Better Perspectives with Big Data in City of Atlanta

Transcription

1 Better Perspectives with Big Data in City of Atlanta Xuewen Le, PE, PTOE Atkins, Renew Atlanta/TSPLOST Program Management Team October 8, 2018

2 Atlanta s North Avenue smart corridor demonstration A public demonstration and living lab for Internet of Things (IoT) deployment, data collection/analytics, autonomous vehicles, and partnerships

3 North Avenue smart corridor

4 North Avenue smart video APP analytics

5 North Avenue pedestrian detection

6 North Avenue pedestrian and bicycle detection

7 North Avenue: connected vehicle dedicated short range communication

8 Downtown special event area Mobi devices

9 Data streaming video detection data (per vehicle data and aggregated)

10 SPaT and BSM Bluetooth signals

11 Scenario 1: Traffic signal timing before/after evaluation

12 Traditional method: Travel time runs

13 ATSPM: Average delay per vehicle Percent AoR for selected period

14 Bluetooth probe data: Average speed, travel time, limited to where devices deployed

15 Inrix probe data: Average speed and travel time

16 Google API and vehicle detection analytics: Delay/travel time weighted with traffic volume

17 Results: Average delay= sum(delay_change * average_volume) / sum(average_volume)

18 Scenario 2: Realtime congestion and emission monitoring Smart city vision Integration of smart technologies with physical infrastructure Real-time traffic simulation model Real-time data integrated into operational analysis Handling large amount of data Use of big data concepts for injecting data into simulation

19 Simulation model - North Avenue smart corridor

20 Model architecture primary components 1 Inject real-time data into the simulation model 2 Run the traffic simulation model 3 Generate and visualize KPIs 5 2 Dynamic link between three tasks Simulation runs faster than real-time operations 6

21 Model architecture Component 1 Component 2 Component 3

22 Model architecture components: Dynamic KPIs Energy-Emission Computation Architecture Energy and CO2 emissions profile based on Motor Vehicle Emission Simulator (MOVES) matrix is estimated in real-time using data from the trajectory output file Compute Energy and Emissions from Vehicle Position Generate Heat Maps

23 Result and discussion: Real-time performance measures computation Energy heat map generated at the end of the 47 th simulation minute Presenting the cumulative energy consumption during the simulation minute interval throughout the corridor

24 Result and discussion: Model sensitivity to real-time input Energy and Emissions Energy and emissions estimated from preset and real-time inputs were compared Results were comparable as shown in the scatterplots and CDF

25 Result and discussion: Model sensitivity to real-time input Vehicle Travel Time Travel times compared for 10 random seeds of simulation with preset and real-time inputs Travel times varied within plausible bounds Average vehicle travel time versus simulation time intervals plots for (a) Westbound Route 4 (b) Eastbound Route 6

26 Scenario 3: Mobility situational awareness

27 Live traffic control room

28 Live traffic control room

29 Live alert: Speed

30 Live alert: Bottlenecks

31 Recorded live alerts

32 Game day traffic comparison: Falcons vs Panthers (60k) Falcons vs Saints (66k)

33 Game day traffic comparison: Falcons vs Panthers (60k) Falcons vs Saints (66k)

34 What s next? North Avenue Phase 2 deployment North Avenue simulation model to be validated with field data experiment and to be tested on larger scale with more real-time data Mobi: Game day usage to fine tune application Predictive simulation

35 Questions? Xuewen Le, PE, PTOE Atkins, Renew Atlanta/TSPLOST PMT