Friday 30 November 2012

3D simulation with JMonkeyEngine 3.0

I have been looking for a reasonable high level 3D library for Java for some time. Recently I have been using JME 3.0 (www.jmonkeyengine.org) and it seems to be doing the trick. Here are a couple of pics, one from an accessibility model of Cambridge, UK and one from an on-the-fly pedestrian simulation.


The accessibility model has been produced by a simple tool that takes the a shapefile (.shp) or other GIS polygon file and extrudes it polygon based on one of its attributes. The colour of each resulting prism may represent the same or a different attribute.


This second one is from a thin 3D viewer sitting on top of a pedestrian simulation suite. The 3D viewer receives information about the position, speed and direction of each pedestrian from the pedestrian simulation and updates the positions and kinematics of the skeletons accordingly. The colour of each avatar may represent any of the metrics associated with their motion (e.g. speed, estimated distance to destination, local density, delay etc.).

GPS traces of Taxis in Beijing

Last couple of weeks I have been playing around with a dataset of taxi GPS traces from Beijing. This includes positions and timestamps from 10.000 individual taxis for a week. 

Here is a map of speeds (red low, green high). It has been created by calculating the distance between consecutive pairs of traces from the same vehicle. When the time difference between a pair of such timestamps is less than 60 seconds, they are used to estimate a value instance based on their euclidean distance and time difference. 


Datasets like this one are becoming increasingly available and they provide new information-rich ways to explore the spatial and temporal dynamics of urban systems. We are planning to evaluate this specific dataset, using spatiotemporal autocorrelation and if the results are encouraging we will be using it to estimate the speeds of the links of a transport model of Beijing.

I will come back to this to discuss the autocorrelation method we devised and share some results when we have made some more progress.