The Macroscopic Fundamental Diagram (MFD) relates traffic flow (vehicles/hour) and density (vehicles/km), and can be used to support decisions on how to mitigate traffic congestion in some region. The MFD is usually computed over an area characterized by a homogeneous traffic pattern. For this reason, when considering multiple traffic assignments for the same transportation infrastructure, the different patterns of traffic densities arising result in MFDs computed over different areas, which cannot be meaningfully compared. In order to allow the use of MFDs to compare the impact of different traffic assignments, partitioning of the region needs to be done based on an infrastructure metric that will not change the resulting area. This paper assesses the use of the administrative boundaries of a city to partition the area in a way that satisfies this requirement. Using this partitioning method, we show how MFDs can be used to quantify the impact of a mode shift from cars to powered two wheelers on traffic congestion in a city-scale multi-modal mobility simulation of Monaco. Our results show that it is possible to use administrative boundaries to generate MFDs, and to use them to evaluate the impact of multiple traffic assignments on the same transportation infrastructure.

A digital twin is a digital representation of a physical process, person, place, system or device. Digital twins were originally designed to improve manufacturing processes using simulations that have highly accurate models of individual components. However, with increasingly large and accurate building information models (BIM) combined with big data generated from IoT sensors in a smart city, it is now possible to create digital twin smart cities. An accurate 3D model of a city can be published online and walked around by the public to view proposed changes in urban planning and policy. This allows for easier dissemination and transparency to the public before putting these decisions into practice. This open and public model allows for an additional virtual feedback loop where citizens can interact and report feedback on planned changes in the city. Citizens can also interact with components to tag and report problems in their area. The digital twin also allows for additional experimentation where 3D data is necessary, such as flood evacuation planning. In this paper, we demonstrate a public and open digital twin of the Docklands area in Dublin, Ireland and show how this model can be used for urban planning of skylines and green space allowing users to interact and report feedback on planned changes.

Multi-modal journey planning for large numbers of simultaneous travellers is a challenging problem, particularly in the presence of limited transportation capacity. Fundamental trade-offs exist between balancing the goals and preferences of each traveller and the optimization of the use of available capacity. Addressing these trade-offs requires careful coordination of travellers’ individual plans. This paper assesses the viability of Deep Reinforcement Learning (DRL) applied to simulated mobility as a means of learning coordinated plans. Specifically, the paper addresses the problem of travel to large-scale events, such as concerts and sports events, where all attendees have as their goal to arrive on time. Multi-agent DRL is used to learn coordinated plans aimed at maximizing just-in-time arrival while taking into account the limited capacity of the infrastructure. Generated plans take account of different transportation modes’ availability and requirements (e.g., parking) as well as constraints such as attendees’ ownership of vehicles. The results are compared with those of a naive decision-making algorithm based on estimated travel time. The results show that the learned plans make intuitive use of the available modes and improve average travel time and lateness, supporting the use of DRL in association with a microscopic mobility simulator for journey planning.

In this paper, we define a workflow and a toolchain to support fast mobility scenario prototyping based on open data and open-source software. SAGA is an activity-based multi-modal mobility scenario generator for the Simulation ofUrban MObiltiy (SUMO). Starting from an OpenStreetMap (OSM) file, SAGA extracts the data required to build an amulti-modal scenario, and in a step-by-step fashion, generates the configurations needed to execute it, including the intermediate steps required to refine the scenario with additional data, allowing the iterative improvement of realism and representativeness. The workflow implemented, extended, and automated by SAGA was developed while hand-crafting the Monaco SUMO Traffic (MoST) Scenario. Based on the fast prototyping capabilities added by SAGA, the creation of a multi-modal mobility scenario is readily achievable, and the incremental process to fine-tune it is supported by a workflow instead of being solely based on expert knowledge and experience. Based on previous experience, the generation of the first working prototype of a city-scale multi-modal mobility scenario may take months of work and expert knowledge. SAGAautomatically generates such a prototype, and all the intermediate configuration files are made available for further iterative improvements.

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. The multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now, the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent, advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. The results demonstrate its optimality, robustness, and sample efficiency over the other state-of-the-art decentralized MARL algorithms.

In the last decade, many efforts to solve traffic congestion and sustainable growth issues are going in the direction of research and investments in smart cities and consequently smart mobility. We use the proposed simulation framework is compatible with SUMO 1.1.0. We use it to study multi-modal commuting and parking optimization issues in a state-of-the-art large-scale mobility scenario, and we intend to demonstrate the ease of use and its capabilities.

SUMO2019:Editor's Preface This volume contains the papers presented at the SUMO Conference 2019 Simulating Connected Urban Mobility. The conference was held in Berlin from 13-15 May 2019. The goal of the conference was to present new results in the field of mobility simulation and modelling using traffic tools and data which are open available.There were 32 submissions. Each submission was reviewed by at least 2 program committee members. The committee decided to accept 22 papers. Traffic simulations have a high value for traffic research studies. New traffic strategies can be tested and evaluated in advance with little costs. For realistic simulation results a complex traffic simulation framework is needed. One microscopic traffic simulation for this purpose is the open source tool Eclipse SUMO (Simulation of open mobility) which is available since 2001. SUMO provides a wide range of transport planning and modelling applications. The major topic of the 7th SUMO conference is the simulation of connected vehicles. This volume contains articles about simulator coupling, connected and automated Vehicles. Furthermore, the journal includes also papers about new algorithms for traffic light systems and new applications for the simulation of other traffic modes or reinforcement learning strategies. We would like to thank EasyChair for the conference support and its helpfull conference management tool. Laura Bieker-Walz Melanie Weber Robert Hilbrich Michael Behrisch July 24, 2019 Berlin

The Internet of Things (IoT) has accelerated the innovations in the connected car domain. Together with Intelligent Transportation Systems (ITS), the IoT technologies envision unleashing efficient and more sustainable transportation systems. A connected car is already considered as an IoT resource whose capabilities can be exposed through an IoT platform, but few testbeds provide full-stack, cloud-based environment for connected car services experimentation. This paper formulates several research and engineering challenges for developing such testbed. Our proposed testbed exploits the IoT and microservices to provide advanced functionalities such as (i) the integration of heterogeneous sensors and vehicular communication technologies in a complete end-to-end stack and (ii) an Edge Server for vehicular data annotation, local processing with actuation. The testbed exposes the vehicular resources securely through a Cloud based framework and open APIs. They are supported by The Web of Things (WoT) based interoperable descriptions of vehicular devices and interactions among relevant actors. Finally, the Cloud system deploys the connected car services using microservices philosophy. In addition to that, we present a functional IoT architecture of the testbed, describe its operational phases, novel aspects, prototyping and experimentation details.

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.

We collectively decided that investing in smart cities, and consequently smart mobility, is the appropriate direction to solve traffic congestion and sustainable growth issues. Among the problems linked to traffic congestion, we find the complexity of efficient multi-modal commuting and the eventual search of a parking spot. Ideally, mobility should be a transparent service for the users and the quest to find parking should not exist in the first place. In order to achieve this goal, we need to study large-scale parking management optimizations. Recently we reached the computational power to simulate and optimize large-scale cities, but problems such as the complexity of the models, the availability of a reliable source of data, and flexible simulation frameworks are still a reality. We present the general-purpose Python Parking Monitoring Library (PyPML) and the mobility simulation framework. We discuss the implementation details, focusing on multi-modal mobility capabilities. We present multiple use-cases to showcase features and highlight why we need large-scale simulations. Finally, we evaluate PyPML performances, and we discuss its evolution.

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.

Both the industrial and the scientific communities are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sources. Usually, a vehicular traffic simulator, with an appropriate scenario for the problem at hand, is used to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is made based on the type of simulation required, but a common problem is finding a realistic traffic scenario. The aim of this work is to provide and evaluate a scenario able to meet all the basic requirements in terms of size, realism, and duration, in order to have a common basis for evaluations. In the interest of building a realistic scenario, we used information from a real city with a typical topology common in mid-size European cities, and realistic traffic demand and mobility patterns. In this paper, we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with our evaluation and validation of the traffic demand and mobility patterns.

The information that we are collecting in the cities is enhancing our understanding of their dynamics. Cooperative Intelligent Transportation Systems (C-ITS) are using mobility information from vehicles and vulnerable road users to improve road traffic and safety in urban and extra-urban environments. In this field, optimizations are usually done through simulations. An interactive mobility scenario has to fully integrate different means of traffic to achieve this goal; nonetheless, a scenario able to integrate vulnerable users such as pedestrians, bicycles, and motorbikes, is not yet available. In this paper, we present the analysis of the requirements and preliminary work to create a realistic scenario for urban mobility able to integrate vulnerable users. Based on the Principality of Monaco, the Monaco SUMO Traffic (MoST) Scenario presents a perfect playground to study advanced parking management solutions, and alternative transport modes applications, while keeping into account realistic telecommunication models.

Interconnected vehicles are a growing commodity providing remote access to onboard systems for monitoring and controlling the state of the vehicle. Such features are built to facilitate and strengthen the owner's knowledge about its car but at the same time they impact its safety and security. Vehicles are not ready to be fully connected as various attacks are currently possible against their control systems. In this paper, we analyse possible attack scenarios on a recently released all-electric car and investigate their impact on real life driving scenarios. We leverage our findings to change the behaviour of safety critical components of the vehicle in order to achieve autonomous driving using an Open Vehicle Monitoring System. Furthermore, to demonstrate the potential of our setup, we developed a novel mobile application able to control such vehicle systems remotely through the Internet. We challenge the current state-of-the-art technology in today's vehicles and provide a vulnerability analysis on modern embedded systems.

Various vehicular communities ranging from telecommunication to infrastructure are working on problems related to traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. In order to test the solutions, the first step is to use a vehicular traffic simulator and an appropriate scenario. Many mobility simulators are available, but a common problem is finding a realistic traffic scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible uses.

Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible use cases.

Dear reader, You are holding in your hands a volume of the series „Reports of the DLR-Institute of Transportation Systems“. We are publishing in this series fascinating, scientific topics from the Institute of Transportation Systems of the German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt e.V. - DLR) and from his environment. We are providing libraries with a part of the circulation. Outstanding scientific contributions and dissertations are here published as well as projects reports and proceedings of conferences in our house with different contributors from science, economy and politics. With this series we are pursuing the objective to enable a broad access to scientific works and results. We are using the series as well as to promote practically young researchers by the publication of the dissertation of our staff and external doctoral candidates, too. Publications are important milestones on the academic career path. With the series „Reports of the DLR-Institute of Transportation Systems / Berichte aus dem DLR-Institut fur Verkehrssystem¬technik“ we are widening the spectrum of possible publications with a bulding block. Beyond that we understand the communication of our scientific fields of research as a contribution to the national and international research landscape in the fiels of automotive, railway systems and traffic management. This volume contains the proceedings of the SUMO2015 – Intermodal Simulation for Intermodal Transport Data, which was held from 7th to 8th May 2015 in Berlin-Adlershof, Germany. SUMO is a well established microscopic traffic simulation suite which has been available since 2002 and provides a wide range of traffic planning and simulation tools. The conference proceedings give a good overview of the applicability and usefulness of simulation tools like SUMO ranging from new methods in traffic control and vehicular communication to the simulation of complete cities. Another aspect of the tool suite, its universal extensibility due to the availability of the source code, is reflected in contributions covering rapid scenario prototyping and interfacing improvements to govern microscopic traffic simulation results. The major topic of this third edition of the SUMO conference is the interaction of different types of traffic and intermodality. Several articles cover heterogeneous traffic networks as well as logistics and pedestrian extensions to the simulation. Subsequent specialized issues such as disaster management aspects and applying agile development techniques to scenario building are targeted as well. The conference’s aim was bringing together the large international user community and exchanging experience in using SUMO, while presenting results or solutions obtained using the software or modeling mobility with open data. Let you inspire to try your next project with the SUMO suite. There are many new applications in your environment. Prof. Dr.-Ing. Karsten Lemmer

There are many studies concerning the problem of traffic congestion in cities. One of the best accepted solutions to relieving congestion involves optimization of resources already available, by means of balancing traffic flows to minimize travel delays. To achieve this optimization, it is necessary to collect and process Floating Car Data (FCD) from vehicles. In this paper we evaluate the repercussions of partial information on the overall traffic view, and consequently on the outcome of the optimization. Our study focuses on the role of the user participation rate and the availability of Road Side Units to collect the FCD. By means of simulation we quantify the impact of partially-available information on the computation of route optimization, and how it impedes traffic flows. Our results show that even minor uncertainties can significantly impact routing strategies and lead to deterioration in the overall traffic situation.

Due to the increasing deployment of city-wide IEEE 802.11 networks for nomadic Internet access, there is a great potential for users wanting to access the network while being on the move. However, due to the limited coverage range of single access points in these networks, handovers between them need to be carefully managed. This issue becomes critical if we consider vehicular users, where the high speed and mobility pattern greatly affect the connectivity performance. In this paper, we evaluate the performance of different handover approaches in a real commercial deployment under vehicular mobility constraints. In particular, we consider using a second wireless interface in order to achieve make-before-break handovers. Additionally, we identify some open issues that constrain IEEE 802.11 commercial deployments in providing completely seamless connectivity for moving vehicles.