For driving the roads of cities into enjoyable and relaxing places with parks, trees, and seating, a paradigm change in everyone’s commuter behavior is needed. Still, individual transport via cars increases, and thus, the space required for parking and driving these cars shapes our cities — not the people. Next to the space needed, vehicles pollute the environment with CO2, diesel particulate, and even electric cars with tire abrasion. Alternative modes of locomotion, like public transportation and shared mobility, are still not attractive to many people. Intelligent intermodal mobility networks can help address these challenges, allowing for efficient use between various transportation modalities. These mobility networks require good databases and simulation combined into digital twins. This paper presents how such a digital twin can be created in the Simulation of Urban Mobility (SUMO) software using data from available and future city sensors. The digital twin aims to simulate, analyze, and evaluate the different behaviors and interactions between traffic participants when changing commuting incentives. Using the city of Osnabrück and its different available sensor types, the data availability is compared with other towns to discuss how the data density can be improved. Creating a static network from open street data and intersection side maps provided by the city of Osnabrück shows how these data can be integrated into SUMO for generating traffic flows and routes in SUMO based on a database of historical and live data. Within the conclusion, the paper discusses how developing a digital twin in SUMO from static and dynamic data can be improved in the future and what common misconceptions need to be overcome.
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Title: Simulating Traffic Networks: Driving SUMO towards digital twins
[Music] yeah so thank you for the kind introduction so as you all mention if you are um yeah older folks in the sumu uh Society we are new so uh this year or last year we started to using Zumo for our uh project and uh yeah we want to use zumu for digital Twins and um before uh going into the deep um we do have an issue and this issue is that uh we want to simulate traffic without any personal reference so using traffic data that is uh in line with the laws of privacy and uh therefore yeah we came up okay can we use zumu in this kind of uh setting and um as usually um I start with a comic and uh this is uh all my students knew that uh yeah my Comics have uh something uh related to the topic and uh here we go and uh two years from now everyone uh noticed that uh this could continue like this so that the artist is painting a beautiful picture and nowadays we do have this so that people starting to create a prompt and by the use of AI we yeah we picture this uh woman and what is more gentle and what is more nice this is something you might uh decide however the first image is with person Al reference and the second image is without personal reference so the first image is not if you do not ask the person in for in forehand and the reality of the uh paint is in this way that the uh people or that the woman is uh identic uh can be identified uh really nicely then it’s with personal reference and the other thing is without personal reference and we want to simulate traffic without the reference of um vehicles and this is where our project um of uh intelligent Intermodal Comm commuter traffic is about we are a consortia of uh yeah of the osam brook of the uh bus companies of osnar Brook of University of Minster of University of Appliance science osnar BG as well as other industry partner and uh osnar Brook and Minster this is not the famous city all of you should know but uh they are nice little cities uh with a population uh about um 160,000 and 300,000 uh um people and we do have a lot of uh traffic in these cities and between the cities and the big thing is how to create a digital twin without breaking any law restriction as you know in Germany we are not allowed to uh track uh traffic plates so we are not allowed to store traffic plate numbers and we use this information later on and um therefore we will have a close look to the data of a crossing crossing and by go to uh by going to a Crossing this is the famous Crossing uh in osnar Brook on the Val we are looking okay what can we measure without personal reference and the uh first thing is that we can identify the numbers of right Turners the numbers of left Turners the numbers of traffic uh of trucks uh per minutes the number of cars per minutes then we can count uh on The Pedestrian light the number of pedestrians then we can count uh in the buses the number of passengers the delay in minutes the position of the buses as well as the rout then we can also um sense the bus stops so how many passengers are waiting on the bus stop the delay of uh the buses coming into a certain bus stops the position of the bus stops this is funny because uh we recognize that even the city does not know exactly where the bus stops are located they only have a rough idea it could be on this Edge but uh no one catches the uh GPS position of the sign correctly um and then of course we do have uh the bicycle lane where we can detect the number of bicycles the direction and the lane quality and the last thing that we could measure is the parking lot so uh how many parking lots are occupied and the type of parking lots so in the 17s um our city uh say okay we want to be a city of future and they remove all public Transportation because car is the future and this was nowadays a bad decision but in these days it was a valid decision and so we do have a lot of paring parking lot we do have uh a lot of uh roads but we did not have any longer any train or a light Railway or other things unfortunate and the last thing that we can measure is the environment so the temperature the wind as well as the air pollution concentration and this leads us to the roots of the yeah of the people traveling through the city and we do have the cars uh we do have the bike and for a car and for the bike we have the origin and the um Destin uh destination as well as for the bike but we are not allowed to store any photos of the bicycle driver to re-identify the bicycle driver on the other uh traffic light we are not allowed to store the number plate of the um car to re identify the car on the next uh stop and so we have a nonconcrete view of our traffic situation so this is somehow black and white and this is where we say okay can we use this microscopic data so the counting data of sensors to simulate a microscopic uh simulation of our town and this is where we looked to different tools we ask our city we uh discuss uh with uh the yeah with other companies providing cities with such systems and they say okay everything is closed source and you can buy a module from us to uh do this but um yeah we decided okay this is not the way we want to go and so we say okay we do have access to all the sensors due to um the shut worker as well as the bus provider and let’s go for sensor based uh traffic simulation and so we want to new the rootes of the pedestrian of the buses of the car as well as the number of passengers within the car the cyclists the trucks that we do have because uh next to the city we do have two highways and if there is an accident on the highways every navigation system say okay please drive through the city and then uh there is a big uh traffic gem uh based due to uh the big trucks and so on and we want want to improve the public transportation we want to reduce the environmental pollution we want to have a human centered urban planning we want to have uh uh traffic flow optimization we want to have improving quality of the life within the city uh we want to uh prevent accidents because why is this an important Point uh due to the fact that we do not have any light rail system in osnar book we have a lot of cyclists and so we do have a lot of accidents with cyclists and so this is an an really issue uh that unfortunate there are a lot of cyclists that pay the price for the traffic with their lives and um this is something that we need to avoid and then wheel time traffic management this is brand new to cities uh that this is uh feasible and possible and uh they are interesting Concepts and uh so on and so on and therefore we have a look to other cities and one uh City that came in our mind is Barcelona and Barcelona provides uh super uh zones and the super zones and in the super zones you do not have a car traffic that is allowed to pass these super zones so you can enter the super Z zes but you will um leave the super zones on the same direction as you entered then the um city of uh Minster uh has a new system for guiding bicycle Riders so if you see this uh greenish uh this greenish over here then you have the perfect speed to match the next Green bicycle light and so you have a flow system for your bicycles as well as uh the city of grown inion the city of grown inion say okay we have uh some one ways in our city and we want to yeah therefore increase our things and all this are static and we do not want to have static things in the city and usually if you set something like this static in the city you do not know how this works in forehand and so we want to simulate the different things that you can build on the city and then we discuss in the project team what can we do and the first thing as the big companies say okay we ask the people where GPS tracker so that they voluntary offer uh their data to us and uh then uh with the voluntary data we can model the traffic and who of you is voluntary sharing their data only one only two three okay who of you read the um license agreement of Google street uh uh and other navigation services so have in mind that all of you or most of you by navigating with your phone you um provide the data to the big companies and the big discussion is what can we give the users to share the data of their GPS tracker with us and uh the city have a good idea and say okay everyone who shared uh the data with us uh will get one coffee for free and yeah so there this is an approach that does not work because we do not have something to offer so you use um the services of Google Maps or other things because you get in return a good navigation system you use other tools because you get in return a good functionality and working software but for a city what will a city return and so having GPS it’s something that from the idea worked perfectly but unfortunate the coffee idea is lovely but uh does not work out then the the next idea were okay we placing camaras so we do have our infrastructure placing their surveillance cameras uh and from the technical point of view it’s it’s not a big issue to re-identify people it’s not a big issue to detect a number plate and to re identify the number plate but then um our lawyers say okay this is not clever and this is forbidden to track people in the city even if you working with the city and so um this does not work out then the next the Le the next thing this is the uh way we are going right now is to have some sensors on our infrastructure and these sensors are counting sensors so we are counting how many people are there how many cars are there what kind of cars what kind of people what kind of bicycle drivers and um so on and so on and based on this data we can somehow theorize where the people were going in the city and use this theoretic uh pass to simulate our traffic and the interesting thing by talking to the city uh where to place the sensors is that not every traffic light is owned by the city so if there is a a street that is uh built by the uh by the federal state and not by the city then the traffic lights belong to the federal state and there you need then an agreement that uh you are allowed to put a sensor on the traffic light from the federal state so it’s it’s not as simple as it uh sounds so um there’s a lot of things going on yeah and um this ends up in a lot of work that we are currently doing uh to drive zumu towards a digital Trend so we are working with zumu since uh half a year and what we did so far is that we have all the static data and we put all the static data to create our traffic Network then the next thing that we are right now setting up is by the use of passive uh infrared sensors and smart cameras that we do have microscopic information so counting information of our wheel data and um these number plate uh recognition this is something that we are discussing uh with uh our um local uh lawyer uh that we are allowed to store a check sum of the number plate if the check sum is generated directly within the uh camar and not transferred to server this is somehow tricky all Germans know that uh this section control is still forbidden in Germany due to the fact that we are storing the um number plate data and we have maybe a clever concept to um get this information and with this real data integration we can do the simulation and on top on the simulation we um want to connect to our whe time uh traffic computer of the state and uh also integrate this data and then have a digital twin and with the digital twin we want to answer if else scenarios so for example if you um yeah if you close this route or if you change the speed of a certain thing then then um um yeah then the traffic will behave in this way and um I got the sign that five minutes is left so static data this is something that you all know by working with Zumo um that we embed uh the G information that we create a traffic Network that we Define the sensor POS positions that we defin the positions of bus stations etc etc and we put it in our uh cugus uh and and uh here we go here we have uh the open street map and then the detailed information on the traffic light systems and uh where we have the traffic light s then we also recorded 360° uh videos on our own um yeah and we try to match all of this what is the so this is the easy steps so 360° uh um uh recording and then use this recording to get a um GPS position Etc this is easy but the hardest thing is to get the traffic light information and uh we ask for a standardized format and we get PDFs and uh so um yeah a student uh of my is now writing his uh Bachelor thesis on how to identify this uh uh traffic light schedule and to quite important we do have different traffic light uh schedules and for me as a non um yeah traffic expert it was completely new that the traffic lights only have different programs and not where configurable complete freely so they have only state one state two state three and all of these different programs or states are written in one beautiful PDF and then you need to re identify which light on this um traffic light has which uh cycle for switching uh green red and yellow and therefore we are capable to simulate quite beautiful our um our uh Crossings and with the dynamic data we are now uh starting to uh position uh passive inar sensors for counting the um yeah number of traffic for measuring the speed and for measuring uh the different roads by uh the vehicle length so how the vehicle is turning then a smart camaras we are also going to put smart Camas in the city um and further sens of Technology this is something not yet in the project but uh in other project that we can reuse or use for our things and um these are our um dependencies requirements so tronic behavior and speed and uh with this thing we are creating first temporal data and you see that we have also blanks in the temporal data as usual and by having this data together uh we can or we might uh generate uh the data from the count data and uh this is where we stand and the Outlook is that we want to connect uh collect and fuse traffic data of one year and provide this data to the public uh because uh I think this data from Germany could be uh a big value um then um we have uh the challenges uh right now is to get the correct meta data because we have also a lower van Network in the city and The Meta data is not good documented and there’s a lot work to do and uh we want to have our macroscopic data and drive with microscopic data in microsc opic simulation and this is where yeah we are currently working on and as I say okay I start with a comic you will also get the solution of the comic so if you put this prompt in today’s computers you will get this result so this is stable diffusion and we are hopefully to do the same by having the counting data and putting this counting data in a clever algorithm ISM to be more or less next or more or less um uh close to the reality in the simulation of traffic and this is without any personal references but close to reality and so thank you