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This paper presents a novel traffic simulation scheme capable of modeling most forms of urban, chaotic traffic. Different from other lane-based or following-based approaches, ours models traffic as a large navigational problem in an agent based simulation context. While this generalization makes the traffic more reflective of certain scenarios, it...
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Citations
... • Fully Agent-Based: MATSim (Horni et al., 2016), ITSUMO (Bazzan et al., 2010), MovSim (Treiber and Kesting, 2010), MAS-CAT (Guériau et al., 2016), MATISSE (Torabi et al., 2018), PO-LARIS (Auld et al., 2016), AgentPolis (Jakob and Moler, 2013), OPUS (Waddell et al., 2006), MOSAIIC (Czura et al., 2015), MARS (Weyl et al., 2018), SimMobility (Adnan et al., 2016), SITRAS (Hidas, 1998), ArchiSim (Champion et al., 2001), SEM-Sim (CityMOS) (Xu et al., 2012), JTSS (Tao and Huang, 2009), Megaffic + XAXIS (Osogami et al., 2012), SD-Sim (Dumbuya et al., 2002), SM4T (Zargayouna et al., 2014), VCTS (Chaurasia et al., 2010), SIMTUR (Nakamiti et al., 2012), MUST (Pathania et al., 2013), CAMiCS (Levesque et al., 2008), OpEMCSS (Clymer, 2002), DEFACTO (Schurr et al., 2005), MAGE (Banos and Charpentier, 2007), CityScape (Ion et al., 2015), BAE Systems (Handford et al., 2011), AITSPS (Zhou and Zhao, 2010), SeSAm (Klügl et al., 2006), IMAGES (Yoo et al., 2009), Mobiliti (Chan et al., 2018), CUPSS (Wang et al., 2004), KLMTS1.0 (Chen and Pang, 2008), CARLA (Dosovitskiy et al., 2017), AgentStudio (Radecký and Gajdoš, 2008), ILUTE (Salvini and Miller, 2005), SIMU-LACRA (Batty et al., 2013), TransWorld (Wang, 2010) • Featuring Agent-Technology: ATSim (Chu et al., 2011), FastTrans (Thulasidasan et al., 2009) • Not Agent-Based: TRANSIMS (Institute, 1999), SUMO (Krajzewicz et al., 2002), OpenTraffic (Miska et al., 2011;Tamminga et al., 2014), CONTRAM (Taylor, 2003), PTV VISSIM/VISUM (Fellendorf, 1994), GETRAM/AIMSUN (Barceló and Casas, 2005), PARAMICS (Cameron and Duncan, 1996), MITSIM (Yang et al., 2000), FreeSim (Miller and Horowitz, 2007), TSIS/CORSIM (Owen et al., 2000), VATSIM (Lei et al., 2001), DRACULA (Liu, 2010), RENAISSANCE (Wang et al., 2006), SimTraffic (Sorenson and Collins, 2000), DynaMIT (Ben-Akiva et al., 1998), DYNASMART (Mahmassani and Peeta, 1993), MITSIMLab (Yang and Koutsopoulos, 1996), CUBE Voyager (Bentley Systems, 2021), PELOPS (Wallentowitz et al., 1999), TransModeler (Balakrishna et al., 2009), Dynameq (Mahut and Florian, 2010), CORFLO (Lieu et al., 1992), PACSIM (Cornelis and Platbrood, 2002), SIMSCRIPT II.5. (Bernhard and Portmann, 2000), CTSP (Elci and Zambakoǧlu, 1982), CityMob (Martinez et al., 2008), VanetMobiSim (Härri et al., 2006), FIVIS (Schulzyk et al., 2007), THOREAU (Wang and Glassco, 1995), GENIVI , SLX (Henriksen, 2000), SALT (Song and Min, 2018), SIM-ENG (Creagh, 1999), KAIST (Kwon et al., 2001), UMTSM (Zhang et al., 2009), SES/MB (Chi et al., 1995), SISTM (Hardman, 1996), INTERGRA-TION (Van Aerde et al., 1996), MATDYMO (CHOI et al., 2006), TRANSYT (Byrne et al., 1982) As our objective is to address modelling of individuals, there is a primary focus on the approaches of the first and the second category. ...
Individual traffic significantly contributes to climate change and environmental degradation. Therefore, innovation in sustainable mobility is gaining importance as it helps to reduce environmental pollution. However, effects of new ideas in mobility are difficult to estimate in advance and strongly depend on the individual traffic participants. The application of agent technology is particularly promising as it focuses on modelling heterogeneous individual preferences and behaviours. In this paper, we show how agent-based models are particularly suitable to address three pressing research topics in mobility: 1. Social dilemmas in resource utilisation; 2. Digital connectivity; and 3. New forms of mobility. We then explain how the features of several agent-based simulators are suitable for addressing these topics. We assess the capability of simulators to model individual travel behaviour, discussing implemented features and identifying gaps in functionality that we consider important.
A traffic simulation model with detailed simulation and realistic rendering serves the purpose of global traffic prediction as well as individual driver behavior analysis. Though there has been research in traffic simulation for years, the focus has been mainly on centralized approaches. The main drawback with the centralized approaches is that they are quite intractable, unstable and unrealistic. Recently many decentralized models are developed to reproduce global parameters like traffic flow rate from collective behavior of intelligent agents. But decentralized approaches which simulate local parameters like individual speed preferences do not consider the lane-less chaotic traffic existing in most Indian roads. They do not provide sufficient details for realistic rendering. As of now, there are no detailed simulation models available for realistic rendering that captures the essential features of Indian traffic scenario.
The proposed work tries to avoid the above mentioned problems and aims to develop a decentralized system for detailed simulation and realistic rendering of Indian traffic. Some of the nationally significant applications of the proposed tool are urban planning, training novice drivers and serving as a test bed for new traffic implementations apart from games and entertainment.
From fig. 1, it can be seen that research in traffic monitoring and simulation was started a century ago. Over the years, advanced techniques such as loop detectors and sensors have refined the traffic simulation models. Recently with the availability of devices and tools to capture and process real-time videos, there are new inputs to the learning algorithms used in traffic simulation. Loop detectors and road-side cameras are known to provide global traffic information such as vehicle type and density in a road, traffic jam and queue length at traffic junctions. But they are not sufficient to develop detailed simulation models which require realistic rendering. Though vehicle velocity may be measured using vehicle mounted devices, that alone is not sufficient for the task. We need to learn the driver actions with respect to the acceleration and break controls to simulate the realistic path of the vehicle on the road. This emphasizes the need to use vehicle mounted cameras for capturing vehicle traffic on a road.
