Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions
The safety of unsignalized intersections is evaluated by correlating the number of crashes with traffic volume and intersection geometry. However, crash-based safety assessment has known drawbacks related to data quality and coverage. Further, the crash-based safety analysis does not account that no...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/9965733 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548794985611264 |
|---|---|
| author | Jaydip Goyani Ninad Gore Shriniwas Arkatkar |
| author_facet | Jaydip Goyani Ninad Gore Shriniwas Arkatkar |
| author_sort | Jaydip Goyani |
| collection | DOAJ |
| description | The safety of unsignalized intersections is evaluated by correlating the number of crashes with traffic volume and intersection geometry. However, crash-based safety assessment has known drawbacks related to data quality and coverage. Further, the crash-based safety analysis does not account that not all vehicles interact unsafely. Therefore, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban unsignalized T-intersections with varying intersection geometry. Initially, the crossing conflicts were analyzed using post encroachment time (PET); based on that, they are bifurcated into critical and noncritical conflicts. The C-SPFs were modeled as a function of traffic volume and intersection geometry using the generalized estimating equations with the Tweedie distribution (GEE_TD) regression approach. The results revealed the time of the day, intersection geometry, vehicular composition, and traffic volume of both offending and conflicting approaches as significant variables influencing the number of critical and noncritical crossing conflicts. Further, to check the predictive power of the GEE_TD model, the model errors are compared with those obtained using the negative binomial (NB) model. The result revealed that for both critical and noncritical conflicts, the GEE_TD model has better predictivity (lesser error) than the NB model. |
| format | Article |
| id | doaj-art-0cdcbe3296904181ad345bfd1f5d3767 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0cdcbe3296904181ad345bfd1f5d37672025-02-03T06:12:59ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/9965733Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic ConditionsJaydip Goyani0Ninad Gore1Shriniwas Arkatkar2Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringThe safety of unsignalized intersections is evaluated by correlating the number of crashes with traffic volume and intersection geometry. However, crash-based safety assessment has known drawbacks related to data quality and coverage. Further, the crash-based safety analysis does not account that not all vehicles interact unsafely. Therefore, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban unsignalized T-intersections with varying intersection geometry. Initially, the crossing conflicts were analyzed using post encroachment time (PET); based on that, they are bifurcated into critical and noncritical conflicts. The C-SPFs were modeled as a function of traffic volume and intersection geometry using the generalized estimating equations with the Tweedie distribution (GEE_TD) regression approach. The results revealed the time of the day, intersection geometry, vehicular composition, and traffic volume of both offending and conflicting approaches as significant variables influencing the number of critical and noncritical crossing conflicts. Further, to check the predictive power of the GEE_TD model, the model errors are compared with those obtained using the negative binomial (NB) model. The result revealed that for both critical and noncritical conflicts, the GEE_TD model has better predictivity (lesser error) than the NB model.http://dx.doi.org/10.1155/2022/9965733 |
| spellingShingle | Jaydip Goyani Ninad Gore Shriniwas Arkatkar Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions Journal of Advanced Transportation |
| title | Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions |
| title_full | Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions |
| title_fullStr | Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions |
| title_full_unstemmed | Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions |
| title_short | Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions |
| title_sort | modeling crossing conflicts at unsignalized t intersections under heterogeneous traffic conditions |
| url | http://dx.doi.org/10.1155/2022/9965733 |
| work_keys_str_mv | AT jaydipgoyani modelingcrossingconflictsatunsignalizedtintersectionsunderheterogeneoustrafficconditions AT ninadgore modelingcrossingconflictsatunsignalizedtintersectionsunderheterogeneoustrafficconditions AT shriniwasarkatkar modelingcrossingconflictsatunsignalizedtintersectionsunderheterogeneoustrafficconditions |