Video
One day of floating car data gathered by 1500 vehicles in Stockholm, Sweden. Red points show the current location of the vehicles. Blue points keep track of the history of the locations. Starting from early in the morning, as the points are accumulated, they gradually shape the underlying road network. The city center is located in the bottom while an important attraction point, the airport, is located on the top.
Reference:
M. Rahmani, H. N. Koutsopoulos, and A. Ranganathan, Requirements and Potential of GPS-based Floating Car Data For Traffic Management: Stockholm Case Study, Intelligent Transportation System, IEEE ITSC 2010
Floating car data
On 13 October, the year's biggest IBM event: IBM Software Day held at the Annex, the Globe Arena in Stockholm. A new section of this year's event was the price for a smarter planet assigned to a company or an organization that have a distinctive contribution towards a smarter planet, i.e. a solution that is intelligent, instrumented and connected together. After an exciting open ballot, iMobility Lab from KTH the Royal Institute of Technology was selected as the winner with 38 percent of the vote.
KTH won IBM’s Smarter Planet award 2010
The use of probe vehicles in traffic management is growing rapidly. The reason is that the required data collection infrastructure is increasingly in place in urban areas with a significant number of mobile sensors constantly moving and covering expansive areas of the road network. In many cases, the data is sparse in time and location and includes only geolocation and timestamp. Extracting paths taken by the vehicles from such sparse data is an important step towards travel time estimation and is referred to as the map-matching and path inference problem. This work introduces a path inference method for low-frequency floating car data, assesses its performance, and compares it to recent methods using a set of ground truth data.
The video shows the inferred paths as the result of the proposed method applied on a set of sparse floating car data reported by taxis in Stockholm, Sweden.
Reference:
M. Rahmani, and H. N. Koutsopoulos, Path inference from sparse floating car data for urban networks, Transportation Research Part C: Emerging Technologies, Volume 30, May 2013
Path Inference of Sparse Floating Car Data
A comparison between travel time of a route in Stockholm estimated from sparse FCD and travel time of the same route observed by ANPR (automated number plate recognition) system. The data belongs to the period of Sep 2012 to Sep 2013. The lower plot shows the average travel time for each 15-minute interval together with its standard deviation. The upper plot depicts corresponding distributions for each time interval.
Reference:
M. Rahmani, and H. N. Koutsopoulos, Path inference from sparse floating car data for urban networks, Transportation Research Part C: Emerging Technologies, Volume 30, May 2013
Route Travel Time Estimated from Sparse FCD
