A Car-Following Model Based on Quantified Homeostatic Risk Perception
This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is...
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| Veröffentlicht in: | Mathematical problems in engineering 2013-01, Vol.2013 (2013), p.1-13 |
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| creator | Lin, Qingfeng Wang, Yunpeng Cheng, Bo Lu, Guangquan |
| description | This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT. |
| doi_str_mv | 10.1155/2013/408756 |
| format | Article |
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On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2013/408756</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Behavior ; Calibration ; Car following ; Computer simulation ; Distributed memory ; Driving ; Engineering ; Homeostasis ; International conferences ; Laboratories ; Mathematical models ; Perceptions ; Researchers ; Risk acceptance ; Risk perception ; Safety margins ; Studies ; Traffic flow ; Vehicles ; Velocity</subject><ispartof>Mathematical problems in engineering, 2013-01, Vol.2013 (2013), p.1-13</ispartof><rights>Copyright © 2013 Guangquan Lu et al.</rights><rights>Copyright © 2013 Guangquan Lu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-e65b494794bc9d8175552d71382e7d0d0ca02d4e5f27b4b74846551d660d1b2d3</citedby><cites>FETCH-LOGICAL-c389t-e65b494794bc9d8175552d71382e7d0d0ca02d4e5f27b4b74846551d660d1b2d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Cruz-Hernandez, Cesar</contributor><creatorcontrib>Lin, Qingfeng</creatorcontrib><creatorcontrib>Wang, Yunpeng</creatorcontrib><creatorcontrib>Cheng, Bo</creatorcontrib><creatorcontrib>Lu, Guangquan</creatorcontrib><title>A Car-Following Model Based on Quantified Homeostatic Risk Perception</title><title>Mathematical problems in engineering</title><description>This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT.</description><subject>Behavior</subject><subject>Calibration</subject><subject>Car following</subject><subject>Computer simulation</subject><subject>Distributed memory</subject><subject>Driving</subject><subject>Engineering</subject><subject>Homeostasis</subject><subject>International conferences</subject><subject>Laboratories</subject><subject>Mathematical models</subject><subject>Perceptions</subject><subject>Researchers</subject><subject>Risk acceptance</subject><subject>Risk perception</subject><subject>Safety margins</subject><subject>Studies</subject><subject>Traffic flow</subject><subject>Vehicles</subject><subject>Velocity</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0E1Lw0AQBuBFFKzVk3cJeBEldmc_s0ctrRUqfqDgbdlkN7o1zdZsSvHfmxIP4sXTzMDDMPMidAz4EoDzEcFARwxnkosdNAAuaMqByd2ux4SlQOjrPjqIcYExAQ7ZAE2ukrFp0mmoqrDx9VtyF6yrkmsTnU1CnTyuTd360nfTLCxdiK1pfZE8-fiRPLimcKvWh_oQ7ZWmiu7opw7Ry3TyPJ6l8_ub2_HVPC1optrUCZ4zxaRieaFsBpJzTqwEmhEnLba4MJhY5nhJZM5yyTImOAcrBLaQE0uH6Kzfu2rC59rFVi99LFxVmdqFddTACVaUqu7xITr9Qxdh3dTddRpUJkAJJWWnLnpVNCHGxpV61filab40YL2NVG8j1X2knT7v9buvrdn4f_BJj11HXGl-YQqZxPQbJS18nA</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Lin, Qingfeng</creator><creator>Wang, Yunpeng</creator><creator>Cheng, Bo</creator><creator>Lu, Guangquan</creator><general>Hindawi Publishing Corporation</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20130101</creationdate><title>A Car-Following Model Based on Quantified Homeostatic Risk Perception</title><author>Lin, Qingfeng ; Wang, Yunpeng ; Cheng, Bo ; Lu, Guangquan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-e65b494794bc9d8175552d71382e7d0d0ca02d4e5f27b4b74846551d660d1b2d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Behavior</topic><topic>Calibration</topic><topic>Car following</topic><topic>Computer simulation</topic><topic>Distributed memory</topic><topic>Driving</topic><topic>Engineering</topic><topic>Homeostasis</topic><topic>International conferences</topic><topic>Laboratories</topic><topic>Mathematical models</topic><topic>Perceptions</topic><topic>Researchers</topic><topic>Risk acceptance</topic><topic>Risk perception</topic><topic>Safety margins</topic><topic>Studies</topic><topic>Traffic flow</topic><topic>Vehicles</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Qingfeng</creatorcontrib><creatorcontrib>Wang, Yunpeng</creatorcontrib><creatorcontrib>Cheng, Bo</creatorcontrib><creatorcontrib>Lu, Guangquan</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Qingfeng</au><au>Wang, Yunpeng</au><au>Cheng, Bo</au><au>Lu, Guangquan</au><au>Cruz-Hernandez, Cesar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Car-Following Model Based on Quantified Homeostatic Risk Perception</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2013-01-01</date><risdate>2013</risdate><volume>2013</volume><issue>2013</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2013/408756</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Behavior Calibration Car following Computer simulation Distributed memory Driving Engineering Homeostasis International conferences Laboratories Mathematical models Perceptions Researchers Risk acceptance Risk perception Safety margins Studies Traffic flow Vehicles Velocity |
| title | A Car-Following Model Based on Quantified Homeostatic Risk Perception |
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