**2. Case study of project**

#### **2.1 Case study field**

This project has been started since April 2017 under SATREPS program, which is government founded joint research project between Japan and India. The case study filed was chosen in the Ahmedabad city of Gujarat state India, which is one of typical rapid economic growing and faced to serious traffic issues. The population of Ahmedabad city is over 8 million [1] in 2018 from 5 million in 2011 and the number of vehicles is about 4 million in 2017 [2]. More than 70% vehicle is two wheelers, which is typical percentage in developing countries. Based on city government or Ahmedabad Municipal Corporation (AMC) agreement, the case study field was chosen in west side of the city called "New City" where there will have more commercial business and new building constructions (**Figure 1** and **Table 1**).

#### **2.2 Monitoring traffic in Ahmedabad**

First, it is necessary to have monitoring tools for measuring traffic condition in the city. In this program, we installed traffic video monitoring cameras shown in **Figure 2**. This is general camera in traffic industry. And the special high resolution 4 K camera is installed at the major junction—Pladi junction as detail analysis at junction.

Second, the total number of cameras is 36 including 4 K camera. The camera location is shown in **Figure 3** (The number is camera ID and 4 K camera has no number, but it is in the center among 2000s ID cameras in the map). The traffic video camera has several functions such as counting number of vehicles on the road, average vehicle speed, traffic density, occupancy and so on. Traffic density is the number of vehicles per kilo meter on the road (vehicle/km) which is defined in the traffic flow theory. Occupancy is how much percentage occupied by vehicles on the road which is also defined in the traffic flow theory. The detail parameter explanation is described in later of this chapter.

Third, here is an example of traffic condition data which is shown in **Figure 4**. There are two graphs shown which are time-based traffic volume and average vehicle speed. From those graphs. we see the traffic condition (in this case camera #2 location). It is clear that the traffic congestion was occurred around 20:00 o'clock because its traffic volume was peek and average vehicle speed is lowest. The data here is a month data in June 2019 and each graph shows average point and standard deviation.

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**2.3 Traffic flow theory and measurement**

In this section, it is necessary to understand the basic Traffic Flow Theory then compare between the theory and measurement result of traffic flow. The traffic flow analysis originally comes from fluid mechanism theory. When average vehicle speed (*v*) (km/h)on a road and the density of vehicle which is so called Traffic Density (*k*)(veh/km), the Traffic Volume (*q*) (veh/h) is obtained by Eq. (1).

**Co-ordinates:** 23.03° N 72.58° E **Area:** 466 Sq.km. (year 2006) **Population:** 55,77,940 (year 2011 Census) **Density:** 11,948 /sq.km **Literacy Rate:** 89.60% **Average Annual Rainfall:** 782 mm **Popularly known as:** Amdavad **STD Code:** 079

*q kv* = × (1)

*2.3.1 Traffic flow theory*

*Profile: Ahmedabad City [3].*

**Figure 1.**

**Table 1.**

*Ahmedabad city map [3].*

*Traffic Flow Analysis and Management DOI: http://dx.doi.org/10.5772/intechopen.95087* *Traffic Flow Analysis and Management DOI: http://dx.doi.org/10.5772/intechopen.95087*

#### **Figure 1.**

*Design of Cities and Buildings - Sustainability and Resilience in the Built Environment*

method combined with traffic flow theory.

traffic issues and some countermeasures.

constructions (**Figure 1** and **Table 1**).

**2.2 Monitoring traffic in Ahmedabad**

explanation is described in later of this chapter.

**2. Case study of project**

**2.1 Case study field**

counters, it becomes challenge to understand the real traffic condition from the data rather than in advanced counties. In this book, it is introduced traffic flow analysis

After collecting traffic data and analysis, it digs into traffic flow problem and shows important traffic flow parameter for traffic congestion with using actual traffic measurement data in a major city in India. And it shows city level traffic flow visual analysis by Geographic Information System (GIS) tool to understand its

Based on this project, it mentioned what we learn and how to approach for solving the issues. Technology becomes higher and provides us better solution. In conclusion, author wants to share the idea that technology is one of tool for helping collecting data and we need to know how to creating our experience and how to use it as sustainable concept. At last part, author adds traffic fatality issues from fifty years Japanese experience with some enhanced experimental equation in appendix.

This project has been started since April 2017 under SATREPS program, which is government founded joint research project between Japan and India. The case study filed was chosen in the Ahmedabad city of Gujarat state India, which is one of typical rapid economic growing and faced to serious traffic issues. The population of Ahmedabad city is over 8 million [1] in 2018 from 5 million in 2011 and the number of vehicles is about 4 million in 2017 [2]. More than 70% vehicle is two wheelers, which is typical percentage in developing countries. Based on city government or Ahmedabad Municipal Corporation (AMC) agreement, the case study field was chosen in west side of the city called "New City" where there will have more commercial business and new building

First, it is necessary to have monitoring tools for measuring traffic condition in the city. In this program, we installed traffic video monitoring cameras shown in **Figure 2**. This is general camera in traffic industry. And the special high resolution 4 K camera is

Second, the total number of cameras is 36 including 4 K camera. The camera location is shown in **Figure 3** (The number is camera ID and 4 K camera has no number, but it is in the center among 2000s ID cameras in the map). The traffic video camera has several functions such as counting number of vehicles on the road, average vehicle speed, traffic density, occupancy and so on. Traffic density is the number of vehicles per kilo meter on the road (vehicle/km) which is defined in the traffic flow theory. Occupancy is how much percentage occupied by vehicles on the road which is also defined in the traffic flow theory. The detail parameter

Third, here is an example of traffic condition data which is shown in **Figure 4**. There are two graphs shown which are time-based traffic volume and average vehicle speed. From those graphs. we see the traffic condition (in this case camera #2 location). It is clear that the traffic congestion was occurred around 20:00 o'clock because its traffic volume was peek and average vehicle speed is lowest. The data here is a month data in June 2019 and each graph shows average point and standard deviation.

installed at the major junction—Pladi junction as detail analysis at junction.

**116**

*Ahmedabad city map [3].*


**Table 1.** *Profile: Ahmedabad City [3].*

### **2.3 Traffic flow theory and measurement**

### *2.3.1 Traffic flow theory*

In this section, it is necessary to understand the basic Traffic Flow Theory then compare between the theory and measurement result of traffic flow. The traffic flow analysis originally comes from fluid mechanism theory. When average vehicle speed (*v*) (km/h)on a road and the density of vehicle which is so called Traffic Density (*k*)(veh/km), the Traffic Volume (*q*) (veh/h) is obtained by Eq. (1).

$$q = k \times v \tag{1}$$

**Figure 2.** *Traffic video monitoring camera and high resolution 4 K camera (photo by zero sum ltd).*

And the fundamental relationship between (*k*) and (*v*) is defined as Greenshields [4] in Eq. (2).

$$\upsilon = \upsilon\_f \left( 1 - \frac{k}{k\_f} \right) \tag{2}$$

where *vf* is free speed which is theoretical maximum speed at zero traffic density and *kj* is jam traffic density which is theoretical zero vehicle speed condition. From Eq. (1) and Eq. (2), Eq. (3) is obtained.

$$q = v\_f \left( 1 - \frac{k}{k\_f} \right) k \tag{3}$$

**119**

**Figure 6.**

**Figure 5.**

**Figure 5**.

**Figure 4.**

traffic volume Q is decreasing at *q*c.

in **Figure 6** as an example of Camera#2 in June 2019.

*2.3.2 Traffic flow measurement*

*Fundamental traffic flow characteristics.*

*Fundamental traffic flow characteristics at camera#2.*

*Traffic Flow Analysis and Management DOI: http://dx.doi.org/10.5772/intechopen.95087*

Traffic Volume (veh/h)

**Traffoc Volume@0002 (2019.6)**

Time

0 2 468 10 12 14 16 18 20 22

Eq. (4) is obtained by changing Eq. (3) as traffic density quadratic equation.

*Example of traffic condition at camera#2. (a) Time-based traffic volume (b) Time0based vehicle speed.*

 =− − +

*j*

*q k <sup>k</sup>*

2

*v k vk*

Speed (km/h)

15

(a) (b)

20

25

30

By using Eq. (2) and Eq. (4), we have two funder mental traffic characteristics in

From K-Q curve in **Figure 5**, traffic congestion occurs between *k*c to *k*j because

From measurement data, the fundamental traffic flow characteristics are shown

By comparing **Figures 5** and **6**, the boundary line of each characteristics is similar shape. But the measured data spreads under the boundary line. It is

2 4 *f j fj* **Speed @0002 (2019.6)**

Time

0 2 468 10 12 14 16 18 20 22

(4)

#### **Figure 4.**

*Design of Cities and Buildings - Sustainability and Resilience in the Built Environment*

*Traffic video monitoring camera and high resolution 4 K camera (photo by zero sum ltd).*

And the fundamental relationship between (*k*) and (*v*) is defined as

*v v*

*<sup>f</sup>* 1

*<sup>f</sup>* 1

*<sup>k</sup> qv k <sup>k</sup>* = − 

*j k*

(2)

(3)

*k* = − 

*j*

where *vf* is free speed which is theoretical maximum speed at zero traffic density and *kj* is jam traffic density which is theoretical zero vehicle speed condition. From

**118**

**Figure 3.**

**Figure 2.**

Greenshields [4] in Eq. (2).

*Traffic video monitoring camera location.*

Eq. (1) and Eq. (2), Eq. (3) is obtained.

*Example of traffic condition at camera#2. (a) Time-based traffic volume (b) Time0based vehicle speed.*

Eq. (4) is obtained by changing Eq. (3) as traffic density quadratic equation.

$$q = -\frac{\upsilon\_f}{k\_f} \left( k - \frac{k\_f}{2} \right)^2 + \frac{\upsilon\_f k\_f}{4} \tag{4}$$

By using Eq. (2) and Eq. (4), we have two funder mental traffic characteristics in **Figure 5**.

From K-Q curve in **Figure 5**, traffic congestion occurs between *k*c to *k*j because traffic volume Q is decreasing at *q*c.

#### *2.3.2 Traffic flow measurement*

From measurement data, the fundamental traffic flow characteristics are shown in **Figure 6** as an example of Camera#2 in June 2019.

By comparing **Figures 5** and **6**, the boundary line of each characteristics is similar shape. But the measured data spreads under the boundary line. It is

**Figure 5.**

*Fundamental traffic flow characteristics.*

**Figure 6.**

*Fundamental traffic flow characteristics at camera#2.*

difficult to figure out the traffic congestion condition from measurement data. We see the traffic congestion occurs around 20:00 at Camera#2 from **Figure 4**. Therefore, the measurement funder mental traffic flow is shown by time zone base in **Figure 7**. There are six time zone from 7:00–10:59 as T1, 11:00–14:59 as T2, 15:00–18:59 at T3, 19:00–22:59 as T4, 23:00–2:59 as T5, and 3:00–6:59 as T6. Then T4 is most critical traffic congestion condition. From **Figure 7**, we see the traffic congestion at Camera#2 in June 2019 occurs the area of the funder mental traffic flow characteristics under its boundary line. This is one of typical features of traffic flow characteristics in India. The traffic congestion occurs before its critical traffic volume (*q*c) (refer to **Figure 5** K-Q curve). This research has been done in previous study for time-zone based visualization of traffic flow [5].

When we use the boundary line as its traffic flow characteristics, we get the traffic flow parameter. The example of boundary line for **Figure 6** is shown in **Figure 8**. The we have the following parameter *vf* = 38, *kj* = 250 in this case. This is called in previous research as Boundary Observation Method [6].
