Comparison of Flow- and Bandwidth-Based Methods of Traffic Signal Offset Optimization

Shams, Andalib; Mahmud, Shoaib; Day, Christopher M.

doi:10.1061/jtepbs.teeng-7404

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The PCD shows each detected vehicle arrival relative to the local green times. The presence of platoons and their arrival time relative to green intervals can be quickly ascertained from these views, distilled into aggregated metrics, and can also support the optimization of offsets [24] [25] [26] [27] [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Mahmud¹,

Day²

2023

JTTs

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Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor's performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Mahmud¹,

Day²

2023

JTTs

Self Cite

View full text Add to dashboard Cite

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“…However, although the Internet of Things and deep learning have been applied in traffic flow prediction, they still need to overcome many challenges. Data quality issues, such as sensor failures, network transmission issues, incomplete data, etc., can all affect the availability and accuracy of data [13,14]. In terms of model design and optimization, how to design and optimize the model based on specific traffic flow prediction tasks and how to improve the interpretability of the model are all issues that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Construction of Short-Term Traffic Flow Prediction Model Based on IoT and Deep Learning Algorithms

Sun,

Dou

2024

IJACSA

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On a global scale, traffic problems are an essential factor affecting urban operations, particularly challenging the frequent occurrence of traffic congestion and accidents. The solution to the problem requires real-time and accurate prediction of traffic flow. This article mainly explores the application of the Internet of Things and deep learning in traffic flow prediction, aiming to solve the problem where existing methods cannot meet the requirements of real-time and accuracy. IoT devices, such as road sensors and in-vehicle GPS devices, which provides rich information for traffic flow prediction. With the ability of deep learning, it can not only learn and abstract a large amount of complex traffic data but also handle traffic flow prediction tasks in various complex situations. During the model construction process, the complexity of the road network was fully considered, practical algorithms were designed to fuse multi-source data, and the structure of the model was optimized to meet the needs of real-time prediction. The experimental results show that the absolute error of the test results is generally less than 6km/h, which can better reflect the traffic speed of the road section in the future.

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Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Shahbazi,

Nowaczyk

2023

Smart Cities

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In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

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Comparison of Flow- and Bandwidth-Based Methods of Traffic Signal Offset Optimization

Cited by 3 publications

References 23 publications

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Construction of Short-Term Traffic Flow Prediction Model Based on IoT and Deep Learning Algorithms

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

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