Exercise 4 - Flow Phenomenon Aditya Nugroho e

EXERCISE 4

FLOW PHENOMENON

CE 5203 TRAFFIC FLOW AND CONTROL

ADITYA NUGROHO

HT083276E

DEPARTMENT OF CIVIL ENGINEERINGNATIONAL UNIVERSITY OF SINGAPORE

2010

2

Department of Civil Engineering

CE 5203 Traffic Flow and Control

1.0 INTRODUCTION

Traffic phenomena are complex and nonlinear, depending on the interactions of a largenumber of vehicles. Due to the individual reactions of human drivers, vehicles do not interactsimply following the laws of mechanics, but rather show phenomena of cluster formation andshock wave propagation, both forward and backward, depending on vehicle density in a givenarea and highway design. In this Exercise 4 the objectives are:

To apply all concepts learned to interpret traffic phenomena To discuss phenomena based on traffic flow theories To identify traffic flow abnormalities the flow patterns on AYE (in city bound

direction) before and after ClementiAvenue 2

2.0 TRAFFIC STREAM METHOD

2.1 Data collection

In observing the traffic stream at AYE (city bound diretion), it is essential to address theproblems that signify the importance of the study. This study was done by identifying themain variables to visualize a traffic stream: speed (v), density (k), and flow (q). Traffic stream(upstream and downstream) have been measured by using video camera in 30 min record. Inthis Exercise 4, several points should be considered before do the measuring of traffic stream.

A reference line- a certain line should be set as a reference to count vehicles. Flow record- Flows were recorded when the vehicles pass a reference point on a lane

measured from front bumper to front bumper. Speed record - two reference line in the same direction has been set to determine spot

speed both of upstream and downstream (see Figure 1). Measurement over a shortsection (by which is meant less than about 10 meters, it is cause by angle of videocamera)

Density record - number of vehicles occupying a given length of highway or lane andis generally expressed as vehicles per km.

Type of traffic flow – in this study since vehicles traveling on an expressway thereforecan be characterised are uninterrupted flow condition.

2.2 Traffic stream measurement

To visualize a traffic stream parameters the empirical analysis equation measured in thesurvey data. The vehicle densities for each interval are calculated by the following equation:

/ sk q u (1)

1/s

iiu N

u (2)

where, su =spot speed, k=density, q=flow

3



Equation of speed-density properties of upstream and downstream flow will employed byregression analysis as follow:

y x e (3)

Therefore Greenshield model can be applied for comparing between upstream anddownstream flow. Greenshield made the assumption that, under uninterrupted flowconditions, speed and density are linearly related. In addition this model is able to develop amodel of uninterrupted traffic flow that predicts and explains that are observed. While modelis not perfect, it is fairly accurate and relatively simple.

(1 / )f ju u k k (4)

Two parameters model,(1 / )f jq ku k k (3)

Derivation of capacity,(1 / )f jq ku ku k k

(1 / )

= (1 2 / )

ff j

j

f j

kuu

u

dq k kdk k

k k

(4)

where, u= speed, q=flow, fu =free flow speed, k=density, jk = jam density.

3.0 Site analysis (Ayer Rajah Expressway)

All data were collected from two stream sections located in AYE (City bound direction). Fielddata were collected during morning peak period. The raw data used in analyses were speed,and density. Slow vehicles (Bus, Truck) in the upstream and downstream of traffic havesignificant effect. The proportion of total bus and truck in the selected sites are high (25.86%),so in our analysis we assume that there are effects on traffic stream caused by of these typesof vehicles.

Diverging and merging occurs in two separate traffic streams as illustrated in Figure 1. Basedon the site analysis, the ramp vehicle diverging process is a complex pattern at the upstreamsection. It can be seen from the video that at the upstream jam the vehicles come from freeflow or synchronized flow and must reduce their speed. It is causes by lane capacity reductionhas where the most outerlane (ramp vehicles) has dedicated for diverging vehicles. While, atthe downstream flow the vehicles might accelerate to the free flow speed and occuring largegap.

Hence this site was taken during the morning peak period, many of road type user arecommuting work driver towards city direction, therefore driver which performs several

4



diverging process such as lane changes of expressway mainstreams into ramp of vehicles arefew.

Figure 1 Study site of AYE (City bound direction)

Figure 1 shows the site arrangement for video recording purposes and road geometry of studysite (note that the estimation length of road geometry was obtained from Google EarthPro5.2).

Figure 2 Upstream and downstream flow abnormalities

4.0 Quantitative description

4.1 Upstream section

Upstream flow analysis was done by transform the flow equations into linear form. However,due to the nature of traffic there is underlying error in adopting this form. In simple linearregression form, there is exists only one error term, it might be possible will be errors in theindependent variable as the stream flow is intrinsic and extrinsic relationship.

12

Weaving section

3

310 m

4

123

4

Videocamera

Viewing angle23 mSpot speed 10 m Spot speed 10 m

Diverge points Merge points

Large gapHigh density/Congested

5



y = -0,3399x + 53,859R² = 0,6687

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Spee

d (K

m/ H

r )

Density (Veh/Km)

Speed-Density

Figure 2 Speed-Density upstream section

A study of AYE flow has resulted in a calibrated speed-density relationship, as follows:

[ / ] 53.86-0.34[ / ]ukm h veh km (4)

Thus, from the above empirical equation we can obtained fu =53.86 say 54 and jk =158.41say 158. Therefore Greenshields model graph can be calculate as follow:

B=Uf/(Kj) = 0.341772152

max flow: q=Uf^2/(4*B) = 2133 vehicles

Table 1 Greenshield graph calculations

% of Jam Density (k) Speed (u) Flow (q)Density k=%*(Kj) u=Uf-B*K q=k*u

0% 0 54 010% 15.8 48.6 767.8820% 31.6 43.2 1365.1230% 47.4 37.8 1791.7240% 63.2 32.4 2047.6850% 79 27 213360% 94.8 21.6 2047.6870% 110.6 16.2 1791.7280% 126.4 10.8 1365.1290% 142.2 5.4 767.88100% 158 0 0

6



0

10

20

30

40

50

60

0 100 200 300

Spee

d (

v)

Density (k)

Speed -Density

0

10

20

30

40

50

60

0 1000 2000 3000 4000

Spee

d (

v)

Flow (q)

Speed - Flow

0

500

1000

1500

2000

2500

0 100 200 300

Flow

(q)

Density (k)

Flow - Density

Figure 3 Greenshield model –upstream section

4.2 Downstream section

Downstream flow analysis was done by transform the flow equations into linear form.However, due to the nature of traffic there is underlying error in adopting this form. In simplelinear regression form, there is exists only one error term, it might be possible will be errors inthe independent variable as the stream flow is intrinsic and extrinsic relationship.

7



y = -0,4962x + 63,473R² = 0,6793

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Spee

d (K

m/ H

r )

Density (Veh/Km)

Speed-Density

Figure 4 Speed-Density downstream section

A study of AYE flow has resulted in a calibrated speed-density relationship, as follows:

[ / ] 63.47-0.496 [ / ]ukm h veh km (4)

Thus, from the above empirical equation we can obtained fu =63.47 say 63 and jk =127.9182say 128. Therefore Greenshields model graph can be calculate as follow:

B=Uf/(Kj) = 0.4921875

max flow: q=Uf^2/(4*B) = 2016 vehicles

Table 2 Greenshield graph calculations

% of Jam Density (k) Speed (u) Flow (q)Density k=%*(Kj) u=Uf-B*K q=k*u

0% 0 63 010% 12.8 56.7 725.7620% 25.6 50.4 1290.2430% 38.4 44.1 1693.4440% 51.2 37.8 1935.3650% 64 31.5 201660% 76.8 25.2 1935.3670% 89.6 18.9 1693.4480% 102.4 12.6 1290.2490% 115.2 6.3 725.76100% 128 0 0

8



0

10

20

30

40

50

60

70

0 100 200 300

Spee

d (

v)

Density (k)

Speed -Density

0

10

20

30

40

50

60

70

0 1000 2000 3000 4000

Spee

d (v

)

Flow (q)

Speed - Flow

0

500

1000

1500

2000

2500

0 100 200 300

Flow

(q)

Density (k)

Flow - Density

Figure 4 Greenshield model –downstream section

5.0 Conclusion and recommendations

This study was carried out to determine the abnormalities flow between upstream anddownstream section at AYE (city bound direction). The analyses presented in the previoussection shows the stream flow both upstream and downstream. The conclusions can be drawnfrom the exercise is the accuracy of the speed and density measurements can be improved ifthe angle spot camera has 4 spot point (upstream diverging and downstream merging). Inaddition, following recommendations are needed to improve traffic stream:

While numerous variables can impact the traffic stream, it is clear that design speedinteractions can positively or negatively impact performance.

The roadway section has certain geometric elements that affect driver expectancy andjudgments. The lane reduction, contributing to the congestion already occurring becauseof capacity problems through the section.

9



One alternative is added a collector-distributor line to remove the weaving section fromthe freeway. A second alternative is added an additional freeway lane through theweaving section.

In addition it is suggested that better signing and lane markings for driver notification canimprove the flow through the weaving section.

REFERENCES

Google Earth Pro 5.2. Google.

Highway Capacity Manual. 2000. Special Report 209, 4th Ed., TRB, National ResearchCouncil, Washington, D.C.,

May, A. D. (1990), Traffic Flow Fundamentals, Prentice-Hall, Inc., Englewood Cliffs, N.J.

Traffic Monitoring Guide, 3rd Ed. Federal Highway Administration (FHWA)

Traffic Flow Theory A State-of-the-Art Report (2001). Committee on Traffic Flow Theoryand Characteristics (AHB45). TRB, National Research Council, Washington, D.C.,

Exercise 4 - Flow Phenomenon Aditya Nugroho e

Documents

Transcript of Exercise 4 - Flow Phenomenon Aditya Nugroho e