2.3.1 Introduction
In recent years, the rapid growth of ITS applications is generating an increasing demand for tools to support in designing and assessing the performance of proposed strategies. Traffic simulators are cost-effective tools to achieve these objectives. There are several reasons which make traffic simulators play an important role in traffic research area: (1) It is expensive and difficult to test and evaluate most proposed traffic strategies in real-world traffic networks; (2) For some studies, it is extremely difficult to establish ex- pected traffic parameters in order to set up the experimental environment in real-world traffic networks as in simulation models; (3) Traffic simulators are a powerful tool which allows users to determine the correctness and efficiency of a proposed strategy before it is actually constructed. Therefore, the overall cost of constructing a specific strategy would be reduced significantly. Users also can use traffic simulators to compare the con- sequences’ of a number of alternative strategies and improvement plans. Consequently, traffic simulators are one of the widely used methods in research of modelling and plan- ning as well as the development of traffic networks and systems,Kotusevski and Hawick
(2009).
Currently, there are several traffic simulation software, such as SUMO, VISSIM, MAT- Sim, AIMSUN, and Paramics. According to the level of detail which transport simula- tors can represent, they are divided into three categories: microscopic, mesoscopic, and macroscopic simulators. Macroscopic simulators describe the traffic at a high level of aggregation without considering its parts. They are mainly used in traffic flow analysis. The dynamics of every single vehicle are modelled by microscopic traffic models based
Figure 2.3: The structure of the node file of a traffic scenario simulated by SUMO,
Krajzewicz et al.(2019).
Figure 2.4: The structure of the edge file of a traffic scenario simulated by SUMO,
Krajzewicz et al.(2019).
Figure 2.5: The structure of the traffic light file of a traffic scenario simulated by SUMO,Krajzewicz et al.(2019).
on the interactions between the vehicles and their neighbourhood in detail. Mesoscopic traffic models have an intermediate level of detail, for instance, describing the individual vehicle without their interactions. Microscopic traffic simulation has proven to be a use- ful tool to support the evaluation process of ITS’s deployment, B D Venter and Barcelo
(2001). Comparative studies of traffic simulators can be found atPell et al. (2017) and
Figure 2.6: The Netconvert command to generate a traffic network file of a scenario simulated by SUMO, Krajzewicz et al.(2019).
Figure 2.7: The structure of the route file of a traffic scenario simulated by SUMO,
Krajzewicz et al.(2019).
2.3.2 Simulation of Urban Mobility (SUMO)
Simulation of Urban Mobility (SUMO) is a well-known and widely used microscopic traffic simulators Kotusevski and Hawick (2009). SUMO is a microscopic traffic simu- lation package which is highly portable, open-source and created to handle large road networks. The development of SUMO started in the year 2000 and it is mainly devel- oped by employees of the Institute of Transportation Systems at the German Aerospace Centre to provide the traffic research community a tool to implement and assess their own studies. SUMO is multi-modal which means that not only car movements are mod- elled, but also public transports, such as bus and train networks, can be included in the simulation. Due to SUMO’s high portability, it may be used on different operating systems.
There are two main components to construct a traffic simulation using SUMO which are road network representation and traffic demand. The road networks represent real-world traffic network as directed graphs, where intersections and roads are represented by nodes and edges, respectively, and they are described in XML files. The nodes are declared in the node file. Figure2.3illustrates an example of a node file. The edges contains certain attributes such as the position, shape, and speed limitKrajzewicz et al.(2012) as shown in Figure2.4. A SUMO network also can contain traffic lights, roundabouts and other transport components. An example of the traffic light file is provided in Figure 2.5.
Figure 2.8: The structure of the configuration file of a traffic scenario simulated by SUMO,Krajzewicz et al.(2019).
All the information about road network are described in the net.xml file. SUMO road networks can be either generated from XML files or converted from other input data. “Netconvert” is a road network importer which is used to import road networks from other traffic simulators as Vissim, MATsim, or VISUM and produces road network that can be used by other tools in SUMO, Krajzewicz et al. (2019). Figure 2.6 de- scribes the Netconvert command. SUMO can also read other common formats such as OpenStreetMap. The existing road network file can be edited using NETEDIT tool,
Krajzewicz et al. (2019).
The second major component in SUMO scenarios is traffic demand defining routes of vehicles. The structure of a route file is provided in Figure2.7. Routes can be generated either by using existing origin/destination matrices (O/D matrices) and convert them into route descriptions or specifying them manually. The first approach is applied mostly within the traffic science when dealing with large real-world scenarios. The second one is used when the researchers would like to have their own wishes about the traffic movements of the scenarios, Krajzewicz et al. (2012). SUMO also can import routes from other simulations. Additional information such as traffic light timing data can be integrated into the traffic simulation through additional files.
After creating network and route files, a configuration file is generated to glue every files together and the simulation scenario can be visualized in the SUMO-GUI. The structure of the configuration file of a traffic scenario simulated by SUMO is shown in Figure2.8. A large number of measurements can be generated for each simulation run in SUMO. The output can be unaggregated vehicle-based information such as positions and speed
for every simulation step or aggregated information of vehicles in their journeys. SUMO also provides information about simulated detectors, traffic lights, and values for lanes or edges. Besides common traffic measures, other metrics such as noise emission, pollutant emission, and a fuel consumption are also included in SUMO,Behrisch et al. (2011).