde/SST/Tupolev Tu-144 (but SST was never really widespread, less than 1% of aviation market share)
  • Cycling is making a comeback, especially bikesharing (still really small market share in North America and Australia, but in China this seems a big deal)
  • Jitney/taxi
  • Trolleys/LRT is making a comeback (also small market share)
  • Time machines. They were everywhere for a few years until someone went back and killed the inventor. Now we have none.

Safety in Numbers: Pedestrian and Bicyclist Activity and Safety in Minneapolis

Recent Report:
This investigation aims to evaluate whether the Safety in Numbers phenomenon is observable in the midwestern U.S. city of Minneapolis, Minnesota. Safety in Numbers (SIN) refers to the phenomenon that pedestrian safety is positively correlated with increased pedestrian traffic in a given area. Walking and bicycling are increasingly becoming important transportation modes in modern cities. Proper placement of non-motorized facilities and improvements has implications for safety, accessibility, and mode choice, but proper information regarding estimated non-motorized traffic levels is needed to locate areas where investments can have the greatest impact. Assessment of collision risk between automobiles and non-motorized travelers offers a tool that can help inform investments to improve non-motorized traveler safety. Models of non-motorized crash risk typically require detailed historical multimodal crash and traffic volume data, but many cities do not have dense datasets of non-motorized transport flow levels. Methods of estimating pedestrian and bicycle behavior that do not rely heavily on high-resolution count data are applied in this study. Pedestrian and cyclist traffic counts, average automobile traffic, and crash data from the city of Minneapolis are used to build models of crash frequencies at the intersection level as a function of modal traffic inputs. These models determine whether the SIN effect is observable within the available datasets for pedestrians, cyclists, and cars, as well as determine specific locations within Minneapolis where non-motorized travelers experience elevated levels of risk of crashes with automobiles.
Recent publications from this report include:

Speed vs. Safety

March 21 [Updated with more accurate estimate/figure after fixing an excel bug] How fast should we drive? From a social cost perspective, faster speeds save time, which has a value, but faster speeds cost lives, which also have a value. To illustrate the trade-off I did some back of the envelope calculations, imagining, like a macro-economist, a single road represents the whole t

Speed vs. Safety (updated)
Speed vs. Safety (updated)

ransport system. Annually there are about 30-40,000 people killed in the US, there are an annual Vehicle Miles Traveled of 3,208,517,000,000. The average speed of travel isn’t known directly, but if we assume the average person travels in a car 60 minutes per day (the 1 hour travel time budget) this implies, at approximately 30 miles of travel per day per traveler, about 30 MPH, which seems about right (including 1/4 of travel on freeways at higher speeds and 3/4 on surface streets and roads at lower speeds, and including traffic signals). As the saying goes, Your Mileage May Vary, and this is intended to be indicative — not a universal answer. Some additional assumptions:

  • We take the Value of Life to be $10,000,000, and assume fatalities are the only cost associated with crashes (they are about 78 % of total crash costs according to our analyses, so we should inflate this number to get total crash costs) [US DOT says $9.6 M]
  • We take the Value of Time to be $15/hour [US DOT gives a lot of ranges, but this number is high for all surface travel excluding freight]
  • We assume the number of deaths drops linearly with speed, to zero at zero MPH. The improvement is likely non-linear, as reductions in speeds from high speeds are more valuable than from low speeds.
  • We assume the value of travel time savings is constant, independent of the amount of time saved.

To be clear, these are huge assumptions. Examining the figure we see the lines cross at about 75 MPH, which is the minimum total cost. So why don’t we set the speed limit to  75MPH? Note that:

A Political Economy of Access: Infrastructure, Networks, Cities, and Institutions by David M. Levinson and David A. King
A Political Economy of Access: Infrastructure, Networks, Cities, and Institutions by David M. Levinson and David A. King
  • Travel time savings are, while still speculative in terms of their valuation, both private and real,
  • The statistical value of life is far more abstract. The value of my life to me is infinite. The value of your life to me is, sadly, not. Yet, I am willing to take risks that increase the probability of my dying in order to save time or earn more money. These are the kinds of factors that allow an estimate of value of a statistical life.
  • Death and crashes are probabilistic affairs, while the time lost is deterministic. People are gamblers.
  • There are some other benefits to faster travel not accounted for, such as more or longer trips (to better destinations, or the ability to get better real estate at the same price), which increase consumer surplus. The analysis here does not consider user response to lower speeds, which would be to travel less (or higher speeds and travel more).  There are also issues like travel time reliability.
  • Since 1988 The Statistical Value of Life has risen 6-fold in US DOT estimates, the value of time has little more than doubled. (If we cut the value of life to $3M, (effectively holding the tradeoff more similar to 1988 levels), the tradeoff is much higher .)
  • Speed limits reflect what travelers will travel at, not what we wish they would travel at.

If you dislike these number, you can roll your own analysis on individual roads. The difficulty is not measuring the speed of those roads, but measuring their safety. There is a Highway Safety Manual for such purposes, but crashes are highly random events.

UPDATE 2: Axel Waleczek made an interactive Tableau, so you can test your own scenarios.

Additional Readings

Uber’s self-driving car killed someone today

Uber’s self-driving car killed someone today. This is terrible tragedy, and in retrospect, it will probably be judged to have been preventible. Future versions of the software will better address the scenario that led to this crash. But mistakes are how people and systems learn, and someone was going to be the first. The victims are scarcely remembered.

A few key points.

  • The safety rate for Uber AVs collectively is now worse than that for human drivers (1.25 pedestrian deaths / 100MVMT) (MVMT = Million vehicle miles traveled) (Uber is at about 1 MVMT, Waymo at about 4MVMT). It will undoubtedly get better.
  • Don’t assume Uber AVs are the same as Waymo or others. Different software, vehicles, sensors, driving protocols, safety cultures. The stats for each will differ.
  • Also we need to see the full investigation (from NHTSA, NTSB).
    • How much victim blaming will there be?
    • Was it just sadly unavoidable?
    • Or was it preventable?
  • The opposition will use this to bang on against AVs while supporters will be quiet for a while.

Hopefully the developers learn something and this type of crash is rare. Other AV makers will take the scenario and run it through their own simulations and field tests.

Still the technology trajectory is strong, and even if the US slows down development, it’s a big world. China won’t slow down development.

How Railways Dealt With The First Notable Fatality:

The Liverpool and Manchester Railway killed former Leader of the House of Commons and cabinet member, William Huskisson during the opening day ceremonies. It was the UKs 2nd significant steam railway and the first that was opened with a big deal with   such publicity. We write in The Transportation Experience

On September 15, 1830, the opening ceremonies for the Liverpool & Manchester Railway were held. The Prime Minster (the Duke of Wellington), Cabinet members, Members of Parliament, and other assorted dignitaries were present. Among those were an MP from Liverpool, and a 60 year old former Leader of the House of Commons and cabinet member, William Huskisson. The dignitaries had been riding on a train pulled by one of Stephenson’s Rockets. Reports differ, but Lady Wilton, an observer on the same train wrote to Fanny Kimble:

The engine had stopped to take a supply of water, and several of the gentlemen in the directors’ carriage had jumped out to look about them. Lord Wilton, Count Bathany, Count Matuscenitz and Mr. Huskisson among the rest were standing talking in the middle of the road, when an engine on the other line, which was parading up and down merely to show its speed, was seen coming down upon them like lightening. The most active of those in peril sprang back into their seats; Lord Wilton saved his life only by rushing behind the Duke’s carriage, and Count Matuscenitz had but just leaped into it, with the engine all but touching his heels as he did so; while poor Mr. Huskisson, less active from the effects of age and ill-health, bewildered, too, by the frantic cries of `Stop the engine! Clear the track!’ that resounded on all sides, completely lost his head, looked helplessly to the right and left, and was instantaneously prostrated by the fatal machine, which dashed down like a thunderbolt upon him, and passed over his leg, smashing and mangling it in the most horrible way.

Stephenson personally helped Huskisson onto a locomotive and traversed 15 miles in 25 minutes (57.9 km/h) to receive medical attention in the nearby town of Eccles. But it was for nought. Huskisson amended his will and died within the hour. (Garfield)

This was not the first death by steam locomotive, it was at least the third, but it was still the most notable. Wikipedia notes

5 December 1821, when a carpenter, David Brook, was walking home from Leeds along the Middleton Railway in a blinding sleet storm. He failed to see or hear an approaching train … and was fatally injured.” — Richard Balkwill; John Marshall (1993). The Guinness Book of Railway Facts and Feats (6th ed.). Guinness. ISBN 0-85112-707-X.

According to parish council records, a woman in Eaglescliffe, Teesside, thought to be a blind beggar, was “killed by the steam machine on the railway” in 1827– “Corrections and clarifications.” The Guardian. 2008-06-21. Retrieved 2009-02-05.

Despite this inauspicious beginning, both passengers and freight services (the latter opened in 1831) were immediate successes.

Spatiotemporal Traffic Forecasting: Review and Proposed Directions


Abstract: This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

Postdoctoral Research Associate in Transport – Closing date: 11:30pm, Tuesday, 27 March 2018

Postdoctoral Research Associate in Transport

School of Civil Engineering

Faculty of Engineering and IT

Reference no. 779/0417B

  • Join an organisation that encourages progressive thinking
  • Be valued for your exceptional knowledge and experience in Transport Networks
  • Full-time fixed-term for 3 years, remuneration package: $106k (which includes base salary, leave loading and up to 17% superannuation) 

About the opportunity

Applications are invited for the appointment of one Postdoctoral Research Associate (Level A) in the School of Civil Engineering, within the Faculty of Engineering and IT at the University of Sydney. The position will support the research and leadership of School of Civil Engineering in the newly launched Transport Engineering program.


The successful applicant(s) will help build the new research group headed by Professor David Levinson to further the analysis of Transport Networks, understand the relationships between Transport Networks and Land Use, and consider the implications of changing Transport Technologies on optimal Network Structure.


About you

The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance in recruiting talent aligned to these values in the pursuit of research excellence. We are looking for a Postdoctoral Research Associate who:


  • Holds a PhD in civil engineering, or related fields
  • Has published ground-breaking research in the area of transport networks, geo-spatial analysis, and/or econometrics in high quality international journals
  • Possesses strong communications skills as the position requires liaising with government and industry stakeholders.

About us
We are undergoing significant transformative change which brings opportunity for innovation, progressive thinking, breaking with convention, challenging the status quo, and improving the world around us.

Since our inception 160 years ago, the University of Sydney has led to improve the world around us. We believe in education for all and that effective leadership makes lives better. These same values are reflected in our approach to diversity and inclusion, and underpin our long-term strategy for growth. We’re Australia’s first university and have an outstanding global reputation for academic and research excellence. Across 9 campuses, we employ over 7600 academic and non-academic staff who support over 47,000 students.


The University of Sydney encourages part-time and flexible working arrangements, which will be considered for this role.


For more information about the position, or if you require reasonable adjustment or support filling out this application, please contact Emmen Saeed, Recruitment Partner, on

If you would like to learn more, please refer to the Candidate Information Pack for the position description and further details.


To be considered for this position it is essential that you address the online selection criteria. For guidance on how to apply visit: How to apply for an advertised position.


Closing date: 11:30pm 27 March 2018

The University of Sydney is committed to diversity and social inclusion. Applications from people of culturally and linguistically diverse backgrounds; equity target groups including women, people with disabilities, people who identify as LGBTIQ; and people of Aboriginal and Torres Strait Islander descent, are encouraged.


If we think your skills are needed in other areas of the University, we will be sure to contact you about other opportunities.


The University reserves the right not to proceed with any appointment.

Candidate Information Pack

Selection Criteria

How to apply:

How much time is spent at traffic signals?

The 30-Minute City by David M. Levinson
The 30-Minute City by David M. Levinson 

While working on another piece, I came upon the question of how much time is spent at traffic lights, for which there is not a well-sourced answer. I posted to Twitter and got some useful replies.

With that and some additional digging, I attempt to answer the question.

As the saying goes: Your Mileage May Vary. This depends on your origin and destination and path and mode and time of day and local traffic signal policies and street design. Tom VanVuren notes: “Much of the impact is in slow moving queues, rather than waiting for the signal cycle to complete. I expect you can make this number smaller than 10% (time at the stop line) or larger than 50% (time affected by traffic lights).” For simplicity, I am considering vehicles that would be stopped if they could either move at the desired speed or must stop (i.e. they are subject to “vertical” or “stacking” queues), but clearly measurement will depend on assumption. Still, there must be a system average. I had heard the number 20% bandied about, which feels right, but let’s first begin with some thought experiment, then look for some empirical results. We take different modes in turn.

A signalized but porkchop-islanded crosswalk at a Free Left (Free Right for those in the right-side drive countries). Notice the pedestrian light is red (don't walk) but the pedestrians cross anyway. If the free left is not eliminated in a more comprehensive redesign, it could easily be de-signaled and the crosswalk raised, so pedestrians dominate, and cars travel when they can.
Pedestrian Crossing at Broadway and City Road, Sydney. Pedestrians crossing against the light.

Motor Vehicles

Thought Experiments

Thought Experiment 1 A

Imagine an urban grid.

  • Assume 10 signalized intersections per km.
  • Assume a travel speed of 60 km/h when in motion. (This is probably too high with so many intersections and no platooning, but we are imagining here that you would not be stopping.)
  • Time to traverse 1 km=1 minute + signal delay.  (Some of the distance traversal time overlaps some of the signal delay time, but we will imagine a stacking queue, rather than one that has physical distance for simplicity, we can correct this later if it matters.)
  • Assume each intersection has only 2 phases.
  • Assume fixed time signals at each intersection evenly distributing green time between N/S and E/W directions.  So red time = 1/2 cycle length.
  • Assume 1 minute cycle length
  • If a vehicle stops, it waits 1/2 red time.
  • Vehicles obey traffic signals.
  • Assume no platooning.

This means that the average vehicle will stop at 5 intersections for 15 seconds each = 75 seconds (or 1.25  minutes) (vs. 1  minute in motion time). In this case, 1.25/2.25 minutes (55.5%) is spent waiting at signals.

Thought Experiment 1 B

In contrast.

  • Assume near perfect platooning.

In this case, the vehicle will stop at 1 intersection per km, for 15 seconds = 15 seconds. In this case 0.25/1.25 = 20% of the time is spent waiting at signals.


Now, not all travel takes place on an urban grid.

  • Assume 25% of travel is on limited access roads (this is approximately true in the US),  75% on non-limited access roads.

With perfect platooning on the grid, and 25% off-grid, then 15% of travel time is intersection delay with near perfect platooning.

Clearly in practice platooning is far from perfect. My guess is the green wave breaks down after one or two intersections during peak times, but can survive well in the off-peak. As a rule of thumb, about ~10% of travel is in the peak hour, ~30% peak period. ~60% AM + PM Peak.


GPS Studies

Eric Fischer of MapBox was kind enough to offer to run this question on their open traffic data. The results are not yet in. I will update when they are.

Arterial Travel Time Studies

There are a variety of Arterial Travel Time studies for specific corridors, but nothing that is universally generalizable.  (And logically where people do arterial travel time studies, there is a congestion problem, otherwise why study it.)

I recall that in my childhood, I did a study in Montgomery County, Maryland using such data (from 1987 traffic counts and a floating car study published by Douglas and Douglas), I did not actually compute the percentage, but fortunately I reported enough data that allows me to compute the percentage now. (The sample is of course biased to what is measured). For the average arterial link, the speed was

 Variable Inside the Beltway  Outside the Beltway
 Speed (km/h) 34.88  41.60
 Length (km)  0.46  0.72
 Time (min)  0.792  1.04
 Downstream Delay (min)  0.27  0.24
 Percentage of Signal Delay  25%  18.75%

Which is consistent with expectations that signals are more significant in more urbanized areas (inside the beltway is basically Bethesda and Silver Spring, MD), and with our general estimates. Now of course the speed here is impacted by downstream signals, and so is lower than the speed limit and certainly lower than the free-flow speed sans-signals. More details are in the paper.

Engine Idling Studies

Moaz Ahmed pointed me to a Vehicle Idling Study by Natural Resources Canada.

The percent of time of vehicle idling ranged from 20-25%. (Not all vehicle idling is at signalized intersections).

(Engine idling of course burns fuel without doing work, so if the engine is going to be idling for an extended period, it would save fuel (and reduce air pollution) to turn it off. Turning the engine on and off also has costs, so the estimate was if idling was going to be longer than 10 seconds, it uses more fuel, but considering other wear and tear costs, the recommended threshold is if idling is longer than 60 seconds, then turn off the engine.  But at a signalized intersection, how will vehicles know how long they will wait? Smart traffic signals with connected vehicles could provide this, but now they don’t. Eventually this will be moot with a full electric vehicle fleet. Until that time, it matters. I suspect given the longevity and sluggishness of the traffic control sector, smart signals informing trucks will not be widely or systematically deployed before trucks are electrified.)


Now as noted above, Your Mileage May Vary. If you are a pedestrian, you are unlikely to hit a greenwave designed for cars, though of course your travel speed is slower is well. So redoing the Thought Experiment

Thought Experiment 2

Imagine an urban grid.

  • Assume 10 signalized intersections per km.
  • Assume a travel speed of 6 km/h when in motion. (this is a bit on the high side, average pedestrian speed is closer to 5 km/h)
  • Time to traverse 1 km=10 minutes + signal delay.  (Some of the distance traversal time overlaps some of the signal delay time, but we will imagine a vertical stacking queue, rather than one that has physical distance for simplicity, this is a much better assumption for pedestrians than vehicles.)
  • Assume each intersection has only 2 phases.
  • Assume fixed time signals at each intersection evenly distributing green time between N/S and E/W directions.  So red time = 1/2 cycle length.
  • Assume 1 minute cycle length
  • If a pedestrian stops, she waits 1/2 red time. (That is the “walk” phase for pedestrians is as long as the green phase for cars. Strictly speaking this is not true, it is more true in cities with narrow streets than it is in suburban environments with wide streets, as narrow streets can be crossed more quickly, so the amount of “walk” time allocated can be most of the phase. This is certainly not true in Sydney, where the “walk” phase is cut short so turning cars have fewer conflicts with late pedestrians.)
  • Pedestrians obey traffic lights.  (This is not as good an assumption as vehicles obey signals, pedestrian signal violation is probably higher. This is not a moral judgment one way or the other, people tend to obey authority, even when authority abuses power.)
  • Assume no platooning. (This is probably too severe, a quick pedestrian with some signal coordination can probably make a couple of lights in a row).

Here the average pedestrian will stop at 5 intersections for 15 seconds each = 2.5 minutes (vs. 10  minute in-motion time). In this case, 2.5/(2.5+10) minutes (or 20%) is spent waiting at signals. Now, this number is probably true for more pedestrians than the vehicle delay estimate is for vehicles, since pedestrians are more likely to be found on an urban grid and less in a suburban or limited access environment. (Self-selection at work).


If you are a bicyclist, you are unlikely to hit a greenwave designed for cars unless you travel at exactly an integer fraction (1/1, 1/2, 1/3) of the green wave, as your travel speed is slower is well. So redoing the Thought Experiment

Thought Experiment 3

Imagine an urban grid.

  • Assume 10 signalized intersections per km.
  • Assume a travel speed of  20 km/h when in motion. (This is a typical for experienced riders). Time to traverse 1 km=3 minutes + signal delay. (Assume a stacking queue)
  • Assume each intersection has only 2 phases.
  • Assume fixed time signals at each intersection evenly distributing green time between N/S and E/W directions.  So red time = 1/2 cycle length.
  • Assume 1 minute cycle length
  • If a bicyclist stops, she waits 1/2 red time. (That is the ‘bike’ phase for bicyclists is as long as the green phase for cars.)
  • Bicyclists obey traffic lights.  (This is not as good an assumption as ‘motor vehicles obey signals’, bicyclists signal violation is probably higher.)
  • Assume no platooning. (This is probably too severe, a quick bicyclists with some signal coordination can probably make a couple of lights in a row).

In this case the average bicyclists will stop at 5 intersections for 15 seconds each = 2.5 minutes (vs. 3  minute in-motion time). In this case, 2.5/(3+2.5) minutes (or 45%) is spent waiting at signals in an urban environment.

Strava Data

Strava, an app for tracking bicyclists and runners can produce some useful data. Andrew Hsu, e.g., reports “28 mile bike commute. 1:30-ish moving time. 10-15 minutes waiting at lights.” From this, for him, we estimate 15 / (15+90) = 14%. To be clear, 1:30 is an extreme commute. I don’t have access to the full database, and obviously this is biased by the nature of the trip.


Alejandro Tirachini produced an estimate of travel time for buses finds delay at traffic signals (in suburban Blacktown, Sydney, NSW) is 10-13% of total time.

On the Second Amendment and the Right of Revolution

There are several reasons some people in the United States support the private ownership of guns. School shootings and the rest are unfortunate collateral damage of ensuring the principle of individual arms can be readily obtained.

Flintolock musket

The Second Amendment confusingly says

“A well regulated militia being necessary to the security of a free state, the right of the people to keep and bear arms shall not be infringed.”

It is not clear on what arms people can have. Almost everyone agrees people should be allowed to have knives and muskets. Almost everyone also agrees no private nuclear bombs. I like the idea that original intent of the word “arms” means that no weapons invented since 1789 are implicitly constitutionally covered. The current interpretation of the second amendment is a modern one.

In addition to the “well regulated militia” rationale, there are other reasons people might want guns, including:

  • Personal Self-defense
  • Hunting
  • Committing crimes (some of which is self-defense while doing illegal things)
  • Over-throwing a `tyrannical’ government (which also relates in part to self-defense when rebelling against a well-armed opponent, as well as offense against the same opponent). This is also known as the “right of revolution.” It is discussed in this Federalist article.

I believe most gun supporters are, in fact, though most won’t admit it, about the last point. That is, their minds foresee a dystopian outcome when a fascist (or communist, but same thing) comes to power and must be resisted by weapons that have yet to be confiscated by a weak liberal regime.

The Civil War is a morally repugnant example of this kind of resistance, in that case by a south defending slavery; but one can equally imagine a world where a slightly less demographically and economically powerful  north was resisting imposition of slavery  by the southern states.

Or, their mind foresees the US being invaded by a foreign (or alien) army which somehow the military was unable defeat. Having grown up in the 1980s and seeing Red Dawn and V, I have some empathy for that view in principle. In practice, not so much.

Yet, if you are right wing, and believe the previous administration was the illegitimate dystopia that fuels your nightmares, where was your uprising? I missed it. If you are truly anti-fascist, where is your uprising now? You, gun-owners of America, are as well-armed as any citizenry in history. The US government’s ICE brown shirts are taking people from their homes and deporting them. Police officers are systematically killing people of color. And gun owners are not systematically challenging them. Hmm. Oh, I missed the part that it was the right of revolution for white people.

Which leads me to the conclusion that over-throwing the US government with the citizenry’s privately owned weapons is just not going to happen. Which means, we can strike the justification of needing guns for keeping the government in check. At this point in history, the US government can keep the populace in check, even if armed. At best you can take someone out before being killed yourself. You will not actually win.

The counter-argument is that it is the well-armed citizenry that is keeping the government in check, and thereby keeps it from confiscating guns (and eliminating other freedoms, but those are secondary to the guns). But if that were true, they wouldn’t be worried about the government confiscating guns. The reasoning is circular. The reason to have guns is to keep the government from confiscating your guns. If the government could confiscate your guns at any time with a change in legislation, the guns aren’t actually buying you your freedom. Instead it is that the government cannot effectively act without the consent of the governed.

Since the ‘committing crimes’ is also not really a good reason to keep guns, and ‘hunting’ doesn’t require sophisticated weapons, and ‘personal self-defense’ with guns is only necessary because everyone else also has guns and may be committing crimes, the US should just throw in the towel and follow the civilized world, or even Australia, and more significantly reduce access to firearms.