DATA ANALYSIS:

Our achieved data can be analyzed from different point of view and purpose. Like previously

mentioned-Planning, Modeling, Designing. For these application several parameters are need

to be determined from this type of study. Like

ADT,AADT,VECHILE COMPOSITION,FLOW RATE,FLOW VARIATION

VECHILE COMPOSITION :PROPRTION OF AVAILABLE TYPE OF VECHILE. Helps to count total pcu

and geometric designing .

From our data (APPENDIX 1) a pi-chart of vehicle composition is shown below.

Fig:1.1a:vechile composition 1

Bus (B) 0%

Truck (T) 0%

Light Vehicle (LV) 49%

Auto Rickshaw (AR) 11%

Small Public-transport (SP) 10%

Motor Cycle (MC) 10%

NMV 20%

0%

VECHILE COMPOSITION(%)

Group 5: Rassel square to panthopath

GROUP 4: PANTHOPATH TO RASSEL SQUARE

Fig:1.1b:vechile composition 2

So,we can say light vehicle is predominate here.

Directional Distribution:

Distribution of traffic vechile is varied according with their demand.It varies with time also

A classified comparision is shown below

IN first figure actual number is shown

In next one they are converted to pcu

0% 0%

55%

12% 3%

8%

22%

VECHILE COMPOSITION(%)

bus Truck Light vehicle Auto Rickshaw (AR) Small Public-transport (SP) Motor Cycle (MC) nmv

Fig:1.2a:Directional distribution

Also a comparison of total pcu is needed.Below pcu in both direction along with composition

is shown

Fig:1.2b:Directional distribution

050

100150200250

Bus (B) Truck (T)Light Vehicle

(LV)Auto Rickshaw

(AR)Small Public-

transport (SP)Motor Cycle

(MC)NMV

group 5 1 2 205 46 40 40 86

group 4 1 0 169 36 9 24 66

0

100

200

300

400

group 5 group 4

nmv 34.4 26.4

motor cycle 16 9.6

small public transport 72 16.2

Auto rickshaw 32.2 25.2

light vehicle 205 169

truck 4 0

bus 2 2

Axi

s Ti

tle

Directional distribution(pcu)

AS stated earlier this distribution is a demand function, which is a time function. Directional distribution along with

time is shown below

Time group Bus Truck L.V cng SPT m.BIKE

9.45 1 & 8 12 6 429 109.9 0 61.6

10 2 & 7 20 2 305 105 0 38

10.15 3 & 6 4 6 421 93.1 0 26.8

10.3 4 & 5 4 4 374 57.4 88.2 44

IF we want to show in graph

Fig:1.2c:Classified vehicle Directional distribution vs time

Another important parameter for design, forecast and modeling is ADT and AADT

-50

0

50

100

150

200

250

300

350

400

450

500

0 10 20 30 40 50

bus truck L.V cng+Sheet4!$F$2 spt m.bike

ADT : The equivalent hourly traffic flow measured in less than one hour

AADT: Total yearly volume divided by number of days

This two parameter are shown below

GROUP Flow rate EXP. FAC 7day volume ADT

1 1563.2 7.012 10961.16 1565.88

2 856.8 7.012 6007.882 858.2688

3 1430 7.012 10027.16 1432.451

4 993.6 7.012 6967.123 995.3033

5 1462.4 7.012 10254.35 1464.907

6 1064.8 7.012 7466.378 1066.625

7 1023.2 7.012 7174.678 1024.954

8 1610 7.012 11289.32 1612.76

Exp.FAC AADT 1 DIRECTION AADT 2 DIREC Average

0.948 1484.454

0.948 813.6388 3013.29

0.948 1357.964 1785.2

0.948 943.5475 2369 2375

0.948 1388.732 2332.2

0.948 1011.161

0.948 971.6564

0.948 1528.896

Another parameter flow rate is shown below-

Panthopath to rassel square

Rassel square to panthopath

total

total pcu 248.4 365.6 614

elapsed time 15 15 15

Flow rate(pcu/day)

993.6 1462.4 2456

Flow fluctuation:

Travel demand varies with time,season ,weather .Urban and rural fluctuation has definite

pattern.

For our work fluctuation curve is like below

Fig 1.3a:flow fluctuation

In urban area two peaks should be there.But as we take a very short count it is not occurring here

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20 25 30 35 40 45

Series1

Series2

Series3