All roads lead to ROMA
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The presentation slides of the public defense of my master's thesis for Science Communication at Delft University of Technology. The defended work is titled: The networked brand identity - Management support tool for tension analysis in brand identity networks concerning privacy. A final score of 8/10 was obtained for the project, the written thesis, and this presentation. The thesis can be found at: https://www.researchgate.net/publication/292139858_All_roads_lead_to_ROMA_Design_and_evaluation_of_a_Robust_Online_Map-generation_Algorithm_based_on_position_traces
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All roads lead to ROMA
- 1. 1 / 32 All roads lead to ROMA Friday, 6 March 2014 Roelof P. van den Berg
- 2. 2 / 32Roelof P. van den Berg Setting the scene • disaster relief scenario • no up-to-date map available • changing environment • requirements • fast construction of a best guess • adapt to new information
- 3. 3 / 32Roelof P. van den Berg Outline 1. Introduction • Research question • Relevance • Methodology 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion
- 4. 4 / 32Roelof P. van den Berg Introduction – Research Question How to create and maintain an accurate and dynamic map based on position traces?
- 5. 5 / 32Roelof P. van den Berg Introduction – Relevance • scientific • insight in map dynamics • novel description of road decay • novel method of map comparison • practical • situations with no a priori map • speedy creation of a best guess map
- 6. 6 / 32Roelof P. van den Berg Introduction – Methodology 1 3
- 7. 7 / 32Roelof P. van den Berg Introduction – Methodology 2 3
- 8. 8 / 32Roelof P. van den Berg Introduction – Methodology 3 3 Zoeterwoude-Dorp Inhabitants: • 4.514 Traffic model: • shortest path • random
- 9. 9 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion
- 10. 10 / 32Roelof P. van den Berg Algorithm – Layout I. Pre-processing II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics node edge measurement
- 11. 11 / 32Roelof P. van den Berg Algorithm – Pre-processing I. Pre-processing • Filter on accuracy • Filter on distance II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics
- 12. 12 / 32Roelof P. van den Berg Algorithm – Path estimation I. Pre-processing II. Path estimation • Match edges to measurements • Apply forward tree search III. Path generation IV. Path adjustment V. Map dynamics
- 13. 13 / 32Roelof P. van den Berg Algorithm – Path generation 1 2 I. Pre-processing II. Path estimation III.Path generation IV. Path adjustment V. Map dynamics
- 14. 14 / 32Roelof P. van den Berg Algorithm – Path generation 2 2 I. Pre-processing II. Path estimation III.Path generation • Backward tree search • Bridge shortest gap IV. Path adjustment V. Map dynamics
- 15. 15 / 32Roelof P. van den Berg Algorithm – Path adjustment I. Pre-processing II. Path estimation III. Path generation IV.Path adjustment • measurement interpolation • node influencing V. Map dynamics
- 16. 16 / 32Roelof P. van den Berg Algorithm – Map dynamics I. Pre-processing II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics • node merging • road decay average interval time position PP CDF-1
- 17. 17 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments • Comparison • Road introduction • Road removal • Map dynamics 4. Discussion 5. Conclusion
- 18. 18 / 32Roelof P. van den Berg Two vector-maps Map & World . Match nodes Map <-> World Experiments – Comparison 1 2
- 19. 19 / 32Roelof P. van den Berg Experiments – Comparison 2 2 Recall = 𝑖=0 |𝑆| 𝑗=0 |𝑆| 𝑆 𝑖,𝑗 𝑊 𝑖,𝑗 |𝑒 𝑤| Precision = 𝑖=0 |𝑆| 𝑗=0 |𝑆| 𝑆 𝑖,𝑗 𝑀 𝑖,𝑗 |𝑒 𝑚| F-score = 2 × prec × rec prec + rec False Positive False Negative True Positive Similarity
- 20. 20 / 32Roelof P. van den Berg Experiments – Road Introduction 1 2 • Map stabilizes • for 50, 100, and 200 cars • Clear overfitting • for ≥ 1000 cars simulated • within 3.5 – 6.5 minutes • faster for more traffic
- 21. 21 / 32Roelof P. van den Berg Experiments – Road Introduction 2 2 observed overfitting 2000 cars; overfitting at 5.5 minutes
- 22. 22 / 32Roelof P. van den Berg Experiments – Road removal 1 2 • Roadblocks added • 10, 20, and 50% • Map recovers • generally within 30 minutes • faster for more traffic
- 23. 23 / 32Roelof P. van den Berg Experiments – Road removal 2 2 road removal recovery 2000 cars; recovery within 25 minutes
- 24. 24 / 32Roelof P. van den Berg Road introduction Road removal Experiments – Map dynamics
- 25. 25 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion • Novelties • Noise • Improvements 5. Conclusion
- 26. 26 / 32Roelof P. van den Berg Discussion – Novelties • Description of road decay method • Investigation in formation of noise • Graph based method for map comparison
- 27. 27 / 32Roelof P. van den Berg Discussion – Noise • Outliers in measurements • Many roads on the map • One road in the world • Creep towards centreline
- 28. 28 / 32Roelof P. van den Berg Discussion – Improvements • Traffic model in simulator • Filtering methods • Outlier removal • Road decay
- 29. 29 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion • Conclusion • Future work “How to create and maintain an accurate and dynamic map based on position traces?”
- 30. 30 / 32Roelof P. van den Berg Conclusion • ROMA is a proof of concept • Dynamic vector-map generation in an online fashion • Framework for map-generation evaluation
- 31. 31 / 32Roelof P. van den Berg Conclusion – Future work • Topological and geographical map dynamics • Distributed application of ROMA Alice knows: Bob knows:
- 32. 32 / 32 All roads lead to ROMA Friday, 6 March 2014 Roelof P. van den Berg
- 33. 33 / 32Roelof P. van den Berg Appendix – Road Introduction
- 34. 34 / 32Roelof P. van den Berg Appendix – Road Removal
Hinweis der Redaktion
- Thanks to committee: Catholijn Jonker, prof at Interactive Intelligence Leon Rothkrantz, prof at Interactive Intelligence Caroline Wehrmann, assistant prof at Science Communication Freek van Polen, researcher at Sense Observation Systems. Mentor Research performed during my internship at Sense, Daughter of Almende Design and Evaluation of a Robust On-line Map-generation Algorithm (ROMA) based on position traces Road-maps in a changing environment, such as in the next slide
- Translated setting -> Research Question Relevance of answering this questions Methodology to answer the question The design of the algorithm is followed by the evaluation thereof in the experiments We conclude this presentation with the discussion of results and conclusion.
- Accurate: Best possible representation of the world Dynamic: Quickly responds to changes in the world
- Young field: The main methods were described in this millenium 2003, 2006, 2009 Current literature describes how to create a map, not how to maintain Description of map dynamics and how roads decay, advances the field Map comparison for quantitative evaluation, which is rare in literature
- Controlled environment: simulator Determine spatial accuracy as similarity map and world Determine temporal accuracy as speed of adaption to changes Changes: Click -> introduction of roads to the world -> map generation Click -> removal of roads from the world -> road decay
- Road Block Simulator developed by PhD-student at Almende Click -> Provides a controllable ground-truth. Simulated world: compare ground truth world to developed world representation. Validate algorithm performance We developed both the cartography and analysis modules
- For validation we used Zoeterwoude-Dorp, near Leiden, as our world Navigable map representations is stored as a vector-map, consisting of positions (or nodes) with connections (or edges) between them. The vector-map is a specialized form of a graph, which is a mathematical structure commonly used to represent all kinds of problems. Vector-map has nodes representing geographical locations. Shortest path between two random points on the world. Roadblocks can be added to the world -> changes to traffic routes.
- Cartography module contains ROMA Robust -> responds to changes in the world Online -> provides best guess based on available measurements Map-generation Algorithm ROMA provides a vector-map representation of the world
- We use a sequential approach common in map generation Map dynamics is added as last step to describe how to handle changes over time ROMA is fed a stream of GPS measurements (CLICK), consisting of position information and estimate of measurement error (CLICK) The measurement error of GPS generally folows a Gaussian distribution on the horizontal plane CLICK -> It tries to match and merge these measurements to the existing map Map consists of nodes (CLICK) and edges (CLICK)
- Stream of measurements first goes through pre-processing -> remove outliers CLICK -> Accuracy < threshold are removed CLICK -> Inter-measurement > threshold is split Remaining measurement sequence is further processed per 6 measurements in this example, to enable look-ahead, shown in next slide
- Fitness-function: distance and directionality Tree search: start at E0 and further
- Best path sought CLICK Look-ahead No path possible CLICK
- V: TODO: dynamics, decay
- CLICK -> - Welch’s T-test - represent the same location Road decay: EXAMPLE AVG and SD of interval CLICK -> results in the inverse cumulative distribution function of a T-distribution. T-distribution was chosen because the number of measurements differs and influences the certainty of interval estimation. We also added a static initial time to live so that roads with only one measurement have a time to live estimate.
- Wij hebben gekozen om de twee kaarten direct met elkaar te vergelijken (topology, geometry) Kaart Zoeterwoude toevoegen
- CLICK -> For comparison of the developed map to the simulated world we first lay the map on the world CLICK -> We then match nodes from map to the nearest node in the world and vice versa CLICK -> Conflicts are solved by first accepting the nearest matches
- With the matched nodes, we can determine the true positives (CLICK) These are found when an edge is present between matched nodes in world and map CLICK -> False postives are those edges only present in the map With these two classifications, we can determine the precision (CLICK) as it is called in information retrieval Normally items from both domains are equal of size In our case, edges can differ in shape and length We therefore added scoring (CLICK) based on similarity In the world domain (CLICK), we can find the false negatives (CLICK) With false negatives and true positives we then determine recall A one value measure of spatial accuracy is obtained through the harmonic mean or F-score (CLICK)
- In experiment 1 we introduced new roads to the world. This was simulated by starting traffic movement. For lower amounts of traffic the map showed a rather stabilizing map quality. For higher amounts of traffic the map showed overfitting similar to overfitting in machine learning where noise gets the overhand. Overfitting is mainly found in the precision value since Recall generally stabilizes for all traffic densities. Stabilization of recall occurs within 10 minutes of all simulations. overfitting occurs earlier for larger amounts of traffic.
- Recall > precision in all simulations Max. Recall generally > 0.8 Max. Precision > 0.5, seems lower for more traffic CLICK -> Here you see precision decreasing after 5.5 minutes.
- For larger amounts of traffic we simulated road decay by adding roadblocks to the world at the moment of overfitting in experiment 1. This kind of change is visible in precision because the size of the world changes instantly. We expected the map to adapt to this change and recover to a stable value. In all experiments we observed recovery within 30 minutes and this also occured faster for larger amounts of traffic. In the simulations with large amounts of noise we measured the time to recover against a baseline without roadblocks as shown on the next slide.
- In the simulations with large amounts of noise we measured the time to recover against a baseline without roadblocks. Here you see the baseline experiment as a blue line, with lines for different percentages of roadblocks. CLICK -> Roadblocks are added after 5.5 minutes, the moment of overfitting. CLICK -> You see that the precision values return to baseline values within 25 minutes of road removal. What this looks like for the map is shown on the next slide
- After road removal, we see that the southwestern (CLICK) area loses many roads. This also happens on other locations on the map given that roadblocks are placed randomly on the world. Another observed difference is seen in the noise, or web-like structures. Because traffic takes other routes, other roads are affected by the growth of web-like structures.
- Practice and science Relevance for both Methodology for answers
- Many contributing factors: Used simulation and traffic model, applied filters, static time to live
- Practice and science Relevance for both Methodology for answers
- Potential for
- With this vision of the future I would like to conclude this presentation. Thank you for your time.
- TODO: wijs mensen op diktes van wegen -> meer verkeer
- SHORT IO: data belongs to user CAD: backdoor, helpdesk CL: No excessive collections DQ: errors over time (age v.s. dob)