Regional Traffic Monitoring System for Maryland’s Eastern Shore Dr. Gang-Len Chang Traffic Safety and Operations Lab University of Maryland, College Park Aug, 2012

Contents Project motivation & goals Eastern shore overview Traffic monitoring system Travel time prediction system Evacuation system Hurricane Irene Summary

Motivation (1) During the peak season, the Eastern shore is plagued with heavy congestion from tourists desiring to enter the Ocean City area. An accurate travel time prediction system is needed to provide motorists with information effecting route choice

Motivation (2) Maryland’s Eastern shore is prone to hurricane threats During hurricane season, the Eastern shore is crowded with tourists An evacuation plan is needed to safely and efficiently move people from the immediate impact area

Research Goals 1.To provide a safe and efficient evacuation plan based on empirical data 2.Provide accurate travel time information to mitigate traveler delay

Eastern Shore Region Counties: Cecil, Kent, Queen Anne’s, Talbot, Caroline, Dorchester, Wicomico, Somerset, Worcester Total population: 449,226 (2010 U.S Census Bureau) Major arterials: US 301, US 50, US 13, US 113, MD 662, MD 565, MD 16, MD 90

Eastern Shore Region Real-Time Traffic Monitoring System 43 microwave sensor stations are maintained and operated by UM Wireless communication between sensors and UM Data collection 30 seconds interval data by each lane (volume, speed, occupancy) UM provides data to MDSHA, DelDOT, and other agencies via RITIS

Sensor Stations

Eastern Shore Region Real-Time Traffic Monitoring System Provides real-time traffic conditions (speed and volume) UM Website ( CHART ( Interactive speed map of eastern shore region with locations of sensor Provides historical sensor data

Website Display Website ( Travel Time Prediction US 50 MD 90 Sensor Locations Current Sensor Data Historical Sensor Data Historical Predicted Travel Time

Interactive mapping Current Detector Data Historical traffic data, travel time, OC traffic, evacuations

Website Display (Cont’d) (a) Real time predicted travel time(b) Current detector data (c) Current speed map(d) Current volume map

Website Display (Cont’d) (e) Historical detector data(f) Historical travel times

Ocean City Ocean City, MD A famous tourist destination in Maryland’s Eastern Shore Population Summer peak season: 150,000 – 300,000 people Off-peak: 7,000 – 25,000 Serious congestion on the major eastbound entry road (US 50 and MD 90)

Ocean City Travel Time Prediction System Two routes from Hall Road to Ocean City (MD90, US50) Travel time may vary from 15 minutes to 90 min 23 detectors for travel time predictions Including 18 HD traffic sensors

Travel Time System Operation Flowchart Prediction travel time every minute Detection of incidents Handling of missing data Database of Traffic Data t=t+1 Real-Time Detector Data at Time t Travel Time Estimation Module Missing Data Estimation Module Incident Detection Module Database of Historical Travel Times Travel Time Prediction Module Predicted Travel Time for Time t Data Missing? N Stop Predicting for Impacted Segments Links with Detected Incident Links with No Detected Incident Y N Links with Unreliable Missing Data Links with Reliable Missing Data Estimation Only

VMS Display ATIS (Advanced Travelers Information System) Travel time information for both routes Route guidance to travelers Traffic conditions toward to Ocean City

LPR System Two LPR trailers are deployed Hall Road and Inlet Isle Lane Data is collected for travel time estimation and for prediction module calibration

Travel Time Information System Provide real-time travel time information to drivers The system showed positive effects on drivers’ route choice behavior during congested condition Assist travelers in making proper route choice More efficient use of existing roadway capacity

Efficiency in Relieving Congestion and Increasing Throughput from VMS Time-varying system throughputs to Ocean City (With VMS versus without VMS) Peak-season Saturday

Evacuation System Organizing an evacuation plan Identifying critical control points Develop traffic control parameters Improving efficiency Satellite image of hurricane Irene Aug 2011

Evacuation System (cont’d) Simulator tool for eastern shore region emergency evacuation Off-line simulator (old version) On-line simulator (new version) User friendly interface Output can be used to analyze evacuation plans by: DOTs (MD, DE, VA) Emergency management agencies Interstate 45 at Houston, Texas Evacuation from hurricane Katrina 2005

Evacuation system System Demonstration

System demonstration Total throughput for each target area

System demonstration Hourly volume for each target area

System demonstration Speed changes over time

System demonstration Identified Bottleneck

Ocean City Traffic Data Before and After Evacuation Evacuation for Hurricane Irene Aug 2011

Number of vehicles in ocean city 8/18/2011 – 9/13/2011 Evacuation 8/25/ :00 ~ 8/26/ :00

Number of Vehicles in Ocean City During Evacuation Evacuation 8/25/ :00 ~ 8/26/ :00

Number of Vehicles Changes in Ocean City During Evacuation MD 528US 50MD 90TOTAL Change DateINBOUNDOUTBOUNDINBOUNDOUTBOUNDINBOUNDOUTBOUNDINBOUNDOUTBOUND 08/25/ : /25/ : /25/ : /25/ : /25/ : /25/ : /25/ : /25/ : /25/ : /25/ : /26/2011 0: /26/2011 1: /26/2011 2: /26/2011 3: /26/2011 4: /26/2011 5: /26/2011 6: /26/2011 7: /26/2011 8: /26/2011 9: /26/ : /26/ : /26/ : /26/ : /26/ : /26/ : /26/ : /26/ :

Inbound/Outbound Traffic Volume from Ocean City 8/18/2011 – 9/13/2011 Evacuation 8/25/ :00 ~ 8/26/ :00

Inbound/Outbound Traffic Volume from Ocean City 8/25/2011 – 8/30/2011 Evacuation 8/25/ :00 ~ 8/26/ :00

Hourly Evacuation Route Usage 8/25/2011 – 8/26/2011 Evacuation 8/25/ :00 ~ 8/26/ :00 * Only outbound volume

Evacuation route usage

Eastern Shore Region Traffic Monitoring System - Summary Traffic Monitoring System Provides 43 detectors’ real-time traffic data Provides historical data Travel Time Prediction System Provides travel time to motorists Improves usage of existing roadway capacity Detects incidents Provides traffic condition ahead Evacuation System On-line and off-line simulation tool with advanced GUI Evaluating evacuation plans Identifying critical control points Estimating time needed for evacuation Improving efficiency of evacuation

Thank you Questions & Comments Dr. Gang-Len Chang Sung Yoon Park