The effect of transportation infrastructure network development on movement patterns (case study: tunggulmas bridge)

This research examines the effect of the Tunggulmas Bridge construction on movement patterns in Malang City, Indonesia, focusing on road network performance through the Volume to Capacity (V/C) ratio and Level of Service (LoS). The study addresses the scientific-applied problem of traffic congestion and network performance evaluation due to new infrastructure. The V/C ratio is a key traffic performance metric that represents the relationship between traffic volume (V) and road capacity (C). A V/C ratio below 0.75 indicates smooth traffic flow (LoS A–C), 0.75–0.85 suggests moderate congestion (LoS D), 0.85–1.0 signals high congestion (LoS E), and a V/C ratio exceeding 1.0 means oversaturated conditions (LoS F), where demand surpasses road capacity, leading to significant delays. The methodology involved a four-step modeling approach using Cube Voyager for network performance and PTV Vissim for intersection performance. The results show that 6 out of 14 road segments experienced a reduction in the V/C ratio, indicating improved conditions, while 5 segments saw increased congestion, and 3 remained unchanged. Additionally, the new bridge led to the formation of two new intersections, impacting existing intersections by increasing queue lengths and delays, particularly from the Batu direction. Quantitative findings highlight that the bridge significantly altered movement patterns. The highest trip origins were from Ketawanggede and Dinoyo, areas known for education and commerce, with traffic redistribution leading to varying impacts on different segments. Without intervention, a 10-year projection indicates further deterioration in road performance, with more segments exceeding a V/C ratio of 1. The study’s practical applicability lies in its recommendations for traffic management, including road widening, additional networks, and toll roads, to optimize network performance. Compared to existing literature, these results provide a localized understanding of infrastructure impacts, offering data-driven solutions to urban traffic challenges