Case Report
Optimizing Toll Booth Performance with M/M/1 Queueing Models: Case Research from Indian Highways
Satendra Chandra Pandey*
,
Vasanthi Kumari
Issue:
Volume 12, Issue 1, March 2026
Pages:
1-14
Received:
3 December 2025
Accepted:
24 December 2025
Published:
23 January 2026
Abstract: Efficient toll plaza operations are critical to minimizing congestion and travel delay on high-volume highway corridors. This study investigates the operational performance of a major toll plaza on National Highway-75 (NH-75), India, using queueing theory–based analytical models. Field data were collected at the Neelmangala–Devihalli Toll Plaza, including traffic volume, time headway, arrival rates, service times, and space mean speeds under mixed traffic conditions. Statistical analysis confirms that vehicle arrivals follow a Poisson process, while service times exhibit a general distribution, justifying the application of both M/M/1 and M/G/1 queueing models. Key performance indicators such as system utilization, average queue length, and vehicle waiting time were derived to evaluate toll booth efficiency during peak and off-peak periods. The results indicate that congestion is primarily driven by high arrival rates during peak hours, limited toll booth capacity, and downstream lane merging constraints rather than insufficient roadway capacity. The analysis further demonstrates that the adoption of electronic toll collection (FASTag), optimized lane allocation, and selective booth expansion can significantly reduce average waiting times and improve throughput. This research highlights the practical applicability of queueing theory as a decision-support framework for diagnosing operational bottlenecks and guiding data-driven improvements in toll plaza planning and management on Indian highways.
Abstract: Efficient toll plaza operations are critical to minimizing congestion and travel delay on high-volume highway corridors. This study investigates the operational performance of a major toll plaza on National Highway-75 (NH-75), India, using queueing theory–based analytical models. Field data were collected at the Neelmangala–Devihalli Toll Plaza, in...
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Research Article
Assessing the Operational Efficiency of Public Transport in Rajshahi: A User and Operator Perspective
Issue:
Volume 12, Issue 1, March 2026
Pages:
15-22
Received:
14 January 2026
Accepted:
23 January 2026
Published:
9 February 2026
Abstract: Sustainable development is a broad concept stems from environmental, social and economic equality and vitality. A well-functioning Public transport system is also an important part of a sustainable transportation system, as it focuses on reducing traffic congestion, pollution and provides affordable mobility facilities for people. So, performance evaluation of this public transportation system is a fundamental requirement for a rapidly growing metropolitan city like Rajshahi. Rajshahi is the fourth largest divisional city, containing both motorized and non-motorized transport facilities. The aim of this study is to evaluate the performance of Rajshahi’s public transport system based on efficiency measurement. The main mode of transportation system auto rickshaw, has been selected for this study. Service efficiency, cost efficiency, system efficiency, utilization efficiency and network efficiency are being assessed from both passenger and operators’ perspectives. For the purpose of this investigation, Railgate is considered as the center point, and the Railgate to Binodpur, Railgate to Bazar, Railgate to Court station and Railgate to Nowhata routes are selected. The data collection process was done by conducting questionnaire surveys of passengers and drivers. Also, other data such as velocity, distance, time of travel and delay time were measured by direct observation. The study result reveals that network efficiency is the highest, securing 73.50%, while system efficiency is the lowest, at around 18.48%. Additionally, service efficiency, cost efficiency and utilization efficiency are 37.21%, 22.71% and 30.95%, respectively. These values indicate that although the vehicle speed is faster, the number of auto-rickshaws is increasing in a considerable amount lead to traffic congestion delay. The study outcomes provide an overview of the Rajshahi city transport system, contributing valuable insights for the development of a long-term and well-planned transportation system.
Abstract: Sustainable development is a broad concept stems from environmental, social and economic equality and vitality. A well-functioning Public transport system is also an important part of a sustainable transportation system, as it focuses on reducing traffic congestion, pollution and provides affordable mobility facilities for people. So, performance e...
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Research Article
Research on Automatic Navigational Buoy Recognition Based on YOLOv11
Issue:
Volume 12, Issue 1, March 2026
Pages:
23-30
Received:
24 January 2026
Accepted:
4 February 2026
Published:
14 February 2026
DOI:
10.11648/j.ijtet.20261201.13
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Views:
Abstract: With the rapid development of intelligent ships, deep learning-based automatic identification of maritime buoys has emerged as a critical research direction for maritime intelligence. To address the challenge of balancing real-time performance and detection accuracy in complex inland waterway environments, this paper proposes an automatic identification method based on the YOLOv11 object detection algorithm. Specifically, by integrating advanced C3K2 and C2PSA modules, the model's capability for feature extraction and global information perception in cluttered backgrounds is significantly enhanced. To mitigate the scarcity of data samples, data augmentation techniques-including rotation and Gaussian noise elimination-were applied to construct buoy dataset, which consists of 913 high-quality annotated images. Furthermore, an incremental learning strategy with multi-stage iterative training was introduced to improve the model's generalization across diverse scenarios. Experimental results demonstrate that while maintaining high-efficiency real-time response, the proposed model achieves a mAP of 93%. This performance outperforms traditional algorithms such as Cascade-RCNN and SSD, as well as previous versions like YOLOv8, providing robust technical support for safe collision avoidance and waterway situational awareness in intelligent shipping.
Abstract: With the rapid development of intelligent ships, deep learning-based automatic identification of maritime buoys has emerged as a critical research direction for maritime intelligence. To address the challenge of balancing real-time performance and detection accuracy in complex inland waterway environments, this paper proposes an automatic identific...
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