And the third factor causing the variability in the

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Unformatted text preview: presence of entrance and exit ramps. The vehicles have to decelerate on exit ramps and the vehicles should be in the shoulder lane to exit. In case of the entrance ramps, the vehicles have to accelerate to reach the posted speed limit on the freeway and they should be in the shoulder. Carter, Rakha, and Van Aerde analyzed the traffic flow at Queen Elizabeth Way in Ontario. The authors suggested that different lane flows have different impacts on the microscopic simulation models. The speed shows variability according to the lanes 14 and they also found that the day of the week does not have a significant effect on the traffic flow measures variability. Chronopoulos and Wang [19] developed a traffic simulation model through parallel processing. Chronopoulos and Wang applied the Lax method (explicit) and the Euler method (implicit) to simulate the traffic data collected from Minnesota. Entrance and exit ramp sections of the freeway are also simulated. The results of the methods used in the simulation were found acceptable when they are compared to the actual data collected from the experiment site. They concluded that the Lax and Euler methods could be used in the solution of a traffic flow continuum model. Dudek et al. [20] completed capacity studies at nine work zones on Texas and Oklahoma freeways. They concluded that individual work zones have characteristics such as the grade, automobile mix, presence of entrance and exit ramps, and number of lane closures that affect the overall traffic flow. They also concluded that it is important to estimate the impact of a lane closure and take appropriate measures to minimize traffic delays. Lemessi [21] developed a car following and lane changing micro-simulation model of a two- lane road section using SLX (Simulation Language with Extensibility). In car following algorithm, the author defines a desired speed for each driver, when the distance between a vehicle and its leader is greater than a pre-defined driver-specific critical distance, the driver tries to maintain its desired speed. When the distance between two consecutive vehicles falls below the critical distance the following vehicle either changes the lane or brakes. In the fo llowing mode the driver follows the leading vehicle, trying to maintain zero speed difference between the leading vehicle and the following 15 vehicle. In the developed model, lane changing mode occurs in three cases; during free driving mode, vehicle perceives a slower vehicle and changes lane without braking; during the braking mode, the driver stops braking and changes lane; during the following mode, driver changes the lane. In modeling lane changing behavior of the vehicles, minimum required headway distance is assumed as 50 meters. 16 3 METHODOLOGY 3.1 3.1.1 I-76 Westbound near Rootstown Construction Site Description of the Work Zone Site Used in the Example The construction work zone chosen to simulate was Ohio State Job Number 534.03, also identified as POR-76-9.50 on construction drawings. It was a bridge repair and pavement resurfacing job on I-76 in Rootstown and Edinburg Townships in Portage County. It extends from State Route 14 on the east to State Route 44 on the west. The map of the construction work zo ne site is given in Figure 1. In the summer of 2004, both lanes of eastbound traffic are crossed over to the westbound direction and the westbound traffic is reduced to a single lane for about 1.1 miles. The speed limit on I-76 was 65 miles per hour (MPH) before the construction zone, and it was 55 MPH through the construction zone. Figure 1: Map of the Work Zone Site used in the Example 17 3.1.2 Data Collection Data used in the simulation study were collected as part of the “Improved Work Zone Design Guidelines and Enhanced Model of Travel Delays in Work Zones” project for Ohio Department of Transportation. The data were collected at I-76 construction work zone in the westbound direction near Rootstown using microwave radar detectors. These detectors use a microwave radar beam as the means of detection that is reflected by passing traffic. Total of nine trailers equipped with microwave radar units were deployed at the construction zone. The locations of the trailers are shown in Figure 2. Microwave radar equipped trailers collect time-stamped individual vehicle data including exact time of passing, speed, vehicle length, and classification. The data used in the example was collected between the dates 08/20/2004 (Friday) and 08/22/2004 (Sunday). Three days of data was collected in order to reduce the affects of day to day variability in the traffic flow behavior. In addition to the trailer records, traffic was recorded on video at all trailer locations for 30 minutes in order to compare and validate trailer records. 18 Entrance Ramp TRAILER 012 Exit Ramp TRAILER 009 TRAILER 005 TRAILER 007 Milemarker 47 44 225 14 TRAILER 006 TRAILER TRAILER 013 008 TRAILER 011 TRAILER 010 1.385 miles (3.9421 miles) 1.109 miles (7.6758 miles) 1.506 miles (6.2695 miles) 0.2973 miles (6.5668 miles) 0...
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