Catch Ringo Chan (Arup) presenting our latest models at the 2018 European Transport Conference in Dublin this week. Presentation linked below:
C Cooper, Cardiff University; R Chan, Arup, UK Combining spatial network analysis with demographics to study the effect of segregation on cycling mode share.
sDNA cycling models have continued to get better and a recent paper in Int Jnl Sustainable Transportation shows how sDNA can model flows, mode choice and targeting investment. These models are all based on mixing lots of different user behaviours (trip types, distances and aversions to cycling in traffic) to match observed flow and mode choice data.
On the pedestrian front, Cardiff’s new Data Innovation Institute funded what we think is the first ever longitudinal (before-and-after backcasting) test of a pedestrian flow model, on our own city centre which saw huge redevelopment from 2007-2010.
Quick update to the conference calendar. Crispin will be at
In both cases presenting the range of current sDNA models both recently published (cycling) under review (walking) and in progress (land use, economic and car use).
And sorry you already missed,
Mr Richard Price completed his MSc dissertation, supervised by Dr Kate Boyer, “Applying spatial analysis modelling to the study of pedestrian networks around railway stations in South East Wales”. The analysis uses network modelling of active travel data to distinguish point where steps represent a barrier to access from points where suitable alternative access is available.
This represents a new research direction for sDNA. A presentation summarizing results is available here.
In 2016 on request of Tongji University we implemented a number of features to assist in analysis of public transport networks. An overview of these (demonstrated on the enormous bus network of Shanghai) is now available here.
Crispin spoke at the BCSD Smart Cities Cardiff event this month. Slides for his talk, “Spatial Network Analysis, Smarter Walking and Cycling & the Role of the Private Sector” are available here.
Dr Crispin Cooper’s paper “Using spatial network analysis to model pedal cycle flows, risk and mode choice” has just been published in the Journal of Transport Geography. This demonstrates use of cycling betweenness in sDNA to simulate cyclist flows based on slope, vehicle traffic, distance and angular change. The vehicle traffic input is also derived from an sDNA model (a simple angular betweenness one as usually works).
Because vehicle and cyclist flows are simulated separately, we can predict where they are likely to cross over and hence where accidents are more likely to occur.
Academic publishing being what it is, these aren’t the most up to date sDNA models. In particular we now have better ways to predict flows and mode choice which (who knows) will hopefully see peer reviewed publication before too long. For now have a look in the appendix to Cardiff Cycle Models and the European Transport Conference paper.
Congratulations to Ringo Chan and team at Arup Cardiff for project “Sustainable Access to Newport City Centre” – winner of the Chartered Institution of Highways & Transportation’s Small Transportation Research & Studies Award 2016. The judging panel praised their commitment to innovative research and analysis to identify optimal transport solutions that meet different planning objectives.
Ok, confession time, we’re not entirely disinterested in promoting this ourselves because the team used sDNA in the study to identify link connectivities and active travel relationships. sDNA, pleased to help win awards for our users! (See also the 2014 RTPI Excellence in Spatial Planning prize and also this year’s finalists for the Hong Kong Walk21 CityTech awards). But more seriously, congratulations again to Arup on their hard work paying off.
The QGIS sDNA plugin received a warm reception at the QGIS user conference, Foss4G Cymru 2016. Slides available here.
The latest sDNA models of cyclist flows and mode choice were presented at the European Transport Conference 2016, UAB, Barcelona. The paper describes use of machine learning techniques to combine multiple sDNA models to fit observed data, incorporate heterogeneous behaviours, and calibrate for agglomeration effects. As well as a nice technique to use in practice (as it can handle the complications of real world data, especially auto- and cross-correlation), this is a step up in rigour for the Spatial Network Analysis tradition – we report cross-validated r2 which brings us more in line with mainstream transport modelling.
The conference paper is available here though this is an outline to be explained in more detail in an upcoming publication:
Cooper, C. (2018) Predictive spatial network analysis for high resolution transport modelling, applied to cyclist flows, mode choice and targeting investment. Int Jnl Sustainable Transportation, https://doi.org/10.1080/15568318.2018.1432730).