Research Article
Assessing Level of Service, Capacity and Future Optimization of Roundabouts in Rajshahi City Using Sidra Intersection
Akhtar Hossain,
Arfan Siddiquee Sazid*
,
Rakibul Hasan
Issue:
Volume 10, Issue 1, March 2024
Pages:
1-14
Received:
14 May 2024
Accepted:
5 June 2024
Published:
26 June 2024
Abstract: This study investigates traffic flow challenges at the bustling Mintu Chattar roundabout Situated in Laxmipur Mor, Rajshahi, Bangladesh. Located in close proximity to a hub of hospitals and medical establishments, this intersection routinely witnesses traffic congestion, becoming a familiar occurrence in the area. From dawn to dusk, traffic officers are required to step in to manage the traffic flow of countless individuals commuting to hospitals, workplaces, educational institutions, and other destinations. Our capacity analysis using Sidra Intersection software reveals significant bottlenecks, particularly during peak hours, necessitating traffic police intervention due to inadequate control measures. The research identifies key contributing factors: insufficient entry and circulatory lanes, uneven traffic distribution, and high overall traffic volume. A detailed case study of the south leg highlights the severity of the situation, with Level of Service (LOS) dropping to F during evening hours, resulting in long queues (over 61 vehicles) and delays exceeding 82 seconds per vehicle. The integration of the design life model from SIDRA software has also illustrated the correlation between Level of Service (LOS) and capacity with the projected rise in traffic volume for the next five years. In order to improve traffic flow efficiency, the study proposes exploring design optimization of the roundabout. This includes investigating the impact of geometric redesigning, such as gradual expansion of approach lanes width, on capacity at the roundabout leg, considering both existing and future traffic scenarios.
Abstract: This study investigates traffic flow challenges at the bustling Mintu Chattar roundabout Situated in Laxmipur Mor, Rajshahi, Bangladesh. Located in close proximity to a hub of hospitals and medical establishments, this intersection routinely witnesses traffic congestion, becoming a familiar occurrence in the area. From dawn to dusk, traffic officer...
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Research Article
Short-Term Traffic Flow Prediction Based on Wavelet Analysis and XGBoost
Xin Wang*,
Fang Fang
Issue:
Volume 10, Issue 1, March 2024
Pages:
15-24
Received:
31 May 2024
Accepted:
18 June 2024
Published:
23 July 2024
Abstract: Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined prediction model based on wavelet decomposition and reconstruction (WDR) and the extreme gradient boosting (XGBoost) model is developed in this paper. Firstly, the Mallat algorithm is applied to perform multi-scale wavelet decomposition on the average travel time series of the original traffic data, and single branch reconstruction is performed on the components at each scale. Secondly, XGBoost is used to predict each reconstructed single-branch sequence, so as to obtain multiple sub-models, and the Bayesian algorithm is used to optimize the hyperparameters of the sub-models. Finally, the algebraic sum of the predicted values of all sub-models is used to obtain the overall traffic prediction result. To test the performance of the proposed model, actual traffic flow data has been collected from a certain link of the Brooklyn area in New York, USA. The performance of proposed WDR-XGBoost model has been compared with other existing machine learning models, e.g., support vector regression model (SVR) and single XGBoost model. Experimental findings demonstrated that the proposed WDR-XGBoost model performs better on multiple evaluation indicators and has significantly outperformed the other models in terms of accuracy and stability.
Abstract: Traffic flow prediction is of great significance for urban planning and alleviating traffic congestion. Due to the randomness and high volatility of urban road network short-term traffic flow, it is difficult for a single model to accurately estimate traffic flow and travel time. In order to obtain more ideal prediction accuracy, a combined predict...
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