The Co-Evolution of Network Investment, Modal Demand, and Land Use

Summary

Travel demand is shaped by investments in new infrastructure and changes in public policy, land use and urban design strategies. Previous research analyzed the relationship between investment (i.e., supply in infrastructure) and demand for various transport modes in selected cases. This research will add cases (cities being examined), and extend the research question. In traditional cross-sectional regression methods, when observations are taken from one point in time, correlations may be observed, however cause and effect cannot be identified. An example is the inability to determine whether a bicycle facility has been built because of many cyclists observed in the area, or whether a newly built bicycle facility attracts more cyclists. This limitation is an apparent drawback for those who regulate and manage the development of cities. If observations are given for different points in time, a typical question that arises within time- series analysis is whether or not one variable can help forecast another one. Granger tests infer the direction of causality by testing whether lagged information on a time-series variable X provides a significant information about another variable Y. That is, a model that includes past values for X and Y should outperform a model that only includes past values of Y. Such tests have been made in the context of land use and transport networks. This research will extend that to travel demand, particularly mode choice.

Supervisor(s)

Professor David Levinson

Research Location

Civil Engineering

Program Type

Masters/PHD

Synopsis

Succinct paragraph to provide sufficient information for prospective students to quickly understand the scope and topic of the research opportunity [maximum 4000 characters]  Hypotheses: The theory being tested in this research is that of co-development. One illustrative hypothesis is that investment in modes have measurable effects on the shares of competing and complementary modes. We define three sets of working hypotheses: (i) Mode share: Mode share is positively (negatively) associated with the lagged increase in share of complementary (substitute) mode.  (ii) Investment Feedbacks (iii) Complementarity

Additional Information

  • Use of research technique / methodology / technology
  • Potential topics of interest for the research opportunity
  • Current PHD and/or Masters topics
  • Eligibility criteria / candidate profile
  • Scholarship(s)  /  funding available

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Keywords

transport, Transportation, networks, GIS, Statistics, Geography, Econometrics, economics, spatial analysis

Opportunity ID

The opportunity ID for this research opportunity is: 2231

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