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Projection of Macroeconomic Impacts on the Toll Plazas Waiting Time Queueing: a Case Study in Brazil

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Projection of Macroeconomic Impacts on the Toll Plazas Waiting Time Queueing: a Case Study in Brazil International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 4 Issue 3 ǁ March. 2016 ǁ PP.68-80 Projection of Macroeconomic Impacts on The Toll Plazas Waiting Time Queueing: A Case Study In Brazil Mariana Gonçalves de Carvalho Wolff*, Marco Antonio Farah Caldas Industrial Engineering, Universidade Federal Fluminense, Niterói, Brazil. Rua Passos da Pátria, 156, Bloco D, Sala 309, São Domingos, Niterói, Brazil. SUMMARY:- This study aims to understand the flows of vehicular traffic in a toll plaza impacted by macroeconomic events. The study provides the traffic demand of commercial and small vehicles on a Brazilian highway until the year 2020. This Highway plays an important role for the oil industry and the development of the region depends mostly on it. The population, the region's Gross Domestic Product and oil production are considered as explanatory variables. The data used is divided by the toll plaza, vehicle type and area of influence. A simulation model for discrete events is used based on the actual scenario and future events. The model concludes that the amount of commercial vehicles will rise significantly, generating an increase in the queue waiting time of the toll plazas, going up to nineteen minutes. To reduce the stakeholders impact the study suggests some steps for planning the highway in the medium term. Keywords:- logistics bottleneck; simulation; macroeconomic development; transportation planning; linear regression. I. INTRODUCTION The country´s economic development needs a quality infrastructure that allows a flow of vehicles and people and also allows developing less accessible areas, facilitating their access to major cities and ports. This dynamics leads to improve production flow, either for domestic consumption or export. The National Transportation Agency (ANTT) regulates road and rail transport in Brazil. Aiming to improve the infrastructure through the concession highways program, granted to private initiative fifteen sections, covering, actually, 5.240 km. The BR-101 in the State of Rio de Janeiro is one of these concessionaires, connecting Niterói to Campos dos Goytacazes. The highway has 320 kilometers and is mostly single track (261 km, or 82%) marked by a heavy volume of traffic, according mainly in the Campos Basin, where is the largest oil reservoirs in Brazil with approximately 77% of total reserves (ANP). The discovery of new oil fields in the pre-salt area in the Campos Basin and the consequent increasing population in the region indicate that vehicular traffic will have significantly impact in the coming years, what motivates calculate how much will be the increase in demand for vehicles on this road. This analysis will allow to propose actions to reduce negative impacts on the highway concerning the exponential growth of vehicles using the road. This study considers several factors, such as increased production of oil and natural gas (discovery of large reserves in the pre-salt region, which could increase 160% by 2020). The increase of private vehicles is another issue that this study takes into account. It is happening because of the constantly income growth of the population. The vehicle demand can vary greatly over a day, month or year, according to the economic activities. The north of Rio de Janeiro region has 700,000 inhabitants, equivalent to 11% of the state Gross Domestic Product (GDP) [1]. The most important city of this region is Campos dos Goytacazes, which has 0.67% participation of Brazilian GDP in 2010. The next section brings a brief of the review of the literature. After that, a methodology that is used is detailed. Finally, the last sections bring the modeling study, followed by results, conclusions and suggestions for future studies. II. LITERATURE REVIEW 2.1 Demand Forecasting Demand forecasting is used to plan a lot of factors as described for the reconstruction plan of a highway in Lithuania, where it should identify how bottled will be the highway in a few years [2]. To this prediction it is necessary to analyze various factors of influence, such as the history of the annual daily average volume (AADT), changes in the economy and the number of people and vehicles, average distance traveled by a vehicle, industrialization rate in the region and fuel price. www.ijres.org 68 | Page Projection of macroeconomic impacts on the toll plazas waiting time queueing: a case study in Brazil A multiple linear regression is applied to predict the average daily traffic volume in Santa Catarina, between 2002 and 2008. The following variables were considered: annual rice production in southern Santa Catarina, annual production of ceramic in Southern Santa Catarina, population in Southern Santa Catarina, GDP, Selic rate, industrial production of Santa Catarina, exchange rate and domestic household expenditure. The Minitab 6.1 software presented the set of variables that best explains the movement of vehicles and their respective coefficients [3]. A determination of the demand for a highway, can exist in two forms: first when you have a new infrastructure will need to generate demand expectations and second when the infrastructure already exists and can be used historical data to forecast. In the second case the forecast will have greater consistency with reality. Is necessary to identify the expected demand for the coming years and determine how vehicles will access this highway, ie, how will be the arrival distribution [4]. The number of road accidents concerning Turkish highways consider as variables: GDP per capita, population, number of vehicles and percentage use of modal (road, rail and air transportation). For this analysis the methods of multiple neural networks and nonlinear regression are used [5]. To predict the number of accidents some variables are designed by simple linear regression and only the percentage of utilization of each mode is designed in three different scenarios from 2007 to 2020, keeping the other constant. Scenario I assumes that there will be no investment in railways, its share falling from 3.0% to 2.6%. The second preview a railroads growth of 15% per year, from 3.1% to 18.8%. Finally, scenario III considers a slight increase in investment by railroads from 2.9% to 10.1%. The study concludes that the transfer of passengers from road to rail, scenario II, significantly reduces the number of traffic accidents in Turkey. 2.2 Simulation of toll plaza Simulate dynamic systems is an advantage over analytic models, since give more information about what is happening in the system over time, which helps in decision making [6]. A toll plaza acts as a bottleneck to the flow of vehicles on highways, since the capacity of the square is usually smaller than the capacity of highway [7]. Service time in the cabins is directly related to the fare (easier change reduces the time), payment (cash or electronic billing), flow, type of vehicle (cars and trucks) and pre- collectors along the queue. Simulating an operation of the toll plaza can conclude that simple attitudes as the adoption of tariffs that facilitate change can increases the efficiency of manual collection, as shown in the study of concessionaires Rio Grande do Sul [7]. To plan the best number of cabins in a toll plaza in Porto Alegre, is simulate their flow in some distinct periods, considering the busiest period, the lowest and intermediate. Thus one can conclude what is the peak usage and idleness. The number of cabins was tested from 3 to 25 (current quantity in the toll plaza). From 20 cabins, the queue time for peak-hour is less than 2 minutes, considering one direction of the highway. Analyzing the volume from the opposite direction at the same time the need for the same queue time is 7, totaling 27 cabins. The total number of cabins is flexible and can be used for both directions [8]. A toll plaza is simulate evaluating the optimal number of cabins according to three variables: current traffic, use of automatic cabins dynamically (can be used when there are large queues in manual cabins) and encouraging motorists to increase use of automatic cabins. The study concludes that an increase of 10% in traffic can generate large queues [9]. A toll plaza simulation consider as variables: service capacity, vehicle arrival pattern, number of available gates and driver behavior. The principal results are the number of vehicles queued at the square when traffic jam occurs on departure and the average vehicle speed when crossing the square. The time of each vehicle in system is the difference between the arrival and departure. Four types of manual payments are considered: small vehicles who pay cash and no need change or toll ticket pre-paid, small vehicles that need change, heavy truck and trailer or bus. Each payment method presents its own service time and generate different queues [10]. III. METHODOLOGY The concessionaire has five toll plazas, distributed along the track and located according to the criteria of the agency. The following table presents consolidated data for commercials and passengers vehicles for each, 2009-2012. Data for 2009 are annualized for comparison purposes, since the database presented values only from February to December, when toll collection started. www.ijres.org 69 | Page Projection of macroeconomic impacts on the toll plazas waiting time queueing: a case study in Brazil 2009* 2010 2011 2012 Toll Plaza km-km Commercial Passanger Commercial Passanger Commercial Passanger Commercial Passanger Vehicles Vehicles Vehicles Vehicles Vehicles Vehicles Vehicles Vehicles São Gonçalo 300,0 - 320,1 960.414 5.899.684 1.076.258 6.491.903 1.165.635 7.176.612 1.222.605 7.602.021 Rio Bonito 253,0 - 300,0 570.524 1.396.801 1.583.703 3.847.039 1.747.367 4.118.825 1.785.839 4.390.132 Casimiro de Abreu 193,0 - 253,0 1.060.677 2.596.829 1.601.788 4.083.883 1.779.910 4.386.437 1.823.825 4.778.789 Conceição de Macabu 124,0 - 193,0 1.310.222 2.230.919 1.415.358 2.445.934 1.530.438 2.595.549 1.582.082 2.795.100 Campos dos Goytacazes 38,2 - 124,0 936.358 1.099.203 1.045.527 1.223.820 1.124.800 1.245.551 1.165.952 1.331.201 * Original data: February to December, annualized.
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