Od_Car Motorcycle Rush Hour Model Input Information:

N gcđ = (0.12-0.14) N ngđ

In which: N gcđ is the traffic volume of cars and motorbikes during rush hour. N ngđ is the traffic volume of cars and motorbikes per day and night.

In this thesis, a coefficient of 0.12 is used to calculate the traffic volume of cars and motorbikes during rush hour.

The MATRIX model (OD_Oto xe may gio rush hour) is presented below.

This:


Figure 4.32 OD model_Cars and motorbikes during rush hour Input information :

Matrix File 1: Matrix file OD_Oto xe may (Ma tran Oto xe may.MAT) taken from MATRIX 7 program.

Result :

Matrix File 1 : Matrix file OD_Oto xe may gio cao diem (OD_Oto xe may gio cao diem.MAT). The output is presented in Appendix C “Output of the 4-step Model using CUBE Citilabs”

Program file:


In there:

Input matrix file:

MI.1.1 input matrix, 1-day and night Car Traffic matrix. MI.1.2 input matrix, 1-day and night Motorcycle Traffic matrix. MW[1]=MI.1.1*0.12 is Car Traffic during rush hour.

MW[2]=MI.1.1*0.12 is the Motorcycle Traffic during rush hour.

MW[3]= MW[3]+ MW[3] is the PCU (Passenger Car Unit) peak hour traffic. (PCU: traffic converted to xcqđ unit)

Figure 4.33 Travel demand by PCU during peak hours between internal zones (II)

4.2.1.7 Network assignment ( Trip Assignment )

The relationship between velocity V and flow rate N : is shown in Figure 4.34 “Relationship between velocity V and flow rate N”. In which there are 5 regional areas limited by 6 different service levels corresponding from A to F (from right to left).


Figure 4.34 Relationship between velocity V and flow rate N

As traffic increases toward the road's capacity, the average speed of traffic flow decreases from the freeway speed (the speed of a vehicle when it is alone on the road) to the speed when traffic flow reaches its maximum.

Level of Service_LOS (Level of Service): According to section 5.4.2 TCXDVN 104:2007 "Urban roads, design standards", Level of service is a measure of the quality of traffic flow operation, which is perceived by vehicle drivers and passengers.

Service levels are divided into 6 different levels, denoted as A, B, C, D, E, F. Level A - the best service quality and level F - the worst service quality. The KNTH utilization coefficient is one of the indicators associated with the service level of a street section.

General operating conditions for service levels:

A – free flow, very high speed, KNTH utilization factor Z < 0.35.

B – not completely free flow, high speed, KNTH utilization factor Z=0.35÷0.50.

C – stable current but the driver is affected when he wants to freely choose the desired speed, the coefficient of using KNTH Z=0.50÷0.75.

D – the starting current is unstable, the driver has little freedom in choosing the speed, the coefficient of using the KNTH Z= 0.75÷0.90.

E – unstable flow, the road is in limit state, any obstacle will cause traffic jam, KNTH utilization coefficient Z=0.90÷1.00.

F – the flow is completely unstable, traffic jam occurs.

Capacity utilization factor (Z) is the ratio between traffic volume (N or V_volume) and capacity (P or C_capacity). Capacity utilization factor is a representative parameter to specify the service level of a road.

The higher the quality of the stream, the higher the speed requirement, the smaller the Z coefficient. Conversely, as Z increases, the average speed of the stream decreases and at a certain value, traffic jams will occur (Z~1).

The design service level and the KNTH utilization coefficient used in street design are specified in Table 4.11.

Table 4.11 Service level and KNTH utilization coefficient



Type of sugar

Technical level

Design speed (km/h)

Service level

KNTH utilization coefficient

Urban highway

100

100


C

0.6-0.7

80

80

0.7-0.8

70

70

0.7-0.8


Main urban street

80

80


C

0.7-0.8

70

70

0.7-0.8

60

60

0.8

50

50

0.8

Street collector

60

60


D

0.8

50

50

0.8-0.9

40

40

0.8-0.9

Internal streets

40

40

D

0.8-0.9

30

30

E

0.9

20

20

0.9

Maybe you are interested!

Source: TCXDVN 104: 2007 “Urban roads_Design standards”

Relationship between Travel Time and Traffic Volume: An important characteristic of road traffic is that travel time increases proportionally with traffic volume: the more vehicles there are on the same stretch of road, the slower the speed of the traffic flow and the longer the travel time.

Figure 4.35 Relationship between Traffic Volume and Travel Time

This relationship is represented by the formula:



In there:

T= T 0 [ 1+ 0.15 ( V/C ) 4 ]


end J

T: Travel time as affected by traffic between starting point I and ending point


T 0 : Travel time in free state between starting point I and ending point JV: Traffic from starting point I to ending point J.

C: Maximum capacity of the route.

Source: Citilabs Cube Manual

The HIGHWAY model (Assignment_An dinh tuyen duong) is presented.

hereafter:


Figure 4.36 Assignment Model_Route Assignment

Input information :

Matrix File 1 : Matrix file OD_Rush hour cars (OD_Rush hour cars.MAT) taken from MATRIX 8 program.

Network File : File Network.NET

Intersctn Data : Intersection setup file (used for intersection evaluation)

(Intersection data.IND)

Result :

Network File : The network file of routes after trips have been assigned (LeadedNetwork.NET)

Tunr Flows : Result file used for intersection evaluation (Intersection data.INT) Path File 1: Result file used for network evaluation (PathFile.PTH)Program file:

In there:

TURNS N=1-99999 T=TURN[1]+TURN[2]*0.25 Turn to define the total turn directions by the sum of turn direction [1] plus turn direction [2]*0.25. Determined by car traffic + motorbike traffic*0.25 for each turn direction. This command is used to determine the traffic at the turns.

T0=((LI.DISTANCE/1000)/(LI.SPEED))*60 Travel time in free state. TC=T0*(1+0.15*(V/C)^4) Travel time when affected by traffic V=VOL[1]+VOL[2]*0.25 PCU traffic = Car traffic + Vehicle traffic

machine*0.25

4.2.2 Evaluation of traffic scenarios

4.2.2.1 Scenario 1 ( Maintain current road network )

Traffic on routes: The final result of the forecasting process is to find out the traffic on each route, thereby evaluating whether the road network is suitable and ensures traffic circulation based on the route's capacity coefficient.

PCU traffic within the district (II) (District 3):


Figure 4.37 PCU traffic within district 3

PCU traffic in the district is distributed on almost all routes, the density of road use is very high, the largest traffic is concentrated on the main routes connecting the centers of the internal Zones together. This traffic arises mainly from people in District 3 traveling within the district.

PCU traffic in the study area:



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