Evaluating the impact and clearance duration of freeway incidents
Transportation agencies use incident duration to report the performance of their incident management programs. Most agencies use the incident clearance duration, which in most cases refers to the duration from the reporting of an incident to the time the incident is cleared. This measure may not nec...
Uloženo v:
| Vydáno v: | International Journal of Transportation Science and Technology Ročník 8; číslo 1; s. 13 - 24 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.03.2019
KeAi Communications Co., Ltd |
| Témata: | |
| ISSN: | 2046-0430 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Transportation agencies use incident duration to report the performance of their incident management programs. Most agencies use the incident clearance duration, which in most cases refers to the duration from the reporting of an incident to the time the incident is cleared. This measure may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study, for the first time, introduced a performance measure called incident impact duration and demonstrated a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incidents detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Moreover, the study assessed the predictive accuracy of the models resulted from both durations. It was observed that the incident impact duration provides a better prediction accuracy than the incident clearance duration. |
|---|---|
| AbstractList | Transportation agencies use incident duration to report the performance of their incident management programs. Most agencies use the incident clearance duration, which in most cases refers to the duration from the reporting of an incident to the time the incident is cleared. This measure may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study, for the first time, introduced a performance measure called incident impact duration and demonstrated a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incidents detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Moreover, the study assessed the predictive accuracy of the models resulted from both durations. It was observed that the incident impact duration provides a better prediction accuracy than the incident clearance duration. Transportation agencies use incident duration to report the performance of their incident management programs. Most agencies use the incident clearance duration, which in most cases refers to the duration from the reporting of an incident to the time the incident is cleared. This measure may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study, for the first time, introduced a performance measure called incident impact duration and demonstrated a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incidents detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Moreover, the study assessed the predictive accuracy of the models resulted from both durations. It was observed that the incident impact duration provides a better prediction accuracy than the incident clearance duration. Keywords: Incident duration, Hazard-based models, Predictive accuracy |
| Author | Haule, Henrick J. Sando, Thobias Lentz, Richard Chuan, Ching-Hua Alluri, Priyanka |
| Author_xml | – sequence: 1 givenname: Henrick J. surname: Haule fullname: Haule, Henrick J. email: h.haule@unf.edu organization: University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, United States – sequence: 2 givenname: Thobias surname: Sando fullname: Sando, Thobias email: t.sando@unf.edu organization: University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, United States – sequence: 3 givenname: Richard surname: Lentz fullname: Lentz, Richard email: r.lentz@unf.edu organization: University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, United States – sequence: 4 givenname: Ching-Hua surname: Chuan fullname: Chuan, Ching-Hua email: c.chuan@unf.edu organization: University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, United States – sequence: 5 givenname: Priyanka surname: Alluri fullname: Alluri, Priyanka email: palluri@fiu.edu organization: Department of Civil & Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3628, Miami, FL 33174, United States |
| BookMark | eNqFkLtOw0AQRbcAiecX0PgHYmbt9T4KiigKDwmJBurVeHYMGwUbrZcg_h4nQRQUUI10Nedq5pyIg37oWYgLCaUEqS9XZVzlMZcVSFuCLgGaA3FcgdIzUDUcifNxjC0oZZQ0oI7FfLnB9Tvm2D8X-YWL-PqGlAvsQ0FrxoQ9cRHe07Qx9MXQFV1i_sDPIvYUA_d5PBOHHa5HPv-ep-Lpevm4uJ3dP9zcLeb3M6or18yUbCUHFdg5R9ZS1zC2bVPr2oXaaiAwgVtSrqqosdg2nVRaWU1mStFSfSru9r1hwJV_S_EV06cfMPpdMKRnjynH6WqvLIAEW6EBUsaRC8aEOrS6M4jYbrvqfRelYRwTdz99EvxWpF_5nUi_FelB-0nkRLlfFMW8E5MTxvU_7NWe5UnRJnLyI0We5IaYmPL0Q_yT_wKfP5Uc |
| CitedBy_id | crossref_primary_10_1080_14680629_2025_2485334 crossref_primary_10_3390_app142310964 crossref_primary_10_1016_j_ets_2025_100016 crossref_primary_10_1155_2022_5272747 crossref_primary_10_1016_j_aap_2021_106303 crossref_primary_10_1016_j_aap_2021_106129 crossref_primary_10_1016_j_trd_2025_104869 crossref_primary_10_1080_15472450_2021_1894936 crossref_primary_10_1016_j_techfore_2024_123621 crossref_primary_10_1016_j_amar_2023_100267 crossref_primary_10_1007_s11750_020_00567_w crossref_primary_10_1080_08839514_2021_2018643 crossref_primary_10_1155_2021_8812740 crossref_primary_10_1155_atr_3748345 crossref_primary_10_1371_journal_pone_0316289 crossref_primary_10_1007_s13177_024_00451_y crossref_primary_10_1155_2021_5543698 crossref_primary_10_3390_fire7040110 crossref_primary_10_1155_2021_6671983 crossref_primary_10_1177_03611981251348461 crossref_primary_10_1016_j_aap_2023_107406 crossref_primary_10_1016_j_aap_2019_105391 crossref_primary_10_1016_j_trc_2022_103721 crossref_primary_10_1080_21680566_2022_2063205 crossref_primary_10_1016_j_tust_2022_104894 crossref_primary_10_3390_s22082933 crossref_primary_10_1080_10298436_2022_2138878 crossref_primary_10_1111_mice_12883 crossref_primary_10_3390_su142214859 crossref_primary_10_1080_19439962_2023_2189339 crossref_primary_10_1080_17517575_2024_2448828 crossref_primary_10_1016_j_cstp_2022_01_033 crossref_primary_10_1108_JICV_03_2021_0004 crossref_primary_10_1038_s41598_025_91744_z |
| Cites_doi | 10.1155/2014/723427 10.1007/0-387-29150-4 10.1016/j.ssci.2012.11.009 10.1016/j.sbspro.2012.04.108 10.3141/2178-13 10.1080/15472450.2015.1082428 10.1016/j.aap.2014.06.006 10.3141/2278-11 10.1007/s12205-012-1632-3 10.1016/S0965-8564(98)00065-2 10.1061/(ASCE)0733-947X(1997)123:6(459) 10.1016/j.aap.2009.08.005 |
| ContentType | Journal Article |
| Copyright | 2018 |
| Copyright_xml | – notice: 2018 |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.ijtst.2018.06.005 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EndPage | 24 |
| ExternalDocumentID | oai_doaj_org_article_48001082a70c479c9d77d3db6f7aaabc 10_1016_j_ijtst_2018_06_005 S2046043018300522 |
| GroupedDBID | 6I. AAFTH ALMA_UNASSIGNED_HOLDINGS M~E AAYXX CITATION GROUPED_DOAJ |
| ID | FETCH-LOGICAL-c3295-41b1ed4de999c88cf5eabb53639d3860c07debc4922c58ab5f146486c7ebca8c3 |
| IEDL.DBID | DOA |
| ISSN | 2046-0430 |
| IngestDate | Fri Oct 03 12:43:44 EDT 2025 Tue Nov 18 20:53:59 EST 2025 Wed Nov 05 20:42:52 EST 2025 Sat Mar 04 13:20:44 EST 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Incident duration Hazard-based models Predictive accuracy |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3295-41b1ed4de999c88cf5eabb53639d3860c07debc4922c58ab5f146486c7ebca8c3 |
| OpenAccessLink | https://doaj.org/article/48001082a70c479c9d77d3db6f7aaabc |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_48001082a70c479c9d77d3db6f7aaabc crossref_primary_10_1016_j_ijtst_2018_06_005 crossref_citationtrail_10_1016_j_ijtst_2018_06_005 elsevier_sciencedirect_doi_10_1016_j_ijtst_2018_06_005 |
| PublicationCentury | 2000 |
| PublicationDate | March 2019 2019-03-00 2019-03-01 |
| PublicationDateYYYYMMDD | 2019-03-01 |
| PublicationDate_xml | – month: 03 year: 2019 text: March 2019 |
| PublicationDecade | 2010 |
| PublicationTitle | International Journal of Transportation Science and Technology |
| PublicationYear | 2019 |
| Publisher | Elsevier B.V KeAi Communications Co., Ltd |
| Publisher_xml | – name: Elsevier B.V – name: KeAi Communications Co., Ltd |
| References | Li, X., 2017. Large-scale traffic incident duration analysis: the role of multi-agency response and on-scene times. In: 96th Annual Meeting for the Transportation Research Board. U.S.DOT Federal Highway Administration, 2017. FHWA Operations - Operations Story [WWW Document]. URL Amer, A., Roberts, E., Mangar, U., Kraft, W.H., Wanat, J.T., Cusolito, P.C., Hogan, J.R., Zhao, X., 2015. Traffic Incident Management: Gap Analysis Primer. Chung, Yoon (b0020) 2012; 16 Chung (b0015) 2010; 42 Margiotta, R., Dowling, R., Paracha, J., 2012. Analysis, Modeling, and Simulation for Traffic Incident Management Applications. FDOT, 2014. State of Florida: Open Roads Policy Agreement. Kaabi, Dissanayake, Bird (b0060) 2012; 2278 Farradyne (b0025) 2006 Li, Shang (b0075) 2014; 2014 Kleinbaum, D.A., Klein, M., 2005. Survival Analysis: A Self_Learning Text, second ed., J. Chem. Inf. Modeling. Springer Science+Business Media, Inc. Transportation Research Board Highway Capacity Manual, Hcm, 2010. Washington, D.C. Schrank, Eisele, Lomax, Bak (b0100) 2015; 39 Ghosh (b0040) 2012; 12 USF, 2005. Best Practices for Traffic Incident Management in Florida. Hojati, Ferreira, Washington, Charles, Shobeirinejad (b0045) 2014; 71 Garib, Radwan, Al-Deek (b0035) 1997; 123 Zeng, Songchitruksa (b0135) 2010; 2178 Jeihani, James, Saka, Ardeshiri (b0050) 2015; 2 Khattak, Schofer, Wang (b0065) 1995; 2 Nam, Mannering (b0090) 2000; 34 Chimba, Kutela, Ogletree, Horne, Tugwell (b0010) 2014 Junhua, Haozhe, Shi (b0055) 2013; 54 Smith, K., Smith, B.L., 2001. Forecasting the Clearance Time of Freeway Accidents. Charlottesville, VA. Zhou, Tian (b0140) 2012; 43 Wang, Chen, Bell (b0125) 2005; 8 (accessed 7.17.17). Park, Haghani, Zhang (b0095) 2016; 20 Washington, Karlaftis, Mannering (b0130) 2003 Ghosh (10.1016/j.ijtst.2018.06.005_b0040) 2012; 12 10.1016/j.ijtst.2018.06.005_b0115 Chung (10.1016/j.ijtst.2018.06.005_b0020) 2012; 16 Li (10.1016/j.ijtst.2018.06.005_b0075) 2014; 2014 10.1016/j.ijtst.2018.06.005_b0080 Wang (10.1016/j.ijtst.2018.06.005_b0125) 2005; 8 Washington (10.1016/j.ijtst.2018.06.005_b0130) 2003 Zeng (10.1016/j.ijtst.2018.06.005_b0135) 2010; 2178 10.1016/j.ijtst.2018.06.005_b0085 Farradyne (10.1016/j.ijtst.2018.06.005_b0025) 2006 10.1016/j.ijtst.2018.06.005_b0120 Park (10.1016/j.ijtst.2018.06.005_b0095) 2016; 20 Schrank (10.1016/j.ijtst.2018.06.005_b0100) 2015; 39 10.1016/j.ijtst.2018.06.005_b0105 Zhou (10.1016/j.ijtst.2018.06.005_b0140) 2012; 43 10.1016/j.ijtst.2018.06.005_b0005 Khattak (10.1016/j.ijtst.2018.06.005_b0065) 1995; 2 Kaabi (10.1016/j.ijtst.2018.06.005_b0060) 2012; 2278 Garib (10.1016/j.ijtst.2018.06.005_b0035) 1997; 123 Hojati (10.1016/j.ijtst.2018.06.005_b0045) 2014; 71 Nam (10.1016/j.ijtst.2018.06.005_b0090) 2000; 34 Chimba (10.1016/j.ijtst.2018.06.005_b0010) 2014 Junhua (10.1016/j.ijtst.2018.06.005_b0055) 2013; 54 Chung (10.1016/j.ijtst.2018.06.005_b0015) 2010; 42 10.1016/j.ijtst.2018.06.005_b0030 10.1016/j.ijtst.2018.06.005_b0110 Jeihani (10.1016/j.ijtst.2018.06.005_b0050) 2015; 2 10.1016/j.ijtst.2018.06.005_b0070 |
| References_xml | – reference: Smith, K., Smith, B.L., 2001. Forecasting the Clearance Time of Freeway Accidents. Charlottesville, VA. – volume: 43 start-page: 349 year: 2012 end-page: 355 ident: b0140 article-title: Modeling analysis of incident and roadway clearance time publication-title: Proc. Soc. Behav. Sci. – volume: 123 start-page: 459 year: 1997 end-page: 466 ident: b0035 article-title: Estimating magnitude and duration of incident delays publication-title: J. Transp. Eng. – volume: 34 start-page: 85 year: 2000 end-page: 102 ident: b0090 article-title: An exploratory hazard-based analysis of highway incident duration publication-title: Transp. Res. Part A Policy Pract. – reference: Li, X., 2017. Large-scale traffic incident duration analysis: the role of multi-agency response and on-scene times. In: 96th Annual Meeting for the Transportation Research Board. – volume: 2 start-page: 291 year: 2015 end-page: 300 ident: b0050 article-title: Traffic recovery time estimation under different flow regimes in traffic simulation publication-title: J. Traffic Transp. Eng. (English Ed.) – reference: U.S.DOT Federal Highway Administration, 2017. FHWA Operations - Operations Story [WWW Document]. URL – volume: 71 start-page: 296 year: 2014 end-page: 305 ident: b0045 article-title: Modelling total duration of traffic incidents including incident detection and recovery time publication-title: Accid. Anal. Prev. – reference: FDOT, 2014. State of Florida: Open Roads Policy Agreement. – volume: 42 start-page: 282 year: 2010 end-page: 289 ident: b0015 article-title: Development of an accident duration prediction model on the Korean Freeway Systems publication-title: Accid. Anal. Prev. – volume: 39 start-page: 5 year: 2015 ident: b0100 article-title: Urban mobility scorecard publication-title: Texas A&M Transp. Institute – volume: 2014 start-page: 7 year: 2014 end-page: 9 ident: b0075 article-title: Incident duration modeling using flexible parametric hazard-based models publication-title: Comput. Intell. Neurosci. – year: 2006 ident: b0025 article-title: Florida Traffic Incident Management Program: Strategic Plan – volume: 12 start-page: 75 year: 2012 end-page: 89 ident: b0040 article-title: Examination of the factors influencing the clearance time of freeway incidents publication-title: J. Transp. Syst. Eng. Inf. Technol. – volume: 20 start-page: 385 year: 2016 end-page: 400 ident: b0095 article-title: Interpretation of Bayesian neural networks for predicting the duration of detected incidents publication-title: J. Intell. Transp. Syst. – reference: Margiotta, R., Dowling, R., Paracha, J., 2012. Analysis, Modeling, and Simulation for Traffic Incident Management Applications. – reference: (accessed 7.17.17). – reference: Kleinbaum, D.A., Klein, M., 2005. Survival Analysis: A Self_Learning Text, second ed., J. Chem. Inf. Modeling. Springer Science+Business Media, Inc. – volume: 2278 start-page: 95 year: 2012 end-page: 103 ident: b0060 article-title: Response time of highway traffic accidents in Abu Dhabi publication-title: Transp. Res. Rec. J. Transp. Res. Board – reference: USF, 2005. Best Practices for Traffic Incident Management in Florida. – volume: 2 start-page: 113 year: 1995 end-page: 138 ident: b0065 article-title: A simple time sequential procedure for predicting freeway incident duration publication-title: IVHSJ – volume: 2178 start-page: 119 year: 2010 end-page: 127 ident: b0135 article-title: Empirical method for estimating traffic incident recovery time publication-title: Transp. Res. Rec. J. Transp. Res. Board – volume: 54 start-page: 43 year: 2013 end-page: 50 ident: b0055 article-title: Estimating freeway incident duration using accelerated failure time modeling publication-title: Saf. Sci. – reference: Amer, A., Roberts, E., Mangar, U., Kraft, W.H., Wanat, J.T., Cusolito, P.C., Hogan, J.R., Zhao, X., 2015. Traffic Incident Management: Gap Analysis Primer. – reference: Transportation Research Board Highway Capacity Manual, Hcm, 2010. Washington, D.C. – start-page: 140 year: 2014 ident: b0010 article-title: Impact of abandoned and disabled vehicles on freeway incident duration publication-title: J. Transp. Eng. – volume: 8 start-page: 75 year: 2005 end-page: 84 ident: b0125 article-title: Vehicle breakdown duration modeling publication-title: J. Transp. Stat. – volume: 16 start-page: 1064 year: 2012 end-page: 1070 ident: b0020 article-title: Analytical method to estimate accident duration using archived speed profile and its statistical analysis publication-title: KSCE J. Civ. Eng. – year: 2003 ident: b0130 article-title: Statistical and Econometric Techniques for Transportation Data Analysis – volume: 2014 start-page: 7 year: 2014 ident: 10.1016/j.ijtst.2018.06.005_b0075 article-title: Incident duration modeling using flexible parametric hazard-based models publication-title: Comput. Intell. Neurosci. doi: 10.1155/2014/723427 – ident: 10.1016/j.ijtst.2018.06.005_b0120 – ident: 10.1016/j.ijtst.2018.06.005_b0070 doi: 10.1007/0-387-29150-4 – volume: 8 start-page: 75 year: 2005 ident: 10.1016/j.ijtst.2018.06.005_b0125 article-title: Vehicle breakdown duration modeling publication-title: J. Transp. Stat. – ident: 10.1016/j.ijtst.2018.06.005_b0080 – ident: 10.1016/j.ijtst.2018.06.005_b0105 – volume: 39 start-page: 5 year: 2015 ident: 10.1016/j.ijtst.2018.06.005_b0100 article-title: Urban mobility scorecard publication-title: Texas A&M Transp. Institute – volume: 54 start-page: 43 year: 2013 ident: 10.1016/j.ijtst.2018.06.005_b0055 article-title: Estimating freeway incident duration using accelerated failure time modeling publication-title: Saf. Sci. doi: 10.1016/j.ssci.2012.11.009 – ident: 10.1016/j.ijtst.2018.06.005_b0005 – ident: 10.1016/j.ijtst.2018.06.005_b0030 – volume: 43 start-page: 349 year: 2012 ident: 10.1016/j.ijtst.2018.06.005_b0140 article-title: Modeling analysis of incident and roadway clearance time publication-title: Proc. Soc. Behav. Sci. doi: 10.1016/j.sbspro.2012.04.108 – volume: 12 start-page: 75 year: 2012 ident: 10.1016/j.ijtst.2018.06.005_b0040 article-title: Examination of the factors influencing the clearance time of freeway incidents publication-title: J. Transp. Syst. Eng. Inf. Technol. – year: 2003 ident: 10.1016/j.ijtst.2018.06.005_b0130 – volume: 2178 start-page: 119 year: 2010 ident: 10.1016/j.ijtst.2018.06.005_b0135 article-title: Empirical method for estimating traffic incident recovery time publication-title: Transp. Res. Rec. J. Transp. Res. Board doi: 10.3141/2178-13 – ident: 10.1016/j.ijtst.2018.06.005_b0110 – ident: 10.1016/j.ijtst.2018.06.005_b0085 – year: 2006 ident: 10.1016/j.ijtst.2018.06.005_b0025 – volume: 2 start-page: 113 year: 1995 ident: 10.1016/j.ijtst.2018.06.005_b0065 article-title: A simple time sequential procedure for predicting freeway incident duration publication-title: IVHSJ – volume: 20 start-page: 385 year: 2016 ident: 10.1016/j.ijtst.2018.06.005_b0095 article-title: Interpretation of Bayesian neural networks for predicting the duration of detected incidents publication-title: J. Intell. Transp. Syst. doi: 10.1080/15472450.2015.1082428 – volume: 71 start-page: 296 year: 2014 ident: 10.1016/j.ijtst.2018.06.005_b0045 article-title: Modelling total duration of traffic incidents including incident detection and recovery time publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2014.06.006 – start-page: 140 year: 2014 ident: 10.1016/j.ijtst.2018.06.005_b0010 article-title: Impact of abandoned and disabled vehicles on freeway incident duration publication-title: J. Transp. Eng. – volume: 2278 start-page: 95 year: 2012 ident: 10.1016/j.ijtst.2018.06.005_b0060 article-title: Response time of highway traffic accidents in Abu Dhabi publication-title: Transp. Res. Rec. J. Transp. Res. Board doi: 10.3141/2278-11 – volume: 16 start-page: 1064 year: 2012 ident: 10.1016/j.ijtst.2018.06.005_b0020 article-title: Analytical method to estimate accident duration using archived speed profile and its statistical analysis publication-title: KSCE J. Civ. Eng. doi: 10.1007/s12205-012-1632-3 – volume: 2 start-page: 291 year: 2015 ident: 10.1016/j.ijtst.2018.06.005_b0050 article-title: Traffic recovery time estimation under different flow regimes in traffic simulation publication-title: J. Traffic Transp. Eng. (English Ed.) – volume: 34 start-page: 85 year: 2000 ident: 10.1016/j.ijtst.2018.06.005_b0090 article-title: An exploratory hazard-based analysis of highway incident duration publication-title: Transp. Res. Part A Policy Pract. doi: 10.1016/S0965-8564(98)00065-2 – ident: 10.1016/j.ijtst.2018.06.005_b0115 – volume: 123 start-page: 459 year: 1997 ident: 10.1016/j.ijtst.2018.06.005_b0035 article-title: Estimating magnitude and duration of incident delays publication-title: J. Transp. Eng. doi: 10.1061/(ASCE)0733-947X(1997)123:6(459) – volume: 42 start-page: 282 year: 2010 ident: 10.1016/j.ijtst.2018.06.005_b0015 article-title: Development of an accident duration prediction model on the Korean Freeway Systems publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2009.08.005 |
| SSID | ssib044741704 |
| Score | 2.2592707 |
| Snippet | Transportation agencies use incident duration to report the performance of their incident management programs. Most agencies use the incident clearance... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 13 |
| SubjectTerms | Hazard-based models Incident duration Predictive accuracy |
| Title | Evaluating the impact and clearance duration of freeway incidents |
| URI | https://dx.doi.org/10.1016/j.ijtst.2018.06.005 https://doaj.org/article/48001082a70c479c9d77d3db6f7aaabc |
| Volume | 8 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources issn: 2046-0430 databaseCode: M~E dateStart: 20120101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: false ssIdentifier: ssib044741704 providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQYmBBIECUlzwwEpEmdmyPBbVigIoBUDfLT9QKpagPRn47PjupMpWFJUPkR3Tn3MP67juEbspC5yVlItNeVxnxebCD1NpMhfFgM4mOVWnvT2w85pOJeOm0-gJMWKIHToK7IxzSFl4olhvChBGWMVtC8RhTSmkD1jdnopNMhZNESHCULPYOLEICmAGxVUs5FMFd09lqCUjKfmLvhOZ1HbcU2fs73qnjcUaH6KAJFfEgfeIR2nH1MRoMG3ru-gOH2A2nKkesaosNdIAAJWK7TnrFc4_9wsH1GIY7dSjKXZ6gt9Hw9eExa9ogZKYsBM1IX_edJdaFWM5wbjx1SmtahtjClrzKTc6s04aIojCUK019sH6EV4YB0Imb8hTt1vPanSFsCRFeKdunXJPKV8qFhKVyAHN1NPy7PVS0UpCm4QiHVhWfsgWDzWQUnQTRyQiJoz10u5n0lSgytg-_B_FuhgK_dXwRtC4brcu_tN5DVasc2YQKKQQIS0237X7-H7tfoP2wpEhQtEu0u1qs3RXaM9-r6XJxHU9ieD7_DH8BZv7jmA |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Evaluating+the+impact+and+clearance+duration+of+freeway+incidents&rft.jtitle=International+Journal+of+Transportation+Science+and+Technology&rft.au=Haule%2C+Henrick+J.&rft.au=Sando%2C+Thobias&rft.au=Lentz%2C+Richard&rft.au=Chuan%2C+Ching-Hua&rft.date=2019-03-01&rft.issn=2046-0430&rft.volume=8&rft.issue=1&rft.spage=13&rft.epage=24&rft_id=info:doi/10.1016%2Fj.ijtst.2018.06.005&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ijtst_2018_06_005 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2046-0430&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2046-0430&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2046-0430&client=summon |