Simulations are the first approach to test ITS applications due to the difficulties involved in deploying real test scenarios. In addition to the use of detailed network simulators, realistic vehicular movements are key to guarantee reliable results. SUMO is one of the tools most used to generate synthetic traces of vehicles for this objective. In this paper, a qualitative review of the five traffic demand generation tools of SUMO: DUArouter, JTRrouter, DFrouter, OD2trips, and MArouter, is presented. We include the advantages and limitations of each of these tools to simulate vehicles’ mobility in real scenarios. Moreover, we compare the vehicular traces obtained with these tools using real input data and a carefully debugged map of the financial district of Quito by using well-known graph metrics for network analysis. We found that despite that the tools used the same input, the value of some metrics of the ad-hoc communication network can differ up to one magnitude order.

由于部署实际测试场景所涉及的困难，模拟是测试ITS应用程序的第一种方法。除了使用详细的网络模拟器之外，逼真的车辆运动对于保证可靠的结果也至关重要。SUMO是为此目的最常用于生成车辆合成痕迹的工具之一。在本文中，对SUMO的五个交通需求生成工具进行了定性评估：DUArouter，JTRrouter，DFrouter，OD2trips和MArouter。我们包含了每种工具的优点和局限性，它们可以在实际场景中模拟车辆的机动性。此外，我们使用实际的输入数据和通过使用众所周知的图形指标进行网络分析的经过仔细调试的基多金融区的地图，比较了使用这些工具获得的车辆走线。