Leveraging data-driven traffic management in smart cities: Datasets for highway traffic monitoring

Mahmud, Shoaib; Day, Christopher M.

doi:10.1016/b978-0-12-817784-6.00009-6

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Such studies are labor and equipment-intensive. More recently, advances in technology have enabled alternatives, such as measuring travel times using automated vehicle identification [14] [15], high-resolution traffic signal event data [15] [16] [17] [18], and aggregated probe vehicle data [19] [20] [21] [22] [23]. Automated traffic signal performance measures (ATSPMs) enabled by high-resolution data include visualizations such as the Purdue Coordination Diagram (PCD).…”

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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“…With the rapid development of intelligent transportation technology, there are increasingly diverse types of sensors and detectors in traffic operations, such as in intelligent transportation systems (ITSs), traffic management and control, and traffic safety analysis and prevention [1][2][3]. Two broad categories of sensors are generally considered: counting sensors and scanning sensors [4][5][6][7].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors

Wang

Zhao

2022

Sustainability

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Monitoring vehicles’ paths is important for the management and governance of smart sustainable cities, where traffic sensors play a significant role. As a typical sensor, an automatic vehicle identification (AVI) sensor can observe the whereabouts and movements of vehicles. In this article, we introduced an indicator called the deployment score to present the deployment priorities of AVIs for a better reconstruction of vehicles’ paths. The deployment score was obtained based on a programming method for maximizing the accuracy of a recurring vehicle’s path and minimizing the number of AVI sensors. The calculation process is data-driven, where a random-work method was developed to simulate massive path data (tracks of vehicles) according to travel characteristics extracted from finite GPS data. Then, for each simulated path, a path-level bi-level programming model (P-BPM) was constructed to find the optimal layout of the AVI sensors. The solutions of the P-BPM proved to be approximate Pareto optima from a data-driven perspective. Furthermore, the PageRank method was presented to integrate the solutions; thus, the deployment score was obtained. The proposed method was validated in Chengdu City, whose results demonstrated the remarkable value of our approach.

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Use of Connected Vehicle Data to Identify Signal Timing Plans on Signalized Arterial Corridors

Mahmud,

Huang,

Sharma

et al. 2023

2023 IEEE International Automated Vehicle Validation Conference (IAVVC)

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Leveraging data-driven traffic management in smart cities: Datasets for highway traffic monitoring

Cited by 4 publications

References 36 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

A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors

Use of Connected Vehicle Data to Identify Signal Timing Plans on Signalized Arterial Corridors

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