A bicycle simulator for experiencing microscopic traffic flow simulation in urban environments

Keler, Andreas; Kaths, Jakob; Chucholowski, Frederic; Chucholowski, Maximilian; Grigoropoulos, Georgios; Spangler, Matthias; Kaths, Heather; Busch, Fritz

doi:10.1109/itsc.2018.8569576

Cited by 17 publications

(12 citation statements)

References 3 publications

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“…Important is also the support of representing the road network up to the lane level, as it is supported by the OpenDRIVE standard. This allows us to visualize the road infrastructure in a realistic three-dimensional manner, as it is done for bicycle simulator studies (Keler et al 2018).…”

Section: Testing Methods For the Realistic Representation Of Bicyclismentioning

confidence: 99%

Enriching complex road intersections from OSM with traffic-related behavioral information

Keler

Grigoropoulos

Mussack

2019

Proc. Int. Cartogr. Assoc.

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<p><strong>Abstract.</strong> Bicycle traffic in urban environments has various complex aspects in relation to the used space and the assigned transport infrastructures, especially in urban environments. Depending on the traffic situations, the built static urban infrastructure design, and, the other traffic participants involved, there are more or less challenging possibilities of its representation. Modelling and simulating urban bicycle traffic should include the interactions to other traffic participants, and, of course, behavioral aspects within each ride. Therefore, we introduce a method for enriching road features from OSM with traffic-related behavioural information. We focus on road intersections, since those are the locations with the highest number of interactions, communications, and, accidents. Our aim is to extend microscopic traffic flow simulations for achieving higher accuracies in simulation results and for applying bicycle movement models that are more adapted to reality. As an example we present a behavioral catalogue for cyclists, focussing on urban roundabouts, which results from real video observations and subsequent classifications.We classify into typical, frequently occurring and unusual bicycle maneuvers, and, include those into a specification procedure when enriching various OSM features.</p>

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Section: Testing Methods For the Realistic Representation Of Bicyclismentioning

confidence: 99%

Enriching complex road intersections from OSM with traffic-related behavioral information

Keler

Grigoropoulos

Mussack

2019

Proc. Int. Cartogr. Assoc.

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“…In [2] the broad idea is to use a discrete event architecture, in which there are logical processes for executing and simulating a number of agents. For a computation intensive aim those processes can be clustered into agent clusters with dependencies between them, but the paper avoids presenting the necessary details on how the distributed mechanics would work.…”

Section: Related Workmentioning

confidence: 99%

A Microservices based Approach for City Traffic Simulation

Becea¹,

Vălean²

2020

IJACSA

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The paper proposes a city traffic software simulation based on actors which run independently of one another and have specific characters in their behavior. To run indepentently actors are modeled as microservices and they are running within an orchestration framework. Their behavior is modeled as with specific algorithms for each of their type, embedded in each actor's type code. They may act based on the data about all the other actors, data which is gathered together by a single entity called city simulator. An orchestration model is proposed and all the actors use a communication protocol to offer data to the city simulator and request data from it.

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“…In case the destination hotspots are not intersecting inferred complicated crossings, we can include enriched information on road intersection complexity by using the approach of Sladewski, Keler, and Divanis (2017). All these approaches might be evaluated in a VR environment using a bicycle simulator (Keler et al 2018). This means evaluation occurs on a microscopic level and might include simulated traffic of three groups of traffic participants: pedestrians, bicyclists and vehicle drivers.…”

Section: Future Workmentioning

confidence: 99%

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

Keler

Krisp

Ding

2019

Geo-spatial Information Science

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Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous research shows that due to service restrictions, boro taxis have typical customer destination locations on selected Saturdays: many drop-off clusters appear near the restricted zone, where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing. Detected crossings imply recent infrastructural modifications. We want to follow up on these results and add one additional group of commuters: Citi Bike users. For selected Saturdays in June 2015, we want to compare the destinations of boro taxi and Citi Bike users. This is challenging due to manifold differences between active mobility and motorized road users, and, due to the fact that station-based bike sharing services are restricted to stations. Start and end points of trips, as well as the volumes in between rely on specific numbers of bike sharing stations. Therefore, we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.

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A bicycle simulator for experiencing microscopic traffic flow simulation in urban environments

Cited by 17 publications

References 3 publications

Enriching complex road intersections from OSM with traffic-related behavioral information

Enriching complex road intersections from OSM with traffic-related behavioral information

A Microservices based Approach for City Traffic Simulation

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

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