Transpo 2012 Mohammed Hadi, Yan Xiao, Ali Daroodi Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International.

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Presentation transcript:

Transpo 2012 Mohammed Hadi, Yan Xiao, Ali Daroodi Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International University Miami, FL October 30, 2012 Utilization of ITS Data to Calibrate Simulation Models

Microscocopic traffic simulation models allow detailed analysis of traffic operations and alternative capacity improvements and traffic operation analysis The complexity of simulation modeling increases with the increase in congestion level and advanced strategy modeling Data from ITS combined with data from other sources allow: – More cost-effective simulation model development – Better calibrated and validated models 2 Introduction

“Investigation of ATDM Strategies to Reduce the Probability of Breakdown” Joint FIU/UF Project: M. Hadi, L. Elefteriadou, Y. Xiao, C. Letter, A. Darroudi – Investigate the implementation of the breakdown probability to an existing real-world deployment of ramp metering. – Investigate the use of speed harmonization by itself or in combination with the ramp metering implementation – Examine how connected vehicle technologies can be used to support the strategies investigated – Provide guidelines to agencies on how to use simulation models to assess and fine-tune ATDM strategies of the types investigated in the project 3 STRIDE Project

Utilize detailed data to identify the variability of congestion between days – How similar are different days Identify day(s)/congestion levels for use in the analysis Examine variation in breakdown attributes between days and associate these attributes with congestion levels Examine the use of new attributes (based on breakdown and queuing) for calibrating simulation models for use in modeling advanced strategies 4 Objectives

Guidelines have been produced for calibration and use of simulation models – FHWA Traffic Analysis Toolbox (TAT) Volumes 3 and 4 are examples Calibration data has consisted of measures of capacity; traffic counts; and measures of system performance such as travel times; speeds, delays, and queues TAT specifies that system performance data (travel times, delays, queues, speeds) must be gathered simultaneously with the traffic counts 5 Simulation Model Calibration

Multi-scenario analysis for “normal”, incidents, special events, weather has been proposed in recent years. Range of “normal” conditions considered – low, median, and heavy based on VMT 6 Multi-Scenario Modeling

Mean Median Speed Contour Plot (16 days) Based on Similarity 4/26/ /11/2010 5/12/2010 Less Congestion 11/18/2010 More Congestion 10/6/2010 CORSIM Results

Similarity Based on Euclidean Distance: 16 Days Distance Results Based on Speed

Speed Contour Plot (7 days) Based on Similarity of Speed Less Congestion More Congestion CORSIM Results 5/12/2010 6/17/2010 Mean Median 4/26/2011 2/11/ /30/2010

Similarity Based on Euclidean Distance: 7 Days Distance Results Based on Speed Only

Speed Contour Plot (7 days) Based on Similarity of Speed and Volume Less Congestion 3/15/2011 More Congestion 2/11/2011 CORSIM Results Mean Median 4/26/2011 5/12/2010 6/17/2010

Similarity Based on Euclidean Distance: 7 Days Distance Results Based on Speed and Volume after Normalization

Speed Contour Plot (7 days) Based on Congestion Index Less Congestion More Congestion CORSIM Results 5/12/2010 6/17/2010 4/26/2011 Median Mean 2/11/ /30/2010

Congestion Index (7 days):

Capacity, Breakdown Flow, and Queue Discharge

Congestion Index Speed before breakdown (mph) Average Speed of breakdown (mph) Speed Reduction due to beakdown (mph) Starting time Duration (hr:min) Maximum pre-Breakdown Flow upstream and downstream of ramp (veh/hr/lane) Breakdown Flow (veh/hr/lane) Queue Discharge (veh/hr/lane) Recovery Flow (veh/hr/lane) Queue Build-up Rate and Queue Dissipation Rate 16 Congestion/Breakdown Attributes

Breakdown Analysis Results: Date Congestion Index Speed before breakdown (mph) Average Speed of breakdown (mph) Speed Reduction (mph) Starting time Duration (hr:min) Maximum pre- Breakdown Flow (veh/hr/lane) Breakdown Flow (veh/hr/lane) Queue Discharge (veh/hr/lane) Recovery Flow (veh/hr/lane) 2/11/ % :40:004: /30/ % :05:004: /18/ % :50:003: /15/ % :30:002: /26/ % :25:003: /17/ % :05:002: /12/ % :25:002:

18 Thank You