Analyzing and Simulating Traffic Collision Data to Recommend Policy for Autonomous Taxi Deployment in Dubai
| dc.Location | TD | |
| dc.Supervisor | Professor Bassam Abu-Hijleh | |
| dc.contributor.author | MAHBOOB, MOHAMED | |
| dc.date.accessioned | 2024-01-04T11:10:10Z | |
| dc.date.available | 2024-01-04T11:10:10Z | |
| dc.date.issued | 2023-12 | |
| dc.description.abstract | The adoption of appropriate and suitable Autonomous vehicles regulations and guidelines is one of the main challenges that governments and transportation decision makers are facing nowadays. Appropriate guidelines to operate autonomous vehicles successfully and to maximize their benefits is crucial for safe and seamless deployment. This research focuses on analyzing the key safety benefits of deploying autonomous taxis for traffic collision avoidance. The research also suggests and recommends policies and guidance to accelerate appropriate and safe autonomous vehicles deployment. The research aims at supporting policy makers and governments with the recommendations for safer deployment of autonomous taxi. The Al Muraqabat area was selected for the simulation model as it had a high frequency of collisions. Statistical analysis of taxi traffic collision data was conducted to identify the human factors that contribute to traffic collisions. The statistical model was developed using the SPSS software. Ten reasons for traffic collisions were studied and 76% of the traffic collisions were due to three reasons that were associated with human factors. Experience, Age and fatigue were identified as main causes of traffic collision which are associated with human drivers. The outcome of the statistical model was used to build a traffic simulation model using the Vissim traffic simulation Software. Seven scenarios were simulated which included a baseline model and three autonomous driving behavior scenarios that were simulated on both 100% and 50% penetration levels. When autonomous scenarios were simulated, the lowest number of traffic collisions occurred for the case of 100% normal driving behavior (367) which was 21% lower than baseline scenario. Policy analysis was used to identify gaps in the current legislations and exploring best practice globally. The interview questions were formulated based on the outcome of statistical analysis, simulation and legislation gaps. The thematic analysis was used to identify the experts’ ideas and thoughts using Nvivo software. Experts suggested focused on engaging the government and providing incentives to the private sector. Restudying urban planning, dedicated autonomous vehicles centers and developing smart infrastructure were the main recommendations. Revising the traffic and developing autonomous vehicles standard are critical for safe deployment. | |
| dc.identifier.other | 2016139056 | |
| dc.identifier.uri | https://bspace.buid.ac.ae/handle/1234/2478 | |
| dc.language.iso | en | |
| dc.publisher | The British University in Dubai (BUiD) | |
| dc.subject | traffic collision, autonomous taxi deployment, United Arab Emirates (UAE), Dubai, autonomous vehicles, safe deployment | |
| dc.title | Analyzing and Simulating Traffic Collision Data to Recommend Policy for Autonomous Taxi Deployment in Dubai | |
| dc.type | Thesis |