Characteristics of Transitions in Freeway Traffic

By

Manasa Rayabhari

Soyoung Ahn

Outline: Dynamic Transition

IntroductionObjectivesLiterature ReviewSites and dataDatabase developmentPreliminary analysisSummary of findingsFuture work and time-frame

Introduction

Significance and Priorities

U.S. DOT’s mobility strategic plan - Congestion and bottlenecks have negative impact on the quality of life in terms of air quality, energy consumption and our economy.

Wasted time and fuel resulting from congestion are equivalent to $68 billion a year.

This research will provide a valuable insight on how congested traffic behaves under various transitions that frequently occur on urban freeways.

Understanding of the transition properties will expand the current knowledge on traffic congestion and serve as a building block for future traffic modeling and management practice as well as other outputs such as delay & travel time.

Objectives Understanding different types of transitions that freeway traffic undergoes

at different queue locations.

The Tail of a Queue : Studying the relationship between the characteristics of transition and

traffic variables such as initial flow (or speed) and changes in flow upon a regime change.

The Head of a Queue : Properties of regime transition at as vehicles discharge from an active

bottleneck. Quantifying characteristics such as the length of transition , discharge

rate, free-flow speed, etc from congested to freely flowing regimes.

Inhomogeneous Section : Analyzing the transitions near freeway ramps, lane-reduction and/or

grade change. Quantifying features such as the length and their relationship with traffic

variables (e.g. change in flow, congestion level, etc.) on an individual lane basis.

Literature Review

1. Kinematic wave model by Lighthill-Whitham (1955) and Richards (1956) (LWR) model and its simplified version by Newell (1993).

» Key traffic evolutions at a macroscopic level

2. Cassidy (1998) » Identification of periods of stationary traffic » congested flow-occupancy relationship using the aggregated

data over the stationary periods.

3. Muñoz and Daganzo (2003)» “Transition zones” emerge when a queue forms at a bottleneck.» Propagates as a “shock” upstream and then dissipates with

decreasing demand

Proposed Research

Dynamic Transition What we have done so farTransition near the tail-end of a queue while

expanding and receding

Static TransitionTransition near the head of a queue (i.e. near an

active bottleneck) Transition near inhomogeneous section

» Ramps» Lane reduction or expansion

Sites and Data

Site 1: Queen Elizabeth Way (QEW)

Canadian freeway, “Queen Elizabeth Way”

Schematic of QEW

Site 2: M4

British expressway M4

Schematic of M4

Travel Direction

Site Characteristics

QEW M4

No of lanes 3 3 and then 2

No of loop stations 16 13

Length 10.05 KM 6.8 KM

Bottleneck type Merge Lane-drop

Congestion Time 6:00 – 10:00 AM 6:00 – 9:00 AM

No of ramps 5 on-ramps

3 off-ramps

None

Data

QEW M4

Data type 20-sec loop data Event data aggregated to 20 seconds

Dates 09/13/1999 – 09/24/1999 (weekdays)

11/1/1998 – 12/05/1998 (weekdays)

AM and/or PM peak AM Peak AM Peak

Speed Contour: QEW

QUEUE FORMATION

6:12:00 AM – 6:35:00 AM

QUEUE DISSIPATION

9:49:00 AM – 9:56:40 AM

Speed Contour: M4

6:00 7:00 8:00 9:00 10:00 11:000

1

1.5

2

2.5

3.3

3.8

4.3

4.8

5.3

5.8

6.8 November 2th, 1998,M4

Time

De

tecto

r L

oca

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km

)

10

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1001

4

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8 8

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Movement ofQueue

QUEUE FORMATION6:24:00 AM - 7:15:20 AM

QUEUE DISSIPATION7:49:20 AM - 9:13 AM

Database Development

Variables Included

Dependent variable: Transition durationPotential explanatory variables

Speed or flow changeSpeed or flow before transitionWave speedPresence of on-ramp or off-ramp (QEW only)Lane numberDistance from the bottleneckWeather

Measurement of Variables

Transition duration: speed curves

Transition Start Time (t1)

Transition End Time (t2)

Transition from Free Flow State to Congested State

Transition Duration (t2 – t1)

Measurement of Variables Speed : Sb and Sa are the average speeds before and after transitions

respectively Change in Speed : Sb ~ Sa

Onset Regime Clear Regime

Sb = Average (si)

i

j Sa = Average (sj) i Sb = Average (si)

Sa = Average (sj)

j

Measurement of Variables

Flow and change in flow: Oblique cumulative count curves

Qb = Average (qi)

Qa = Average (qj)

Change in flow = Qb ~ Qa

Measurement of Variables

Wave speed The wave speed is obtained using the following formula: Wave speed = Distance traveled by the queue --- (1)

Time Duration

= Distance between detector stations --- (2) Time Duration

Distance from BN

Tim

e D

urat

ion

1

1

2

3

4

5

(1)

(2)

Measurement of Variables

Presence of on-ramp or off-ramp

Presence of Off -ramp

Presence of On-ramp

Measurement of Variables

Distance from bottleneck For M4 : The bottleneck is assumed to be at the

merge i.e., at the 2nd station. For QEW: There are 2 bottlenecks during queue

formation at this site.

- One bottleneck is situated in between stations 47 and 48.

- The bottleneck is between the stations 51 and 52. Distance from bottleneck is thus obtained for each

station.

Measurement of Variables

Weather Weather data for the study days was obtained from

the following website.

http://www.wunderground.com It was found that the weather was quite consistent in

all the study days. There was no precipitation on the analyzed days

and temperatures were above freezing.

Example Database

Analysis

Analysis Process

1. Transitions Duration (for each site) Duration vs. Distance from Bottleneck Duration vs. Distance from Bottleneck (average,

standard error for each location) Duration Vs Ramp type (QEW) Durations Vs Average Wave-speed of queues

2. Lane – Specific Behavior of transition (for each site) Arrival times of queues in each lane Duration of transition in each lane

1. Transition Duration - Distance from BN

Queue Formation : As the queue propagates backward from the bottle-neck, the transition duration increases.

Queue Dissipation : As the queue propagates forward towards the bottle-neck, the transition duration increases.

Presence of On-ramp/ Off ramp affects the transition duration .

Transition Duration: QEW (Onset, BN : 47)

- sudden reduction in transition duration- quicker transition from free flow - congestion

Transition Duration: QEW (Onset, BN : 51)

- sudden reduction in transition duration- quicker transition from free flow - congestion

Transition Duration: QEW (Clear)

- sudden reduction in transition duration- quicker transition from congestion – free flow

Transition Duration: M4 (Onset)

Transition Duration: M4 (Clear)

2. Transition Duration Vs Dist BN (QEW)- Average and Std. Error Values

Transition Duration Vs Dist BN (M4)- Average and Std. Error Values

3. Transition Duration – Ramp Condition

Queue Formation: Backward Propagation On-ramp adds more traffic to the queue thus accelerating the

transition from free flow to congested flow. This decreases the transition duration.

Off-ramp reduces the traffic on the freeway thus decelerating the transition from free flow to congested flow. This increases the transition duration.

Queue Dissipation: Forward Propagation On-ramp adds more traffic to the queue thus decelerating the

transition from congested flow to free flow. This increases the transition duration.

Off-ramp reduces the traffic on the freeway thus accelerating the transition from congested flow to free flow. This decreases the transition duration.

Transition Duration vs. Ramp condition

On- Ramp Off - Ramp

Onset Decreases Increases

Clear Increases Decreases

Change in Transition Duration with On Ramp and Off Ramp

4. Transition Duration vs. Wave speed for each queue

The Average Wave speed of each queue is calculated using the total queue formation/queue dissipation time.

Avg. Wave Speed :Total Distance traveled

from St 8 to St 1Total time for queue

formation/dissipation

Wave Speed was found to be inversely related to Transition Duration

6:00 7:00 8:00 9:00 10:00 11:000

1

1.5

2

2.5

3.3

3.8

4.3

4.8

5.3

5.8

6.8 November 2th, 1998,M4

Time

De

tect

or

Lo

catio

n (

km

)

10

20

30

40

50

60

70

80

90

1001

4

5

6

7

3

8 8

7

6

5

4

3

1

Movement ofQueue

QUEUE FORMATION6:24:00 AM - 7:15:20 AM

QUEUE DISSIPATION7:49:20 AM - 9:13 AM

Transition Duration vs. Wave speed : M4

Transition Duration vs. Wave speed : QEW

Noise in the data due to the presence of On and Off Ramps

5. Lane-wise Arrival Time (M4 onset) On most of the study days, queuing started in Lane 2 first , then Lane 1

and finally Lane 3.

Lane-wise Arrival Time (M4 clear) On most of the study days, clearing started in Lane 2 first, then Lane 1

and finally Lane 3.

Lane-wise Arrival Time (QEW onset, BN at 47)

On most of the study days, queuing started in Lane 2 first , then Lane 1 and finally Lane 3.

Lane-wise Arrival Time (QEW onset, BN at 51)

On most of the study days, queuing started in Lane 2 first , then Lane 1 and finally Lane 3.

Problems/Issues with the Database

Noisy QEW Data : QEW database was found to be very noisy and the transitions were not clear.

Difficulty in identifying clearing queue: QEW database has 20 second loop data starting from 6 AM to 10 AM. But, on few days, the final clearing occurred after 10 AM.

Data Precision: 20 second data was not precise enough for transition identification. Using 1 second data will increase the accuracy.

Correlations: Most of the variables were found to be highly correlated making Statistical Modeling difficult.

For developing linear models for transition duration, larger database is required.

Summary of Findings

Change in Duration with respect to the following variables

Variable Onset Clear

Distance from BN

Increases Decreases

On-Ramp Decreases Increases

Off-Ramp Increases Decreases

Wave Speed Decreases Decreases

On-going Analysis

Head of the queue: The head of a queue will be analyzed using the trajectory data

available for U.S. Highway 101 in Los Angeles, CA. Microscopic level: Evolution of speed-spacing relations for

individual vehicles in the vicinity of the active bottleneck. Macroscopic level : Examining the flow-density relations in an

attempt to bridge the micro- and macro-level features.

Inhomogeneous sections: Trajectory data from I-80E near San Francisco and U.S.

Highway 101 in Los Angeles, CA will be included in the analyses.

Distance over which a transition occurs due to a merge, a diverge or a lane-reduction is analyzed.

Freeway stretch near an inhomogeneous point will be divided into multiple contiguous segments, and a flow-density relation will be estimated for each segment.