International Journal of Traffic Management in Transportation Network (p: 3107-9504)

Mitigating Port-Induced Traffic Congestion in Chattogram: A Theoretical SUMO Simulation Framework for the Agrabad–Freeport Corridor

Wed, 23 Sep 2026 00:00:00 +0000

Port-related freight activity, heterogeneous traffic, weak lane discipline, and roadside friction contribute to persistent congestion along major urban corridors in Chattogram, Bangladesh. This study develops an evidence-informed microscopic traffic simulation framework using the Simulation of Urban Mobility (SUMO) platform for the Agrabad–Freeport corridor, with particular focus on the Barik Building–Saltgola section. Five scenarios are considered: the existing-condition baseline, adaptive signal control, lane channelization, a dedicated Non-Motorized Transport (NMT) lane, and a combined strategy. Because corridor-specific traffic counts, signal timings, trajectory data, and validated travel-time observations were unavailable, the study is structured as a theoretical framework rather than a locally calibrated simulation. Published intervention effects were transferred only where sufficiently comparable evidence existed, while unsupported outcomes were retained as theoretical or sensitivity-based projections. Adaptive signal control showed the most comprehensive evidence base, with reported reductions of 13.6% in travel time, 14.3% in intersection delay, 8.9% in queue length, and approximately 9% in both CO₂ emissions and fuel consumption, together with a 47.9% increase in mean network speed. Lane channelization indicated a 10%–30% travel-time reduction based on Bangladesh-specific evidence. In contrast, the dedicated NMT lane revealed a context-dependent safety–capacity trade-off, while the combined strategy was treated as non-additive because the interventions influence overlapping network processes. The framework provides a transparent basis for prioritizing future field data collection, calibration, validation, and integrated scenario testing in a freight-intensive urban corridor.

Smart Traffic Management System: Real-Time AI-Based Adaptive Traffic Light Control Using Vehicle Density Analysis

Mon, 12 Oct 2026 00:00:00 +0000

Urban traffic gridlock in Nigerian cities, particularly Lagos and Port Harcourt, exacts a severe toll on both the economy and public health a toll that conventional fixed-cycle signal systems are structurally ill-equipped to address. This study presents the design, construction, and experimental validation of a software-driven adaptive traffic control system that modifies signal timing in real time by measuring vehicle density from live camera feeds. At its core, the system employs a YOLOv5 object detection model trained on a purpose-built Nigerian traffic dataset that spans cars, buses, okadas, danfos, and trucks, enabling per-lane vehicle counting; a bounded proportional algorithm then translates those counts into green-signal durations while a fairness mechanism prevents any lane from being indefinitely starved. The full processing chain operates on a Raspberry Pi 4 edge device and is supervised through a Flask-based web dashboard. On the Raspberry Pi, detection accuracy reached mAP@0.5 = 92.4% at 15–20 FPS. When benchmarked against a conventional 60 s fixed-cycle signal across ten SUMO simulation trials per scenario, the adaptive system delivered average reductions in vehicle waiting time of 27%–42%, queue length cuts of 38%–43%, and throughput gains of 17%–28%. Full documentation of the experimental protocol is provided to support independent replication.

TrafficVisionAI: A Real-Time Deep Learning Framework for Automated Vehicle Counting and Speed Estimation using YOLOv8 and ByteTrack

Maloani Saidi Georges — Fri, 25 Sep 2026 00:00:00 +0000

Rapid urbanization has escalated the urgent need for intelligent transportation systems capable of alleviating traffic congestion, enhancing road safety, and enabling data-driven urban planning. Traditional manual surveillance methods are notoriously prone to human error, labor-intensive, and incapable of delivering real-time analytics at scale. This paper presents the design, implementation, and evaluation of TrafficVisionAI, a lightweight and cost-effective deep learning framework for automated vehicle detection, tracking, and speed estimation. The system leverages the YOLOv8n model to perform object detection across four specific COCO classes: Cars, Motorcycles, Buses, and Trucks. To ensure consistent identity preservation in complex, occluded traffic environments, the ByteTrack algorithm is integrated to assign persistent IDs to moving vehicles. The core novelty of this work lies in the seamless integration of a custom pixel-to-meter scaling factor for real-time speed estimation (in km/h), coupled with a professional, feature-rich Heads-Up Display (HUD) that provides live traffic statistics and system status (FPS, timestamp). Additionally, automated video archiving via OpenCV ensures high-volume annotated data is retained for post-hoc analysis. Experimental results demonstrate the system's robust capability to classify and count vehicles, while preliminary testing highlights a performance trade-off of 4 FPS on standard CPU hardware. The findings confirm that deep learning provides a scalable, accessible, and highly efficient solution for modern intelligent transportation systems.

Spatiotemporal Analysis of Road Crash Frequency and Severity in Dhaka, 2007-2021: Trend Detection, Change-Point Identification, and Corridor-Level Risk Persistence

Antora Roy, Fofana Mamoudou, Md. Elias Fakir — Tue, 29 Sep 2026 00:00:00 +0000

Road crash research in Dhaka has relied on cross-sectional severity models fitted to short observation windows, leaving the temporal behavior of the city's safety performance untested. This study analyses 17,497 police-reported crashes recorded in the Dhaka Metropolitan Area from 2007 to 2021, comprising 180 consecutive monthly observations, and combines non-parametric trend and change-point testing, interrupted time-series regression, a forecasting benchmark, and empirical-Bayes corridor ranking. Crash frequency and severity diverge. Annual counts rise significantly while the fatality share falls from 72.4% to 54.5%, yet annual fatal counts show no monotonic trend. The declining ratio therefore reflects a growing denominator rather than fatality reduction, and a 4.6-fold rise in recorded minor injuries indicates improving capture of less severe events. Structural breaks occur in the mid-2010s, preceding both the Road Transport Act 2018 and COVID-19 restrictions, whose effects prove statistically indistinguishable from the dominant secular trend. Twenty corridors carry 58.1% of all crashes, yet 59 of 64 corridors are indistinguishable from the city-wide fatality share of 64.2%, and concentration remains stable. Available covariates carry negligible severity information, with classification performing below the majority-class baseline. The findings identify a small, persistent set of treatable corridors and argue for expanding the crash record itself.