Quantifying Impacts of Connected and Automated Vehicles on Traffic Operation of a Diamond Interchange in UAE Using Micro-simulation

Tulimat, Mohamad; Obaid, Osama; Obaid, Lubna; Hamad, Khaled

doi:10.1109/dese54285.2021.9719571

Cited by 1 publication

(2 citation statements)

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“…This research aimed to develop, calibrate, and validate a VISSIM-based microsimulation model, to reveal the car-following behavior of Abu Dhabi city, as a representative of the GCC and Middle Eastern drivers, where such models are not highly used for research and traffic management policy evaluations. However, the majority of sustainable road research in the region has focused on alternate material research [14] and traffic safety behavioral studies from a psychological response perspective [15,16], with minimal attention to actually modeling the driver behavior. The model was then applied to analyze the efficacy of extending public transport services to a major highway in the City of Abu Dhabi, using in-field data and traffic projection models, which can be used to perform scenario modeling of not only traffic management strategies, as demonstrated here, but traffic safety research when considering the impact of flow management policies on changes in aggressive driving behavior.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that none of these benefits can be assessed without properly creating, calibrating, and validating a dedicated microsimulation model. Although, some macroscopic models exist for the urban planning purposes in Abu Dhabi and the GCC region at large [16,25], these are largely concerned with personal mobility or alternate vehicle type evaluations and do not model the general driving behavior of the city's residents on a major urban road, which is required to compare different traffic management strategies at a micro level.…”

Section: Introductionmentioning

confidence: 99%

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Microsimulation Modelling and Scenario Analysis of a Congested Abu Dhabi Highway

Hasan¹,

Jassmi

Hasan³

2023

Eng

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Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%