Geography Reference
In-Depth Information
techniques to spatial analysis with JTW data and present JTW data with
different form better serving transport analysis. Firstly, we employed an area
interpolation technique to transform two data sets (1996 and 2006 JTW
matrices) into consistent geographical units that independent of the two
original and inconsistent zonal structures. This method provides new
opportunities to examine spatial and temporal changes in urban transport
patterns. Secondly, we applied a GIS-based network analysis to compute the
average travel distance between the origin-destination traffic zones. The
method accounts for all possible routes between randomly generated points in
order to give more advanced measures of effects of multiple residential
locations, workplace and road choices on the resulting travel distance. The
GIS network modelling is also used to model the distribution of traffic flows
through road networks. The procedure calculated the number of trips travelling
on every road (network link) to estimate a traffic map. The map is used to
analyse the distribution of traffic flow over time and congestions. The result is
spatially disaggregated at 1km by l km grid cells, making them suitable to
inform the transport analysis and policy making at local level (e.g. traffic
noise, carbon emission and energy consumption at street block level). Both
these GIS techniques are found to be very useful tools to model the spatial
dynamics of commuting from the complex JTW datasets.
The second part of the chapter has shown that at a disaggregated scale, a
flow mapping technique was used to explore spatio-temporal dynamics of
public bicycle. In most of the previous research into public bicycle data
capturing, the stock data has been the predominant source of data to study their
underlying dynamics. In this study, we have highlighted the utility of flow or
trip-level data that offers new opportunities for research to examine the spatio-
temporal dynamics of public bicycle. Developing an understanding of the
complex spatio-temporal dynamics of public bicycle at a local scale is critical
to compiling evidence base with the capacity to ensure the system is
configured in a manner that meets the needs of the public bicycle users. The
data necessary to establish such an evidence base often exists in the form of
disaggregate trip-level records. The challenge now for transport planners and
researchers is to draw upon existing data, and where necessary, develop new
tools and techniques to examine these data in a manner that has the potential to
improve the operation of public bicycle systems. This paper has attempted to
progress research in this area through the development of a visual analytic, the
flow map, to explore new insights into the association of bicycle trip patterns.
Gaining a better understanding of these underlying dynamics is a first step to
establishing a fully automated monitoring tool with the capacity to identify
