Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network

2000 ◽  
Vol 1727 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Loren Bloomberg ◽  
Jim Dale
Keyword(s):  
Sensitivity Analysis ◽  
Performance Measures ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Design Decision ◽  
Simulation Tools ◽  
Critical Design ◽  
Complex Program ◽  
Comparative Information ◽  
Model Approach

Traffic simulation packages like CORSIM and VISSIM are frequently used as tools for the analysis of traffic since they are effective approaches for quantification of the benefits and limitations of different alternatives. The concern of those who are cautious or skeptical about the application of a complex program to making a critical design decision is often appropriate, as many models are unproven or little information about their accuracy is available. As these simulation models become easier to use, it may be practical to use more than one model in some studies. The two-model approach was applied as a means of making the analysis more reliable and the results more defensible. The results proved the consistency and reasonableness of the simulation tools and provided everyone involved with confidence about the analysis. The study also illustrated the value of using a range of performance measures and a sensitivity analysis. More generally, it proved the value of providing as much comparative information as possible before making a design decision. The results were generally consistent, and the end product was a set of clear, defensible, and well-supported conclusions. Although the experience gained through the application of CORSIM and VISSIM was in some ways unique to the study area, this experience can provide insight to other transportation professionals charged with selecting and applying these simulation models to similar networks. To that end, some of the characteristics of both models are contrasted.

Gaussian Process Metamodels for Sensitivity Analysis of Traffic Simulation Models

2013 ◽  
Vol 2390 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Biagio Ciuffo ◽  
Jordi Casas ◽  
Marcello Montanino ◽  
Josep Perarnau ◽  
Vincenzo Punzo
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Traffic Simulation ◽  
Simulation Models

Variations in Capacity and Delay Estimates from Microscopic Traffic Simulation Models

2002 ◽  
Vol 1802 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Zong Z. Tian ◽  
Thomas Urbanik ◽  
Roelof Engelbrecht ◽  
Kevin Balke
Keyword(s):  
Performance Measures ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Accurate Estimate ◽  
Microscopic Simulation ◽  
Link Length ◽  
Microscopic Traffic Simulation ◽  
Traffic Demand ◽  
Headway Distribution ◽  
Simulated Network

One of the issues involved in using microscopic simulation models is the variation in the simulation results. This study examined some of the more popular microscopic traffic simulation models, CORSIM, SimTraffic, and VISSIM, and investigated the variations in the performance measures generated by these models. The study focused on the capacity and delay estimates at a signalized intersection. The effects of link length, speed, and vehicle headway generation distribution were also investigated. With regard to variations in performance measures, the study found that CORSIM yields the lowest variations, whereas SimTraffic yields the highest. The highest variation in each simulation model normally occurs when the traffic demand approaches capacity. It was also found that delays are affected by the link length and speed in simulation models. Such an impact on delays is closely related to the range of speed variations. In general, shorter links and higher link speeds result in lower delays. There is no strong evidence that the headway distribution used to generate vehicles in the simulated network has any effect on capacity and delay estimates. Multiple simulation runs are necessary to achieve an accurate estimate on the true system performance measures. With a 10% error range in estimated delay, two to five runs may be enough for under-capacity conditions, but more than 40 multiple runs may be necessary to accurately estimate delay at, near, or over capacity.

Model-Based Design of Mechatronic Systems by Means of Semantic Web Ontologies and Reusable Solution Elements

2012 ◽  
Author(s):  
Felix Oestersötebier ◽  
Viktor Just ◽  
Ansgar Trächtler ◽  
Frank Bauer ◽  
Stefan Dziwok
Keyword(s):  
Semantic Web ◽  
Design Process ◽  
Simulation Models ◽  
Semantic Search ◽  
Design Stage ◽  
Design Decision ◽  
Functional Capability ◽  
Mechatronic Systems ◽  
Simulation Tools ◽  
Suitable Solution

When designing complex mechatronic systems, a team of developers will be facing many challenges that can impede progress and innovation if not tackled properly. In meeting them simulation tools play a central role. Yet it is often impossible for a single developer to foresee the overall impact a design decision will have on the system and on the other domains involved. For this task multi-domain simulation tools exists, but because of its complexity and the different levels of detail that are needed, the effort to specify a complete system from scratch is very high. Another challenge is the selection of the most suitable solution elements provided by the manufacturers. Currently they are often chosen manually from catalogues. The development engineer is therefore usually inclined to employ well-known solution elements and suppliers. To tackle both challenges our aim is an increase in efficiency and innovation by means of generally available solution knowledge, such as well-proven solution patterns, ready-to-use solution elements, and established simulation models [1]. Our paper presents a tool-supported, sequential design process. From the outset, the comprehensive functional capability of the designed system is supervised by means of multi-domain simulation. At significant points in the design process, solution knowledge can be accessed as it is stored in ontologies and therefore available via Semantic Web [2]. Thus, one can overcome barriers resulting from different terminologies or referential systems and furthermore infer further knowledge from the stored knowledge. The paper focuses on an early testing in the conceptual design stage and on the subsequent semantic search for suitable solution elements. After the specification of a principle solution for the mechatronic system by combining solution patterns, an initial multi-domain model of the system is created. This is done on the basis of the active structure and of idealized simulation models which are part of a free library and associated with the chosen solution patterns via the ontologies. In further designing the controlled system and its parameters with the completed model, the developer defines additional criteria to be fed into the subsequent semantic search for solution elements. Information on the latter is provided by the manufacturers as well as detailed simulation models, which are used to analyze the functional capability of the concretized system. Therefore, the corresponding idealized models are replaced automatically with the parameterized models of the solution elements containing for example the specific friction model for the chosen motor. We show this process using the concrete example of a dough-production system. In particular, we focus on its transport system. Resulting requirements for the simulation models and their level of detail are expound, as well as the architecture and benefits of the ontologies.

Sign in / Sign up

Export Citation Format

Share Document