Developing Industrial Trip Generation Model for Himatnagar Industrial Area

Developing Industrial Trip Generation Model for Himatnagar Industrial Area

B. V. Patel1,

1M. E. Student,

Tatva Institute of Technological, Modasa.

1. R. Varia3

   3Principal,

   1. K. Kadiya2,

      2Assistant Engineer R&B Department Himatnagar.

      Tatva Institute of Technological, Modasa.

      AbstractThis work is to determine the factors affecting trip generation for the selected groups of industries within the region and to develop trip generation model. To develop trip generation model considering all the affecting parameters for the future trips estimation, the industries are classified based on the plot area and numbers of employee. The model has been developed using several regression analyses by means of Statistical Package for the Social Sciences (SPSS), which establishes relationship between numbers of trips each activity produce or attract by the employees. A model for trip generation has been developed. The model result gave an effective value of R2 equal to 0.99, indicating that the explanatory variables such as area of industries, income of employee, travel distance, travel time, raw material and finished material included in the model explain 99% of the dependent variable. Income of employee and raw material are the main factors affecting trip generation. A detailed work is necessary to use this model for planning purpose. Reliable forecasting of future trips using this model can be done.

      Keywords Regression, Trip Generation, SPSS

      1. INTRODUCTION

Urban transportation covers the movement of both people and goods within an urban area. At the individual level, urban transportation can be characterized by a trip Personal trips are commonly classified based on their main purpose (Barber, 1995); work trips, shopping trips, social trips, recreational trips, school trips, home trips and business trips. This study focuses on industrial trips, and the factors that determine the aggregate number of industrial trips generated in urban areas. The transportation planning process can be split up into four stages like trip generation, trip distribution, mode choice, and route assignment. As on date majority of the research on trip generation have been concentrated on home based person trip generation, using either regression analysis or cross classification analysis as the mathematical technique.

Due to urbanization process developing cities are surrounded by different types of industrial and recreational activities. These activities giving impact on existing road network by increased vehicular trips. It can create traffic congestion, delay, air and noise pollution etc. Hence, it is necessary to estimate these types of vehicular trips generated by industrial or recreational activities. In India

few researches have been carried out to develop the industrial trip generation model. Keeping this in view the study is aimed to develop industrial trip generation model for the developing city like Himatnagar. Himatnagar city is facing urbanization problems due to increased outer growth of the area. Due to ceramic industries, packaging industries, laminates industries, agriculture industries, GIDC, and Sabar Dairy, trips by industrial employees, goods vehicle trips for raw material (in coming trips) and finished material (outgoing trips) creating traffic congestion on existing road network.

The scope of the work is confined to development of trip generation model for the Himatnagar city area. The following are the objectives:

To get the information of the existing locations of the industries near by the Himatnagar. Their types, numbers of workers, floor area, details of raw materials and finished material. To understand the trip making characteristics of various industrial workers and generating trip attraction model (employee trips).To determine the independent variables for the trips generated by goods/freight transport vehicles for the different types of industries and developing their trip generation models.

The study is mainly focused on industrial trip generation behavior of the employees and goods/freight transport vehicles for the different types industries near by the Himmatanar city. This study enables to understand the significant parameters for the industrial trip generation. This study also facilitates to understand the tendency of trip makers for choosing particular time, route, location, cost etc. This study also enables to estimate the workers trips and goods/freight vehicle trips for any new industry established in future.

II METHODOLOGY AND DATA COLLECTION

The study region for this paper is Himatnagar Industrial region, one of the most important industrial estates of Gujarat, located in district Sabarkatha of Gujarat state in India.

1. Employees Trips Survey

2. Goods Trips Survey

3. Land/floor

1. Trip Model of Employees.

2. Trip Model of Goods Vehicle

Fig.1: Methodology chart for study

Employees Trips Data collection

Industrial survey had taken at five categories of industries divided into study area. The process consist collection of origin and destination data. The information on the travel pattern includes number of trips made, their origin and destination, purpose of trip, travel mode, travel time and so on. The information on industrial employee interview survey characteristics includes type of Employee name, age, salary, vehicle ownership and so on. Based on these data it is possible to relate the amount of travel to industry and zonal characteristics and develop equations for trip generation rates.

The sections are described below: Category 1: Ceramic Industries Category 2: Agriculture Industries

Category 3: GIDC Industries and Sabar Diary Category 4: Packaging Industries

Category 5: Laminates Industries

Fig.2: Distribution of daily trips to Ceramics industries.

Fig.3: Distribution of daily trips to Agriculture Industries.

Fig.4: Distribution of daily trips to GIDC &Sabar dairy industries

Fig.5: Distribution of daily trips to Packaging industries.

Fig.6: Distribution of daily trips to Laminates industries.

Goods/Freight Vehicle Trips Data collection

The information on industrial goods/freight survey characteristics includes type of workers name, age, salary, row materials, finished materials, vehicle type, and commodity bulk and so on. Based on these data it is possible to relate the amount of travel to industry and zonal characteristics and develop equations for trip generation rates.

Fig.7: Distribution of daily goods vehicle trips to Ceramics industries

Fig.8: Distribution of daily goods vehicle trips to Agriculture Industries.

Fig.9: Distribution of daily goods vehicle trips to GIDC &Sabar dairy industries.

Fig.10: Distribution of daily goods vehicle trips to Packaging industries.

Fig.11: Distribution of daily goods vehicle trips to Laminates industries.

Category Analysis of Employees Trips/Day

From the Employees trips survey, it is clear vision about the share of Industries size with respect to total trips per day as a various types of mode and travel time.

Fig.12: Ceramic industries trips/day (Mode wise)

Fig.13: Agriculture industries trips/day (Mode wise)

Fig.14: GIDC & Sabar Dairy industries trips/day (Mode wise)

Fig.15: Packaging industries trips/day (Mode wise)

Fig.16: Laminates industries trips/day (Mode wise)

Fig.17: Ceramic industries trips/day (Time wise)

Fig.18: Agriculture industriestrips/day (Time wise)

Fig.19: GIDC & Sabar Diary industries trips/day (Time wise)

Fig.20: Packaging industries trips/day (Time wise)

Fig.21: Laminates industries trips/day (Time wise)

Ceramic Industries Design line Diagram

Figure Design line diagram to indicate the origin and destination of the employees trips for ceramic industries.

Agriculture Industries Design line Diagram

Figure Design line diagram to indicate the origin and destination of the employees for agriculture industries.

GIDC & Sabar dairy Industries Design line Diagram Figure Design line diagram to indicate the origin and destination of the employees for GIDC and Sabar dairy industries.

Packaging Industries Design line Diagram

Figure Design line diagram to indicate the origin and destination of the employees for packaging industries

Laminates Industries Design line Diagram

Figure Design line diagram to indicate the origin and destination of the employees for laminates industries

(5.) Y=616.08-11.65X2-0.017X3+0.83X4 R2=0.86

(6.) Y=159.18+0.017X3-0.23X4+2.18X5 R2=0.82

(7.)Y=887.31+0.001X1-12.03X2 R2=0.63

(8.)Y=712.02-12.87X2+0.017X3 R2=0.85

(9.) Y=223.6+0.019X3+1.25X4 R2=0.80

(10.) Y=173.34-4.66X4+9.04X5 R2=0.34

III MODEL DEVELOPMENT

Employees Trips Model Estimation

After understanding the technique of development of regression model, the industrial trip generation models- dependent and independent variables based will be estimated. The model will be formulated using the multiple linear regression method by regression the dependent variable on each of the explanatory variables.

Table 1: List of independent variables used in the Employees trips generation model

Table 2: List of dependent variables used in the Employees trips generation model

Y

Num. of Employees Trips

Employees Trip Generation Model Development from Regression Analysis:

From the collected data and analysis of data, trip generation model developed for some selected base:

Ceramic industrial Trip generation model:

Using the various data from the industries, the trip generation model is developing using the multiple regression analysis. The regression analysis conducted few times. In each stage, the regression model is evaluated according to statistical tests. The final projected ceramic industrial trip generation model is:

(1.) Y=624.28-0.01X1-14.73X2+0.026X3+1.35X4-0.395X5 R2=0.90

(2.) Y=595.86-0.01X1-14.23X2+0.025X3+1.1X4 R2=0.90

(3.) Y=648.48-12.2X2+0.01X3+1.12X4-0.45X5 R2=0.86

(4.) Y=720.39-0.001X1-15.43X2+0.024X3 R2=0.88

(11.) Y=525.7+0.002X1 R2=0.52

(12.) Y=1326.86-23.5X2 R2=0.29

(13.) Y=310.41+0.019X3 R2=0.77

(14.)Y=538.46+1.640X4 R2=0.05

(15.)Y=277.2+4.18X5 R2=0.22

Y=223.6+0.019X3+1.25X4

Where,

Y=Num. of employees trips

X3=Ave. monthly salary X4=Ave. Daily Raw material (Ton)

The full SPSS results of the employee trip generation model using the SPSS package. The interpretation of these results is discussed below:

Interpretation of Regression Coefficients

This model (eq. 9) is best because of the coefficient of X3 (Ave. monthly salary) and X4 (Ave. Daily Raw material (Ton)) are 0.019 and 1.25 respectively. These coefficients have positive sign indicate that the monthly salary and Ave. Daily Raw material increases, the daily trips will increases.

Table 3: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.80 indicates that the independent variables entered into the model explain about 80% of the variation in the dependent variable. In the case of cross- section data, such an R-squared value is considered practical reasonable.

Agriculture industrial Trip generation model:

The final projected agriculture industrial trip generation model is:

Where,

Y=Num. of employees trips

Where,

Y=20.02+6.2X4

X3=Ave. monthly salary

X4=Ave. Daily Raw material (Ton)

Y=Num. of employees trips X4=Ave. Daily Raw material (Ton)

Table 4: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.99 indicates that the independent variables entered into the model explain about 99% of the variation in the dependent variable

GIDC & Sabar Dairy industrial Trip generation model:

. The final projected GIDC & Sabar Dairy industrial trip generation model is:

Y=-41.4+0.011X1-41.33X2+0.091X3

Where,

Y=Num. of employees trips X1=Area (sq.m)

X2=Ave. trip time (min) X3=Ave. monthly salary

Table 5: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.97 indicates that the independent variables entered into the model explain about 97% of the variation in the dependent variable.

Packaging industrial Trip generation model:

The final projected packaging industrial trip generation model is:

Y=-176.78+0.013X3+4.98X4

Table 6: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.98 indicates that the independent variables entered into the model explain about 98% of the variation in the dependent variable.

Laminates industrial Trip generation model:

The final projected laminates industrial trip generation model is:

Y=-847.9+27.28X4

Where,

Y=Num. of employees trips X4=Ave. Daily Raw material (Ton)

Table 7: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.99 indicates that the independent variables entered into the model explain about 99% of the variation in the dependent variable.

Goods/freight Vehicle Trip Generation Model Development from Regression Analysis:

From the collected data and analysis of data, trip generation model developed for some selected base:

Ceramic industrial Goods Trip generation model:

The final projected ceramic industrial trip generation model is:

Y=5.37-0.002X3+0.40X4

Where,

Y=Num. of employees trips

X3= Ave. Daily Finished material trips distance (km) X4=Ave. Daily Raw material (Ton)

Table 8: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.75 indicates that the independent variables entered into the model explain about 75% of the variation in the dependent variable.

GIDC & Sabar Dairy industrial Trip generation model: The final projected GIDC & Sabar Dairy industrial trip generation model is:

Y=20.81-0.032X3+0.498X4

Employees trip generation model

Goods Vehicle trip generation model

Where,

Y=Num. of employees trips

X3= Ave. Daily Finished material trips distance (km) X4=Ave. Daily Raw material (Ton)

Table 9: Observed T-test value

Testing Goodness of Fit: R-Squared (R2)

The R-squared value of 0.671 indicates that the independent variables entered into the model explain about 67% of the variation in the dependent variable.

Conclusion

In this study industrial trips calculated from the employee trips survey and goods/freight survey. The sample size of industries is 31 i.e., approx 1063 employees. Another sample size is also 31 industries for goods trips survey.

The multiple regression method, which is one of the popular methods used to predict the trip generation, was used in this study. The relevant conclusions are as follows:

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