Probabilistic speed–density relationship for pedestrian traffic
• Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nikolić, Marija [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Umfang: |
24 |
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| Übergeordnetes Werk: |
Enthalten in: New approach for proportioning of controlled low strength materials - Alizadeh, Vahid ELSEVIER, 2018, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:89 ; year:2016 ; pages:58-81 ; extent:24 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.trb.2016.04.002 |
|---|
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ELV040019896 |
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| 520 | |a • Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. | ||
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| 700 | 1 | |a Farooq, Bilal |4 oth | |
| 700 | 1 | |a de Lapparent, Matthieu |4 oth | |
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10.1016/j.trb.2016.04.002 doi GBVA2016006000023.pica (DE-627)ELV040019896 (ELSEVIER)S0191-2615(16)30165-5 DE-627 ger DE-627 rakwb eng 380 380 DE-600 690 VZ 56.45 bkl Nikolić, Marija verfasserin aut Probabilistic speed–density relationship for pedestrian traffic 2016 24 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. Bierlaire, Michel oth Farooq, Bilal oth de Lapparent, Matthieu oth Enthalten in Elsevier Alizadeh, Vahid ELSEVIER New approach for proportioning of controlled low strength materials 2018 Amsterdam [u.a.] (DE-627)ELV001588265 volume:89 year:2016 pages:58-81 extent:24 https://doi.org/10.1016/j.trb.2016.04.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 56.45 Baustoffkunde VZ AR 89 2016 58-81 24 045F 380 |
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10.1016/j.trb.2016.04.002 doi GBVA2016006000023.pica (DE-627)ELV040019896 (ELSEVIER)S0191-2615(16)30165-5 DE-627 ger DE-627 rakwb eng 380 380 DE-600 690 VZ 56.45 bkl Nikolić, Marija verfasserin aut Probabilistic speed–density relationship for pedestrian traffic 2016 24 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. Bierlaire, Michel oth Farooq, Bilal oth de Lapparent, Matthieu oth Enthalten in Elsevier Alizadeh, Vahid ELSEVIER New approach for proportioning of controlled low strength materials 2018 Amsterdam [u.a.] (DE-627)ELV001588265 volume:89 year:2016 pages:58-81 extent:24 https://doi.org/10.1016/j.trb.2016.04.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 56.45 Baustoffkunde VZ AR 89 2016 58-81 24 045F 380 |
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• Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. |
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• Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. |
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• Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. • Data-driven approach which is motivated by the empirically observed heterogeneity. • Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). • Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. |
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