Microsimulation of an autonomous taxi-system in Munich

Dandl, Florian; Bracher, Benedikt; Bogenberger, Klaus

doi:10.1109/mtits.2017.8005628

Cited by 35 publications

(32 citation statements)

References 5 publications

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“…At first, MATsim was not able to simulate autonomous vehicles, so researchers often use this tool to combine with other simulation tools or with their coded program. Until [19] and [21] add the autonomous vehicle toolkit into MATsim, it has been a most useful platform to model the operation of the autonomous vehicle system.…”

Section: Discussionmentioning

confidence: 99%

Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review

Jing

Zhan

et al. 2020

IEEE Access

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Autonomous vehicles (AVs) may have a transformative influence on transportation systems. AV use is expected to reduce crash, transport costs, and land use. However, the net effects of the autonomous vehicle are unknown because there are no fully-autonomous vehicles in reality. The simulation systems are indispensable to be built up not only for cost but also for obtaining massive data. An agent-based simulation is capable of simulating the entities in a complex system, such as the AV system. Considering the potential of agent-based models to complement existing models and the difficulty to understand, to duplicate and to compare different agent-based models, this paper review existing agent-based modeling of autonomous vehicles with the help of ODD protocol, which has been applied in the review of agent-based land-use change models and agricultural policy evaluation. The significant variables that researchers have taken into consideration in sensitivity analysis and in the different scenarios are explored in the paper. Fleet size is the primary variable researchers considered, and we also discussed the probable causes of different AV replacement rates. We have assessed the quality of reviewed articles to gain more acceptance from the agent-based modeling community. Most of the reviewed papers follow the ODD protocol of agent decision making. However, in order to improve the level of modeling transparency, the executable and related data shall be available, at least the data source and the programming language/simulation platform should be provided. Several recommendations for further research are also presented. INDEX TERMS Agent-based model, autonomous vehicle, systematic literature review, ODD+D, multi-agent systems.

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Section: Discussionmentioning

confidence: 99%

Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review

Jing

Zhan

et al. 2020

IEEE Access

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“…The operating area (A x = 7 km and A y = 10 km), the trip rates λ i and trip lengths d i are based on a prior MoD study [22]. The average velocities per time interval are extracted from a traffic microsimulation model [23]. The penalty factor for demand not served is chosen to be 5 e per customer.…”

Section: A Scenario Setupmentioning

confidence: 99%

Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

Dandl¹,

Fehn²,

Bogenberger³

et al. 2020

Preprint

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<div>Electrifying mobility-on-demand (MoD) fleets is</div><div>an important step towards a more sustainable transportation</div><div>system. With increasing fleet size, MoD operators will be</div><div>able to participate in the energy exchange market and will</div><div>have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of</div><div>charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.</div>

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“…We utilize a microsimulation model of the network developed in Aimsun microsimulator [46], [47]. It consists of 37,650 street sections (about 4,962 km in length) with 16,790 nodes [7]. After reducing the network to the minimum routing network, the networks contain 8,380 nodes with 20,574 edges (A99 area), 5,818 nodes with 14,642 edges (B2R area) and 959 nodes with 2,364 edges (Schwabing area).…”

Section: Case Studymentioning

confidence: 99%

“…florian.dandl@tum.de by contraction hierarchies [3]- [5], enables fleet control in real-time, but traffic planners interested in the bigger picture and researchers not focusing on the routing aspect might want to use even faster routing algorithms to simulate more scenarios in the same time. Since advanced fleet control is computationally very demanding, integrating the fleet routing problem into a DTA further limits the number of scenarios that can be investigated [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

Dandl¹,

Tilg²,

Rostami-Shahrbabaki³

et al. 2020

Preprint

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The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

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Microsimulation of an autonomous taxi-system in Munich

Cited by 35 publications

References 5 publications

Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review

Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review

Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

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