Traffic sensor health monitoring using spatiotemporal graphical modeling

Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.

Designing a Comprehensive Procedure for Flagging Archived Traffic Data: A Case Study

Archived data management systems (ADMS) are extensively used for storing historical traffic data (e.g., volume, speed, occupancy) collected from traffic sensors. Archived traffic data have important uses for engineering and planning applications such as ramp meter timing, work zone planning, and performance management. They are also an important data source for transportation research. Various flagging procedures have been implemented in ADMS to identify invalid or questionable archived traffic data, however, those flagging procedures may not be comprehensive enough to maintain adequate data quality. This study presents the findings of a literature search and a user survey to discuss the possible gap between the state-of-the-practice and the state-of-the-art validity tests, identifies complex yet effective validity tests which are favored by users, and recommends the procedure that prioritizes the implementation of validity tests in ADMS. To aid the implementation, different methods to establish quantitative rules and practical thresholds for candidate validity tests have been proposed. This study underscores the importance of keeping the basic validity tests required to maintain minimum data quality and adding more advanced tests to detect less obvious yet important data issues. The recommended tests along with the flagging procedure are demonstrated through a case study based on one detector station in Wisconsin. Results of the case study show that the guide is useful in the development of a comprehensive flagging procedure for better data quality.

Approximative Network Partitioning for MFDs from Stationary Sensor Data

The macroscopic fundamental diagram (MFD) measures network-level traffic performance of urban road networks. Large-scale networks are normally partitioned into homogeneous regions in relation to road network topology and traffic dynamics. Existing partitioning algorithms rely on unbiased data. Unfortunately, widely available stationary traffic sensors introduce a spatial bias and may fail to identify meaningful regions for MFD estimations. Thus, it is crucial to revisit and develop stationary-sensor-based partitioning algorithm. This paper proposes an alternative two-step partitioning algorithm for MFD estimations based on information collected solely from stationary sensors. In a first step, possible partitioning outcomes are generated in the road networks using random walks. In a second step, the regions’ MFDs are estimated under every possible partitioning outcome. Based on previous work, an indicator is proposed to evaluate the traffic heterogeneity in regions. The proposed partitioning approach is tested with an abstract grid network and empirical data from Zurich. In addition, the results are compared with an algorithm that disregards stationary detectors’ biases. The results demonstrate that the proposed approach performs well for obtaining the quasi-optimal network partitions yielding the lowest heterogeneity among all possible partition outcomes. The presented approach not only complements existing literature, but also offers practice-oriented solutions for transport authorities to estimate the MFDs with their available data.