Nico Kuehnel, Hannes Rewald, Steffen Axer, Felix Zwick, Rolf Findeisen
{"title":"基于智能体的高效动态拼车模拟的流量膨胀选择性抽样","authors":"Nico Kuehnel, Hannes Rewald, Steffen Axer, Felix Zwick, Rolf Findeisen","doi":"10.1177/03611981231170624","DOIUrl":null,"url":null,"abstract":"Agent-based simulations are powerful tools for simulating emergent mobility modes, but they often require significant memory and computing power. To address this issue, researchers have previously used sampling techniques, where only a fraction of agents are explicitly simulated while others are simulated through teleportation. However, recent studies have highlighted the challenges of scaling ride-pooling simulations because of their reliance on demand density, which does not scale linearly. In this study, we introduce a new methodology for simulating dynamic ride-pooling services called flow-inflated selective sampling (FISS). Unlike previous approaches, FISS considers all agents but—for selected modes—only explicitly simulates a fraction of their trips while simulating the remaining trips through teleportation. Here, we explicitly simulate all public transport and ride-pooling trips and sample private car trips. The capacity consumption of explicitly simulated cars is scaled up to obtain realistic traffic flows, rather than adjusting the network capacity as in previous approaches. We implement FISS in the MATSim simulation environment for a large scenario in Munich, Germany, and show that it preserves traffic flows while keeping key performance indicators of ride-pooling services stable and unbiased. Mode choice decisions based on FISS also remain stable, and runtimes of the assignment can be almost halved. Overall, FISS is a simple yet effective approach that can significantly reduce the computational burden of agent-based simulations while maintaining the accuracy of the results. It can be particularly useful for simulating ride-pooling services, which can be challenging to scale because of their dependence on demand density.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flow-Inflated Selective Sampling for Efficient Agent-Based Dynamic Ride-Pooling Simulations\",\"authors\":\"Nico Kuehnel, Hannes Rewald, Steffen Axer, Felix Zwick, Rolf Findeisen\",\"doi\":\"10.1177/03611981231170624\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Agent-based simulations are powerful tools for simulating emergent mobility modes, but they often require significant memory and computing power. To address this issue, researchers have previously used sampling techniques, where only a fraction of agents are explicitly simulated while others are simulated through teleportation. However, recent studies have highlighted the challenges of scaling ride-pooling simulations because of their reliance on demand density, which does not scale linearly. In this study, we introduce a new methodology for simulating dynamic ride-pooling services called flow-inflated selective sampling (FISS). Unlike previous approaches, FISS considers all agents but—for selected modes—only explicitly simulates a fraction of their trips while simulating the remaining trips through teleportation. Here, we explicitly simulate all public transport and ride-pooling trips and sample private car trips. The capacity consumption of explicitly simulated cars is scaled up to obtain realistic traffic flows, rather than adjusting the network capacity as in previous approaches. We implement FISS in the MATSim simulation environment for a large scenario in Munich, Germany, and show that it preserves traffic flows while keeping key performance indicators of ride-pooling services stable and unbiased. Mode choice decisions based on FISS also remain stable, and runtimes of the assignment can be almost halved. Overall, FISS is a simple yet effective approach that can significantly reduce the computational burden of agent-based simulations while maintaining the accuracy of the results. It can be particularly useful for simulating ride-pooling services, which can be challenging to scale because of their dependence on demand density.\",\"PeriodicalId\":23279,\"journal\":{\"name\":\"Transportation Research Record\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Record\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/03611981231170624\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/03611981231170624","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Flow-Inflated Selective Sampling for Efficient Agent-Based Dynamic Ride-Pooling Simulations
Agent-based simulations are powerful tools for simulating emergent mobility modes, but they often require significant memory and computing power. To address this issue, researchers have previously used sampling techniques, where only a fraction of agents are explicitly simulated while others are simulated through teleportation. However, recent studies have highlighted the challenges of scaling ride-pooling simulations because of their reliance on demand density, which does not scale linearly. In this study, we introduce a new methodology for simulating dynamic ride-pooling services called flow-inflated selective sampling (FISS). Unlike previous approaches, FISS considers all agents but—for selected modes—only explicitly simulates a fraction of their trips while simulating the remaining trips through teleportation. Here, we explicitly simulate all public transport and ride-pooling trips and sample private car trips. The capacity consumption of explicitly simulated cars is scaled up to obtain realistic traffic flows, rather than adjusting the network capacity as in previous approaches. We implement FISS in the MATSim simulation environment for a large scenario in Munich, Germany, and show that it preserves traffic flows while keeping key performance indicators of ride-pooling services stable and unbiased. Mode choice decisions based on FISS also remain stable, and runtimes of the assignment can be almost halved. Overall, FISS is a simple yet effective approach that can significantly reduce the computational burden of agent-based simulations while maintaining the accuracy of the results. It can be particularly useful for simulating ride-pooling services, which can be challenging to scale because of their dependence on demand density.
期刊介绍:
Transportation Research Record: Journal of the Transportation Research Board is one of the most cited and prolific transportation journals in the world, offering unparalleled depth and breadth in the coverage of transportation-related topics. The TRR publishes approximately 70 issues annually of outstanding, peer-reviewed papers presenting research findings in policy, planning, administration, economics and financing, operations, construction, design, maintenance, safety, and more, for all modes of transportation. This site provides electronic access to a full compilation of papers since the 1996 series.