Traffic Simulation Modeling for Major Intersection

Abstract

Nowadays, computer simulation is widely used in industries to support decision-making and performance enhancement. Real situations often require optimal solutions in the presence of multiple constraints. Present study aims to solve a transportation problem with the help of simulation modeling. Different kind of models are investigated using multiple iterations to reduce vehicle cycle and waiting time at roundabout intersection. Discrete Event Simulation (DES) is done to compare base model against two alternatives, while, ARENA soft ware is used for data analysis and computations. Results indicated that the alternative 2 of round about only is the best option considering cycle time, waiting time and number of vehicles transferred out of the system. Findings are beneficial to improve traffic flow at similar intersections, where commuters face long waiting time during peak hours.

New Extensions and Applications of the Modified Chumanov Model for Calculating Entry Capacity of Single-Lane Roundabouts

“…These models have received support by advances in computer systems, allowing to manage an extremely large number of events, albeit with considerable efforts in calibration and application to real cases, including modern intelligent transport systems and automated vehicles [39]. The recent literature proposes numerous models and solutions based on commercial (e.g., Paramics [40], Aimsun [41], and Vissim [42]) or open source (e.g., SUMO [43]) proprietary microscopic simulation programs, or using for example discrete event simulations (e.g., [44]), game theory (e.g., [45,46]), multi agent systems (e.g., [47]), cellular automata (e.g., [48][49][50]), and various artificial intelligence learning approaches (e.g., [51][52][53]).…”

Testing roundabout capacity models: a practical aid for choosing the right one based on total capacity calculation

“…It is worth noting that, although it is possible to obtain context-specific information thanks to widespread traffic data acquisition technologies, psychotechnical time values from literature are often used, without delving into the specificity of the design site. Micro-simulations models have spread enormously in recent years, with varied solutions based on commercial (e.g., Paramics (SIAS, 2019), Aimsun (TSS, 2019), and Vissim (PTV, 2019)) or open source (e.g., SUMO (Behrisch et al, 2019)) proprietary simulation programs, or using for example discrete event simulations (e.g., (Bakhsh, 2020)), game theory (e.g., (Banjanovic-Mehmedovic et al, 2016;Tian et al, 2018)), multi agent systems (e.g., (Doniec et al, 2008)), cellular automata (e.g., (Wang et al, 2005;Małecki, 2017)), and various artificial intelligence learning approaches (e.g., (Gritschneder et al, 2016;Zhao et al, 2017;Capasso et al, 2020)). Despite having a great flexibility allowing to manage an extremely large number of events, these models require significant efforts to produce case-by-case implementations, with considerable efforts in calibration and application to real cases.…”