SUMO2018:Papers with Abstracts

Abstract. This paper presents how emergency vehicles can be modeled and simulated in the microscopic traffic simulation SUMO (Simulation of Urban MObility). The special rights of emergency vehicles are implemented in the SUMO framework and can be switched off and on in the simulation with a blue light device. The surrounding traffic reacts accordingly to the emergency vehicle and form an emergency lane. In addition real world data from emergency vehicles are used to evaluate the driving behavior of emergency vehicles and compare it to real world and simulated vehicle characteristics. The evaluation results show that the simulated vehicles pass an intersection generally faster than in real world. For emergency vehicle a time saving of in average one second at a single intersection could be measured for right turning vehicles.
Abstract. Airports build the pillar for international mobility and are core elements of intermodal traffic. They are interface between ground level transportation and air transport and commonly act as central points for logistics.
The DLR project “” investigates the interaction between relevant traffic modes and develops innovative approaches for integrated traffic management of ground and air level to extend the management of an airport not only to airport landside and to terminal processes but to go even further and incorporate feeder traffic in the management of airport processes. The realization of an integrated traffic planning is the foundation for several research questions, how the interaction between planes and ground transport can be improved. While the overall airport management benefits from more efficiency, travelers should be supported with a door-to-door management service.
The simulated scenario consists of four cities in a multi airport region. The simulation of the region includes three airports in different sizes with the simulation of the feeder traffic to the airports. All cities are connected by road and railroad and public ground level transport is available.
The procedure switching between a ground level vehicle and a plane needs several time consuming steps at the airport for individual passengers, including security checks, boarding and so on. Therefore, these steps are also simulated at the airport on a passenger level. Furthermore, the dispositioning of flights must be considered. On the ground level individual traffic, public road transport and railways are included in the simulation environment. In total, the complete simulation environment is built of nine simulation models with various abstraction levels. For the simulation of ground level vehicles in this environment, SUMO is used. This paper discusses the coupling and the data exchange between the different simulators and further focuses on the integration of SUMO in this setup.
Abstract. Traffic information services and traffic simulations represent a crucial element for to- day’s mobility. Traffic data may be gained using different types of sensor technologies and measurement approaches. However, there is no “one fits for all solution” related to the application of sensor technology and providers of traffic information services need to care- fully decide when to apply which kind of sensor technology and measurement approach to provide traffic information.
In Upper Austria, ITS Upper Austria represents such a traffic information provider. For the calculation of travel times and delays, real-time traffic sensors and a traffic simulation are currently in use. The latter is required when the amount of current real–time traffic information related to a link is too low for providing reasonable traffic information.
ITS Upper Austria implemented its traffic simulation using the SUMO software. The demand model used for the simulation was built years ago, mainly using data from a household survey in Upper Austria in 2012. Based on this demand model, a route file was composed, which serves as input for the mesoscopic simulation. However, to increase the quality of the simulation, the route file needs to be continuously updated with respect to changing traffic behaviors (e.g. route traces, amount of cars). Different types of sensor data might trigger the calibration of traffic simulation models. For example Floating-Car-data, Bluetooth-data, data gained by permanent counting stations or even traffic times gained within test rides. Triggering updates of the traffic simulation model requires a careful analysis of the data basis and an appropriate update algorithm.
This paper presents a traffic simulation update algorithm based upon diverse traffic data sources. Furthermore, findings related to the applicability of different sensor technologies for triggering simulation model updates are discussed. The findings stem from developments and empirical tests of ITS Upper Austria. The results could inform traffic information service provides when selecting sensor technology or when designing update mechanisms related to traffic simulation models.
Abstract. Cooperative Intelligent Transportation Systems (C-ITS) are a viable solution when it comes to the optimization of the ever-growing population moving in the cities. C-ITS studies have to deal with telecommunications issues and location errors due to the urban environment, while keeping into account realistic mobility patterns. A detailed and state of the art scenario is complex to generate and validate. There is a trade-off between precision and scalability. Additionally, precise information may be problematic to obtain or use due to privacy issues. There are some general-purpose freely-available scenarios, but none of them provides a 3D environment with intermodal traffic. Nonetheless, the 3D environment is a requirement to have reliable C-ITS simulations in a realistic setting, and the importance of intermodal mobility cannot be overlooked in planning the future of smart cities. The Monaco SUMO Traffic (MoST) Scenario aims to provide a state of the art 3D playground with various kind of vehicles, vulnerable road users and public transports to test C-ITS solutions. This paper presents the data requirements, characteristics, possible use cases, and finally, the limitations of MoST Scenario.
Abstract. Driving simulators provide a safe testing environment for reactions of human drivers in traffic. Traffic simulations traditionally focus on deducing infrastructure efficiency mea- sures. Both types of simulation model similar aspects but differ in scale and detail. They mostly target different research domains, too, like psychologists, automotive or traffic engi- neers. Current traffic studies tend to simplify driving assistance systems a lot. As those get developed further, interdisciplinary collaboration may help to model their impact on the traffic system. A short literature overview of simulation couplings in this field, their appli- cations and challenges is given. In this work, a coupling mechanism is being developed to run the driving simulation SILAB and the microscopic traffic simulation SUMO in parallel. Components include a mutual traffic participant exchange, traffic light states transfer, and an automatic road network converter. Here, the technical concept of a closed loop as well as the planned application are presented. The human-driven car from SILAB is placed into SUMO and makes the other vehicles react, and vice versa: SUMO-controlled vehi- cles act as surrounding traffic in SILAB. Firstly, this facilitates the driving simulation of urban road networks with many random traffic participants. Secondly, microscopic traffic simulation may profit from the insights gained from the test persons driving in SILAB.
Abstract. In this paper, Chula-Sathorn SUMO Simulator (Chula-SSS) has been proposed as an educational tool for traffic police and traffic engineers. The tool supports our framework to develop actuated traffic signal control logics in order to resolve urban traffic congestion. The framework design aims to incorporate the tacit traffic control expertise of human operators by trying to extract and extend the human-level intelligence in actuating logically traffic signal controls. In this regard, a new software package has been developed for the microscopic-mobility computer simulation capability of the SUMO (Simulation of Urban MObility) platform. Using the SUMO TraCI, our package implements the graphical user interface (GUI) of actual traffic light signal control panel, recently introduced in Bangkok (Thailand) for traffic police deployment in the Chulalongkorn University’s Sathorn Model project under the umbrella of Sustainable Mobility Project 2.0 of the World Business Council for Sustainable Development (WBCSD). The traffic light signal control panel GUI modules can communicate via TraCI in real-time to SUMO in order both to retrieve the raw traffic sensor data emulated within SUMO and to send the desired traffic light signal phase manually entered via GUI by the module users. Each of the users could play a role of traffic police in charge of actuating the traffic light signal at each of the controllable intersections. To demonstrate this framework, Chula-SSS has been implemented with the calibrated SUMO dataset of Sathorn Road network area. This area is one of the most critical areas in Bangkok due to the immense traffic volume with daily recurring traffic bottlenecks and network deadlocks. The simulation comprises of 2375 intersection nodes, 4517 edges, 10 main signalised intersections. The provided datasets with Chula-SSS cover both the morning and evening rush-hour periods each with over 55,000 simulated vehicles based on the comprehensive traffic data collection and SUMO mobility model calibration. It is hoped that the herein developed framework and software package can be not only useful for our Thailand case, but also readily extensible to those developing and least- developed countries where traffic signal controls rely on human operations, not yet fully automated by an area traffic controller. In those cases, the framework proposed herein is expectedly an enabling technology for the human operators to practice, learn, and evolve their traffic signal control strategies systematically.
Abstract. Emerging developments in the field of automotive technologies, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems, are expected to enhance traffic efficiency and safety on highways and urban roads. For this reason, substantial effort has been made by researchers to model and simulate these automation systems over the last few years. This study aims to integrate a recently developed car-following model for ACC and CACC equipped vehicles in the microscopic traffic simulation tool SUMO; the implemented ACC/CACC simulation models originate from empirical ones, ensuring the collision-free property in the full-speed-range operation. Simulation experiments for different penetration rates of cooperative automated vehicles, desired time-gap settings and network topologies are conducted to test the validity of the proposed approach and to assess the impact of ACC and CACC equipped vehicles on traffic flow characteristics.
Abstract. Recent technological advances in vehicle automation and connectivity have furthered the development of a wide range of innovative mobility concepts such as autonomous driving, on-demand services and electric mobility. Our study aimed at investigating the interplay of these concepts to efficiently reduce vehicle counts in urban environments, thereby reducing congestion levels and creating new public spaces to promote the quality of live in urban cities. For analysis, we implemented the aforementioned factors by introducing the concept of robo-taxis as an autonomous and shared mobility service. Using SUMO as the simulation framework, custom functionalities such as ride sharing, autonomous driving and advanced data processing were implemented as python methods via, and around, the TraCI interface. A passenger origin-destination matrix for our region of interest in Milan was derived from publically available mobile phone usage data and used for route input. Key evaluation parameters were the density-flow relationship, particulate-matter emissions, and person waiting- times. Based on these parameters, the critical transition rate from private cars to robo- taxis to reach a free-flow state was calculated. Our simulations show, that a transition rate of about 50% is required to achieve a significant reduction of traffic congestion levels in peak hours as indicated by mean travel times and vehicle flux. Assuming peak- shaving, e.g. through dynamic pricing promised by digitalization, of about 10%, the threshold transition rate drops to 30%. Based on these findings, we propose that introducing a robo-taxi fleet of 9500 vehicles, centered around mid-size 6 seaters, can solve traffic congestion and emission problems in Milan.
Abstract. The microsimulation of larger cities would be of a considerable gain for urban planning. However, compared with traditional traffic flow assignment models, transport networks suitable for microsimulations require a large amount of data (connectivity, properties, road/rail signaling, etc.). One persistent problem with microsimulation models has been that the creation of large urban networks is very resource consuming. Therefore, it would be of considerable value if the modeling process of microsimulation networks could be largely automated.
SUMO is one of the most versatile, open source mobility simulators with the capability of modeling virtually all urban transport systems, including roads, rail and even water- ways. Openstreetmap (OSM) is an open and feature-rich editable map of the world that is potentially the best data-source for creating micro-simulation networks. But OSM data is incomplete, imprecise and imperfect as it is edited by volunteer laymen, and it has many descriptive, but ambiguous attributes that leave ample room for interpretation.
The present work proposes an alternative method to import OSM data into SUMO with the goal to generate a functioning SUMO microsimulation network that is as close as possible to reality, while overcoming some of the deficiencies buried in OSM data. For this purpose, a recent software package, called OSMNx is used, which generates a directed graph (as networkx graph object) from OSM data, by resolving many topological issues. Both, OSMnx and networkx are Python packages. Within the SUMOPy framework, the following additional software is developed: (i) a converter from networkx graph to a SUMO network; the conversion is performed by processing additional information from the original OSM data; (ii) a realistic traffic light system generator for major intersections.
It is shown that the software converts even complex junctions correctly into a SUMO network. However, the creation of efficient traffic light systems (a prerequisite for simulating a larger urban area) turns out to be problematic as deficient information from OSM is difficult to replace by heuristic methods.
Abstract. This article explains a travel demand generator developed within the SUMOPy frame- work which aims at providing person-based plans for the SUMO micro-simulator. The plan generation has four principal steps: 1.) a population needs to be generated, with specific attributes for each person; 2.) activities and their associated locations need to be identified, 3.) travel plans need to be generated, with the aim to connect the various activities in an efficient manner. 4.) A microsimulator determines the effective travel times for each plan which persons can use to modify or change their plan. In a first part, this article briefly describes other software packages which allow activity based demand models. It is further explained that the use of SUMO as microsimulator is particularly suited to evaluate multi-modal travel plans.
The article then focuses on SUMOPy’s activity based demand model and in particular on the population synthesizer, plan generation and plan selection step. SUMOPy’s activity based demand framework has two distinguishing features: 1.) the time travel budget can be controlled during the population synthesizing process; 2.) The concept of abstract mobility strategies – each person may have different feasible plans, following different mobility strategies. The SUMO micro-simulator is used to evaluate the effective travel time of plans for the entire population. Regarding the plan selection method, a method is described if and how persons change plans based on the the effective travel times experienced after each simulation run. It is shown by means of a synthetic network and a realistic city network that the proposed algorithm is converging and total travel times are decreasing after each simulation run until an equilibrium is reached. Some preliminary attempts were undertaken to improve the speed of convergence. For both of the analyzed networks an equilibrium has been reached after approximately 10 simulation runs.
Abstract. We detail the motivation and design decisions underpinning Flow, a computational framework integrating SUMO with the deep reinforcement learning libraries rllab and RLlib, allowing researchers to apply deep reinforcement learning (RL) methods to traffic scenarios, and permitting vehicle and infrastructure control in highly varied traffic envi- ronments. Users of Flow can rapidly design a wide variety of traffic scenarios in SUMO, enabling the development of controllers for autonomous vehicles and intelligent infrastruc- ture across a broad range of settings.
Flow facilitates the use of policy optimization algorithms to train controllers that can optimize for highly customizable traffic metrics, such as traffic flow or system-wide average velocity. Training reinforcement learning agents using such methods requires a massive amount of data, thus simulator reliability and scalability were major challenges in the development of Flow. A contribution of this work is a variety of practical techniques for overcoming such challenges with SUMO, including parallelizing policy rollouts, smart exception and collision handling, and leveraging subscriptions to reduce computational overhead.
To demonstrate the resulting performance and reliability of Flow, we introduce the canonical single-lane ring road benchmark and briefly discuss prior work regarding that task. We then pose a more complex and challenging multi-lane setting and present a trained controller for a single vehicle that stabilizes the system. Flow is an open-source tool and available online at
Abstract. The Project Modelling and Control of Urban Traffic in the City of Medell ́ın (MOY- COT) has produced multiple results in modelling, simulation and control of multimodal urban traffic using the SUMO simulator. As the simulations became more complex the necessity to distribute the computational load rose. Therefore, an approach for network partitioning and border edges management was introduced. In this paper a new border edge management strategy is presented for distributed simulation with SUMO. Unlike the previous approaches, which were developed in Python programming language using the corresponding TraCI client and tools such as sumolib, the strategy presented in this work was developed in C++ using the TraCI client for this language. Additionally, this strategy involves a simplified process for network partitioning since the border edges are preserved in every partition, without the need of splitting them. In this case, neighboring partitions behave in a master-slave fashion, depending on whether the border edge is an incoming edge or an outgoing edge. Concretely, a given partition is a master for its incoming edges and a slave for its outgoing ones. Furthermore, all the vehicles are found in the master and the slave partitions, where the master partition controls its slaves through the TraCI commands slowDown and moveTo that correct the position of these vehicles. Simulation results show that this new strategy presents better precision than the previous one. The description of the new procedure for border edge management is detailed. Finally, it is compared with the previous approach and the non-distributed simulation using a free flow scenario and a scenario with queue formation is presented.
Abstract. To overcome the data insufficiency and achieve reasonable simulation results this paper has proposed to use the Webster’s delay model, together with given route information, to optimize traffic signal programs generated by SUMO. The pre-timed traffic signal cycle length and the green time allocation will be optimized for each given traffic signal program accordingly. The proposed approach takes also signal sharing among intersections into account. Two cases studies, i.e. one single intersection and one traffic signal controlled area, are conducted to evaluate the performance of the proposed approach. The simulation results show that there are apparent reductions in average trip duration, waiting time, time loss and departure delay with use of the proposed approach. The traffic efficiency can be improved consequently. In addition, some issues are pointed out as future works for extending the proposed approach.
Abstract. We present an algorithm which calculates a list of routes in a street network, approximating the flows given on the streets by traffic counts. We prove optimality with respect to maximizing the flows treating the counts as constraints and show that the algorithm can cope very well with missing data using a real motorway example.
Abstract. In the mobility sector, autonomous driving will become more and more part of our daily life. Most of all, in public transportation the research to exploit the new possibilities of autonomous driving has increased drastically. But the problem of the last mile is still unsolved, for example. The last mile is the problem to transport people from a transportation network (examples of endpoints are parking lots or bus station) to their final destination. A promising solution to this problem are autonomous RoboShuttles. Because of their low velocity, they can operate in the shared space where pedestrians and vehicles share the same traffic area. Therefore, in a shared space the interaction between them is greater than under normal traffic conditions, where the traffic flows are separated as much as possible. Through this higher interaction, new requirements on the autonomous vehicle arise. To explore the new requirements and to understand the interaction of a RoboShuttle in a shared space, a simulation scenario in SUMO is set up.
Abstract. This paper describes the validation of Chinese driver models in SUMO that enables a Chinese traffic simulation in the ChAoS framework. For validation a multi-level-concept is used, meaning that microscopic and macroscopic parameters are used. The results are discussed with respect to the characteristics of Chinese traffic, especially the improvements made by the sublane model.
Abstract. Dynamic Route Optimization is a generic problem for the commuter traveling diagonally in the smart cities; complex road network poses challenges for the heterogeneous agents to opt for route from source to destination. In smart grid of road network where intersections, roundabouts, footpaths, pedestrian bridges and tunnels having variant topographic features as a result route optimization create diversity. In various part of the cities where gridlock observed, consequently routing application recommend a route where a grid of intersecting streets completely congested where no vehicular movement is possible. In the paper we explored that how to enhance the utility of an existing application, prevent the gridlock affects and non- deterministic delay by considering topographic features of road networks using optimal shortest path routing algorithm Dijkstra. For this purpose, instituting a profile of Agents and feature recording of road network, e.g. height, width, speed and capacity is a prerequisite. Augmentation of Dijkstra algorithm according to the topographies of a Heterogeneous Agent, road network and agent simulation using SUMO (Simulation of Urban Mobility) in real time environment.
Commuter described the intricate parameter of an agent, e.g. height, width and capacity at the time profile creation, recall the parameter while devising route and its calculation of alternative preferences. Functional utility authenticated and compared with the existing application, e.g. Google Maps, OSM (Open Street Maps).