Network Traffic Surveillance Research Articles

A class of distribution-free one-sided Cucconi schemes for joint surveillance of location and scale parameters and their application in monitoring cab services

Simultaneous distribution-free surveillance (SDFS) of location and scale parameters of a process based on a single plotting statistic has emerged as a prevalent research issue in the past seven years. Most of the SDFS schemes are, however, appropriate for monitoring two-sided shifts in location-scale. In monitoring service quality or time to an event, among others, decreasing process median or variability are usually considered as a process improvement and not something to worry about in practice. We only desire to have a quick signal when there is an increase in the median or scatter as they refer to a deterioration in the process quality. On the contrary, in network traffic surveillance, a decrease in process variability could be a matter of concern as it may result in network congestion where a decreasing shift may be of more interest. In such instances, specific one-sided shifts are of more importance, and a one-sided SDFS scheme would be more appropriate than traditional two-sided schemes. In this article, we propose several Shewhart-Cucconi type SDFS schemes for jointly monitoring one-sided deviations in location or scale or both parameters of a process. We discuss the implementation steps and establish the in-control robustness of the new surveillance plans. We compare proposed schemes with existing one-sided Shewhart-Lepage type schemes using run-length properties. Numerical results substantiate that the proposed schemes perform quite well in many cases. We explain the application of newly designed schemes in surveillance of waiting time during a taxi ride in some cities.

Alternative method to generate three-dimensional (3D) ultrasonographic images in veterinary medicine

Simultaneous distribution-free surveillance (SDFS) of location and scale parameters of a process based on a single plotting statistic has emerged as a prevalent research issue in the past seven years. Most of the SDFS schemes are, however, appropriate for monitoring two-sided shifts in location-scale. In monitoring service quality or time to an event, among others, decreasing process median or variability are usually considered as a process improvement and not something to worry about in practice. We only desire to have a quick signal when there is an increase in the median or scatter as they refer to a deterioration in the process quality. On the contrary, in network traffic surveillance, a decrease in process variability could be a matter of concern as it may result in network congestion where a decreasing shift may be of more interest. In such instances, specific one-sided shifts are of more importance, and a one-sided SDFS scheme would be more appropriate than traditional two-sided schemes. In this article, we propose several Shewhart-Cucconi type SDFS schemes for jointly monitoring one-sided deviations in location or scale or both parameters of a process. We discuss the implementation steps and establish the in-control robustness of the new surveillance plans. We compare proposed schemes with existing one-sided Shewhart-Lepage type schemes using run-length properties. Numerical results substantiate that the proposed schemes perform quite well in many cases. We explain the application of newly designed schemes in surveillance of waiting time during a taxi ride in some cities.

Real-Time Freeway Network Traffic Surveillance: Large-Scale Field-Testing Results in Southern Italy

This paper reports on some large-scale field-testing results of a real-time freeway network traffic surveillance tool that has recently been developed to enable a number of real-time traffic surveillance tasks. This paper first introduces the related network traffic flow model and the approaches employed to traffic state estimation, traffic state prediction, and incident alarm. The field testing of the tool for these surveillance tasks in the A3 freeway of 100 km between Naples and Salerno in southern Italy is then reported in some detail. The results obtained are quite satisfactory and promising for further future implementations of the tool.

Reliable sensor deployment for network traffic surveillance

New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).

RENAISSANCE – A unified macroscopic model-based approach to real-time freeway network traffic surveillance

The paper presents a unified macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.

A real-time freeway network traffic surveillance tool

This paper presents a real-time freeway network traffic surveillance tool RENAISSANCE. Based on a stochastic macroscopic freeway network traffic flow model and the extended Kalman filter, RENAISSANCE is fed with a limited amount of real-time traffic measurements to enable a number of freeway network traffic surveillance tasks, including traffic state estimation and prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction (queue tracking), and incident alarm. The paper first introduces the stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which a complete dynamic system model for freeway network traffic is established, with a special attention to the handling of some important model parameters. The addressed traffic surveillance tasks are described along with the functional architecture of RENAISSANCE. A simulation test was conducted for the tool with respect to a hypothetical freeway network example, while the traffic state estimator of RENAISSANCE was also tested with real traffic measurement data collected from a Bavarian freeway.

RENAISSANCE: A Real-Time Motorway Network Traffic Surveillance Tool

The paper presents a real-time motorway network traffic surveillance tool called RENAISSANCE that enables traffic state estimation and short term prediction, travel time estimation and prediction as well as queue length estimation and prediction based on a limited amount of real-time measurements. The macroscopic motorway network traffic flow model of RENAISSANCE is introduced first, followed by a description of the various traffic surveillance tasks addressed by RENAISSANCE along with the functional architecture of the tool. Preliminary simulation testing has been conducted via application of RENAISSANCE to a hypothetical motorway network and the corresponding results are presented in some detail. Final conclusions and future steps are outlined.