From fires in the Amazon and in Australia, to flash flooding in Europe, right across the globe we are witnessing the impact of climate change on the environment. But how does a changing climate affect key infrastructure? Engineering experts explain.
How does climate change affect transport infrastructure?
School of Civil Engineering academic, Professor David Levinson is a transport engineering expert who says transport infrastructure both contributes to climate change, but can also be affected by it.
“The issue goes two ways in transport. Typically, road and air transport are considered major contributors to climate change, making up 23 percent of CO2 emissions,” said Professor Levinson.
“But transport is also vulnerable, particularly due to so many facilities like roads and bus terminals being in flood zones, for instance New Jersey Transit lost $US120 million in damage to buses after Hurricane Sandy.
“Many transport facilities are also below grade, tunnels like those in the New York Subway were severely damaged from flooding during Sandy. Airports being at low elevations along the coast are at risk of sea-level rise. Extreme heat can cause road buckling, freeze-thaw cycles cause pavement cracking and potholes.
“Extreme weather increases the variability of weather, and roads designed for a particular climate range may fail more quickly. All of these add costs to design and retrofit, as well as decreasing reliability for users.”
Driver lane-changing behaviours have a significant impact on the safety and the capacity of the vehicle-based traffic system, therefore, modeling lane-changing maneuvers has become an essential component of driving behaviour analysis. Among microscopic LC models, game theory based lane-changing models highlight the interaction or competition of drivers, which reveal a more realistic image of driving behaviours compared to other classic models. However, the potential of game theory to describe the human driver’s lane-changing strategies is currently under-estimated. This paper aims to review the recent development of game theoretic models that are classified according to their different methodologies and features. They are designed for both human-driven (User-optimal) and autonomous vehicles (User and system-optimal), and we hope they can find applications in future AV industries.
This paper examines the changes that occurred in the tram and train networks and density of population in Sydney between the early 1860s and 1930s when both trains and trams were developing. A set of statistical analysis has been conducted using panel data representing 593 districts of Greater Sydney at suburb (neighborhood) level over each decade from 1861 to 1931. We find that trams and population density are positively associated in a positive feedback process, tram deployment leads population growth and population growth leads tram deployment, both satisfying a Granger causality test.
How much of the variation in transit mode share is attributable to accessibility is not well understood, despite its significant policy implications. It is hypothesized that better transit accessibility leads to higher transit mode share. This paper explains block-group level transit mode share using transit accessibility in a logistic model for 48 major US metropolitan areas. Transit accessibility alone explains much of the variation in transit mode share for all 48 regions despite their geographical differences (adjusted R2 0.61, potential accessibility); models for individual cities have stable and interpretable parameters for transit accessibility. The models better explain mode share in cities with higher person weighted transit accessibility and larger populations; an adjusted R2 of 0.76 is achieved for New York City with transit accessibility as the only explanatory variable. Additional automobile accessibility and income variables modestly improve model fit. Time-decay functions fitted to accessibility measures better explain mode choice than the isochrone accessibility, and suggest the catchment area affecting transit mode choice to be within 35 minutes. This work contributes to the understanding of transit mode share by solidifying its link with accessibility, which is determined by the structure of the transport network and land development.
Davis, Blake, Ji, Ang, Liu, Bichen, and Levinson, D. (2020) Moving Array Traffic Probes. To be presented at the Transportation Research Board Annual Meeting, January 2020.
This paper explores the potential of moving array `probes’ to collect traffic data. This application simulates the prospect of mining environmental data on traffic conditions to present a cheap and potentially widespread source of traffic conditions. Based on three different simulations, we measure the magnitude and trends of probe error (comparing the probe’s `subjective’ or time-weighted perception with an `objective’ observer) in density, speed, and flow in order to validate the proposed model and compare the results with loop detectors. From these simulations, several conclusions were reached. A single probe’s error follows a double hump trend due to an interplay between the factors of traffic heterogeneity and shockwaves. Reduced visibility of the single probe does not proportionately increase the error. Multiple probes do not tend to increase accuracy significantly, which suggests that the data will be still useful even if probes are sparsely distributed. Finally, probes can measure the conditions of oncoming traffic more accurately than concurrent traffic. Further research is expected to consider more complex road networks and develop methods to improve the accuracy of moving array samples.
Estimating the OD travel time matrix is an essential step in transport demand forecasting, accessibility analysis, spatial interaction modeling, and reliability analysis. Many tools have been developed to estimate the travel time between origins and destinations by different transport modes. This paper systematically reviews three tools: ArcGIS Network Analyst, OpenTripPlanner, and Google Maps Application Programming Interface, that are able to estimate an OD travel time matrix, and compares the results for 30 stations of Sydney Trains network with a same set of parameters. Bus, train (rail), and transit modes are tested. Results indicate that OpenTripPlanner produces higher transit travel times than Google Maps API (2.8 minutes) while ArcGIS Network Analyst estimates a travel time 6 minutes higher on average than Google Maps API for transit modes.
Access, or the ease of reaching destinations, influences property values. Most studies investigating the relationship between the two either confuse access for proximity, or measure accessibilities in ways that make interpreting elasticities difficult. We specify, and estimate, a hedonic pricing model for house sales in the Sydney region where access by auto and transit are measured as the cumulative number of jobs (opportunities) reachable within a determined time threshold, here 45 minutes. The model was set up with structural, and neighborhood attributes in addition to the access measures, and resulted in a statistically significant fit. The results indicate a greater influence of transit access on house prices compared with auto access. Although a synergy would play out in reality, a unit increase in jobs reachable within 45 minutes by transit was estimated to increase sale price by more than six times than auto. We also demonstrated the impact of improving access on the value of postal code areas. Although the results are specific to the Sydney region, the method can be adapted to any region to estimate elasticities of sale price with respect to access.
I mentioned that I will be at the New Zealand GEN 2019 Annual Conference – 6th December. I will not coincidentally also be presenting at the New Zealand Transport Knowledge Hub, December 5, 2019, presenting on: Accessibility measurement and valuation and how they contribute to wellbeing.
University of Sydney News published an article about our research. Repeated below:
13 November 2019
A new study conducted by researchers from the University of Sydney reveals public transport investments in large metropolitan areas reap a better return, with more passengers adopting public transport, than those in smaller cities.
The research, published in Nature Sustainability, analysed data collected from 48 metropolitan areas in the United States, including New York, Boston, Minneapolis and Atlanta, which showed a positive correlation between ridership, residential density, and the convenience of using public transport.
The research drew on data collected by the University of Minnesota, measuring the number of jobs over 48 metropolitan areas that were accessible by public transport. Efficiency was measured by assessing whether improvements to public transport influenced residential density.
It also revealed it was more beneficial for transport operators to expand in big cities than launch into greenfield markets (undeveloped areas), as larger cities yield a greater return on transit improvement than smaller towns.
“Public transport is relatively sustainable and is more suited to grow in larger cities than smaller ones. For example, accessibility improvement on the same scale would likely induce more transit patronage and residential land use intensification in New York City than in Las Vegas,” said Mr Wu.
Dr Somwrita Sarkar believes learnings from the research could be adopted in Australia’s major cities.
“These findings are very relevant to Australia, as large cities like Sydney and Melbourne plan to accommodate future population growth,” said Dr Sarkar.
“The current scenario, which sees low density, suburban sprawl surrounding jobs located in CBD areas is inefficient. Instead, residential and employment density growth must be paired with planned and prioritised growth in transit infrastructure,” she said.
Car use declines with higher transit accessibility
Using a scaling model, the researchers found that with increased residential density, coupled with better public transport accessibility, the density of car commuters levelled-off and began to decline.
“Our paper finds that while public transport intensifies residential density, fewer people use cars,” said Mr Wu.
“With an increase in transit accessibility, transit commuter density catches up to, and begins to exceed, auto commuter density. This was most apparent in the New York City,” he said.
Residential location and commute mode choice are affected by accessibility of transport to jobs
The researchers’ models were derived from a database that measured each minute of the morning peak period (between 7am – 9am) over 11 million areas in the United States. They found that jobs within a 45 minute commute from home most affected transit rider density. The findings support that transit investment should focus on mature, well-developed regions such as Sydney or Melbourne, which would attract proportionally more ridership than smaller cities.
“The choice on where to live, and how one goes to work are generally intertwined, and are not usually separate decisions,” said Mr Wu.
“Our research analyses the residential density of the working population, and transit commuters to reflect that joint decision process,” he said.
Transit rider density rises faster than population density with increasing transit accessibility
Efficiency was measured by ‘returns of scale’, assessing whether improved access to public transport influenced residential density.
The research found that residential density did increase with improvements to the transport network, but not at the same rate as the take up of public transport.
“There is a paradigm shift which indicates commuters favour public transport in areas that are well serviced. Residential density increases in places with convenient transport and a higher percentage of commuters switch to public transport to go to work,” said Mr Wu.
“Improvements to transit accessibility, such as increasing the number of jobs that can be reached within 30 minutes using public transport, increases the number of commuters faster than it does residential density,” he concluded.