A. Field of the Invention
The present invention relates generally to traffic simulators, and, more particularly, to a computer-implemented system and method for simulating motor vehicle and bicycle traffic.
B. Description of the Related Art
Because most real-world systems are too complex to be evaluated analytically, they are often studied by means of simulation. In a simulation a computer is used to evaluate a model numerically, and data are gathered in order to estimate the desired true characteristics of the model. Another definition states that simulation is the process of designing a computerized model of a system (or process) and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies for the operation of the system.
Computer simulation models play a major role in the analysis of the transportation system and its components. For this purpose simulation can be defined as a numerical technique for conducting experiments on a digital computer, which may include stochastic characteristics, be microscopic or macroscopic in nature, and involve mathematical models that describe the behavior of a transportation system over extended periods of real time. By representing a traffic system as a simulation model, the effects of traffic management strategies on the system's operational performance can be measured and expressed in terms of Measures of Effectiveness (MOE).
One of the advantages of traffic simulation is its lower cost and time consumption than field experiments. Simulation can generate MOE, which cannot, in a practical sense, be obtained empirically. Disruption of traffic operations can be avoided and physical changes to existing facilities, not acceptable in the field, can be tested. Also simulation provides a high level of detail and accuracy for analyses of operational impact of future traffic demand. Computer simulation can be used for the comparison of actual planning and design alternatives, as well as for the research and development of new methods and strategies. One of the main advantages of simulation is the possibility to test different alternatives in exactly the same traffic situation in the office. Another is the great amount of detailed data about vehicle movements that can be collected, assuming that the simulation model is able to describe correctly the basic functions and interactions between vehicles, the traffic environment, and the signal control.
Traffic simulation models can be categorized, based on their level of detail, as macroscopic and microscopic. In microscopic traffic simulation the traffic is composed of individual vehicles rather than being a continuous flow. The flow and process type of traffic behavior should appear as a consequence of a large number of vehicles and their interactions. Thus the vehicle is the most active component with a major role in microscopic simulation. Macroscopic models take into consideration only the aggregate characteristics of vehicles composing the flow.
To be useful, traffic simulation must provide reasonable estimates of real world data, the time required to simulate the problem must be reasonable, and the results of the simulation must be accessible in a meaningful format. When modeling a complex real-world system it is usually not necessary to have a one-to-one correspondence between each element of the system and the model. It must be determined which aspects of the system are needed in the model, and what aspects can be ignored. Given a limited amount of time, money, and data available to develop the model, the focus should obviously be on the most important factors. Models are not universally valid since they are designed for specific purposes. On the other hand, the model must have enough detail to be credible.
The flexibility of simulation makes it possible not only to create simplified models of real systems, but also to take into account some of the basic laws governing the real world, which are the dynamic and stochastic natures of systems.
Because of the dynamic nature of most real world systems, one of the main elements of the simulation models is time. One of the principal approaches for advancing the simulation clock in a discrete simulation model is the fixed-increment time advance. With this approach, after the simulation clock is advanced by some appropriately chosen Δt time period, a check is made to determine if any events should have occurred during the previous interval of length At. Any observable change in the status of the simulated system is considered an event. The system state variables and statistical counters are updated accordingly. A set of rules must be built into the model to decide in what order to process events when two or more events are considered to occur during the same interval.
The main disadvantages of this approach are the errors introduced by processing events in time intervals, and the necessity to decide which event to process first. These problems can be made less severe by making At smaller. This on the other hand increases the number of checks for event occurrences, and thus the execution time.
The main reason for using this time scanning principle in traffic simulation models is, that in this kind of detailed model the number of events is high in relation to time, and thus the number of parallel occurrences is high. In case of event-oriented simulation this would lead to extremely short time steps. When the average number of events during a time period is significantly higher than one, the use of the time scanning approach is recommended. Another reason is that in traffic simulation programs a complex interaction process is modeled, which makes it difficult to forecast future events. Because the fixed-increment time advance method operates on “here and now” basis, it is more suitable for modeling these processes.
The simulation of any stochastic system or process requires generating or obtaining numbers that are random, in some sense. Random variates generated from the U(0, 1) distribution are called random numbers. Although the numbers generated by the random number generators are pseudo-random numbers, this inaccuracy does not have an impact on most of the practical simulation applications. Random variates from other distributions can be obtained from U(0,1) random numbers through various transformation techniques.
Exponential random variates, necessary to model Poisson arrival processes, can be generated by the inverse-transform algorithm. This method is based on the property that the cumulative distribution functions of random variables are on interval [0,1], which corresponds to the range of uniformly distributed random numbers. Based on this method the algorithm for generating exponential random variates can be written as:

1. Generate U˜U (0, 1)
2. Return X=−βln U
where X is an exponential variable with the mean β>0. This algorithm is used to generate inter-arrival times of Poisson arrival processes.
Although traffic simulation models exist, most only simulate vehicular or motor vehicle traffic. These models fail to take into consideration bicycle and pedestrian traffic. Thus, there is a significant need in the art to provide a model that simulates motor vehicle traffic, as well as bicycle and pedestrian traffic.
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