*2.3.3 Traffic congestion*

In previous Section 2.3.2, we have traffic flow fundamental characteristics curve and traffic flow parameter i.e. free speed, jam traffic density. In terms of traffic congestion analysis, we only have an observation method by using Time-based Traffic Volume and Vehicle speed shown in **Figure 4**. In this section, we try to get one of some traffic parameters as its traffic congestion condition. In previous research [7], we focus on occupancy (OC) parameter. The occupancy is obtained by Eq. (5) from traffic theory.

$$OC = 100 \times \frac{\underline{q}}{v} \times \overline{l} \text{ (\%)}\tag{5}$$

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**Figure 9.**

*2.3.4 Traffic congestion condition*

The occupancy is useful to understand the traffic congestion condition from the traffic flow parameters in 2.3.2. By using the occupancy parameter in whole city, **Figure 10** shows the trend of traffic congestion condition in Ahmedabad.

*Traffic flow parameter in Ahmedabad June 1st 2019. (a) Traffic Volume (q) in Ahmedabad June 1st 2019. (b) Travel Time (= 1/v) in Ahmedabad June 1st 2019. (c) Occupancy (OC) in Ahmedabad June 1st 2019.*

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

*Traffic flow characteristics with boundary line at camera#2.*

**Figure 8.**

where (q) is traffic volume, (v) is vehicle speed, and *l* is average length of vehicle.

Here is an example from our measurement data in **Figure 9**.

**Figure 9** shows traffic condition of total traffic monitoring cameras in **Figure 3**. From **Figure 9**, occupancy shows traffic congestion condition well. In case of occupancy as traffic congestion parameter, we are able to see the traffic congestion condition. It is not necessary to use two parameter i.e. traffic volume (*q*) and average vehicle speed (*v*) like in **Figure 4**.

**Figure 7.** *Time zone based fundamental traffic flow characteristics.*

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

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

previous study for time-zone based visualization of traffic flow [5].

called in previous research as Boundary Observation Method [6].

*2.3.3 Traffic congestion*

traffic theory.

speed (*v*) like in **Figure 4**.

vehicle.

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

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

In previous Section 2.3.2, we have traffic flow fundamental characteristics curve and traffic flow parameter i.e. free speed, jam traffic density. In terms of traffic congestion analysis, we only have an observation method by using Time-based Traffic Volume and Vehicle speed shown in **Figure 4**. In this section, we try to get one of some traffic parameters as its traffic congestion condition. In previous research [7], we focus on occupancy (OC) parameter. The occupancy is obtained by Eq. (5) from

100 %( ) *<sup>q</sup> OC l*

where (q) is traffic volume, (v) is vehicle speed, and *l* is average length of

**Figure 9** shows traffic condition of total traffic monitoring cameras in **Figure 3**. From **Figure 9**, occupancy shows traffic congestion condition well. In case of occupancy as traffic congestion parameter, we are able to see the traffic congestion condition. It is not necessary to use two parameter i.e. traffic volume (*q*) and average vehicle

Here is an example from our measurement data in **Figure 9**.

*<sup>v</sup>* = ×× (5)

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**Figure 7.**

*Time zone based fundamental traffic flow characteristics.*

**Figure 8.** *Traffic flow characteristics with boundary line at camera#2.*

**Figure 9.**

*Traffic flow parameter in Ahmedabad June 1st 2019. (a) Traffic Volume (q) in Ahmedabad June 1st 2019. (b) Travel Time (= 1/v) in Ahmedabad June 1st 2019. (c) Occupancy (OC) in Ahmedabad June 1st 2019.*

#### *2.3.4 Traffic congestion condition*

The occupancy is useful to understand the traffic congestion condition from the traffic flow parameters in 2.3.2. By using the occupancy parameter in whole city, **Figure 10** shows the trend of traffic congestion condition in Ahmedabad.

#### **Figure 10.**

*Traffic congestion by occupancy in Ahmedabad June 1st 2019.*

#### **Figure 11.**

*Padli junction location relation map and traffic volume at Paldi. (a) Location Map (by Google MAP) and Paldi junction Camera position. (b) Traffic Volume at Paldi Junction (Camera#2001 to 2004).*

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The detail analysis is done in our future work.

**3. Traffic safety management**

ArcGIS.

**Figure 12.**

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

*Drone traffic monitoring at Padli junction.*

The data is used all traffic monitoring cameras and interpolation of Inverse Distance Weighted (IDW) with Geographical Information System (GIS) such as

From **Figure 10**, it is clear about traffic congestion condition in Ahmedabad by using occupancy parameter. The left bottom area if **Figure 10** is crowed area because of shopping center and new business office along the road (132 Feet Ring Road) and the right side of middle area along the road (Ashram Road), which connects with the right side of city which is called "old city", where there are government office Ahmedabad Municipal Corporation (AMC), court, bus garage etc.

In Paldi junction sounded by Camera#2001 through Camera#2004, it is expected

to become traffic congestion because the fly-over is under development Ashram Road from Camera#1 to Paldi junction (refer to **Figure 11**). There is METRO under development which will run Ashram Road in parallel (red line in **Figure 11(a)**). In **Figure 11(b)**, there are four graphs about traffic volume at Paldi Junction from Camera#2001 through 2004. From these graphs, there is interesting trend of Paldi Junction traffic. For Camera#2001 and 2002, there is more traffic in the evening rather that in the morning. On the other hand, for Camera#2003 and 2004, its trend is opposite. The direction of each cameras face to the center of Junction, this means many traffic moves from old city or east side of city to new city or west side of city. From these measurements of traffic condition in Ahmedabad, the value of occupancy is lower than 25% in wide area in the city. According to our experience of the project, there is not always congested against our expectation before this project stars. The Ahmedabad traffic congestion occurs by some reason, not always crowed by traffic. This is important evidence and hints how to solve traffic congestion issues in India. Here is undergoing research activities for monitoring traffic condition. We use Drone at Paldi junction. **Figure 12** shows the video capture at Paldi junction in order to understand vehicle movement behavior. The Drone flied about 10-meterhigh at Paldo junction. From the Drone video monitoring, it becomes clear vehicle behavior rather than that by traffic video monitoring camera because traffic video monitoring camera is installed at the fixed point and not high position in the sky.

In previous section, we have traffic flow analysis at Paldi junction. From **Figure 12**, we see clear real vehicle behavior by Drone and each four direction *Traffic Flow Analysis and Management DOI: http://dx.doi.org/10.5772/intechopen.95087*

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

*Padli junction location relation map and traffic volume at Paldi. (a) Location Map (by Google MAP) and* 

*Paldi junction Camera position. (b) Traffic Volume at Paldi Junction (Camera#2001 to 2004).*

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**Figure 11.**

**Figure 10.**

*Traffic congestion by occupancy in Ahmedabad June 1st 2019.*

**Figure 12.** *Drone traffic monitoring at Padli junction.*

The data is used all traffic monitoring cameras and interpolation of Inverse Distance Weighted (IDW) with Geographical Information System (GIS) such as ArcGIS.

From **Figure 10**, it is clear about traffic congestion condition in Ahmedabad by using occupancy parameter. The left bottom area if **Figure 10** is crowed area because of shopping center and new business office along the road (132 Feet Ring Road) and the right side of middle area along the road (Ashram Road), which connects with the right side of city which is called "old city", where there are government office Ahmedabad Municipal Corporation (AMC), court, bus garage etc.

In Paldi junction sounded by Camera#2001 through Camera#2004, it is expected to become traffic congestion because the fly-over is under development Ashram Road from Camera#1 to Paldi junction (refer to **Figure 11**). There is METRO under development which will run Ashram Road in parallel (red line in **Figure 11(a)**). In **Figure 11(b)**, there are four graphs about traffic volume at Paldi Junction from Camera#2001 through 2004. From these graphs, there is interesting trend of Paldi Junction traffic. For Camera#2001 and 2002, there is more traffic in the evening rather that in the morning. On the other hand, for Camera#2003 and 2004, its trend is opposite. The direction of each cameras face to the center of Junction, this means many traffic moves from old city or east side of city to new city or west side of city. From these measurements of traffic condition in Ahmedabad, the value of occupancy is lower than 25% in wide area in the city. According to our experience of the project, there is not always congested against our expectation before this project stars. The Ahmedabad traffic congestion occurs by some reason, not always crowed by traffic. This is important evidence and hints how to solve traffic congestion issues in India.

Here is undergoing research activities for monitoring traffic condition. We use Drone at Paldi junction. **Figure 12** shows the video capture at Paldi junction in order to understand vehicle movement behavior. The Drone flied about 10-meterhigh at Paldo junction. From the Drone video monitoring, it becomes clear vehicle behavior rather than that by traffic video monitoring camera because traffic video monitoring camera is installed at the fixed point and not high position in the sky. The detail analysis is done in our future work.
