**3.1. Scenario-I: Conventional transportation planning paradigm**

In this section, an example application of the proposed land use planning procedure is given for the city of Denizli. Note that the data required for the application is taken from DTMP [19]. Projection year is taken as 2030 considering the 20 years projection period of the DTMP.

As it was explained in the previous section, land use pattern, travel demand between all O-D pairs, transportation network characteristics such as link capacities, free flow travel times and signal timings for signalized intersections are used to calculate the performance indicators of the road network. In this context, a SUE assignment has been applied in order to calculate the link traffic volumes for the base-case and the projection year under Scenario-I. Note that the analyses are carried out for the morning peak periods between 07:00 and 09:00 a.m. The resulting traffic volumes are shown on the road network for 2010 and 2030 are given in **Figures 5** and **6**, respectively.

**Figure 5.** Traffic volumes on the road network for 2010.

**Figure 6.** Traffic volumes on the road network for 2030.

As can be seen in **Figures 5** and **6** that the highest traffic volumes occur along the links meeting at the study intersection. It may also be stated that the increasing demand will lead to worse traffic conditions by 2030 considering the increasing traffic volumes through the road network. In order to investigate the performance of the selected intersection, turn movements and resulting link traffic volumes are given in **Figure 7** and **Table 1**, respectively.

Relation Between Land Use and Transportation Planning in the Scope of Smart Growth Strategies: Case Study of Denizli, Turkey http://dx.doi.org/10.5772/62783 325

**Figure 7.** Turning movements in the intersection.

**Figure 5.** Traffic volumes on the road network for 2010.

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**Figure 6.** Traffic volumes on the road network for 2030.

As can be seen in **Figures 5** and **6** that the highest traffic volumes occur along the links meeting at the study intersection. It may also be stated that the increasing demand will lead to worse traffic conditions by 2030 considering the increasing traffic volumes through the road network. In order to investigate the performance of the selected intersection, turn movements and

resulting link traffic volumes are given in **Figure 7** and **Table 1**, respectively.


**Table 1.** SUE link flows under Scenario-I.

As can be seen in **Table 1**, traffic volumes along the approaches of the intersection are expected to increase with varied ratios by 2030. The highest increase will occur on the second move‐ ment with about 80% while the lowest one is about 33% on the third movement. At this point, VISSIM traffic simulations have been made for Scenario-I considering the traffic volumes for base-case and 2030. **Figure 8** shows VISSIM snapshots for Scenario-I.

As can be seen in **Figure 8a** that queues occur over the upstream links in a similar way to **Figure 4b**. Considering results of the simulations that represent the base-case, those queues are manageable due to the available queue storage on the upstream links. On the other hand, **Figure 8b** shows that the increasing travel demand will lead to longer queues that the vehicles may not discharge in a single green period in 2030 under Scenario-I. The resulting perform‐ ance indicator values of the simulations are given in **Table 2**.

**Figure 8.** Traffic simulation snapshots for base case **(a)** and projection year **(b)**.


**Table 2.** Performance indicators for base-case and Scenario-I.

**Table 2** shows that the number of stops, delay times and total travel time increase over 100% by 2030 considering the traditional land use planning decisions. Meanwhile, the average speed in the intersection decreases by about 36%.

#### **3.2. Scenario-II: Transportation planning paradigm based on smart growth (SG)**

Configuring the transportation demand, which leads to traffic problems when it is assigned to the road network, may be dealt with in the SG manner. Herein, city block densities constitute the main factor which determines the trip attraction and trip generation rates. **Figures 9** and **10** show the trip generation and trip attraction increases in the city of Denizli for 2030 in zonal case [19].

Relation Between Land Use and Transportation Planning in the Scope of Smart Growth Strategies: Case Study of Denizli, Turkey http://dx.doi.org/10.5772/62783 327

**Figure 9.** Trip generation increase for 2030.

**Figure 8.** Traffic simulation snapshots for base case **(a)** and projection year **(b)**.

**Table 2.** Performance indicators for base-case and Scenario-I.

in the intersection decreases by about 36%.

case [19].

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Average delay time per vehicle (s) 85.15 166.21 95 Average number of stops per vehicle 1.55 3.08 99 Average stopped delay per vehicle (s) 60.93 128.77 111 Total delay time (h) 140.17 285.29 104 Number of stops 9210 19033 107 Total stopped delay (h) 100.30 221.02 120 Total travel time (h) 182.58 334.97 83 Average speed (km/h) 17.58 11.24 −36

**Table 2** shows that the number of stops, delay times and total travel time increase over 100% by 2030 considering the traditional land use planning decisions. Meanwhile, the average speed

Configuring the transportation demand, which leads to traffic problems when it is assigned to the road network, may be dealt with in the SG manner. Herein, city block densities constitute the main factor which determines the trip attraction and trip generation rates. **Figures 9** and **10** show the trip generation and trip attraction increases in the city of Denizli for 2030 in zonal

**3.2. Scenario-II: Transportation planning paradigm based on smart growth (SG)**

**2010 2030 Change (%)**

**Figure 10.** Trip attraction increase for 2030.

**Figure 10** shows that attractive activities are clustered in the southwest of the intersection for the case 2030. The zones which have higher trip generation values also take place in the same area. On the contrary of this kind of location choice, several zones which have high trip generation values take place on the eastern part of the intersection. The zones which have attractive characteristics take place at the western side of the intersection. Note that there is no alternative access between the urban districts without using the study intersection. In this case, higher trip generation values at the eastern district of the intersection should be questioned because using intersection for access may be an obligation. To decrease the trip generation characteristics of the zones which take place at the eastern part of the intersection is an alternative land use planning paradigm for urban planners. Residential development areas on the eastern part of the intersection may be transferred to other side of the intersection in order to decrease the traffic congestion.

In the SG context, residential area densities at the eastern part of the intersection have been reduced by 50% in Scenario-II. Therefore, trip generation rates reduce directly proportional to the O-D matrices. This reduction has been applied by evaluating the land use plan of the city. Empty areas which are proper for residential development have been taken into account and all reductions and increases have been reflected to the O-D demands. **Figures 11** and **12** show the trip generation and attraction changes in zonal case after new land use modifications were carried out in Scenario-II.

**Figure 11.** Rearranged trip generation increase for 2030.

Relation Between Land Use and Transportation Planning in the Scope of Smart Growth Strategies: Case Study of Denizli, Turkey http://dx.doi.org/10.5772/62783 329

**Figure 12.** Rearranged trip attraction increase for 2030.

area. On the contrary of this kind of location choice, several zones which have high trip generation values take place on the eastern part of the intersection. The zones which have attractive characteristics take place at the western side of the intersection. Note that there is no alternative access between the urban districts without using the study intersection. In this case, higher trip generation values at the eastern district of the intersection should be questioned because using intersection for access may be an obligation. To decrease the trip generation characteristics of the zones which take place at the eastern part of the intersection is an alternative land use planning paradigm for urban planners. Residential development areas on the eastern part of the intersection may be transferred to other side of the intersection in order

In the SG context, residential area densities at the eastern part of the intersection have been reduced by 50% in Scenario-II. Therefore, trip generation rates reduce directly proportional to the O-D matrices. This reduction has been applied by evaluating the land use plan of the city. Empty areas which are proper for residential development have been taken into account and all reductions and increases have been reflected to the O-D demands. **Figures 11** and **12** show the trip generation and attraction changes in zonal case after new land use modifications were

to decrease the traffic congestion.

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carried out in Scenario-II.

**Figure 11.** Rearranged trip generation increase for 2030.

As can be seen in **Figures 11** and **12** that the land use densities are sprawled overthe area more homogenously in comparison with Scenario-I as shown in **Figures 9** and **10**. For Scenario-II, a SUE assignment has been applied with the new O-D travel demand in order to calculate the link traffic volumes. The resulting volumes are given in **Table 3**.


**Table 3.** SUE link flows for the scenarios.

As can be seen in **Table 3** that the traffic volumes along the approaches of the intersection may be decreased from 6% to 35% by applying Scenario-II. In order to evaluate the impacts of the SG strategies in terms of the performance indicators, VISSIM simulations have been made for Scenario-II and the resulting values of those indicators are provided in **Table 4**.


**Table 4.** Performance indicators for scenarios.

**Table 4** shows that the number of stops in the intersection may be decreased by about 10% while the total delay time decreases by about 5%. Meanwhile, the average travel speed in the study intersection increases by about 4% in comparison with Scenario-I. Therefore, it may be stated that the traffic congestion may be reduced, and performance of the road network could be improved by applying the SG land use planning strategies.
