Investigation of Sources of Congestion at the t th Hampton Roads - PowerPoint PPT Presentation

Investigation of Sources of Congestion at the t th Hampton Roads Bridge Tunnel (HRBT) Mecit Cetin, Ph.D. Filmon G. Habtemichael, Ph.D. Khairul A Anuar Khairul A. Anuar 1

Outline Outline 1. Introduction 2. Purpose and scope of the project 2. Purpose and scope of the project 3. Data sources 4. Exploratory data analysis 5 Quantifying incident ‐ induced delay 5. Quantifying incident induced delay 6. Identifying bottleneck locations 7. Conclusions 2

Introduction Introduction • HRBT connects the cities of Norfolk and Hampton (I ‐ 64) p ( ) – Two lanes per direction, shorter tunnel clearance in the WB direction (14’6” EB and 13’6” WB) • HRBT suffers from heavy congestion due to: HRBT ff f h i d – Demand >> Capacity – Incidents 3

Purpose and scope of the project Purpose and scope of the project 1. Identifying specific sources of congestion at the HRBT • Estimate the impact of delay along the HRBT, and • Estimate the impact of over ‐ height vehicles and traffic p g incident 2. Evaluate the spatial and temporal 2 Evaluate the spatial and temporal characteristics of traffic congestion • Identify specific bottleneck locations, and Id tif ifi b ttl k l ti d • Determine the capacity of those bottlenecks 4

Data sources Data sources Provided by VDOT and INRIX: Year 2013 1. Incident records (two different databases) ( ) • Incident time, clearance time, type of incident …. 2. INRIX speed and travel time data 2 INRIX speed and travel time data • Probe ‐ based speed and travel time for segments on HRBT HRBT 3. Continuous count stations • Volume speed and occupancy aggregated at 5 &15 • Volume, speed and occupancy aggregated at 5 &15 minutes 5

Matching incident databases Matching incident databases • T Two databases (B/T Corridor only) d t b (B/T C id l ) – VA Traffic DB: • All incidents impacting traffic including disabled vehicles, crashes, B/T stoppage events • 2,556 B/T stoppage events: Don’t know the cause? – Response Time DB: • Detailed log for responders and events (more records than VA Traffic DB) • Th The cause for B/T stoppage events given, e.g.: over ‐ height truck, debri, etc. f B/T i h i h k d b i • Match the B/T stoppage events in VA Traffic to Response Time DB – Time stamps, travel direction, duration Out of 2,556 events, 1,280 matched confidently • – 64% of the matched events classified as “over ‐ height truck”, 20% as “disabled vehicle”, 10% as “crashes” … disabled vehicle , 10% as crashes … – The rest 1,276 were then proportionally distributed using the proportions obtained from the matched ones 6

Exploratory data analysis – incident data Exploratory data analysis – incident data Incident frequency by category 7

Exploratory data analysis – delay at HRBT Exploratory data analysis delay at HRBT Median travel time by day of the week (WB) y y ( ) 8

Exploratory data analysis – delay at HRBT Exploratory data analysis – delay at HRBT Median travel time by day of the week (EB) y y ( ) 9

Exploratory data analysis INRIX data Exploratory data analysis ‐ INRIX data 10

Methodology adopted Methodology adopted • Available techniques for quantifying incident ‐ induced A il bl h i f if i i id i d d delay (IID) 1. 1 Deterministic queuing theory ‐ based D t i i ti i th b d 2. Shockwave theory ‐ based 3. 3. Simulation ‐ based, and Simulation based, and 4. Statistical procedures ‐ based • This project: Statistical procedures Thi j t St ti ti l d • Data driven • Estimate IID by examining similar traffic patterns and • Estimate IID by examining similar traffic patterns and establishing reference traffic profile under “normal” traffic conditions 11

Methodology adopted Methodology adopted • Previously, researchers estimated IID by grouping traffic based y, y g p g on day ‐ of ‐ the ‐ week, time ‐ of ‐ the ‐ day and daily volume of traffic. • Can be misleading: – Similar total daily volumes but different profiles 300 Sep. 11, 2013 (DT = 34207 veh) 250 WB) h in 5 minutes (W 200 Oct. 13, 2013 (DT = 34376 veh) 150 Count of veh Aug. 31, 2013 (DT = 34904 veh) 100 50 0 12 0:00 0:50 1:40 2:30 3:20 4:10 5:00 5:50 6:40 7:30 8:20 9:10 10:00 10:50 11:40 12:30 13:20 14:10 15:00 15:50 16:40 17:30 18:20 19:10 20:00 20:50 21:40 22:30 23:20

Methodology adopted Methodology adopted • To estimate IID, a reference travel time profile needs to be p established. This was done by examining similar traffic patterns . 35 Incident affected An incident happens subject travel time and travel time 30 The question is: profile p increase more than  H  How to establish t t bli h 25 usual reference profile? me (min) Incident-  How to select 20 induced delay Travel tim Delay due to demand Delay due to demand candidate or d d (IID) 15 similar profiles?  What weighting 10 Incident-free I id f mechanism should reference travel 5 be adopted? Queue due to incident clears time profile and traffic is back to normal 0 7:00 7:35 8:10 8:45 9:20 9:55 10:30 11:05 11:40 12:15 12:50 13:25 14:00 14:35 15:10 15:45 16:20 16:55 17:30 18:05 18:40 19:15 19:50 20:25 21:00 21:35 22:10 22:45 23:20 23:55 13

Measuring similarity between traffic profiles g y p 14

How to establish the reference profile? How to establish the reference profile? 1 1. D t Determine which TYPE of traffic profiles to use i hi h TYPE f t ffi fil t  Volume ‐ based (flow at upstream and downstream sensors),  Travel time ‐ based, and ,  Hybrid of volume and travel time ‐ based 2. Identify CANDIDATE (similar) traffic profiles y ( ) p  Day ‐ of ‐ the ‐ week ‐ based,  K ‐ nearest neighbor ‐ based, and  Cluster analysis based  Cluster analysis ‐ based 3. Apply weighted summation to establish a REFERENCE profile  Equal weight  Equal weight,  Inverse distance (Shepard’s method), and  Rank ‐ based (Rank Exponent method) 15

Which TYPE of traffic profiles to use? Which TYPE of traffic profiles to use? 16

How to identify CANDIDATE traffic profiles? How to identify CANDIDATE traffic profiles? • Day ‐ of ‐ the ‐ week ‐ based  Grouping days of the week by season of the year   Grouping days of the week by ADT d f h k b • K ‐ nearest neighbor (K ‐ NN) ‐ based, and  Select the nearest K data points 7  K was in the range from 1 to 10 6 • Cluster analysis ‐ based Cl t l i b d 5 4  Ward’s minimum variance was used Height 3  Optimum number of clusters was 13 2 1 0 17

Establishing the REFERENCE profile Establishing the REFERENCE profile 18

Experiments Experiments 19

C l Calendar based d b d methods not so good. K ‐ NN method with k=6, β β = 0.90 and α = 0.10 0 90 and α 0 10 provided the least prediction error

Delays at the HRBT Delays at the HRBT Percentage of total delay and volume of traffic 18% 18% 16% 16% Percentage of 14% 14% Total Delay Total Delay 13% 13% 12% Percentage of 10% 9% 9% 9% 9% Volume of traffic 8% 9% 8% 8% 8% 8% 9% 9% 8% 8% 8% 8% 8% 8% 7% 7% 8% 7% 6% 4% 4% 4% 4% 4% 4% 3% 2% 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 21

Total delay by day of the week Total delay by day of the week 22

Sources of total delay Sources of total delay Debris Other Other Crash C h 1% 4% 8% Disabled Disabled Vehicle 7% Over ‐ height 8% Demand 72% 23

Breakdown of incident induced delay Breakdown of incident ‐ induced delay Vehicle V hi l WB Mainten Fire Vehicle Other EB Wide ance 1% Fire 3% Wide 2% 2% 1% 4% 4% Hazmat Other Maintena 3% 13% nce Over- Over- Debris height 1% height g 3% 3% 27% 27% 28% Vehicle Hazmat 3% Accident Debris 15% 7% 7% Vehicle Multi- Disabled Disabled Accident Vehicle Vehicle Vehicle Vehicle Vehicle 13% 13% A Accident id 25% 23% 20% Multi- Vehicle Accident Accident 6% 24

Cost of delay at the HRBT Cost of delay at the HRBT Constants used (Schrank et al., 2012 and Caltrans, 2011) 25

Annual cost of delay at the HRBT corridor Annual cost of delay at the HRBT corridor 26

Identifying bottleneck locations Identifying bottleneck locations N ‐ curves and speed heat ‐ maps were used to identify the p p y location of bottlenecks and their capacities 27

Inside or at the entrance of the tunnel Congestion inside the tunnel the tunnel Congestion before the tunnel entrance the tunnel entrance 28

Flow rates observed inside and at the entrance of the tunnel EB EB 29

Flow rates observed inside and at the entrance of the tunnel WB WB 30

Vehicle classes Vehicle classes L Lane 1 EB 1 EB 31

Vehicle classes Vehicle classes Very few trucks Very few trucks L Lane 2 EB 2 EB in Lane 2 32

Queue discharge flow rates by travel lanes Queue discharge flow rates by travel lanes • EB direction 33