University of Central Florida, USA

Transcription

1 Dr. Mohamed Abdel-Aty, PE Trustee Chair Pegasus Professor and Chair Department of Civil, Environmental & Construction Engineering University of Central Florida, USA Editor-in-Chief, Accident Analysis & Prevention

2 Challenges in the transportation system Increase in travel demand Growth in congestion Need to improve safety Reality of limited resources Solution: (Pro)Active Traffic Management Dynamically manage recurrent and non-recurrent (incident) congestion based on prevailing traffic conditions Benefits Maximize the efficiency of the facility Increase safety 2

3 Traditional Approach Where congestion/queues have formed Where the incident has occurred Where inclement weather has been detected Proactive Perspective Where the congestion/queues are about to form Where a crash is more likely to occur Where inclement weather is about to begin 3

4 Application of Intelligent Transportation Systems (ITS) Traffic Detection Systems Automatic Vehicle Identification (AVI) Systems Microwave Vehicle Detection Systems (MVDS) Weather Detection Systems Weather sensors (e.g. temperature, precipitation, visibility, fog, etc.) Countermeasures Variable Speed Limit Ramp Metering strategies Queue Warning Dynamic Rerouting and Traveler Information Adaptive Signals MVDS System AVI system 4

5 In general, there are five kinds of data sources will be utilized in this study. Central Server MAC ID: 123AB 07:01:05 Travel Time = 150 seconds Speed = 34 mph MAC ID: 123AB 07:03:35 Travel Time = 170 seconds Speed = 40 mph MAC ID: 123AB 07:07:05 Crash Data 1. Crash type 2. Crash severity 1.42 mile 1.89 mile Bluetooth Data 1. Travel time 2. Space-mean speed Data Sources Adaptive Signal Data 1. Signal timing 2. Lane-specific volume 3. Maximum queue length 4. Maximum waiting time Weather Data UAV Data 1.Weather type 2. Hourly precipitation 3. Visibility 4. Relative humitity 1. Vehicle trajectory 2. Speed 3. Acceleration 4. Deceleration 5. Headway distance 5/30/2018 5

6 Currently Available Data Source Characteristics Availability Contents Granularity Data Freeway & Arterial Source Agency TMS Expressway 1 SMS 2 Volume Incident MVDS 30s-60s CFX, RITIS AVI(Toll tag) Individual CFX BlueMAC 3 Individual Orange County Iteris 3 Individual Orange County BlueTOAD 3 Individual Seminole County, FTE InSync 15min Orange County SPM Individual 4 Seminole County HERE 1min RITIS INRIX 1min RITIS Twitter Congestion Only Twitter Mining 1 Time Mean Speed; 2 Space Mean Speed; 3 All are Bluetooth system, only BlueMAC provides raw log data; 4 SPM records individual count. 6

7 Size of Current Datasets In order to manage, store, and utilize the collected big data efficiently, the UCF research team has purchased two high-end computing severs with in-house funding Source CFX Seminole County Name of Dataset Monthly raw data size (Gbytes) Expected periods (Years) Expected Size (GB, as of 2017) Format Data collection period MVDS dat From 2013 to 2017 AVI csv From 2013 to 2017 DMS csv From 2013 to 2017 SPM MS SQL Server backup database From January 2015 BlueTOAD csv From 2014 to 2017 InSync csv From 2015 to 2017 Detector deployed Orange BlueMAC csv since Dec Still County under deployment. Iteris csv From 2015 to 2017 MVDS csv From 2014 to 2017 RITIS From Oct, 2013 till HERE csv now Total

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9 MVDS Congestion Index Congestion Index CI = Free Flow Speed Speed Free Flow Speed Free Flow Speed = 85th percentile speed 9

10 Real-time safety analysis A crash Disruptive Implementations Understanding the microscopic crash mechanisms Estimating crash likelihood in real-time Improving traffic safety in real-time

11 Effects of traffic safety on operation Crash-leading congestion Real-time speed profile by lane Real-time speed variation 11

12 Traffic Parameter ( e.g., Speed) What patterns are we looking for? Developing a Hybrid Detailed Crash Prediction System Using ITS Data on I-4 and Evaluating the Application Strategies Speed profile before/after crash on I-4 Time of Crash Crash Precursor Before Crash After Crash Observation Time Slice Time

13 Distance Distance Real-time crash risk for I-4 Station H Station H Station G Station G Station F Station F Space (Direction of Traffic) Station E Station E Station D Station D Station Station C C Time and Location of the crash Time and Location of the crash Time and Location of the crash Time and Location of the crash Traffic Direction Traffic Traffic Direction Direction Station Station B B -30 min min. -25 min. min min. Time min min. -15 min min. -10 min. -5 min. -10 min. Time -5 min. of -5 the Time min. of Time the of the crash=0 crash=0 crash=0 Slice 6 6Slice Slice Slice Slice Slice 54 4Slice 3Slice 4 3Slice 2 Slice 3 Slice 2Slice 1 Slice 2 1Slice 1 Hazard Ratio: Slice Contour 6 Slice plots 5 Slice of hazard 4 Slice ratios 3 corresponding Slice 2 Slice 1 to coefficient of variation in speed (42 model outputs) Real-time Black Spot 13

14 Crash Precursor #1: Variation in speed upstream of crash location Upstream of crash location

15 Crash Precursor #2: Speed upstream differential of crash location with speed downstream Upstream of crash location Downstream of crash location

16 Crash Precursor #3: Covariance of volume across adjacent lanes upstream of crash location Upstream of crash location

17 Stabilization in 30-second speed profiles following ITS strategies implementation

18 Warning Message Variable Speed Limit

19 19 Arrangement of RTMS and AVI Segments The 15-mile on I-70 in Colorado is equipped with AVI, RTMS, and Weather Stations. There were five sets of data used in this study; roadway geometry data, crash data, and the corresponding AVI, RTMS and weather data. The crash data were obtained from CDOT for a 15-mile segment on I-70 for 13 months (from October 2010 to October 2011). Traffic data consists of space mean speed captured by 12 and 15 AVI detectors located on each east and west bounds, respectively along I-70. Volume, occupancy and time mean speed are collected by 15 RTMSs on each direction. AVI estimates SMS every 2-minute while RTMS provides traffic flow parameters every 30-second. Weather data were recorded by three automated weather stations along the roadway section for the same time period. The roadway data were extracted from Roadway Characteristics Inventory (RCI) and Single Line Diagrams (SLD).

20 AVI: RTMS: Lane1 RTMS: Lane2 RTMS: Lane1 RTMS: Lane2 RTMS: Lane1 RTMS: Lane AVI: RTMS: Lane1 RTMS: Lane2 RTMS: Lane1 RTMS: Lane2 RTMS: Lane Speed (mph) Normal Traffic Condition (No Crash) 13:00:00 13:15:00 13:30:00 13:45:00 14:00:00 14:15:00 14:30:00 14:45:00 15:00:00 Time (min) Normal Condition with no Crash Occurrence Speed (mph) PDO Crash 14:40:00 14:55:00 15:10:00 15:25:00 15:40:00 15:55:00 16:10:00 16:25:00 16:40:00 Time (min) Crash Location: Milepost Speed Standard Deviation (mph) AVI: RTMS: Lane1 RTMS: Lane1 Speed Variance (PDO) 14:40:00 14:50:00 15:00:00 15:10:00 15:20:00 15:30:00 15:40:00 Speed (mph) AVI: RTMS: Lane1 RTMS: Lane2 RTMS: Lane2 Fatal Crash 11:30:00 11:40:00 11:50:00 12:00:00 12:10:00 12:20:00 12:30:00 12:40:00 12:50:00 13:00:00 13:10:00 13:20:00 13:30:00 Time (min) Time (min) Crash Location: Milepost Crash Location: Milepost 217.5

21 21 Model-1 (All Data) Variables Var. Import. Model-2 (RTMS) Variables Var. Import. Model-3 (AVI) Variables Var. Import. Model-4 (Weather) Variables Var. Import. Avg. Occ. Upstream1_Time Slice _ Avg. Occ. Upstream 2_Time slice_ Log. Coef. of Var. of Speed Crash Segment Time Slice_ Hour Visibility Avg. Occ. Upstream 2_Time slice_ Log. Coef. of Var. of Speed Upstream 1_Time Slice_ Avg. Speed Downstream Segment Time Slice_ Minute Precipitation Log. Coef. of Var. of Speed Crash Segment Time Slice_ Avg. Speed Upstream 2_Time Slice_ Avg. Speed Downstream Segment Time Slice_ Hour Precipitation Avg. Speed Downstream Segment Time Slice_ S.D. Occ. Upstream 2_Time Slice Avg. Speed upstream Segment Time Slice_ Hour Visibility Grade Avg. Speed Downstream 1_Time Slice_2 Avg. Speed Downstream 2_Time Slice_ S.D. Occ. Upstream 3_Time Slice Avg. Occ. Upstream1_Time Slice _ No. of Lanes Avg. Occ. Upstream2_Time Slice _ Avg. Speed Upstream 1_Time Slice_ Avg. Speed Downstream Segment, Time Slice_ Abs. Deg. of Curve Minute Precipitation Log. Coef. of Var. of Volume Downstream 2_Time Slice Log. Coef. of Var. of Volume Downstream 2_Time Slice_

22 22 Model Model Description Overall Classificatio n Rate True Positive Rate False Positive Rate True Negative Rate ROC Index Model-1 All Data % % 6.481% % Model-2 RTMS % % 7.154% % Model-3 AVI % % 6.393% % Model-4 Weather % % 5.854% % 0.675

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25 Real-Time weather and traffic monitoring system on I-4 FMS 8 FMS 7 FMS 1 FMS 2 FMS 3 FMS 4 Weather Station Area Visibility FMS 5 FMS 6 RTMS 25

26 Real-time crash model for weaving segments Variables Mean Std. p-value Intercept Speed difference (Spddif) Log(Vehcnt) Weaving configuration (1=no lane change) Weaving influence length (Lmax) Road surface condition (1=Wet) Training ROC* Validation ROC Speed difference: 0 if Bm_spd < Em_spd; otherwise Bm_spd- Em_spd L max =5728(1 + VR) N WL Using VSL to reduce Using RM to reduce Then, improve safety of weaving segments in real-time

27 Ramp metering (RM) Updated every 5 minutes based on occupancy and crash risk ) r( k) r( k 1) KR( o ok ) K ( ˆ s p pk 1) 1 Green-phase duration, g k, is calculated as follows, rk ( ) g( k). C rsat g g( k) g min max Queue Control g Queue 10 g 1 10 Queue 20 g g 2 20 Queue 30 g 3 Queue 30

28 Variable speed limit (VSL) When crash risk is higher than critical crash risk, VSLs at the upstream and the downstream of the congested weaving segment are activated RM-VSL RM is always active; When queue is more than 10 vehicles, VSL is activated to reduce speed limit on mainline to provide more gap for ramp vehicles In Microscopic Simulation VISSIM Through Component Object Model (COM) interface Coded by Visual Basic for Application

29 Case VSL RM 1 N/A # NA 2 N/A Ks= N/A Ks= (no Queue Control) 4 N/A Ks= (Queue Control) 5 Upstream 50 mph, Downstream remains N/A 6 Upstream 45 mph, Downstream remains N/A 7 Upstream remains, Downstream 60 mph N/A 8 Upstream remains, Downstream 65 mph N/A 9 Upstream 50 mph, Downstream 60 mph N/A 10 Upstream 45 mph, Downstream 60 mph N/A 11 Upstream 50 mph, Downstream 65 mph N/A 12 Upstream 45 mph, Downstream 65 mph N/A 13 Upstream 45 mph, Downstream 55 mph Ks= (Queue Control)

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31 High risk 55 45

32 ATM results Weaving Non-weaving Description Case Conflict Conflict Average travel Conflict Change OR Conflict Change time for total % % N/A No ATM N/A N/A 98.3 Traditional RM No Queue Queue control Up Up DW VSL DW Up 50, Dw Up 45, Dw Up 50, Dw Up 45, Dw RM- VSL Queue control &Up

33 Four urban arterials in Orlando, Florida were chosen; Crash data were collected from March, 2017 to December, 2017; Space-mean speed data collected by 23 IterisVelocity Bluetooth detectors; Signal timing and traffic volume provided by 23 adaptive signal controllers; Weather characteristics collected from MCO. 5/30/

34 Matched case-control design with a control-tocase ratio of 4:1 was employed to select the corresponding non-crash events for each crash event; Three confounding factors, i.e., location, time of day, and day of the week, were selected as matching factors; The real-time travel speed data were extracted for a period of 20 minutes (divided into four 5- minute time slices) prior to crash occurrence. 5/30/

35 Bayesian Conditional Logistic Models Slice 1 Slice 2 Slice 3 Slice 4 Parameter Mean (95% BCI) Hazard Ratio Mean (95% BCI) Hazard Ratio Mean (95% BCI) Hazard Ratio Mean (95% BCI) Hazard Ratio Avg_speed (-0.071, ) (-0.048, ) Up_Vol_LT (0.007, 0.044) (0.005, 0.044) (0.006, 0.045) (0.014, 0.06) Down_GreenR atio (-0.075, ) Rainy ( , 1.065)* (0.055, 1.274) (0.037, 1.322) (0.078, 1.341) 2.054

36 UCF team has implemented the Real-Time risk estimation in the following: I-4, I-95 and CFX network in Orlando I-70 in Colorado Motorways in the Netherlands City streets in Cyprus Expressways in China Currently Orange county arterials 30 Km of an Expressway in Stockholm is currently operational 36

37 More Proactive (but data intensive) approaches / Real- Time Ever richer information Smartphones, sensors, onboard vehicle hardware, provide continuous data Traffic status, weather conditions in real-time Better operation and safety Bottleneck detection in real-time Crash risk evaluation and prediction in real-time More accurate prediction Formation of congestion, queue length, congestion duration Crash-prone conditions: unstable traffic flow, adverse weather Timely communication Media: smartphone, webpage, DMS, radio Suggested countermeasures: trip planning, route choice, travel time calculation, VSL, speed advice, RM, etc. 37

38 Dr. Mohamed Abdel-Aty, PE Center for Advanced Transportation Systems Simulation Department of Civil, Environmental & Construction Engineering University of Central Florida Orlando, FL