• Publications
  • Influence
Spatio-Temporal Congestion Patterns in Urban Traffic Networks
A methodology for determining congestion clusters is described, which provides a significant amount of flexibility to be able to meet different needs for different applications or cities and provides a basis for potential traffic estimation and forecast systems. Expand
A CEP Technology Stack for Situation Recognition on the Gumstix Embedded Controller
A proof-of-concept implementation of a technology stack for semantic complex event processing on the Gumstix embedded platform is presented and first experimental results about memory consumption are reported. Expand
Online Freeway Traffic Estimation with Real Floating Car Data
The main challenges originating from the sparseness and delay in collecting FCD are addressed and a procedure using the GASM is proposed that allows estimating traffic velocities continuously and outperforms naïve approaches in almost all considered setups. Expand
A phase-based smoothing method for accurate traffic speed estimation with floating car data
The quantitative and qualitative results show that the proposed Phase-Based Smoothing Method reconstructs the congestion pattern more accurately than the other two and is therefore more accurate even if the input data density is low. Expand
Travel time prediction in partitioned road networks based on floating car data
A method is presented that first identifies frequently congested regions of a network by clustering affected links and then predicts travel time losses inside these clusters and achieves significantly better results than naive predictors. Expand
Fusing probe speed and flow data for robust short-term congestion front forecasts
A robust and flexible method is proposed that combines the strengths of detector as well as Floating Car data in order to provide short-term congestion front forecasts that focuses on the difficulty of estimating traffic density in congested traffic conditions with given data. Expand
Assessing the probability of arriving on time using historical travel time data in a road network
A novel prediction model for assessing travel time reliability in a network considering alternate paths and including the total network instead of only relying on the travel time information of one particular path is introduced and it is shown that theTravel time reliability increases. Expand
Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction
This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel timeExpand
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
A dedicated Deep Neural Network architecture that reconstructs space-time traffic speeds on freeways given sparse data using a large dataset of sparse speed data, in particular from probe vehicles is presented. Expand
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks
Most traffic state forecast algorithms when applied to urban road networks consider only the links in close proximity to the target location. However, for longer-term forecasts also the traffic stateExpand