Vienna, July 16th. Enrico Steiger. Fachbereich Bauingenieurwesen und Geodäsie der TU Darmstadt

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1 Enrico Steiger Vienna, July 16th 1

2 Research Coperation in Salzburg Spatial Research Sensors Accessibility models Energy Potential models Indicator Public Transport web: real-time Standards Transport telematics Traffic Models Traffic Planning Mobility Services OpenStreetMap Applications ITS Public Transport Mobile Applications Travel Time Models Software architecture Floating-Car-Data Mobile Information Services ITS Research Applied Research and development in the area of transport telematics and geoinformatics 2

3 Mobility quo vadis? Number of journeys are steadily increasing One consequence are highly frequented roads. According to calculations by the Federal Office of Statistics every German Goal is a detection of bottlenecks inside transport networks as well as a temporally, spatially and modal shift of trips. spends about 70 hours annually in traffic jams. Economy loses about 97 billions each year through traffic jams. Forecast Salzburg 2015: Travel times increases by 15 Minutes / Trip 3

4 Aims - Traffic Map 2.0 Road users move as a active source of information inside traffic networks Objective of forming a Community to detect traffic conditions user generated traffic map Applications: Traffic situation map on the internet Traffic messages (congestions, delays etc.) Level of Service (LOS) criteria: Travel time Tailback lengths Average waiting time Travel speed 4

5 Example: TOMTOM Live Traffic 5

6 Example: Community approach WAZE 6

7 What contribution can the OpenStreetMap project provide concerning traffic detections? 7

8 GPS-Tracks OpenStreetMap JOSM with: OSM Data CC-BY-SA and Contributors 1.9 billion GPS coordinates worldwide (August 2010) Spatial positioning roads, waterways etc. use of GPS-Logger Traffic flow information can be derived from GPS-Tracks 8

9 Method development ( ) Traffic Detection with Floating-Traveller-Data (FTD) for mobile navigation services (Dissertation 2006) for: - Deutsche Telekom AG - Land Hessen (HMWVL) Enrico Steiger Methods of traffic data detection and forecasting from volunteered geographic information for intelligent transportation systems (ITS) (Diploma Thesis 2010) 9

10 Preanalysis of GPS-Tracks Evaluation of characteristic GNSS values x( ti), y( ti) ( t i x( t i v( t i b( t i ) ( t i ) ) ) ) Position Movement direction Path change in a time intervall Speed in a time intervall Acceleration in a time intervall Heading change rate (HCR) in a time intervall Straßenbahn: Tram: Geschwindigkeit: Speed: 49 km/h 49 km/h Beschleunigung: Acceleration: 0.1 0,1 m/s² m/s 2 Drehrate: HCR: 1 /s 1 /s Pkw: Car: Geschwindigkeit: Speed: 25 km/h 25 km/h Beschleunigung: Acceleration: 0.8 0,8 m/s² m/s 2 Drehrate: HCR: 1 /s 1 /s Radfahrer: Cyclist: Geschwindigkeit: Speed: 17 km/h 17 km/h Beschleunigung: Acceleration: 0.2 0,2 m/s² m/s 2 Drehrate: HCR: 3 /s 3 /s 10

11 Traffic Map Process GPS-Data Error detection Mode of Transport recognition GPS-Track MapMatching on OSM Traffic Map

12 Detecting GPS-Errors Signal interference through the underpass -> increased speeds well captured by error filter 12

13 Mode of Transport recognition Transit Point Train/Footway HCR Speed 1. multiple Cluster Analysis: Route segments with similar GPS values t Cluster 1 Train Cluster 2 Footway 13

14 Processing OSM GPS Data OSM-Planet Routing OSM Routable OSM MapMatching MapMatching GPS-Track Traffic-Analyzer Level of Service for each Way segment Velocities Tailback lengths Waiting Times 14

15 Automatic determination of tailback lengths and average waiting times from GPS Data Test area Ignaz-Harrer/ Münchner Bundesstraße dailytime change of tailback lenghts at traffic signals Identification of time-dependent waiting time - cluster centroids Signal Ignaz Harrer- Rudolf Bieblstraße Tailback lengths (in meter) Average tailback length /hour Source: OSM GPS Data CC-BY-SA and Contributors 15

16 Level of Service criterion: traffic signal waiting time Test area Ignaz-Harrer/ Münchner Bundesstraße Standstill grids (resolution 10x10 meter grids) Dependancy frequency distribution of standstills inside road networks and their temporal duration out of town into town gesamt Source: OSM Data CC-BY-SA and Contributors 16

17 Traffic-Analyzer Level of Service - Analyzing speeds 17

18 Traffic-Analyzer GPS-based detection of speeds/ travel times Motorized Traffic Data: - Travel time - Travel speed Geschwindigkeitsprofil 6:30 h - 9:30 h Abschitt 2: Verzweigungstelle - Kavalleriesand - stadteinwärts Speed Graph and traffic forecast Geschwindigkeit [km/h] :30-6:45 6:45-7:00 7:00-7:15 7:15-7:30 7:30-7:45 7:45-8:00 8:00-8:15 8:15-8:30 Uhrzeit [hh:mm] 8:30-8:45 8:45-9:00 9:00-9:15 9:15-9:30 Geschwindigkeitsganglinie +Standardabw. -Standardabw. - Veränderte Reisezeit - Ausmaß der Störung? Speeds [km/h] 50 bis 80 (178) 40 bis 50 (379) 30 bis 40 (314) 10 bis 30 (476) 0 bis 10 (684) 18

19 Source: OSM Data CC-BY-SA and Contributors 19

20 Conclusion Strong penetration of mobile location technologies within the next years allows an efficient traffic detection developed methods and procedures can be a basis for upcoming online traffic models. Historical OSM GPS data provides a very good basis for evaluating the quality of transport networks (LOS) Improving traffic planning 20

21 Questions? - Thank You 21