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Road congestion is a major challenge in rapidly urbanizing cities where public transport is limited. This study examines the bridge-constrained road network of Nasiriyah, Iraq, divided by the Euphrates River into two sectors. A locally calibrated, digital-twin-informed geographic information system framework was developed in ArcGIS Pro with Python-based geoprocessing. It combines elevation-aware network construction, classified traffic counts at 62 stations, volume-to-capacity analysis of the seven river bridges, a side-friction impedance coefficient calibrated with Dash Cam travel-time runs, nearest-facility accessibility analysis, and conditional demand projection to 2040. The framework is digital-twin-informed rather than real-time because the data were collected periodically. In the 2025 morning peak, one bridge (Al-Nasr) operated above capacity (volume-to-capacity ratio 1.02, level of service F) and another (Al-Hadarat) at capacity (0.99); the other five ranged from 0.73 to 0.94. The calibrated model reproduced 62 Dash Cam runs with a mean absolute percentage error of 4.24%, and two independent GPS samples gave a coefficient of determination of 0.94. The mean modeled travel time from 8,492 origin nodes to 42 vital-service facilities was 18.92 minutes, and at the 10-minute threshold the service area was about 28% smaller than under free-flow conditions. With capacity held constant and 3% annual demand growth, all seven bridges exceed capacity by 2040 (ratios of 1.15 to 1.60). In a modeled re-run, the one-way operation of three bridges shortened the mean travel time by 13.1%; eight additional bridges were assessed for connectivity only. The parameters are specific to Nasiriyah and require recalibration elsewhere.
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Naji et al. (2026) studied this question.
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