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Raney, Bryan; Cetin, Nurhan; Völlmy, Andreas; Vrtic, Milenko; Axhausen, Kay W.; Nagel, Kai

doi:10.1023/a:1022096916806

Cited by 116 publications

(16 citation statements)

References 13 publications

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“…The selection of the replanning fraction represents a related issue; 10% is a frequently-used heuristic value that works well in practice [23]. This fraction of person agents both improves the average population score and causes the system to relax as soon as possible by adjusting the travel plans.…”

Section: Overview Of Matsimmentioning

confidence: 99%

“…The second step involves drawing an adjustment value at random from within the range and adding this adjustment to the previous departure time (Balmer et al, 2005;Gao, 2013) [2,22]. The optimization of the travel route is based on static traffic assignment using the user equilibrium (UE) or the stochastic user equilibrium (SUE) models (Raney et al, 2003) [23]. The multinomial logit model and the probit model are often used to optimize the mode of travel, and the optimization of the combinations of activities represented by the activity chain is based on heuristic solution algorithms, such as hill-climbing algorithms or the tabu search algorithm (Balmer and Axhausen, 2009;Nagel and Axhausen, 2016) [20,21,24,25].…”

Section: Introductionmentioning

confidence: 99%

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The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China

et al. 2017

Applied Sciences

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Abstract:As serious traffic problems have increased throughout the world, various types of studies, especially traffic simulations, have been conducted to investigate this issue. Activity-based traffic simulation models, such as MATSim (Multi-Agent Transport Simulation), are intended to identify optimal combinations of activities in time and space. It is also necessary to examine commuting-based traffic simulations. Such simulations focus on optimizing travel times by adjusting departure times, travel modes or travel routes to present travel suggestions to the public. This paper examines the optimal departure times of metro users during rush hour using a newly developed simulation tool. A strategy for identifying relatively optimal departure times is identified. This study examines 103,637 person agents (passengers) in Shenzhen, China, and reports their average departure time, travel time and travel utility, as well as the numbers of person agents who are late and miss metro trips in every iteration. The results demonstrate that as the number of iterations increases, the average travel time of these person agents decreases by approximately 4 min. Moreover, the latest average departure time with no risk of being late when going to work is approximately 8:04, and the earliest average departure time with no risk of missing metro trips when getting off work is approximately 17:50.

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Section: Overview Of Matsimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China

et al. 2017

Applied Sciences

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“…Assuming the agents' travel plans to represent valid choice sets, the system state converges towards the stochastic user equilibrium (Nagel and Flötteröd 2012). A detailed description of the simulation framework is provided in Raney et al (2003) and Raney and Nagel (2006).…”

Section: Traffic Flow Simulation All Agents Execute Their Travel Planmentioning

confidence: 99%

User-specific and Dynamic Internalization of Road Traffic Noise Exposures

2016

Self Cite

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In this study, a noise internalization approach is presented and successfully applied to a real-world case study of the Greater Berlin area. The proposed approach uses an activity-based transport simulation to compute noise levels and population densities as well as to assign noise damages back to road segments and transport users. Iteratively, road segment and time dependent noise exposure tolls are computed to which transport users can react by adjusting their route choice decisions. Since tolls correspond to the transport user's contribution to the overall noise exposures, the incentives are given to change individual travel behavior towards reduced noise exposure costs. Applying the internalization approach to the case study reveals that transport users shift from minor to major roads and take detours in order to avoid areas with high population densities. The contribution of the presented methodology is that the within day dynamics of varying population densities in different areas of the city are explicitly taken into account and affected people at work and places of education may be incorporated, which is both found to have a major impact on toll levels and network utilization. Depending on the time of day and depending on which population groups are considered, noise exposures are reduced by means of different traffic management strategies.

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“…The TRAN-SIMS project [19][20][21] of the Los Alamos National Institute (LANL), for example, has created agent-based simulations of whole cities such as Dallas [22] and Portland [23]. The approach has been recently extended to the simulation of the travel behavior of the 7.5 million inhabitants of Switzerland [24,25]. These simulations are obviously based on parallel computing.…”

Section: Social Super-computingmentioning

confidence: 99%

From social simulation to integrative system design

Helbing

Balietti

2011

Eur. Phys. J. Spec. Top.

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The purpose of this White Paper of the EU Support Action "Visioneer" (see www.visioneer.ethz.ch) is to address the following goals: 1. Develop strategies to build up social simulation capacities. 2. Suggest ways to build up an "artificial societies" community that aims at simulating real and alternative societies by means of supercomputers, grid or cloud computing. 3. Derive proposals to establish centers for integrative systems design. 1 Introduction 1.1 Real-world challenges Since decades, if not since hundreds of years, the world has been facing a number of recurrent socioeconomic problems which are obviously hard to solve because their roots are still not well understood. This includes, in particular, the following problems: 1. Financial and economic (in)stability (government debts, taxation, and inflation/deflation; sustainability of social benefit systems; consumption and investment behavior.. .) 2. Public health (food safety; spreading of epidemics [flu, SARS, H1N1, HIV], obesity, smoking, or unhealthy diets.. .) 3. Sustainable use of resources and environment (consumption habits, travel behavior, sustainable and efficient use of energy and other resources, participation in recycling efforts, environmental protection.. .) 4. Collective social behavior and opinion dynamics (abrupt changes in consumer behavior; social contagion, extremism, hooliganism, changing values; breakdown of cooperation, trust, compliance, solidarity.. .) 5. Security and peace (organized crime, terrorism, social unrest, independence movements, conflict, war.. .) 6. Reliability of critical infrastructures, in particular of information systems (cyber risks, misuse of sensitive data, espionage, violation of privacy; data deluge, spam; education and inheritance of culture.. .

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Untitled

Cited by 116 publications

References 13 publications

The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China

The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China

User-specific and Dynamic Internalization of Road Traffic Noise Exposures

From social simulation to integrative system design

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