Big data in Thessaloniki

Transcription

1 Big data in Thessaloniki Josep Maria Salanova Grau Center for Research and Technology Hellas Hellenic Institute of Transport - Web:

2 Big data in Thessaloniki Traditional sensors (loops, radars and cameras) Stationary sensors network: Point to point tracking of MAC ids along the network through Bluetooth detectors (more than 40). Dynamic sensors fleet: Floating Car Data provided in real time by a professional fleet (more than vehicles). Cooperative technologies (COMPASS4D and COGISTICS): RSU is a static sensor and OBU is a dynamic sensors (CAM message). Social networks content

3 How do we use Big Data? Travel time estimation (average values and distributions) Mobility patterns identification OD matrices generation and validation Traffic flow estimations Route choice models development and calibration Macroscopic traffic models calibration Microscopic simulation models calibration Road Hazards Detection Personalized services for drivers (BMs) Other activities

4 Traditional sensors The surveillance system of the Peripheral Ring Road (more than vehicles per day in both directions) The Thessaloniki s Urban Mobility Management System installed in the city center (more than vehicles per day) The traffic lights management system of the whole city

5 Point to point BT network 10 detectors installed in 2012 ( 2.3 million detections per week (peak period) unique devices detected per day (one intersection) tracked trips per week tracked trips per day (one path) 33 detectors installed in 2014 (SEE-ITS & EASYTRIP) 1.5 million detections per week (off-peak period) detections per day (one intersection) More detectors installed in other cities and in Bulgaria (SEE-ITS & EASYTRIP)

6 Point to point BT network Number of detections Intersection Monday Tuesday Wednesday Thursday Saturday Identity Friday 07/12/12 Sunday 09/12/12 03/12/12 04/12/12 05/12/12 06/12/12 08/12/12 A B C D E F G H I J

7 Point to point BT network Valid trips Path identity Monday Tuesday Wednesday Thursday Friday Saturday Sunday

8 Point to point BT network Data filtering and processing for real time travel time estimation Invalid cases Long stop Other mode (pedestrian) Short stop Possible detour Other mode (bus) Valid cases Detector B Detector A Time (Trip End)

9 Point to point BT network Data filtering and processing for real time travel time estimation

10 Point to point BT network Real time travel time provision to drivers (VMS, internet, smart device)

11 Point to point BT network Traffic flow estimation from detections (20%)

12 Point to point BT network Without filter 15 filter 5 filter 60 filter Without filter Filtering each 5 min Filtering each 15 min Filtering each 60 min Ratio 0,3412 0,2179 0,1972 0,0442 R 2 0,9166 0,9193 0,9337 0,8594 Largest differences -401 / % / 75% -410 / % / 61% -336 / % / 57% -536 / % / 79%

13 Floating Car Data More than vehicles (taxi fleet) Circulating hours per day Pulse generated each 100 meters (10-12 seconds without congestion) or 60 seconds pulses per minute More than trucks (two fleet operators)

14 Floating Car Data Device ID GPS position (X, Y, Z) Orientation (degrees) Speed (km/h) Timestamp Zone

15 Floating Car Data Map matching Low cost GPS receiver (grey) High quality GPS receiver without CDGPS (green) The same receiver with CDGPS (black) The black circle has a 10-metre radius

16 Floating Car Data Detailed and updated maps can be generated from FCD National and international network Regional and interurban network Urban network

17 Floating Car Data Road direction One way Two ways

18 Floating Car Data Maximum, average and minimum speed profiles along Tsimiski (2 kilometers)

19 Combination: FCD & BT detectors Virtual static detectors (free, no maintenance, easy to change position)

20 Social content (Facebook check in) Spatial coverage of the city center locations every 5-15 minutes.

21 Thessaloniki Big Data framework Real time data processes and services provision in Thessaloniki Real world Traffic flows Filter Filter Travel times Historical traffic data Traffic flow estimation Travel time provision services Off-line processes Macroscopic traffic model Short term forecast Real time processes Routing services Real time services Traffic scenaria Data expansion Traffic congestion provision services

22 Thessaloniki Big Data framework Real time traffic conditions information based on a combination of traffic modeling and real time measurements (traffic flow and travel time)

23 Other applications Impacts of high intensity storms on urban transportation: Applying traffic flow control methodologies for quantifying the effects Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 All other locations

24 Other applications Impacts of high intensity storms on urban transportation: Applying traffic flow control methodologies for quantifying the effects 08/02/ /02/ /02/2013 Average daily difference (%) Duration of event s impact Average difference during the event (%) Maximum difference during the event (%) Zone % 5:00-8:00-10,7% -34,8 % Zone % 5:00-12:00-14,1% -47,5 % Zone % 5:00-12:00-15,6% -49,3 % Zone % 5:00-11:00-5,5% -37,7 % Zone % 5:00-19:00-23,1% -67,8 % Athens Region % - -8,3% -31,6% Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Athens region 08/02/ /02/ /02/

25 Other applications Taxi observatory in Barcelona The area of study 18 municipalities habitants 330 km 2 more than daily trips The network km intersections links The taxi market taxis driver licenses 24 dispatching centers 272 taxi stands daily trips The taxi trips database 9 years ( ) valid taxi trips km (50% occupied) hours (33% occupied) trips with GPS data

26 Other applications Taxi observatory in Barcelona Taxi demand density maps O-D matrix estimation from GPS coordinates of customers Origins and Destinations

27 Other applications Agent based model for taxi services

28 Other applications Agent based model for taxi services Minimum number of taxis (taxis/hour*km 2 ) 26 First best solution 34 Second best solution 30

29 Other applications Agent based model for taxi services Access / Waiting time (users) Benefit (drivers)

30 Other applications Agent based model for taxi services

31 Other applications Agent based model for taxi services Waiting time (users) Benefit (drivers)

32 Other applications Agent based model for taxi services

33 Thank you! Dr. Josep Maria Salanova Grau