scholarly journals Uncertainties of Sub-Scaled Supply and Demand in Agent-Based Mobility Simulations with Queuing Traffic Model

2021 ◽  
Author(s):  
Aleksandr Saprykin ◽  
Ndaona Chokani ◽  
Reza S. Abhari
Keyword(s):  
Large Scale ◽  
Public Transit ◽  
Dynamic Traffic Assignment ◽  
Supply And Demand ◽  
Mobility Model ◽  
Traffic Model ◽  
Traffic Network ◽  
Test Statistic ◽  
Agent Based ◽  
Full Population

AbstractAgent-based models for dynamic traffic assignment simulate the behaviour of individual, or group of, agents, and then the simulation outcomes are observed on the scale of the system. As large-scale simulations require substantial computational power and have long run times, most often a sample of the full population and downscaled road capacities are used as simulation inputs, and then the simulation outcomes are scaled up. Using a massively parallelized mobility model on a large-scale test case of the whole of Switzerland, which includes 3.5 million private vehicles and 1.7 million users of public transit, we have systematically quantified, from 6 105 simulations of a weekday, the impacts of scaled input data on simulation outputs. We show, from simulations with population samples ranging from 1% to 100% of the full population and corresponding scaling of the traffic network, that the simulated traffic dynamics are driven primarily by the flow capacity, rather than the spatial properties, of the traffic network. Using a new measure of traffic similarity, that is based on the chi-squared test statistic, it is shown that the dynamics of the vehicular traffic and the occupancy of the public transit are adversely impacted when population samples less than 30% of the full population are used. Moreover, we present evidence that the adverse impact of population sampling is determined mostly by the patterns of the agents’ behaviour rather than by the traffic model.

Macroscopic traffic model for large scale urban traffic network design

2018 ◽  
Vol 80 ◽  
pp. 32-49 ◽  
Author(s):  
Elvira Thonhofer ◽  
Toni Palau ◽  
Andreas Kuhn ◽  
Stefan Jakubek ◽  
Martin Kozek
Keyword(s):  
Network Design ◽  
Large Scale ◽  
Urban Traffic ◽  
Traffic Model ◽  
Traffic Network ◽  
Urban Traffic Network ◽  
Traffic Network Design ◽  
Macroscopic Traffic Model

Network effect in shared supply chain platform value co-creation behavior in evolutionary game1

2021 ◽  
pp. 1-12
Author(s):  
Zou Xiaohong ◽  
Chen Jinlong ◽  
Gao Shuanping
Keyword(s):  
Boundary Condition ◽  
Supply Chain ◽  
Large Scale ◽  
Simulation Analysis ◽  
Supply And Demand ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Network Effect ◽  
Chain Model ◽  
The Cost

The shared supply chain model has provided new ideas for solving contradictions between supply and demand for large-scale standardized production by manufacturers and personalized demands of consumers. On the basis of a platform network effect perspective, this study constructs an evolutionary game model of value co-creation behavior for a shared supply chain platform and manufacturers, analyzes their evolutionary stable strategies, and uses numerical simulation analysis to further verify the model. The results revealed that the boundary condition for manufacturers to participate in value co-creation on a shared supply chain platform is that the net production cost of the manufacturers’ participation in the platform value co-creation must be less than that of nonparticipation. In addition, the boundary condition for the shared supply chain platform to actively participate in value co-creation is that the cost of the shared supply chain platform for active participation in value co-creation must be less than that of passive participation. Moreover, value co-creation behavior on the shared supply chain platform is a dynamic game interaction process between players with different benefit perceptions. Finally, the costs and benefits generated by the network effect can affect value co-creation on shared supply chain platforms.

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

scholarly journals Equity Effects of Congestion Charges: An Exploratory Analysis with MATSim

2017 ◽  
Vol 2670 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Lucas Meyer de Freitas ◽  
Oliver Schuemperlin ◽  
Milos Balac ◽  
Francesco Ciari
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Agent Based ◽  
Promising Tool ◽  
Travel Time Savings ◽  
Time Savings ◽  
Time Changes ◽  
High Level ◽  
Congestion Charging ◽  
Equity Effects

This paper shows an application of the multiagent, activity-based transport simulation MATSim to evaluate equity effects of a congestion charging scheme. A cordon pricing scheme was set up for a scenario of the city of Zurich, Switzerland, to conduct such an analysis. Equity is one of the most important barriers toward the implementation of a congestion charging system. After the challenges posed by equity evaluations are examined, it is shown that agent-based simulations with heterogeneous values of time allow for an increased level of detail in such evaluations. Such detail is achieved through a high level of disaggregation and with a 24-h simulation period. An important difference from traditional large-scale models is the low degree of correlation between travel time savings and welfare change. While traditional equity analysis is based on travel time savings, MATSim shows that choice dimensions not included in traditional models, such as departure time changes, can also play an important role in equity effects. The analysis of the results in light of evidence from the literature shows that agent-based models are a promising tool to conduct more complete equity evaluations not only of congestion charges but also of transport policies in general.

A Low-Fidelity Stochastic Model of Viral Spread in Aircraft to Assess Risk Mitigation Strategies

2021 ◽  
Author(s):  
Leigh McCue
Keyword(s):  
Large Scale ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Traffic Network ◽  
Computationally Efficient ◽  
Passenger Compartment ◽  
Stochastic Effects ◽  
Viral Spread ◽  
Risk Mitigation Strategies ◽  
Baseline Health

Abstract The purpose of this work is to develop a computationally efficient model of viral spread that can be utilized to better understand influences of stochastic factors on a large-scale system - such as the air traffic network. A particle-based model of passengers and seats aboard a single-cabin 737-800 is developed for use as a demonstration of concept on tracking the propagation of a virus through the aircraft's passenger compartment over multiple flights. The model is sufficiently computationally efficient so as to be viable for Monte Carlo simulation to capture various stochastic effects, such as number of passengers, number of initially sick passengers, seating locations of passengers, and baseline health of each passenger. The computational tool is then exercised in demonstration for assessing risk mitigation of intervention strategies, such as passenger-driven cleaning of seating environments and elimination of middle seating.

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