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 … Continue reading
Quick update to the conference calendar. Crispin will be at Modelling World 2018 – June 6th, Birmingham Nectar Cluster 6 Workshop: Accessibility in urban modelling: from measurement to policy instruction – 18th-19th June, Lyon In both cases presenting the range … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading
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, … Continue reading
We are pleased to announce that sDNA version 3.4.5 is considered stable! While sDNA remains very general purpose software, sDNA3 also includes the latest active travel models from our ongoing research projects. Finally, we have incorporated feedback from our external … Continue reading