Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data
This study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Future Transportation |
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| Online Access: | https://www.mdpi.com/2673-7590/5/2/46 |
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| author | Maziyar Layegh Ciprian Alecsandru Matin Giahi Foomani |
| author_facet | Maziyar Layegh Ciprian Alecsandru Matin Giahi Foomani |
| author_sort | Maziyar Layegh |
| collection | DOAJ |
| description | This study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone locations to improve safety and regulate traffic. This study focuses on stop-controlled intersections (SCIs) in Montréal, Québec, to propose a new backlit sign for evaluation. An unbiased experiment utilizing multinomial logistic regression (MNL) was designed to compare drivers’ reactions to different signage. Microscopic models based on observed turning movement counters (TMCs) were calibrated for conflict estimation using a genetic algorithm (GA). Generalized linear models (GLMs) estimated accident and conflict frequencies under different treatment scenarios. The results showed significant conflict reductions at intersections with LED-backlit signs (BLSs), including 65.5% at night and 46.8% in daylight. Pedestrian crossing conflicts decreased by 55.6% and 27.8%. This study introduces an evaluation framework that integrates driver compliance behavior into simulation and crash modeling to assess a newly designed BLS treatment. It provides a framework for assessing safety treatments in contexts where crash data are limited. Findings offer insights for improving SCIs and enhancing transportation safety using LED stop signs. |
| format | Article |
| id | doaj-art-40c5458e6e4b48dda6760d5e8834965a |
| institution | Kabale University |
| issn | 2673-7590 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj-art-40c5458e6e4b48dda6760d5e8834965a2025-08-20T03:26:52ZengMDPI AGFuture Transportation2673-75902025-04-01524610.3390/futuretransp5020046Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict DataMaziyar Layegh0Ciprian Alecsandru1Matin Giahi Foomani2Department of Building, Civil and Environmental Engineering, Concordia University Montreal, Montreal, QC H3G 1M8, CanadaDepartment of Building, Civil and Environmental Engineering, Concordia University Montreal, Montreal, QC H3G 1M8, CanadaDepartment of Supply Chain Management Studies, Algonquin College, Ottawa, ON K2G 1V8, CanadaThis study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone locations to improve safety and regulate traffic. This study focuses on stop-controlled intersections (SCIs) in Montréal, Québec, to propose a new backlit sign for evaluation. An unbiased experiment utilizing multinomial logistic regression (MNL) was designed to compare drivers’ reactions to different signage. Microscopic models based on observed turning movement counters (TMCs) were calibrated for conflict estimation using a genetic algorithm (GA). Generalized linear models (GLMs) estimated accident and conflict frequencies under different treatment scenarios. The results showed significant conflict reductions at intersections with LED-backlit signs (BLSs), including 65.5% at night and 46.8% in daylight. Pedestrian crossing conflicts decreased by 55.6% and 27.8%. This study introduces an evaluation framework that integrates driver compliance behavior into simulation and crash modeling to assess a newly designed BLS treatment. It provides a framework for assessing safety treatments in contexts where crash data are limited. Findings offer insights for improving SCIs and enhancing transportation safety using LED stop signs.https://www.mdpi.com/2673-7590/5/2/46stop-controlled intersectionactive signage systemscrash reductionLED stop signsconflict-accident prediction model |
| spellingShingle | Maziyar Layegh Ciprian Alecsandru Matin Giahi Foomani Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data Future Transportation stop-controlled intersection active signage systems crash reduction LED stop signs conflict-accident prediction model |
| title | Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data |
| title_full | Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data |
| title_fullStr | Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data |
| title_full_unstemmed | Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data |
| title_short | Assessing Crash Reduction at Stop-Controlled Intersections: A Before-After Study of LED-Backlit Signs Using Crash and Conflict Data |
| title_sort | assessing crash reduction at stop controlled intersections a before after study of led backlit signs using crash and conflict data |
| topic | stop-controlled intersection active signage systems crash reduction LED stop signs conflict-accident prediction model |
| url | https://www.mdpi.com/2673-7590/5/2/46 |
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