• conduct high-quality research in quantum-enabled computational approaches for digital twins and complex system simulations

• develop, implement, test, and benchmark computational models, algorithms, data pipelines, and research software prototypes

• contribute to optimisation, simulation, sampling, or uncertainty-modelling approaches for complex systems

• analyse infrastructure, spatial, temporal, networked, or urban systems data
  contribute to the preparation and submission of journal articles, conference papers, technical reports, and research outputs

• collaborate with interdisciplinary scholars across engineering, design, planning, computing, and related research domains

• contribute to stakeholder engagement activities, including workshops, research presentations, and partner-facing materials

• assist with the development of competitive grant applications and future external funding opportunities.

Work Rights
You must have unrestricted work rights in Australia for the duration of this employment to apply. Visa sponsorship is not available for this appointment.

Link: https://usyd.wd105.myworkdayjobs.com/en-GB/USYD_EXTERNAL_CAREER_SITE/job/Darlington-Campus/Postdoctoral-Research-Associate-in-Applied-Quantum-Computing-Research_0153792-2

• Song, C., Levinson, D. and Moylan, E. (2026) A simulation-based policy evaluation of healthcare access using heterogeneous emergency medical services vehicles and incidents. Transport Policy, Volume 187, October 2026, 104293. [doi]

Efficient dispatch policies are critical for ensuring equitable and effective emergency medical services (EMS). While vehicle specialisation offers tangible clinical benefits, integrating vehicle types into dispatch policy presents significant challenges. This study employs an agent-based simulation framework to evaluate the effects of alternative dispatch policies under varying proportions of specialised ambulances. Results indicate that allowing specialised vehicles to respond to a broader range of high-priority incidents enhances overall survival rates. However, the benefits of specialisation depend on how these vehicles are deployed within the system. When the supply of specialised vehicles exceeds demand, restrictive policies lead to longer response times and worsened outcomes for non-prioritised cases. In particular, restricting specialised vehicles to a narrow set of incidents can reduce overall system efficiency by limiting dispatch flexibility and increasing waiting times for other patients. Moderately flexible dispatch policies provide balance between outcome efficiency and response equity. These findings suggest that both the proportion of specialised vehicles and their dispatch eligibility should reflect the underlying mix of incidents in order to avoid over-constraining the system. Spatial analysis shows that survival and response disparities are more pronounced in peripheral areas. These findings underscore the need to align dispatch policy and fleet composition with incident typology and spatial demand, offering actionable insights for EMS policy and resource planning.

Keywords:

Dispatching policy, Emergency medical services, Agent-based simulation, Vehicle classes, Incident types, Priority

• Gao, Y. and Levinson, D. (2026) A stochastic queueing model for travel time reliability with spatial density heterogeneity. Transportation Research Part B, Volume 211, September 2026, 103544. [doi]

This paper constructs a theoretical model connecting day-to-day travel time reliability (TTR) and vehicle density, testing it on freeway stretches in Minneapolis - St. Paul and San Diego. We establish the presence of a counter-clockwise hysteresis loop between average vehicle density and experienced travel time standard deviation. We also observe that vehicle density heterogeneity exerts a more profound effect on TTR during moderate congestion. We find that a timehomogeneous Poisson process accurately characterizes the vehicle arrival process on segments of freeway stretches, both with and without on-ramps, during the morning peak period. The travel times for these segments align well with an exponential distribution. Through an analysis of the travel time correlations among different segments of the freeway stretch, we find that this correlation is higher during congestion offset periods, resulting in more severe travel time fluctuations due to increased density heterogeneity. Using the M/M/1 queueing model, we derive a model encapsulating the variance of experienced travel time, average vehicle density, and density heterogeneity for freeway stretches consisting of multiple segments. We use the `birth-death process’ to model the relationship between average vehicle density and density heterogeneity. By integrating these two models, we derive a novel model that accurately approximates freeway stretch TTR based on average vehicle density and traffic flow. We apply our models to TTR changes on the two selected freeway stretches to validate across different time periods and select two subnetworks, each containing three additional freeway stretches, to validate across spatial dimensions. The results from these experiments corroborate the robustness and generality of our model across both temporal and spatial contexts.

Keywords:

Travel time reliability, Density heterogeneity, Queueing theory

• Levinson, D. (2026) Braess’s Paradox Implies Reduced Demand. Findings, June 30, 2026. [doi]

Induced demand is usually interpreted as additional capacity reducing generalized cost and producing more travel. When a network exhibits Braess’s paradox, an added link raises user-equilibrium cost, and we would thus expect demand to fall. I show this in Braess’s original four-node numerical network with linear link costs and inverse demand. In the default case, the added link raises elastic equilibrium cost from 70.14 to 80.00 and reduces demand from 3.66 to 3.33. The reported 143-point sweep maps the Braess region in inverse-demand space; no sign-inconsistent cells appear. The accompanying browser implementation reproduces the reported comparison and sweep.

See also:

BRAESS– Braess Paradox Explorer

BRAESS demonstrates fixed-demand and elastic-demand user equilibrium on a small editable network.
More >

• Soltani, A, Ramezani, M. and Levinson, D.M. (2026) A stochastic delay model for signal-free intersections. Transportation Research part C. October 2026, 105813, Volume 191. [doi]

Highlights

• Stochastic conflict-probability model estimates delay at signal-free intersections without fixed priority hierarchies assumption.

• Binary service time distribution links conflict probability to M/M/1 and M/G/1 queueing delay formulations.

• Validated using real data across intersections, roundabouts, and merging sections with significant improvement over HCM.

• Extended to autonomous vehicle coordination under FCFS and CfDCA protocols.

• Dynamic parameter evolution accommodates time-varying demand through quasi-steady-state regime switching.

Abstract

This study presents a stochastic delay model that treats signal-free intersections as systems of servers where the probability of conflict between vehicles determines delay. Rather than relying on predetermined major-minor hierarchies, the model builds on interaction between any two approaching vehicles attempting to occupy the same conflict zone simultaneously. This conflict-probability framework applies across many traffic scenarios, from traditional unsignalized multi-modal intersections, merging sections, and roundabouts to signal-free intersections for autonomous vehicles (AVs). The model employs queuing theory formulations where service time depends on conflict probability and clearance time for conflict zones. The model extends to multi-movement intersections through dependent and independent conflict zone networks and accommodates time-varying demand through dynamic parameter adaptation. Theoretical analysis reveals that equal inflows maximize delays due to peak conflict probabilities. Validation using real-world trajectory datasets demonstrates improvements over Highway Capacity Manual and Austroads methods across diverse scenarios. Furthermore, the proposed delay model achieves mean absolute errors of 4.6 to 10.9 s using simulation of signal-free intersections for AVs under first-come first-served and communication-free distributed control algorithm protocols.

Keywords

Signal-free junction; Stochastic modeling; Probability of conflict; Autonomous vehicles; Conflict networks; Queue theory

1. Authors should use paid AI (see 2) before submitting. They should use it not just to clean up English but as a hostile referee. Ask your LLM of choice (mine at this stage of history is ChatGPT and Codex) what is wrong with the paper. Ask it to review the paper like a peer reviewers for the journal you are planning to submit to, ask what is unclear, undefined, multiply-defined, unsupported, unreplicable, under-cited, overclaimed, or likely to irritate Reviewer 2. Once you satisfy your LLM of choice with revisions, use a second LLM and repeat, it will find different things. Of course ask it how it would revise the paper as well, but don’t just accept it at face value. This does not guarantee acceptance, but not doing this will probably soon guarantee rejection. As discussed below, reviewers and editors are likely to be using AI too, and perhaps better (more tokens, more advanced, more expensive) AI than the authors used. Even then, reviewers will find something “wrong”, because that is how reviewers are. They are not paid. Their reward is anonymous criticism. (For-profit journals should pay reviewers.)

2. Newer paid AI is much better than a year ago. This is not just a smoother autocomplete. Free AI and paid AI are different tools now. Paid AI can read the paper, follow the method, look at the code, question the equations, find unsupported claims, and write something that looks a lot like a referee report. For technical work, the Codex (or Claude Code) version is better than the ChatBot version, because transport research is dominated by code, data, equations, maps, tables, and claims about the world. Make the investment in yourself.

3. Reviews are already being done with the help of AI, or entirely by AI. This is happening whether or not journal policies have caught up. This is done by some editors as well, I believe but cannot prove, who are having trouble finding reviewers.

4. Editors should use AI as a screener before sending papers to human review, especially at the desk-reject stage. This should be fully disclosed to authors. This would be a first pass: Is the paper in scope? Is there a research question? Do the claims follow from the evidence? Is the method described? Are the data and code available? Is the literature review just a pile of citations? Is this paper wasting everyone’s time? This would help editors. It would help authors. It would save scarce human reviewer time.

5. AI should be used after human review too. Did the authors actually answer the referee reports, or just write “we thank the reviewer for their insightful comments” 37 times? Are the data and code available? Do the tables support the claims? Are they internally consistent? Are the variables in the equations defined properly, do variable definitions slip across the paper? Are the citations real? Are the limitations stated? These are basic scientific checks. Humans need not do all of this by hand.

6. AI is now, in June 2026, good enough to mostly write, under supervision, an acceptable paper in many transport journals, if there is a good research question, available data, a real method, and a human who knows what they are doing. It can also draft a theory paper. This is no longer hypothetical. It can write, iterate, and revise.

7. AI still does not yet generally generate good questions. That remains, so far, the human job. The scarce skill is knowing what is worth asking, what evidence matters, what claims are justified, and why anyone should care.

FIN

Read more

• Levinson, D. (2026) The ‘b’s’ Knees: Logistic Stage-Transition Rules as Percentile Conventions on a Standardised Time Scale. Findings, June 1, 2026. [doi] [Data/code]

Logistic diffusion is often described as passing through early growth, rapid growth, and maturity, but the dates separating these stages, the knees of the curve, are defined in different ways across fields. This note brings several familiar stage-transition rules for the standard logistic curve into one common form. Each symmetric analytic rule can be written as t0 +/- c/b, where b is the logistic growth-rate parameter and t0 is the inflection date. Equivalently, each rule implies a fixed percentile window of the asymptote K. The paper shows that common stage-transition conventions differ by the implied percentile window, and provides a synthetic Python example comparing analytic rules with a free segmented-regression benchmark.

Keywords: logistic growth, diffusion of innovations, stage transitions, sigmoid curves, technology diffusion, inflection point, percentile conventions, segmented regression

I have also uploaded a lot of our shareable code and data to Github recently, you might follow that.

I will be at WSTLUR 2026 in Beijing this June, and then visit Chengdu and Hangzhou, maybe I will see you there.

Pointers

Transit

• London and New York public transport use seems to have settled at 70–80% of the pre-pandemic peak, reflecting (mostly) WFH. Fridays are down 40%. [LONDON] [NYC] (h/t Ben Evans, Benedict’s Newsletter)

• Sydney’s first electric ferry two years off as ambitions for entire fleet sink

• Egypt opens high-speed monorail linking Cairo to desert capital

Transit Data

• Real-time map of TfL

• Mapping the Speed of Surface Transit

• Timetable World — an archive of historical timetables, with a research section worth poking around in.

Parking

• Beaches should be free: row erupts over Sydney council’s paid parking plan

Security

• Iranian hackers responsible for Los Angeles transit system breach, Israeli researchers say

• US Education provider (KKR-owned) Instructure (d/b/a Canvas) was hacked, bringing the system down globally. ShinyHunters were paid. I really liked Moodle. I feel there has been no substantive progress in Canvas in nearly a decade, so they are just engaged in extraction, and that Universities (and High Schools!) keeping their student data remotely is just begging for trouble.

Infrastructure

• Albanese government slashes funding for Inland Rail project — but HSR continues

Aviation

• Frontier Air flight may have struck person on Denver runway

• Spirit Airlines went under. My review from 2015 must have done it.

• Vivid Sydney Drone Incident (Drones fall from the sky)

Bottlenecks

• Strait of Hormuz payment system in the works (Spatial monopolies be spatial monopolising)

• Austrian protesters shut vital motorway connecting Germany to Italy

Ridesharing

• Lyft gets Gett UK

• Ridesharing Institute Conversation, 22 April — Paul Minett’s lightly-edited recording of a conversation on fuel-crisis responses in Norway, the UK, Germany, and Australia, and on pooling as a demand-side policy lever. I might have said some things.

Reality television and its deleterious consequences for civilisation

• Following the Money on Sean Duffy’s [US DOT Secretary] Road Trip

Speed

• Hazelwood road signs vandalised over reduced speed limits, Victoria

• The Embedded Defaults of Cars [A nice blogpost from a UCLA student]

AI

• Could AI’s leading men become as powerful as Ford or Rockefeller? — The Economist.

• It would be highly ironic if stories about evil AI, warning of the risks of AI, were the parts of the training set that caused AI to act evil. And yet here we are.

Your regular reminder

• Two killed, multiple injuries after car hits bystanders in Leipzig

• Eight injured, four critically, as car rams into pedestrians in Italian city

• Cornell University president accused of backing car into protesters following Israel/Palestine debate

Posts

Publications

Press

• Australia’s $90 billion high-speed rail plan faces major hurdles — 7News. I was quoted.

But the challenging terrain means at least 70km of tunnels, making it the most tunnelled high-speed rail line in the world.

“We have more experience with tunnelling, and tunnel boring machines have gotten better. But this is a significant leap,” said Professor David Levinson from the University of Sydney’s School of Civil Engineering.

Professor Levinson warned the cost per kilometre is higher than any other high-speed rail in the world, including California’s troubled project.

• Previous road closure traffic study suggests commuters more flexible than expected - WAVE. I was interviewed

LOUISVILLE, Ky. (WAVE) - For the upcoming I-65 summer shutdown, KYTC says plan to add 15 minutes during rush hour. But the question hanging on commuters minds, just how bad will traffic be?

Traffic engineers have been able to study major closures to learn about the impact on traffic flow. A professor who wrote a reporton what happened after the I-35W bridge collapse in Minneapolis in 2007 said traffic patterns here may not be as dire as expected.

The I-35W bridge carried 140,000 cars a day. But after it collapsed in August 2007, researchers didn’t find those cars choking up traffic on every other road. They only saw 90,000 cars from that interstate.

“50,000 vehicles disappeared. They no longer crossed the Mississippi River. Now, that doesn’t mean that the people didn’t make trips, but they made trips to different destinations,” said David Levinson, a University of Sydney Professor of Transport.

Levinson said their research after the Minneapolis bridge collapse learned drivers are more flexible than previously thought. “Carmageddon” in Minneapolis didn’t actually occur.

“And things would, of course, be more congested if everyone had traveled the same way, but everyone expected everyone else to travel the same way. So they decided not to travel that day if they could avoid it,” Levinson said.

No one planned for I-35W to collapse. Levinson’s study says there is a difference for construction closures like the I-65 summer shutdown, but not how you might think. Drivers adjust pretty well when they’ve had time to plan.

“I mean, there’s no question that there’ll be more traffic on the parallel roads. But I don’t think it’ll be the worst case scenario. I think people will have some flexibility about when they travel,” Levinson said.

He points out schools are out and people go on vacation. Unlike 20 years ago, GPS is also commonplace. More people may be willing to try a totally new route in place of I-65.

“People are more willing to go places because they know that they will not get lost. And I think fear of getting lost was a huge deterrent to a lot of travel before we had Google Maps and other types of live information,” Levinson said.

Levinson’s study found the traffic network was able to find balance around a planned closure. It took six weeks for traffic to find balance after the I-35W bridge collapse.

Monday’s commute may be influenced by how many people pay attention to the signs, the stories and the warnings about the I-65 summer shutdown.

• Hao, W., Fang, Y., and Levinson, D. (2026) User-organized pre-pooled ride-hailing: exploring a new mode. Transportation Research Part D: Transport and Environment Volume 157, August 2026, 105422 [doi]

User-organized Pre-pooled Ride-hailing (UPR) is a user-coordinated form of shared mobility that layers social media coordination on top of commercial ride-hailing within bounded trust-based communities (e.g., campuses, workplaces, or residential compounds). We designed a comprehensive survey with 24 choice scenarios and integrated sociodemographic, revealed behavior, and attitudinal measures including environmental perceptions, then estimated mixed logit models for all respondents and for organizers versus followers. UPR is most competitive for longer, daytime trips and for first-/last-mile access to metro hubs, offering a lower per-person cost than solo ride-hailing and faster door-to-door travel than public transport. UPR choice is negatively associated with ride-hailing and platform ride-pooling use, implying substitution. Climate-mitigation beliefs increase UPR choice probabilities, while stronger trust requirements and privacy concerns constrain uptake. The findings highlight how environmental perceptions and community trust jointly shape decentralized pooling and its potential role as a low-impact complement to transit in fringe areas.

Keywords:

User-organized pre-pooled ride-hailing (UPR), Competition and cooperation, Travel behavior, Shared mobility, New mobility, Ride-hailing, Carpooling

• MILLS, S., LEVINSON, D. (2026) Missed Opportunities: Using Access to Assess Alternative Historical Urban Rail Networks. Case Studies on Transport Policy [doi]

In this paper, we use Sydney as a case study to reassess past and proposed rail lines through an accessibility-based appraisal framework. We estimate changes in job accessibility under alternative network configurations and monetise those changes using land-value uplift implied by previously estimated Sydney hedonic models. We treat this land-value uplift measure as a partial indicator of capitalised accessibility benefits, rather than a full substitute for conventional cost-benefit analysis. We review Bradfield’s 1916 heavy-rail proposal, the 1974 Sydney Area Transportation Study (SATS), the 2001 Long Term Strategy for Rail, and Sydney Metro 2056 proposals, and we also examine two revised heavy-rail or metro routes and several light-rail options. Costs are benchmarked from recent Sydney and international projects, and benefit/cost ratios are reported under multiple discount rates. The results identify several historical and revised alignments that perform well under this access-based land-value metric, while also showing the importance of cost assumptions and the interpretation of land-value uplift in policy appraisal.

This project was originally funded by NSF and hosted at the University of Minnesota, but had been taken down in a website reorganisation. TransportLab at the University of Sydney is pleased to rehydrate STREET. Most of the code has been updated from java to javascript so it can run in a modern browser, without requiring package installations. Some additional work was done.

The core idea is that students learn transport better when they can change the system and see what happens. Unlike chemistry and physics, we are limited in transport with what we can do with physical experiments. For some reason, road agencies don’t want us to close roads so we can run experiments on traffic. While textbooks, lectures, and homework are useful, seeing how individual choices, network structure, control logic, and spatial development interact can be more salient than solving an equation.

STREET turns those ideas into small interactive models.

• ABODE, estimates origins and destinations with an agent-based model using employment search methods at the individual level. (by Nebiyou Tilahun)

• ANGIE models road network growth, including artificial grid-like cities and the Minneapolis Downtown Skyway network. (by Arthur Huang)

• CLUSTER is an agent-based model of retail location choice across supply chains linking consumers, retailers, and suppliers. (by Arthur Huang)

• HSR Alignment Studio lets students build a corridor, test constraints, and compare defensible route options. (by Joey Zhu)

• OASIS helps students understand the control logic of actuated traffic signals. (by Chen-fu Liao and Henry Liu)

• ROAD gives students an online roadway geometry design tool using a contour map as the design reference. (by Chen-fu Liao, updated by Alireza Soltani and me)

• ROUTE is a hex-grid route design and cost comparison tool with connectivity checks and GMNS export/import. (by Chen-fu Liao)

• ULTIMO: an integrated STREET framework for loading GMNS networks, choosing model assemblies, running simulations, editing networks, and exporting diagnostics. It incorporates and extends the earlier versions of ADAM, SONG, and SAND as well as Network Growth models developed as part of earlier research. (by David Levinson, extending work by Bhanu Yerra, Lei Zhang, Shanjiang Zhu, and Feng Xie)

• LEVINSON, D. (2026) Trends in Twin Cities Travel Behavior, 1970–2023. Findings. [doi]

Restoring the Twin Cities Travel Behavior Inventory back to 1970 and extending analysis forward to 2023 changes the long-run interpretation of regional travel behavior. The main behavioral change is a relocation of work within the day. For adults who worked on the diary day, home time rises by 194.5 minutes between 1970 and 2023, daily travel falls by 16.1 minutes, and on-site work falls by 196.9 minutes. Once work-related destinations and observed telework are added back in, however, the modern series is better read as a shift in work location than as a collapse in total work: telework-aware total work reaches 500.9 minutes in 2019 and 513.7 minutes in 2023. A second result concerns commute travel times. The main auto commute series generally rise from 1970 through 2019 and then decline in 2021–2023, so the longer record supports a rise-to-2019 and post-COVID reversal story rather than a simple monotonic increase. The archival recovery of the 1970 survey, the handling of 1982, benchmark checks for 1990–2010, and remaining 2019–2023 comparability issues are documented in the Supplemental Information.

• Yu L., Li, M., Dai, Z., Cui, M., and Levinson, D. (2026) Access door-to-door: An intercity efficiency and distributional analysis of the costs of travel by plane, train, and automobile. Transport Policy. Volume 184, August 2026, 104188 [doi]

Existing studies typically evaluate air travel accessibility by examining either air network performance or ground access to airports in isolation. This paper offers a complementary perspective by assessing the national air travel accessibility through a door-to-door framework and comparing it against multiple modes of intercity transport. We compare four scenarios: air-only, railway-only, highway-only, and an optimal-mode scenario. The first three rely exclusively on a single mode for intercity trips, whereas the optimal-mode scenario selects the lowest-cost option among air, rail, and direct driving for each origin–destination pair. The results show that air travel provides higher accessibility and more balanced spatial equity than rail or highway travel at higher cost thresholds. Air travel also delivers clear advantages in regions with significant geographical constraints, where land-based transport infrastructure is limited. Although the optimal-mode scenario generally enhances spatial equity, it reduces within-group equity in regions characterized either by highly developed urban cores (e.g., the Yangtze River Delta in East China) or by significant geographic constraints (e.g., the peninsula areas of Northeast China).

Supply Chains and their aftermath

• Panama Canal Traffic Jam Spurs $4 Million Line-Jumping Payment

• Explosion at the Panama Canal | Shoreside Gas Tankers Explode & Threaten the Bridge of the Americas

• Australia’s Victoria State Extends Public Transport Benefits

• Transport Minister Catherine King casts doubt on road user tax over EV uptake concerns

Security

• The Dumbest Hack of the Year Exposed a Very Real Problem | WIRED (Crosswalk messages hacked)

Infrastructure

• Maryland dumps Kiewit as Key Bridge builder, potentially delaying project past 2030

Corruption

• A $1.5 million roundabout from nowhere to nowhere shows the ‘Orbánist economy’

Vehicle Automation

• Robotaxi outage in China leaves passengers stranded on highways

• Norwegian authority approves driverless Karsan e-Atak operation without safety driver

Your regular reminder

• ‘He was erratic’: One dead, one fighting for life after car swerves into pedestrians at showgrounds

• Man charged after seven people hit by car in Derby

Press

• Ghost town fears don’t spook new airport suburb backers

… Despite the eventual addition of a metro line, bus routes and an international airport, the risk of mimicking the Leppington debacle lingers over Bradfield.

“New towns ... removed from city centres have a hard time attracting market development,” Sydney University transport professor David Levinson told AAP.

“Firms want to be near other complementary firms. The defining feature of Bradfield is remoteness.”

In a 2024 submission to an inquiry into transport infrastructure around the new airport, Prof Levinson also argued developments such as Bradfield unnecessarily steered funding away from higher priorities.

“Additional infrastructure investments in the absence of changes in demand will not magically make (success) happen, but will drain resources from solving real problems,” he said.

NSW Planning Minister Paul Scully rejected the assertion Bradfield would be a ghost town.

“We know business is interested and they should be,” he told reporters on Tuesday. …

Time will tell as they say.

Full Interview with Reporter …

1. Are the concerns over hasty, unproven development around the airport which you voiced in that submission still relevant? Has the government addressed them?

Not sure what you mean about “hasty, unproven development”. I think I was arguing there was no evidence development was going to materialise in the amounts hoped for and necessary to justify the infrastructure. Development has yet to materialise.

2. Some have questioned whether this development could be a ‘ghost town’ much like nearby Leppington, where the build-and-they-will-come approach didn’t work. Is this a legitimate concern for this project? Is that a likely outcome?

I think it remains a risk. With enough money, the government could subsidise enough development to occur sooner rather than later, but that’s probably not the best use of resources. Bradfield is pretty remote. It is as far from Liverpool CBD as Liverpool is from Sydney.

3. The government wants the development to contain 10,000 new homes and provide 20,000 new jobs. Is this a likely prospect? How long might it take to deliver?

Eventually perhaps, but new towns on Greenfields removed from city centres have a hard time attracting market development. Firms want to be near other complementary firms. The defining feature of Bradfield is remoteness.

4. What are some of the other risks of a development of this kind which concern you?

5. Are there similar projects elsewhere that have worked well or badly? What can we learn from those?

There have been various new town proposals globally. I grew up in one, Columbia, Maryland, https://en.wikipedia.org/wiki/Columbia%2C_Maryland between Baltimore and Washington, DC., which was intended to be the second largest “city” in Maryland. It eventually grew to ~100,000 people as planned by about 2000 (though the Town Centre is still not fully built from an employment perspective, almost 60 years on (the whole town was expected to be completed about 10-15 years after opening in 1967). It didn’t have a high speed metro to St. Mary’s, but it was also closer to two major cities (one of them the US national capital). Most US new towns don’t get that far before falling over. https://en.wikipedia.org/wiki/Albury-Wodonga_Development_Corporation might be instructive

In my submission, I compared Bradfield and Western Sydney Airport in particular to Dulles Airport outside Washington DC, which took decades to become important. Eventually metro Sydney might grow into it, and planning ahead sounds like it makes some sense, but putting resources into something far in advance of it being useful forecloses investing on other opportunities that would have been more meaningful.

6. Is there a better way the government could go about approaching this development?

The sunk cost fallacy should be kept in mind. The internet says: “The sunk cost fallacy is the tendency to continue investing in a decision based on the resources (time, money, or effort) already committed, even when the current costs outweigh the benefits. This cognitive bias can lead to irrational decision-making, as individuals often feel compelled to stick with their past investments rather than reassess the situation based on new information.”

There are many potential useful investments, and it’s not clear why investing there now is the highest priority.

Posts

Publications

Performances

• Mann, I. and Levinson, D. (2026) The Cost of Convenience: A Hedonic Approach to Travel Time Valuation and Cost‐Benefit Analysis. Journal of Regional Science [doi]

This paper presents a novel revealed-preference approach to estimating the value of travel time (VTT) and calculating consumer surplus for the economic evaluation of transport infrastructure. Departing from traditional stated-preference models, we derive time valuations by linking residential rental transactions in Greater Sydney to employment accessibility. We estimate a hedonic price function that controls for unobserved amenities and spatial sorting, recovering VTT estimates consistent with current cost-benefit analysis practice. Furthermore, we leverage the non-linearity of the price gradient, as well as cross-market variation in the price gradient, to perform a second-stage regression without instrumental variables, recovering the structural demand function for accessibility. This allows us to quantify welfare changes from non-marginal accessibility shocks, demonstrated on the Sydney Metro West project, grounding project appraisal in revealed spatial behavior.

In media, film economists have for years treated movies as bundles of attributes, stars, director, budget, genre, sequel status, studio backing, release timing, reviews, and so on, to explain box office or profitability. The movie is treated as a package (McKenzie 2023).

I have long thought this would make an excellent card-based game, Movie Mogul, where players draw famous actors and actresses, scripts, directors, and play them in a particular time period (scripts can be replayed once per decade, actors and directors age however), and with some luck, fortunes are made.

Music is similar. There are literal hedonic studies of recorded music prices, for example classical CDs, where the recording is decomposed into observable characteristics and those characteristics are related to price (Harchaoui and Hamdad 2000).

My kids watch a lot of anime, and reading Wikipedia, I noticed that Azuma’s (2001) Database Consumption Theory says something similar about anime. The consumer is not mainly consuming a unified narrative. The consumer is consuming elements, character types, visual motifs, settings, relations, and other reusable parts drawn from a shared database. Azuma’s account extends Ōtsuka’s earlier work on narrative consumption, but pushes further away from the primacy of the grand narrative.

Database Consumption is a cultural theory version of economics’ hedonic valuation, decomposing the whole into component characteristics.

As Kirby Ferguson says, Everything is Remix. New art remixes old. And in real estate, new houses recombine elements of older (and sadly, perhaps, not just the successful elements). In cities, new developments do the same for place at large.

Consumers often care more about parts than wholes. Anime reveals those parts, transports them, and reuses them. Many consumers approach the work through them rather than through narrative unity. They see the trees for the forest.1

References

Rosen, Sherwin. 1974. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition.” Journal of Political Economy 82(1): 34–55.

Azuma, Hiroki. 2009. Otaku: Japan’s Database Animals. University of Minnesota Press. English edition of the 2001 Japanese original. (Google Books)

Ōtsuka, Eiji. 2010. “World and Variation: The Reproduction and Consumption of Narrative.” Mechademia 5: 99–116. Translated by Marc Steinberg.

McKenzie, Jordi. 2023. “The economics of movies (revisited): A survey of recent literature.” Journal of Economic Surveys 37(2): 480–525.

Harchaoui, Tarek M., and Malika Hamdad. 2000. “The prices of classical recorded music: a hedonic approach.” International Journal of Industrial Organization 18(3): 497–514.

Footnotes

Read more

Talking about

• How the field of transportation has changed.

• Challenges cities are going to face and lessons learned.

• How cities are going to have to change and be reimagined for the future.

THE PEGGY SMEDLEY SHOW

Transcript — Interview with David Levinson, Professor, University of Sydney [From .mp3, transcribed and cleaned up by Claude AI, I make no guarantees, but far more readable than otherwise.]

============================================================================

[INTRO]

Welcome back to the Peggy Smedley Show, your voice for our Connected World, with your host Peggy Smedley.

----

PEGGY: Hello, listen, and welcome back to the Peggy Smedley Show. I’m your host, Peggy Smedley. Today we’re looking at how transportation is evolving as cities grow, technologies accelerate, and data reshapes every decision we make. Few scholars have done more to explain how networks form, adapt, and influence the way we live than my guest today. His research has helped shape cities, rethink infrastructure, understand long-term investment outcomes, and prepare for the next wave of mobility innovation. Join me in welcoming David Levinson, Professor at the University of Sydney and one of the most respected voices in modern transportation research. David, welcome to the show.

DAVID: Thank you for having me.

PEGGY: So David, as a professor you’ve done some incredible research, and I’m really thrilled you could join us today. I thought maybe we could start — if you don’t mind — how has the field of transportation changed since you began your career?

DAVID: Obviously, aging myself — starting my career in 1989 or so — I worked as a planner, and at the time we were envisioning lots of things about the future. Planners were developing 5, 10, 30-year plans, and what we imagined in 30 years — this was in Montgomery County, Maryland — was essentially continued deployment of the traditional freeway system, some new light rail lines, plans for higher-density development around train stations. But our assumptions were always built in, and it’s still built into the planning field, that technology never changes. So there were no assumptions in 1989 that in the next 30 years there would be shifts in how people went to work, how people drove, whether there’d be autonomous vehicles or electric vehicles. And one could argue there are still not really autonomous vehicles that most people use on a daily basis — but they exist now.

The planning for that didn’t exist at all in the 1980s and 1990s. People were talking about it as a frontier of research, but the planning for it also didn’t exist in the 2000s and 2010s, even though the technology kept getting closer. EVs are useful, but they don’t change how people get around very much. Autonomous vehicles, on the other hand, are going to change significantly how people travel — and that’s still not embedded or considered in transport plans.

There have been other technology shifts. E-bikes is another thing the field completely missed. The rise in delivery — not just from e-bikes but from services like Amazon and grocery delivery — was completely ignored. The field was basically assuming people would continue to behave the way they did in 1988, just extrapolated out 30 years because there would be more people, rather than looking at how things might change.

If you say 1989 plus 30 years, that puts us at 2019–2020. And in 2020 there’s a huge shock — COVID — and the changes that came with it, particularly the increase in work from home. At the time the question was how much of that would be sticky, and we’re starting to get that answer. A lot of it is, for office workers.

Now we’re coming into a new wave — AI and computation — that is also going to change who does what, where they do it, and how they do it. We have no clear sense of how that’s going to affect the changing nature of work, shopping, and life — but from a transport perspective, where people are going to go and when.

So I think we’ve missed in the transport field. On one hand there’s a set of people advancing technology. On the other there’s a set of people planning and engineering transport who are looking at the world as if it will be like today, instead of asking: these technologies will be here, how do we skate to where the puck is going, as Wayne Gretzky said, rather than to where the puck is.

PEGGY: I love that analogy, because I think that’s what we need the next generation of workers to think about — where is the puck — and thinking so broadly. Because when you mentioned autonomous vehicles, we’re just finally getting to level four, which we’re talking about maybe happening this year. We’ve been talking about this for the past decade, so that’s really interesting. And I think AI is going to accelerate it much faster than we ever thought. You raise a good point that brings us to the challenges cities are going to face. We’re talking about this big push with EVs — now EVs have halted or slowed a little in the United States, though there’s still a big push in other countries where they’re growing. We’ve seen some growth here, but not what we had a couple of years ago. So I guess that raises the question: what are the biggest challenges facing cities today, with everything you just mentioned?

DAVID: There are lots of different kinds of cities with lots of different kinds of challenges. If you think of an American city, that’s very different from an Asian city or a European city. The American city is much more auto-oriented. There hasn’t been any meaningful increase in public transport use in decades — it’s been declining since the 1940s, leveled out, with minor upticks or downticks in any given year, but nothing significant. The level of active transport in US cities — there are a few exceptions: Portland, Oregon; Minneapolis — where there’s a plausible amount of bicycle use. But most American cities, most people are not going to consider bicycles a serious mode of transport, whereas in European or Asian cities they will.

Then there’s the question of how many people work in the CBD — the central business district — versus suburban office parks. Are those suburban office parks clustered around train stations or are they low density? The land use pattern the US built — and that other countries have followed to a lesser extent since the 1940s — has made it very difficult to plan for any mode other than the automobile as the primary way people get around in metropolitan areas.

The problems of the CBD are different from the problems of the outer suburbs, and what solutions make sense will differ. How are we going to address them? I think we’ve been looking at and admiring transport problems for a very long time without really solving them, especially in the United States.

The Bloomberg administration proposed road pricing in New York City in 2000. Transport economists had been proposing road pricing for decades before that. It finally got deployed and opened in 2025. Seventeen years to put in a congestion pricing toll in the densest part of the United States — that says a lot about the lack of seriousness about solving problems in the US. We can go along living in mediocrity, or we can choose to live better. And we keep choosing mediocrity.

PEGGY: Let’s look at what’s happening. We have crumbling infrastructure — failing bridges, failing roads. And then factor in COVID: rural and urban areas have changed. Factor in autonomous vehicles — cities have to rethink who’s driving, distracted driving, cameras, connected cities. If all that’s true, our cities have to be different from what they were 30, 50, or 100 years ago when the highway system was first created. So hopefully there are lessons learned — but maybe not, because sometimes we’re a little lazy in how we do things. Are there lessons learned from historical transportation trends, or are we still moving in the mediocrity you mentioned?

DAVID: There are people who have learned those lessons — but they have not been learned by society at large. You can look at plans, and many are thoughtful, and many say yes, I’d rather live in this imagined future than the world we have today. We have a vision, and we take maybe a baby step in that direction, but then lots of other things happen around us that are much more significant.

We built the interstate highway system starting in the 1950s. We’ve replaced large elements of it, but it’s an old system. Anyone who owns an old house knows that systems need to be maintained and replaced. We’ve decided that’s not our first priority. Our first priority — I’ll pick on one thing — was opening up streetcars in US cities that don’t really go anywhere. There was a federal program, and politicians with a nostalgic vision of America from the 1940s because they could remember streetcars from their childhood put them in. It’s an enormous expense these days compared to what it was 120 years ago, because we can’t build anything inexpensively anymore. These streetcars serve almost nobody. They take resources away from useful public transport — buses, bus rapid transit systems that could be deployed immediately in lots of places. It eats up the budget.

We’ve been spending money on new things rather than maintaining existing things, as if we’re growing at the rate we were in the 1950s. But if you look at demographic forecasts — if anti-immigration movements succeed across the developed world — most of those countries are going to be depopulating. You already see it in Europe and East Asia. If the United States didn’t have immigration, it would be depopulating. The same is true of Australia in short order. And you’re planning for demographic growth while investing in new things that eat up the resources for maintaining what you have.

If you let your existing infrastructure fail, that’s much more costly than any gains from putting in new infrastructure on the outer fringe to promote development in a world where population is largely stagnant at best. We have our priorities wrong. And changing people’s mindsets is really hard. You have planners who don’t imagine any technology changes. You have elderly politicians trying to restore a nostalgic vision — in all parties, not just one. And you have technologists bringing new technologies, but not new highways, as if someone else is supposed to take care of the highways. Which yes, someone else is supposed to do — but it’s not necessarily happening. We’re underfunding it.

Users don’t pay the full cost of operating and maintaining the infrastructure they use. The highway trust fund, funded largely through motor fuel taxes, is underfunded at the federal level and has to borrow from the general budget to pay for road expenditures. There’s a fundamental lack of seriousness in how this gets funded. And America seems incapable of learning from what other countries do. Other countries pay for their infrastructure from user fees. Americans are outraged if the price of gas goes up ten cents.

PEGGY: There are a couple of things there. One, we get outraged because of the delays — projects supposed to be done at a set time take three years longer, contractors take longer, and consumers freak out. But the second big thing is: when infrastructure fails and lives are lost — and we both know this has happened many times, not just here but globally — that’s when people notice. And unfortunately it takes a catastrophic event for people to take notice. That’s unfortunate, because we don’t need that to happen. That’s when everybody says, why didn’t we address that sooner? ASCE does a report card every year and points out everything that’s failing. Those are significant things to pay attention to. And I think when you talk about investments and when they succeed or fail long term, that’s something that has to be addressed.

DAVID: ASCE is the American Society of Civil Engineers — I was once a member. I don’t want to say there isn’t failing infrastructure, because obviously I believe there is. But I wouldn’t trust their report card as the final word on the matter. They’re going to advocate for more civil engineering works — that’s logical. That said, you’re correct: we can’t respond in the absence of a crisis, and that’s unfortunate.

There’s routine maintenance that better state departments of transportation will do well, where the politics are more aligned. And yes, construction takes longer than it used to. Some of it is safety regulations so that construction workers are safer. Some of it is environmental regulations. Some of it is rising standards in construction quality. But we wind up paying much more for infrastructure than other countries pay for the same thing. There’s a project at NYU that compares rail infrastructure around the world — the US pays two, four, sometimes ten times what lower-cost developed countries pay.

PEGGY: But why? Is there a reason for that?

DAVID: On the rail side, there are a set of reasons. We over-design things. Take the Second Avenue Subway in New York — subways dug farther underground, larger and more cavernous stations. The stations are a large part of the excavation costs. And there are stations with facilities for workers underground that basically increase the volume of digging, dirt removal, and concrete pouring. Rather than value engineering to get costs out of the system, there’s an incredible amount of padding. And nobody within the organizations has the technical skills to push back on it.

We’ve lost a lot of technical capacity within governments. If you go back 50 or 70 years ago, a lot of construction work was done by the transportation agencies themselves. Beginning in the 1990s, we moved to a much more contractor-oriented system — design work farmed out to engineering firms, construction work to large international firms. There are some advantages to that: if you need to reduce staff, it’s easier to cut a contract than your own employees. But the cost is that contractors will charge much more overhead, and their incentives aren’t aligned with yours. If you work for the agency, you’re motivated to do things efficiently. If you work for a private firm and your fees are proportionate to project size, your incentive is to increase the size and scope of the project.

PEGGY: So we’re talking about unions versus non-unions in some cases.

DAVID: In some cases, but I don’t think it’s just unions. Unions are useful things. A lot of it is more like guilds — engineering consulting guilds and construction firm guilds that have an incentive to drive up costs. They describe it as raising standards of performance, but the trains don’t run more on time in the US than in these other countries.

PEGGY: Is it technology? Can the use of big data and analytics to improve transportation help eliminate some of those costs?

DAVID: I don’t think it’s technology so much as processes, politics, and having skilled people with the right aligned incentives, especially on the cost side. Countries digging subways or underground highways all use tunnel boring machines — you can argue about whether to use two machines in parallel or one large one, and that depends on the site. But Elon Musk with his Boring Company didn’t invent a more efficient way to tunnel subways. He promised this 10 to 15 years ago, he deployed tunnels in Las Vegas and maybe one or two other places, and they’re not taking the world by storm. We’ve known how to build tunnels for a long time.

From an infrastructure perspective, there are things you can do at the margins with more efficient construction or longer-lasting concrete — basic chemistry rather than Silicon Valley-style high tech. More investment in concrete roads versus asphalt. Newer bridge designs that are safer and resilient and don’t have a single point of failure, the way the I-35W bridge in Minneapolis did when it fell in 2007. Those are real technology advances. But on motor vehicles, autonomy is where much greater safety gains will come.

The US hasn’t made safety gains in a very long time. It used to be improving its crash risk on par with other countries. Then it flattened out in the 2000s and we’re at 40,000 deaths per year — better than the 1970s, but where we were 20 years ago. Other countries have continued to make progress. We’ve exhausted a lot of the safety gains that come from safer vehicles. We haven’t exhausted the gains from safer road designs and safer driver behavior.

PEGGY: And our behavior is terrible. You can see it — people driving distracted, on their phones. Not understanding to put the phone down when it comes to mobility. But what are the skills needed — maybe for an urban planner — to make better roundabouts, design better roads that protect against the distracted driver?

DAVID: Roundabouts are part of it, but the broader idea is designing roads for lower speeds. Lower speeds have two advantages: one, the driver has greater reaction time before they’re likely to hit something; two, if they do hit something they’ll do less damage because they’re going slower. Lower speeds are great for safety. Now, of course, people resist lower speeds because it’s inconvenient — it will take a little longer to get where they’re going. This is the trade-off: how much is the value of time worth versus the value of life? Other countries have prioritized life and safety.

And it’s not just fatalities — it’s also injuries, particularly life-changing injuries. Improvements in emergency medicine mean fewer people who get into a crash will die, because we can respond faster thanks to cell phones, treat them better on site, get them to the hospital faster. So the fatality rate has gone down relative to crashes. That’s good. But we’d rather have fewer crashes, and the way to achieve that is to replace the driver or get the driver to behave better.

Even if all new cars were autonomous tomorrow — which they aren’t — it would take 15 to 20 years to replace the whole fleet. So this is going to be a long deployment process. But changing speed limits is something that can literally be done overnight — with a little planning you can change the regulations and road signs very quickly and inform everybody of the new limit.

PEGGY: We’re not going to do that though, David. You look at the highway — the speed limit might be 55 or 70 and you see people passing others going 80 or 90. They’re not going to adhere to that.

DAVID: That’s an enforcement problem. If the government decides it wants more enforcement, you can have it. But you have politicians in Australia, for example, running on pulling out speed cameras or promising to notify people in advance of where speed cameras are. The current Labor leadership is saying: yes, speed cameras are a good thing, but we need to notify people that there’s one coming. If there’s a speed camera everywhere and you notify people in advance, that’s fine. But speed cameras are still rare, and telling people exactly where they are means: okay, I’ll slow down for the camera and then speed right back up. That’s not good from a safety perspective.

And most of the fatalities aren’t on interstates. They’re on rural roads or high-speed urban intersections. The pedestrian fatality rate in particular has gone way up in recent years. A lot of that is the change in vehicle size. The average car today is a truck — used to be a sedan. People have SUVs and pickup trucks where previously they’d have had a sedan. They want to be higher up so they can see farther ahead, particularly when everyone else already has a large vehicle blocking their view. It’s logical from an individual perspective to want the taller car. But the consequence is that when you hit a pedestrian, you’re hitting them in the upper half of their body rather than the lower half. Rather than breaking their legs, you break their chest — their heart, their lungs — and you’re more likely to kill them. The vehicles are also heavier, so the impact at even lower speeds is worse.

PEGGY: David, we have to wrap up here. Two things: is there something you’re working on right now that our listeners should be aware of?

DAVID: I work on lots of different things. I’ve got projects looking at how we value transport investments and the relationship between transport and land use — my favorite topic — and how we measure accessibility and how valuable it is. And then I have another project in a completely different domain: how do we design autonomous vehicles so they behave in socially beneficial ways rather than purely selfish ways? All the AVs out there are designed to move the driver from A to B as quickly and safely as possible for the driver, but they don’t think about their consequences on other vehicles or other people in the network. Maybe we can come up with algorithms that improve traffic overall as well as benefiting the individual in the vehicle.

PEGGY: Great — we’ll have to have that discussion next time. I do want to ask: what advice would you give students or those considering a career in transportation? I think there are great opportunities there.

DAVID: Certainly lots of problems, and there have been for a while. One thing people need to keep in mind is the time frames. When I started — I was a kid who liked drawing lines on maps, connecting things. You want to connect the most things for the least amount of resources. That’s what got me into this. And that’s what transportation planners do. But the amount of time you actually spend doing that is relatively low in most cases — it’s the fun part of the job, but you don’t really get to do it that much.

I think if you’re interested in planning, pay attention to the technology. If you’re interested in the technology side, pay attention to the social and political side. Being able to fuse those two domains, rather than thinking of them in isolation, is necessary going forward.

PEGGY: David, you’ve given me a lot to think about today and I really appreciate you joining me. David Levinson, Professor at the University of Sydney — thank you so much for your time. Where can our listeners go to find out more about you and everything we talked about?

DAVID: My blog and newsletter is at www.transportist.net and my website — which has all of my articles and links to my books — is at www.transportist.org.

PEGGY: David, thank you again.

DAVID: Thank you.

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[OUTRO]

PEGGY: All right, listeners, that is all the time we have. Make sure to share and subscribe to our episodes each week. Share your thoughts with me on X, Connected World, or follow me on LinkedIn, YouTube, and continue the conversation there. Check out our website and newsletter and read our blogs at connectedworld.com or click on the show website at thePeggySmedleyShow.com. Remember, we’re live every Tuesday at 12 p.m. Central. Check out all the podcasts that come out every Thursday. This is the Peggy Smedley Show, your voice for our Connected World. And remember: with great technology comes great responsibility.

With the advent of Artificial General Intelligence (AGI), the landscape is poised to shift again.1 Some say AGI is already here, and I am sympathetic to the view, given intelligence is a continuum, and many AIs can do many aspects of work faster and higher quality than I can (but obviously not everything, everywhere, yet).

How transport will change in response will be revealed in time.

Some thoughts below:

Automated Everything

Eventually, we would expect AGI to automate all transport modes. Decentralised AGI systems would manage cars, trucks, trains, planes, and ships, automating real-time decision-making, routing, trajectory planning, and control. This reduces the marginal cost of transport by eliminating labor requirements, but may increase capital costs depending on the cost of the new vehicles and systems vs labour.

These vehicles are logically robots. But even before we have replaced our entire passenger, commercial, and specialised equipment fleet with in-built AGI, in the short term, humanoid robots (androids) can operate existing human-centric equipment. This might be cheaper than rapidly rebuilding the fleet, as part-time robot operators (who have other functions as well) may come in less expensive than replacing

full vehicles, while still allowing mixed human and robotic operation. This is especially important for capital-heavy specialised vehicles.

Robots serve as backward-compatible solutions, performing tasks in environments not yet designed and optimised for AGI. Over time, more purpose-built systems will be deployed, optimised for AGI from inception, and older human-compatible systems will be deprecated and retired.

Transportist

How will robots get to work?

Our community has, for a long-time, been thinking about autonomous vehicles as the future of transport, and in some ways, they still are. But humanoid robots are also coming: see this article in USA Today, obviously there is still a lot of vapourware…

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8 months ago · 1 like · David M Levinson ⁂

Dematerialisation of Demand

If AGI-enabled virtual agents perform many white-collar jobs better and faster than humans, the need for those jobs, and thus daily commuting to, or business travel for, those jobs, should diminish significantly. (Recognising that like lawyers, bureaucracy creates demand for more bureaucracy).

The question arises: what do those put out of work by AGI do?

Traditionally technological innovation has not created permanent mass unemployment, though of course what people do changes: rural mechanisation drove urbanisation, factory automation drove people to offices, and office automation has made everyone a “Director”, or even “Vice President” of something. (I hope to be President of Prompt Engineering (POPE)). Will everyone transition into personal services, trying to stay one career ahead of automation, or will there be massive unemployment because the transition is too rapid? Time will tell, and it depends on the rapidity of the shock.

There are lots of stories about suddenness, (See the 2028 Global Intelligence Crisis by Citrini Research, or AI-2027 by Daniel Kokotajlo et al.), which get attention. But as we have seen with the AV and EV rollouts, physical systems take time to change, so I’d bet on a slower transition than suggested.

Passenger and freight effects may also diverge. Passenger travel, especially commuting and some business travel, could decline. Freight may become more intense if firms run smaller inventories, replenish more frequently, and depend on tighter logistics. Goods still have to move, even if some people do not.

New Risks

Centralizing transport control in AGI systems creates the possibility of widespread failure. Unlike human error, which is typically localized, a single AGI malfunction could affect entire networks simultaneously. This amplifies the need for designing transport systems with robustness and fail-safes as priorities.

If control is centralized or heavily integrated through AGI systems, failures may become more correlated. A bad model, corrupted data, faulty objective function, cyberattack, or software bug could propagate across large parts of the network at once. The system may become more efficient on ordinary days and more brittle on bad ones.

That makes resilience more important, not less. Redundancy, fallback modes, graceful degradation, local override, institutional diversity, and physical slack may become more valuable even as they appear inefficient in a narrowly optimised world. Transport has always needed spare capacity and recovery capability. AGI does not eliminate that requirement, it strengthens it. But designed redundancy and waste should not be confused.

Slack, Reliability, and Coordination

Most transport capacity is wasted much of the time. Seen from the air, roads are mostly pavement rather than vehicle roofs. Zooming into those vehicles, we see most cars carry empty seats, and then, if we watch them over a daily cycle, we see that they sit idle for most of the day. Traffic signals show a green light to empty approaches, while pedestrians impatiently remain standing for six seconds of “Walk” time. Trucks run light or empty on the return leg. Warehouses hold extra inventory because firms do not trust the network. Ships wait outside ports, or straights. Customers pad orders because they do not trust delivery promises. Slack accumulates at every layer because the system is noisy.

Just as large institutions only do something once McKinsey says so, even if their own staff have been screaming about it for years, many people will respond favourably to the recommendations of the AGI even if it’s an obvious solution that’s been waiting around for awhile. The authoritativeness, and general accuracy of AGI (and its successor Artificial Super Intelligence (ASI)) may break through the torpor keeping broken old systems in place.We may finally be able to do all the things we said we should do. A man can dream.

This also changes how to think about schedules. The Great Synchronisation occurred for a reason. Schedules exist because predictability at one layer creates flexibility at another. Railways and airlines both work this way. The visible and inflexible timetable provides a stable coordination device for users, operators, and connecting services allowing the machinery underneath: vehicle assignment, routing, maintenance, recovery, pricing, and staffing to be fluid. Maybe rigidity isn’t going to be as critical a formwork once the system is smart enough and trusted enough.

Revaluing Human Spaces

If AGI leads to a decline in the need for auto-mobility, and especially the use of street space for transport through improved efficiency, particularly through reduced repetitive commuting and routine administrative travel, cities may place more emphasis on proximity and local access. This would not happen everywhere or automatically. Some places may continue to sprawl, especially if less commuting makes distance easier to tolerate. But city streets may be reallocated both between modes, and from transport to non-transport uses, to serve social, recreational, or ecological functions rather than throughput. Reduced movement demand could reshape urban space toward place-centred design. A man can dream.

FIN

Footnotes

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Simini et al. titled their paper about the radiation model “A universal model for mobility and migration patterns.” That was an extraordinary claim. The model was an intervening-opportunities model with no fitted parameters in its original form. It was not universal in any strong social-scientific sense. It has bugged me for over a decade, but I have a long queue.

The problem is that “Universal” can mean at least four different things.

• The same equation everywhere.

• The same mechanism everywhere.

• The same inputs everywhere.

• The same parameter values everywhere.

The paper suggested all four. The later literature does not support that reading.

Masucci et al. showed that the original radiation model’s thermodynamic-limit assumption causes systematic error in finite systems, especially for large cities. They proposed a finite-size correction. Even then, gravity performed better overall in their England and Wales application. They also asked whether universality means all spatial scales, all times, all places? Their results say no.

Yang et al. introduced an extended radiation model with a scale parameter because the original model did not hold across scales, especially at smaller ones. They also argued that population is not always the right attraction measure. At large scales it may work tolerably, but inside cities, point-of-interest density can do better. Once you need a scale parameter and different destination measures by context, the strong universality claim is over.

Kluge and Levermann argued that the radiation model underestimates long-range migration and contains a conceptual inconsistency with substantial numerical effects. They proposed directional preferences to fix it. Again, the original model did not travel unchanged.

So the history of the model is not: universal law proposed, universal law confirmed. It is: extraordinary claim made, then watered down over time by corrections, extensions, and narrower domains of validity.

That is why the usual horse race between intervening opportunities or radiation and gravity models misses the point. The key issue is not whether gravity or radiation wins on some dataset. The key issue is that the word “universal” was doing work the evidence could not support. A model can be useful, elegant, and influential without being universal. These are not synonymous.

My students reviewing the paper for class back in 2015 noted that the county scale is coarse, that jobs are not proportional to resident population, that travel cost and network structure are ignored, that the behavioural assumptions are thin, and that the universality claim was under-tested across places and scales. They go to the core of the paper.

The paper also overreads what its own commuting data can support. Some of the very long county-to-county “commutes” are prima facie implausible as routine daily out-and-back travel. Census journey-to-work flows are not direct observation of daily mobility as implied in the paper. They are residence-work ties constructed from workers’ reported place of residence and primary place of work during the reference week. The Census residence rules then make the problem sharper: people on a weekly cycle, including commuter workers who live near work part of the week and at a family home the rest, are counted at the residence where they spend most of the week. So these long links do not cleanly measure one behavioural object called daily commuting, instead they mix ordinary daily commuting with multi-residence and weekly-cycle work arrangements. That weakens one of the paper’s claims because a good fit to an administrative residence-work matrix is not the same thing as evidence for a universal behavioural law of daily mobility.

This is not unique to Simini, the same inflation shows up elsewhere in the mobility literature. Noulas et al. write of “universal patterns in human urban mobility” from Foursquare data across sampled cities, Yan et al. offer “universal predictability of mobility patterns in cities,” and later papers escalate further to a “universal model of individual and population mobility on diverse spatial scales” and even “a universal opportunity model for human mobility.” In each case, “universal” does not mean law-like validity across contexts, scales, and measurement regimes. It means something weaker: a common stylized mechanism that performs reasonably well on selected datasets. That is exactly the slippage at issue in Simini as well.

What survived from Simini is Radiation, a model family, which may be a productive starting point for later work. This model family needs finite-size correction, scale parameters, alternative attraction variables, and directional structure, which seems to me that makes it not a universal law. It is another family of approximations.

A social science paper, often written by physicists, borrows a prestige word from physics, makes a broad claim, the editors of Nature get excited, and then the literature quietly retreats to something more modest. Meanwhile the paper collects a large number (over 2000 at this writing) of citations. Fortunately this is a blog post so won’t add to the citations (I hope).

A more accurate title would have been something like: An intervening-opportunities-style model that works ok for some aggregate mobility patterns under some conditions, but that would be less likely to garner over 2000 citations.

References

• Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484, 96–100.

• Masucci, A. P., Serras, J., Johansson, A., & Batty, M. (2013). Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physical Review E, 88(2), 022812. Finite-size correction, scale, heterogeneity.

• Yang, Y., Herrera, C., Eagle, N., & González, M. C. (2014). Limits of Predictability in Commuting Flows in the Absence of Data for Calibration. Scientific Reports, 4, 5662. Extended radiation model, scale parameter, limits of the original claim.

• Kluge, L., Levermann, A., & Schewe, J. (2022). Radiation model for migration with directional preferences. Physical Review E, 106(6), 064138.

• Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., & Mascolo, C. (2012). A Tale of Many Cities: Universal Patterns in Human Urban Mobility. PLOS ONE, 7(5), e37027.

• Yan, X.-Y., Zhao, C., Fan, Y., & Di, Z. (2014). Universal predictability of mobility patterns in cities. Journal of The Royal Society Interface, 11(100), 20140834.

• Yan, X.-Y., Wang, W.-X., Gao, Z.-Y., & Lai, Y.-C. (2017). Universal model of individual and population mobility on diverse spatial scales. Nature Communications, 8, 1639.

• Liu, E.-J., & Yan, X.-Y. (2020). A universal opportunity model for human mobility. Scientific Reports, 10, 4657.

FIN

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• XIE, Z., RAMEZANI, M., LEVINSON, D. (2026) Investigating Platoon Formation and Retention Using Reduced-Scale Mobile Robots with Controllers Based on Established Car-Following Models. Transportation Research Part C. Volume 188, July 2026, 105671 [doi]

This research investigates platoon formation and retention in various traffic conditions using five well-known car-following models, implemented as controllers on reduced-scale mobile robots (RSMRs). Our study moves beyond traditional simulations by directly applying these controllers in a controlled physical environment to observe and measure the dynamic interactions within a platoon of RSMRs. We adapted the Gazis-Herman-Rothery (GHR) model, Gipps model, intelligent driver model (IDM), proportional-integral-derivative (PID) model, and adaptive cruise control (ACC) model into controllers. Experiments were designed to assess controller performance across steady-flow, congested, and stop-and-go traffic conditions, with a preliminary scaling test to support comparison with full-scale vehicles. Overall, the IDM-inspired controller achieved the best safety-efficiency balance, with compact platooning and the smallest speed fluctuations, while ACC was typically second-best; PID and Gipps showed larger oscillations and gaps, and GHR led to collisions. The results also demonstrate noticeable differences between physical and simulation experiments, highlighting the necessity of studying platooning in a physical environment. The fundamental diagram analysis confirms that theoretical and experimental results are generally consistent, reinforcing the usefulness of RSMRs in studying traffic dynamics and providing reproducible baselines for controller evaluation.