VeloCity
LenaKeßler,MauritzRenz
ChairforArchitecturalInformatics DepartmentofArchitecture
TechnichalUniversityofMunich ChairofArchitecturalInformatics TechnicalUniversityofMunich
13 VeloCity Chair of Architectural Informatics Prof. Dr.-Ing. Frank Petzold Critical Modeling Ivan Bratoev, Nick Förster, Frank Petzold Lena Keßler, Mauritz Renz 03728986, 03758144
2 27171511533541 Biking in FirstResearchCitiesStepsinData Collection Sensor ChallengesTrafficConceptPrototypeDevelopmentLightPrototypeandOutlookContact Table of Contents
Biking in Cities
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Compared to the last century, when cities and their infrastructure were designed specifically for getting around by car, cycling has now become a real status symbol. However, this design of our cities cannot change at the same speed, which is why the missing infrastructure for bicycles is still a problem for many cities.
Cycling in cities has become increasingly popular in recent years.
Some are more committed to developing the solutions than others as it can be seen for example in Amsterdam or Copenhagen. These two cities have a reputation as bicycle friendly cities and are therefore becoming role models for many German cities. In the case of Munich, the desire for more bicycle friendliness has been expressed for several years and measures have been adopted.
As part of the solution, a bicycle ring around the old town was presented, which would relieve traffic in the city center and make cycling a more pleasant mode of transport. However, little has happened since the decision was made, as the Corona crisis and the lack of staff at the implementing authorities have slowed down the Sonnenstraßeprocess. as a part of the ring road currently consists of a partially eight-lane car road, which is frightening especially in this location. Bicyclists have a narrow strip on both sides, partly on the car lane, to move forward. Overtaking and driving slowly is very dangerous because it can quickly become dangerous due to the narrowness and proximity to car traffic. As part of our project VeloCity, we want to have a look at bicycle traffic in and around Sonnenstraße and come up with a proposal for future improvement
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511 Research
Before we started to deal with the concrete situation in Sonnenstraße we had to answer some general questions. What makes cycling in Munich so dangerous and to what extent is this the fault of the infrastructure? How could cycling become more pleasant than driving and what influences the choice of transport?
In addition, we were interested in the reasons for the delays in improvement. Our assumptions are that the car, with its sophisticated technology and the many sensors that new cars have, has a better data basis for planning. The bicycle, on the other hand, is often underestimated and there is little data on what routes cyclists use and what obstacles they face. Therefore, it makes a lot of sense to collect data on bicycle traffic and make it available for future urban planning.
Some e-bike companies already collect data about the location of the bike and also partly speeds and braking, but this data is not available in sufficient quantity because not many people already own an e-bike. In addition, it is similar to Google data a question of accessibility. The pictured bicycle helmet is also supposed to collect data, but it is not used for urban planning and thus only about the user and not for the user.
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79 Cyclinginurbantraffihasradicalisedme:I Iaminsulted,endangeredandalmosthitbyacar, Onlyjustsothatmyfellowmotoristsincarscangain 3precioussecondsbeforetheyhavetostopatthe nextredtrafficligh.WhereIcatchupwiththem again. -MarlaStromponsky
810 1/3OFALLKILLEDTRAFFICPARTICIPANTSARE CYLISTS MORETHAN1/2OFALLSERIOUSLYINJURED ACCIDENTVICTIMSARECYCLISTS 56,3%OFALLACCIDENTSARERELATEDTO COLLISIONSWITHCARS
915 20112013201520172019 100 200 300 400 Seriouslyinjuredacidentvictims
1016 20112013201520172019 Mostfrequentcausesofaccidentswithcyclistsinvolved GSEducationalVersion 1 2 3 4 4Accidents 3Useof sidewalksor Streets-wrong waydrivers 2Failuretoobserve rightofway 1Mistakeswhen turningleft/right
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First Steps in Data Collection
Tracking heavy braking, potholes and evasive maneuvers made sense to us. First we started to use the sensors in our smartphones, but we soon found out that it was difficult to make the data (a three-axis vibration meter) comparable, because the positioning of the device had an influence on the collected data. Therefore the data sets were to a large extent data noise e.g. in the trouser pocket. Despite the inaccuracy, we were able to integrate the measured data into Grasshopper and to identify particularly difficult zones through visualization.
In order to collect data, one must first know which data best describe the process of cycling and would therefore be useful to collect.
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SENSORING PROCESSING TARGET GROUP App HumanErrors On/Off IdioticData InaccurateGPS ArduinoSensor CostsOvertaking ComplexeSensoring/InaccurateGPS Manipulationbyuser GoogleData Notaccessable DataCatalog MatchingError (Nouniversal solution) CityPlanningDepartement BicycleDataCollection+ WS21/22 PROBLEMS RESEARCH SpacialInjustice BikeLaneQuality/Safety
Trafficlight BetterSpace Planning Now,2y,5y Interactive Trafficligh InjusticeTool Now,2y,5y MünchenBewegt ADFC PublicAwareness Futureproof Cityplanning NOW PublicAwareness InequalityofParticipants Accidentvictims Traffisafety Accidentcauses
PrototypSensore
Our goal was to equip the sensor for our own benefit. Only after an extensive testing phase would we make the sensor available to users and design a purchasable product. Ideas for positioning here were the hollow spaces in the seat post or handlebars. By collecting data, the user would be able to view his distances and also athletic performance. In addition, he should benefit from the GPS function and have an integrated anti-theft device. Other conceivable uses for the consumer would be an app that informs the rider how to adjust his speed to the rhythm of the traffic lights in order to minimize waiting However,times.
since some functions were already challenging during the development of the prototype, we discarded these concrete ideas and initially focused on the informative value of the data in the urban environment.
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Since the data collection with the smartphone was so chaotic, we switched to an Arduino sensor. With the Arduino software we wrote a code, which together with the hardware should ensure that the data from the GPS sensor and accelerometer should be stored on an SD card to transfer them afterwards to a map and evaluate the data. The Gps sensor caused us some difficulties, because our first model could not receive a satellite signal. After getting a new one we found out that the battery voltage of 9 volts was not enough to store the data successfully. Therefore we looked for a battery with 12 volts and finally had some success. The data could be stored and evaluated afterwards. After a few attempts, however, we found that the new GPS sensor did not write any data either, despite a working signal.
The first Prototype with the smaler battery and the first GPS Module.
The Prototypesecondwith a large battery case and a new GPS Module.
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Especially after the difficulties with the sensor data of our own prototype, we were even happier that we could fall back on bicycle data of the startup Bikesolution. After a clearer presentation by grasshopper, we were able to identify various patterns in the data, which we analyzed in terms of causes. From the combination of data patterns, possible causes and possible improvements, we wanted to develop a catalog of solutions that we could combine with any bicycle dataset to create a faster planning algorithm that would address the staffing shortage at planning Onagencies.closer inspection, however, we realized that the data could only be analyzed with prior knowledge of the specific location, and so it would take a machine learning device an insanely long time to determine correct conclusions from the collected data.
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DevelopementConcept
interesting to note that the braking occurs more frequently on side streets or at crossings.
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The red tracks show particularly popular routes which in the case of Munich are mainly from north to south and from east to west.Itisalso
Similarly, the vibrations indicate an uneven road surface and may pose a problem for Particularlyaccessibility.important for us are zones in which cyclists often have to stop, because here the infrastructure seems to neglect cyclists or is not designed for them.
1923 131.558m² 143.885m²
To verify the assumption that there is not enough space for bicyclists in the street traffic to en sure a trouble-free and safe bicycle traffic, we have sorted the areas in Sonnenstraße according to the means of transport and the comparisons are alarming.
2024 11.325m² 10.335m² = 2.500 m²
It does not seem particularly surprising that bicycle accidents are increasing every year, but this should by no means meet the standards of the city of munich, which after all calls itself a bicycle-friendly municipality.
Looking at the parking possibilities, there is unfortunately an even more drastic picture, because there are hardly any possibilities to park your bicycle, but several thousand for your car. One might think that the city would go to great lengths to compensate for this shortcoming, but one searches in vain for projects that could make cycling more pleasant, but for example a new construction project of the city where 555 parking spaces are newly built in the Alpina car park on Adolf-Kolping-Straße because the existing parking spaces are not the current sizes of the cars. would correspond.
2125 IsarklinikumIsarklinikumSonnenhofplannedAlpinaKarstadtElisenhofParkingmin. Bellatrix GmbH 25€ mon 178€ mon mon mon mon mon tag tag tag tag 199€ 357€ 30€ 16€ 24€ 220€ 199€ 130€ 74P 399P 555P368P 213P 75P 30P =100 Parking Lots 30P 60P 368P 500P HotelPschorrParkinggarageOberpollingerParkingPremierInn Parking Lots for Cars
2226 40P 20P 15P 10P 10P 10P U Herzog-Wilhem-StraßeStachus U Sendlinger Tor U Sendlinger Tor U Stachus U Stachus 3004105 125 m²Area = 110 = Parking Lots for Bikes
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2531 Recognized Datapatterns Suspected Problem Recomended Action Expected Benefit Queue (manydotsinarow) Red-lightData Extramanypoints inonearea manypointsleftand rightoflargerroads redllines (highlyfrequentedroutes) emptylines/spotson planned Altstadt-Radel-Ring hardbrakes frequent‚Bumps‘ manydriverswaiting infrontoftraffilights pedestrianarea? parking? changetoÖPNV? non-functioningcrossing sections referencetopopular routes planningand implementationare inadequate narrowspaces&confusion badquality/sideroads changedrhythm, widewaintingzonein frontofcars secureparkingpossibilities+nearUBahn/SBahn, trafficirculationaround thepedestrianzone increaseandexpand crossingpossibilities expansionofconnections, considertheimportance inequalplanningpriority buildbetteralternatives tohighlyfrequented routes widerpaths/rightofway flatenedsidewalkedges shorteningwaitingtime makingbikeusemore comfortable betterconnectionof differentmeansof transport makingconflicareasof differentmeansoftransportsafer creatingcomfortablelong distanceinfrastructure decentralizedenselypopulatedareas destressbicycletraffi basicforanacessiblecity
Focusing on traffic light rhythms
2632 Recognized Datapatterns (manydotsinarow) Red-lightData Extramanypoints inonearea manypointsleftand rightoflargerroads (highlyfrequentedroutes) emptylines/spotson Altstadt-Radel-Ring hardbrakes frequent‚Bumps‘
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2733 Traffic PrototypeLight
Another issue of equality in road traffic that has already been addressed is the preference given to car traffic in the rhythm of traffic light switching. Cars usually drive at similar speeds due to the speed limits and the traffic light switching is adjusted to create the so-called green phase. Simulations are done to avoid congestion and to allow as many cars as possible to get through. The fact that the passage per time is significantly higher in a pedestrian and bicycle friendly city, since cars take up an extremely large amount of space and thus fewer people are transported per area is ignored. To prove this inequality and to make the unfainess understandable we have developed our own simulation tool to adapt the planning to the different speeds. This tool does not claim to represent reality and is nowhere near as advanced as the city's simulations, but it should give a sense of the preference for cars and the additional waiting time for pedestrians and cyclists. To provide the tool with the real red green phases we took measurements in the Sonnenstraße and then inserted this data into the model using Grasshoper. Our prototype is now able to connect the start and destination of the route in the shortest path and send points with different speeds, which are supposed to represent pedestrians, cyclists and car drivers, over the selected route. In doing so, the points encounter traffic lights that are either green or red. At a green light, the dot can continue and draws a green dot on a diagram. If the light is red, the point stops and a red line is drawn on the diagram indicating the waiting time. At the end of the simulation the relation of the waiting time of the different means of transport can be seen and it shows the clear preference of the cars in the current timing.
All of our results so far are now to be incorporated into a plan that we believe will point the way to the future for traffic in the city. To this purpose, we have drawn up a plan for the next five years. The first section will be completed by 2024, the second by 2027.
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In two years, the expansion and widening of the bike lanes should already have taken place. This will be achieved by eliminating above-ground parking on both sides of the four-lane roadways. In addition, by adjusting the traffic lights to lower speeds, a better passage for cyclists and pedestrians should be ensured.
In five years, one direction of the car lanes will be made available for bicyclists and pedestrians. This will equalize traffic and make it safer for everyone involved. The issue of traffic lights will also be resolved by this step, as cars will now travel separately from cyclists.
2937 Today 2Years 5Years
3038 Today 2Years 5Years
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3240 P P P P 5Years By limiting car traffic so close to the city center, areas are freed up that can now make a valuable contribution to the city's climate and equality. This will make Munich a democratic, environmentally friendly city that provides space for its residents and not now for their cars.
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During our journey we encountered some hurdles and challenges, but we were always able to find a way to pursue our theme. It has also become extremely clear several the importance. By focusing on a single mode of transportation, the needs of other modes of transportation are not taken into account. Despite several changes of direction in the process, our biggest goal throughout the semester has been to improve the current conditions by bringing attention to the issue. To continue this goal after the semester we will contact the ADFC (Allgemeiner deutscher Fahrradclub) and 'München Bewegt' to share our ideas and information with them. Furthermore, we will integrate the data and the simulation into the Digital Twin of Munich in order to confront planners with this important topic and include them in the process.
Challenges and07 Outlook
36 Challenges UrbanSensingand InternetofThings UrbanDataandDataFormats SimulationandAnalysis CityScale3D-Models InterfacesandVisualization Arduino GPS Hardware Problem DifferentExchange Formats;Dataisnot objective,bywho,how,why, whatforgenerated,howis itprocessed,filers,etc. Wich parameters willbe simplifyed Enlightenment or manipulation? DataintheDigitalTwin mostlymorerelevant,than the3DModel
3535 Timestamp Accelerometer Trafficligh Timing GoogleTraffi Data Accelerometer GPS Timestamp DigitalTwin StartUp UpRide json generalgpx csv csv json json json ArticlesGPS Websites Research Papers Hardwareerror Uselesswithout GPS WS21/22 Catalog Grasshopper Prototype DangerousZones BikeLaneQuali Visualisation Crowdedroutes BikeLaneSpeed
36 Catalog Grasshopper Prototype map map map map objects Articles Websites Research Papers DangerousZones BikeLaneQuali Automated Catalog Proposals Visualisation BetterStreets Planning Crowdedroutes BikeLaneSpeed
SENSORING PROCESSING TARGET GROUP App HumanErrors On/Off IdioticData InaccurateGPS ArduinoSensor CostsOvertaking ComplexeSensoring/InaccurateGPS Manipulationbyuser GoogleData Notaccessable DataCatalog MatchingError (Nouniversal solution) CityPlanningDepartement BicycleDataCollection+ WS21/22 PROBLEMS RESEARCH SpacialInjustice BikeLaneQuality/Safety
Trafficlight BetterSpace Planning Now,2y,5y Interactive Trafficligh InjusticeTool Now,2y,5y MünchenBewegt ADFC PublicAwareness Futureproof Cityplanning NOW PublicAwareness InequalityofParticipants Accidentvictims Traffisafety Accidentcauses
4411 08 Contact Lena 5.03728986KeßlerSemester Bachelor Mauritz 1.Semester03758144RenzMaster
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