Urban Planning
mohamadreza elyasi; seyed farzin faezi; Mortaze Pazireh
Abstract
Introduction We are today facing a growing number of vehicles, which poses an interference in the performance of urban intersections, particularly Squares. Urban road network does not make a lot of problems for traffic, but its major cause is the intersections resulting in congestion in the network due ...
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Introduction We are today facing a growing number of vehicles, which poses an interference in the performance of urban intersections, particularly Squares. Urban road network does not make a lot of problems for traffic, but its major cause is the intersections resulting in congestion in the network due to the interference in the continuous flow and its conversion into incontinuous flow. This study attempted to provide the closest simulation to reality for 13 Aban Square in Hamadan by determining drivers' behavior and applying local parameters in EMSAN software. Methodology In this study, in order to evaluate the drivers' behavior at intersections, traffic parameters were performed using field studies and the review of microscopic parameters was simulated in a realistic way. Data were collected in the field is by video recording for one hour without interruption during a peak hour of noon. One of the advantages of this method over traffic counting is the calculation of vehicle origin-destination statistics, which provides a better simulation close to the current situation. First, the required geometric data must be collected from the desired location, including the width of route entries and exits, the radius of circumscribed circle, the radius of central Square and other construction protrusions, and applied carefully close to reality in the software. The statistics of route entries were recorded by video recording without interruption at the peak hour from 12:30 to 13:30 from the high point on the northwest side of the approach on Tuesday, November 21, 2018. After determining the drivers' behavior and localization practices, the data were entered into the software and the results were compared in two scenarios. A new cycle plan was also designed and provided for this intersection with SIDRA software, which was applied simultaneously with localization. First, the effect of traffic light application was determined on the microscopic characteristics of Square traffic flow such as delay, vehicle speed, level of service, number of vehicles waiting in line, and travel time using software. The results showed the lowest value following the application of drivers' behavior and simulation for delay in the first scenario and speed in the current situation and the second scenario. Travel time also showed an increase by 54% for the current situation, by 6% for the no-light mode, and by 50% for the 80-second cycle mode. Results and Discussions Validation performed by fitting approximately 80% with the current situation calculated from the software, such as the same practice regarding the latency after determining the localized parameters, revealed the accuracy of the calculations and the correctness of the path travelled in the localization process. Travel time is one of the criteria used to assess the characteristics of traffic flow in urban road network and intersections. Vehicle travel time indicates characteristics such as delay, line length, flow rate and network level of service. It is not unreasonable to expect a change in travel time charts with increasing delay and decreasing speed, so that we observe a 54% increase for the current situation, a 6% decrease for the non-lighted mode, and a 50% increase for the 80-second cycle duration. Effectiveness and accuracy of localization process can be confirmed by analyzing the effect of localization of different software parameters on the outputs, investigating the fitness of the modeling results with reality, and by comparing the difference between the software output results in the two cases before and after localization. Delay and mean travel time parameters were selected as the most significant and common parameters in lighted intersections in line with the validation of the simulations. There was only a 5.5% difference between the current situation of 13 Aban Square and the field validation, confirming the coherence of localization in the software. Conclusion As can be seen in Tables of this study, it should be noted that the delay parameter has a direct effect on other parameters of an intersection. In this regard, validation was performed for the travel time parameter in line with the delay parameter and the test vehicle method was used. However, the tables were not presented in order to avoid prolonging the article. The results revealed that the validation performed by fitting about 80% with the current situation calculated from the software, such as the same findings regarding the delay, shows the accuracy of the calculations and the correctness of the path travelled in the localization process.
