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.
My Colleague Dr. Emily Moylan is advertising for a post-doc at the University of Sydney.
Opportunity to be valued for your exceptional knowledge and experience in Transport Behavioural Modelling and/or Technology Adoption
Located at Darlington Campus, in the School of Civil Engineering
Full-Time, 24 months Fixed Term, Academic Level A: Base Salary: $94,629 p.a. – $101,018 p.a. plus a generous employer’s contribution to 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 at the University of Sydney. The position will contribute to the research and leadership of the School of Civil Engineering in the newly launched Transport Engineering program.
Emergence of new technologies such as autonomous vehicles, increases in data availability and advances in data science are paving the way for exciting and unprecedented opportunities to shape the next generation of transportation systems. The successful applicant(s) will help build a new research group headed by Dr. Emily Moylan to develop data-driven, stochastic methods in transport system performance assessment to support the adoption of new technologies and understand the evolution of travel behaviour.
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, spatial planning, transport, psychology or related fields
Has published ground-breaking research in the area of behavioural modelling or perceptions of travel time in high quality international journals
Has demonstrated interest and aptitude for formulating methodologies to test hypotheticals associated with the adoption of transport innovations such as autonomous vehicles
Possesses strong communication skills
The School of Civil Engineering is one of 5 schools in the Faculty of Engineering. The School has a reputation for the quality of its graduates, for the breadth, depth and innovation in its undergraduate curriculum, and for the strength of its research and postgraduate teaching programs. To learn more about the School of Civil Engineering click here
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 Australias first university and have an outstanding global reputation for academic and research excellence. Across our campuses, we employ over 7600 academic and non-academic staff who support over 60,000 students.
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.
Intending applicants are welcome to seek further information about the position from Dr Emily Moylan, Lecturer in Transport, School of Civil Engineering on +61 2 8627 7547 or firstname.lastname@example.org
For more information on the Dr Emily Moylan, click here
For recruitment-related enquiries, or if you require reasonable adjustment or support filling out this application, please contact Helen Efstathiou, Recruitment Consultant on +61 2 8627 7137 or email@example.com
Job Reference No. 2377/1119F
Routine pre-employment probity checks will be carried out for this position
Closing date: 11:30pm, Thursday 5 December 2019
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.
The University reserves the right not to proceed with any appointment.
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Transit accessibility to jobs (the ease of reaching work opportunities with public transport) affects both residential location and commute mode choice, resulting in gradations of residential land use intensity and transit (public transport) patronage. We propose a scaling model explaining much of the variation in transit use (transit commuters per km2) and residential land use intensity with transit accessibility. We find locations with high transit accessibility consistently have more riders and higher residential density; transit systems that provide greater accessibility and with a larger base for patronage have proportionally more ridership increase per unit of accessibility. All 48 metropolitan statistical areas (MSAs) in our sample have a scaling factor less than 1, so a 1% increase in access to jobs produces less than 1% increase in transit riders; the largest cities have higher scaling factors than smaller cities, indicating returns to scale. The models, derived from a new database of transit accessibility measured for every minute of the peak period over 11 million US census-blocks, and estimated for 48 major cities (MSAs) across the United States, find that jobs within 45 minutes most affect transit rider density. The findings support that transit investment should focus on mature, well-developed regions.
To capture network dependence between traffic links, we introduce two distinct network weight matrices Wj,i, which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.