A COMPARISON OF DELAY MODELS IN VEHICULAR TRAFFIC

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 12, December 2018, pp , Article ID: IJCIET_09_12_083 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed A COMPARISON OF DELAY MODELS IN VEHICULAR TRAFFIC Jeyanthi.B Associate professor, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan koil Rajaram.R.P.C.S Professor, Annai Mathammal Sheela Engineering College, Namakkal ABSTRACT In India, along with its development, traffic congestion has been increasing substantially. As a result, there is a great delay in travel time due to traffic signals. In Tamil Nadu, Madurai is the 3 rd major city sited on Vaigai river bank. This paper concentrates on simulating and modeling of the signals near Periyar bus stand and Goripalayam, which are located in the heart of the Madurai city. To analyze the traffic pattern, traffic volume survey was carried out for three working days from 7.30 am to 8.30 pm. The total number of vehicles are noted and it is categorized into light motor vehicles, medium motor vehicles and high motor vehicles. This data is used for the simulation of the current situation in Madurai city and delay model was created with the help of AMOS 20 software. Two different graphical models were developed and simulated by using the technique Structural Equation Modeling (SEM) to find out the category of vehicles which has heavy impact on traffic congestion. Keywords: Simulation, Delay Model, Structural Equation Modeling Cite this Article: Jeyanthi.B and Rajaram.R.P.C.S, a Comparison of Delay Models in Vehicular Traffic, International Journal of Civil Engineering and Technology, 9(12), 2018, pp INTRODUCTION Today in Tamil Nadu, India traffic has become a bigger problem than it used to be a few years back. In Tamil Nadu 2, 38, 45,064 vehicles have been registered as on Due to urbanisation and general economic development, there has been remarkable increase in number of vehicles registered in Tamil Nadu. Transport Department is one of the major revenueearning departments of the State Government. This paper is concentrated on Madurai, Tamil Nadu. As per the information from Regional Transport Officer, Madurai has a vehicular population of about 20, 81,153 with reference to the year Due to this number of editor@iaeme.com

2 Jeyanthi.B and Rajaram.R.P.C.S vehicles, traffic congestion is also increased drastically. This resulted in delay in travel time, besides the emission of CO2 from the vehicles waiting in traffic signals. Therefore traffic congestion indirectly accounts for greenhouse gases emission. By simulating the traffic condition we can generate a graphical delay model that will make it easier to understand the prevailing traffic conditions. 2. DATA COLLECTION AND ANALYSIS Piloted survey was carried out prior to the detailed traffic volume survey to finalize the intersections to be taken into account for considerations. Finally, the signals near Periyar bus stand and Goripalayam junction was considered as these two signals cause significant delay in traffic in Madurai city. These two signals are connected via an intermediate signal named Simmakal. Almost one third of the vehicular population from Periyar signal goes to Goriplayam signal along with the vehicular traffic from Simmakal. Therefore there forms an invisible indirect connection among these two signals. Traffic volume survey was conducted from morning 7.30 am to 8.30 pm for three working days and the corresponding length of the queue formed by those vehicles while waiting was also measured. The total number of two wheelers, cycles, cars, three wheelers, jeep, buses and trucks noted were then categorized into the following Low motor vehicles (two wheelers and cycles). Medium motor vehicles (cars, three wheelers and jeeps). High motor vehicles (buses and trucks). The collected data is then fed into Microsoft excel to find out the distribution of the vehicles 2.1. Signal near Periyar bus stand Figure 1. Signal near Periyar bus stand The corresponding directions are given as below D1- near Periyar bus stand D2- towards bridge D3- from bridge D4- towards Simmakal D5- from Simmakal D6- towards town hall road editor@iaeme.com

3 A Comparison of Delay Models in Vehicular Traffic The distribution of the vehicles along all the six directions for the study period is shown in the figures 2-7 below. From these figures it is obvious that 2 wheelers are in majority when compared with the other two types of vehicles. Thus theoretically these low motor vehicles will have greater impact on traffic congestion in the signal near Periyar bus stand. From Fig 3, it is noted that the population of high motor vehicles is very much negligible along the direction d2 when compared with all the other directions. Therefore the impact of high motor vehicle on D2 can be neglected. Figure. 2 - D1 Figure. 3 D2 Figure. 4 D3 Figure. 5 D4 Figure. 6 D5 Figure. 7 D editor@iaeme.com

4 Jeyanthi.B and Rajaram.R.P.C.S 2.2 Signal near Goripalayam: Figure. 8 Signal near Goripalayam The corresponding directions are given as below D1- From Periyar bus stand D2- Towards hospital D3- From hospital D4- From Matuthavani D5- Towards Matuthavani D6- Towards Periyar The distribution of the vehicles along all the six directions is shown in the figures below. It is observed from these figures that the distribution of low motor vehicles is more or less equal in all the directions. Therefore it is obvious that the impact of low motor vehicles on traffic congestion on all the directions will be the same. But the distribution of heavy motor vehicles and medium motor vehicles are fluctuating in each direction thus their impact on traffic congestion will also be different from each direction. Figure. 9 D1 Figure. 10 D editor@iaeme.com

5 A Comparison of Delay Models in Vehicular Traffic Figure. 11 D3 Figure. 12 D4 Figure. 13 D5 Figure. 14 D6 3. SIMULATION AND MODELING For simulating the traffic conditions prevailing in Periyar and Goripalayam AMOS 20 software is used. AMOS is a graphical modeling tool which is extensively used in management field Data input The collected data cannot be directly fed into AMOS. In order to feed the data it must be entered in SPSS working sheets. Thus entered data can then be used for processing Data processing For generating a graphical model Structural Equation Modeling (SEM) is used here. It refers to a diverse set of mathematical models, computer algorithms, and statistical methods that fit networks of constructs to data. The links between constructs of a structural equation model may be estimated with independent regression equations or through more involved approaches such as those employed. In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variable (or 'predictors')use of SEM is commonly justified in the social sciences because of its ability to impute relationships between unobserved constructs (latent variables) from observable variables. The graphical delay model developed for both signals near Periyar bus stand and Goripalayam intersection is as given in the figure 15 and figure 16 below respectively editor@iaeme.com

6 Jeyanthi.B and Rajaram.R.P.C.S Figure. 15 Graphical Delay Model near Periyar Bus Stand Figure. 16 Graphical Delay Model at Goripalayam Where, D1- D6- no of low motor vehicles along the six directions in signal near Periyar bus stand

7 A Comparison of Delay Models in Vehicular Traffic MD1- MD6- no of medium motor vehicles along the six directions in signal near Periayar bus stand. HD1- HD6- no of high motor vehicles along the six directions in signal near Periyar bus stand. LVDI LVD6- no of low motor vehicles along the six directions in signal near MVD1- MVD6- no of medium motor vehicles along the six directions in signal near HVDI- HVD6- no of high motor vehicles along the six directions in signal near Queue length D1- queue length formed along the direction D1 in signal near Periyyar bus stand. Queue length D3- queue length formed along the direction D3 in signal near Periyyar bus stand. Queue length D5- queue length formed along the direction D5 in signal near Periyyar bus stand. D1QUEUE- queue length formed along the direction D1 in signal near D3QUEUE- queue length formed along the direction D3 in signal near D4QUEUE- queue length formed along the direction D4 in signal near The queue lengths formed and different categories of the vehicles are considered as latent variables since they are the observed values and represented in boxes. And all other variables used that are represented in oval and circle shapes are unobserved variables or may else be defined as disturbance factors in the selected system. 4. RESULT The output of the two graphical delay models corresponding to the signals near Periyar and Goripalayam intersections are given in the table 1 and editor@iaeme.com

8 Jeyanthi.B and Rajaram.R.P.C.S Table.1 Output of graphical delay model signal near Periyar bus stand Table 2 Output of graphical delay model signal at Goripalayam 5. CONCLUSION Since the impact of the low motor vehicles is very high on the traffic congestion. Only the positive values should be taken into consideration and the negative values can be neglected since they are of very low or negligible significance. From table 1 and table 2 it is observed editor@iaeme.com

9 A Comparison of Delay Models in Vehicular Traffic that the low motor vehicles have greater impact on traffic congestion when compared with other two categories of vehicles in both the signals. While the high motor vehicles have negligible significance in signal near Periyar bus stand whereas it has noticeable significance in signal near Goripalayam intersection when compared with the signal near Periyar bus stand. REFERENCES [1] Khodakaram Salimifard and Mehdi Ansari (2013) Modeling and Simulation of Urban Traffic Signals published in International Journal of Modeling and Optimization, Vol. 3, No. 2. [2] Aiswaria, Mohan, Janet Lourds, Rani (2013) An Intelligent Algorithm for Traffic Signal Scheduling, International Journal of Chemical, Environmental & Biological Sciences (IJCEBS) Volume 1, Issue 1 ISSN (Online). [3] R. Akçelik, B. Christensen and E. Chung, (1998) A comparison of three delay models for sign-controlled intersections. Third International Symposium on Highway Capacity, Copenhagen, Denmark, Volume 1. [4] S. Velmurugan,E.R.Rampalli Madhu, k. Ravinder, k. Sitaramanjaneyulu & S. Gangopadhyay (2010) Critical evaluation of roadway capacity of multi-lane High speed corridors under heterogeneous traffic Conditions through traditional and microscopic Simulation models, Journal of the Indian Roads Congress. [5] Osigwe Uchenna Chinyere, Oladipo Onaolapo Francisca,Onibere Emmanuel Amano (2011), Design and simulation of an intelligent traffic Control system, International Journal of Advances in Engineering & Technology, IJAET ISSN: [6] K.Vidhya, A.Bazila Banu (2014), Density Based Traffic Signal System, International Journal of Innovative Research in Science, Engineering and Technology,Volume 3, Special Issue3 [7] Vincent O, Olayiwola O. E, Kosemani O. O (2014), Estimating Traffic Intensity at Toll Gates Using Queueing Networks,(IJACSA) editor@iaeme.com