Real-time urban traffic state estimation and prediction using a data-fusion framework based on link neighbors 10535 Luuk de Vries, Sweco, The Netherlands Luc Wismans, DAT.Mobility, The Netherlands Eric van Berkum, University of Twente, The Netherlands 1. INTRODUCTION
4. CASE STUDY & ASSESSMENT FRAMEWORK
Effective ITS and traffic management purposes require complete and accurate information about current and predicted traffic states in the transport network. In this research we expand the current state-of-the-art by developing a data fusion framework, branded neighborhood link method (NLM) with the ability to fuse multiple data sources and data from link neighbors, to yield accurate traffic state estimations and predictions in an urban transport environment!
For assessing performance of NLM, we choose to simulate the ground truth using the microsimulation package Paramics. The case study selected for this research is the Enriched Sioux Falls Scenario by Chakirov & Fourie (2014). We feed NLM, all (live) loop detector data, but only 5% of floating car data from the simulation. We then assess NLM’s traffic state estimation & prediction performance, by comparing it’s output with the ground truth for three traffic flow variables; velocity, intensity and density.
2. BACKGROUND In this research two challenges related to urban traffic state estimation and prediction are addressed. These are (Wismans et al, 2017): • Low penetration rate of traffic data sources, leading to gaps in information; • Complexity of urban networks with e.g. a high density of intersections and dynamic interactions between other modes of transport It becomes apparent that generating a robust and accurate image of the traffic state within an urban environment from few traffic data sources is no easy task.
Through use of NLM
Traffic data within an urban environment
(Ideal) robust and accurate image of the traffic state
3. METHOD DEVELOPMENT We have developed a data-driven framework (NLM) for network state estimation/prediction as a solution to the above described challenges. NLM is based on the idea that by using patterns in traffic data, the current traffic state of road segments can be used as indicators for the traffic state on neighboring road segments, as shown below. We refer to our paper for the technical details.
5. RESULTS From our research, we conclude that NLM is able to deliver real-time traffic state- estimations and predictions. Example output from a Sioux Falls road segment is shown to the right. • NLM traffic estimation yields on average for 50% of the roads in the network, a velocity estimation within 5 km/h of the ground truth. • NLM traffic state prediction (5 minute horizon) performs nearly as good, with a respective percentage of 49%.
6. CONCLUSIONS This research shows that a NLM-based framework yields very reasonable traffic state estimation/prediction results in a modelled and simulated environment. NLM is intuitive and light on resources and is therefore easily transferred to real world scenarios.
7. REFERENCES Neighborhood Link Method (NLM) Traffic State Estimation
Traffic State Prediction
• Chakirov, A. & Fourie, P.J. (2014). Enriched Sioux Falls Scenario with Dynamic And Disaggregate Demand. Singapore. • Wismans, L.J.J., L.O. de Vries & E.C. van Berkum (2017). Comparison of interpolation techniques for state estimation on urban networks. In proceedings NECTAR conference, May 31 – June 2nd, Madrid, Spain.