Presence Of Incidents Research Articles

Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.

Learning Experience and Clinical Outcomes with Standardized Patient Simulation: A Mixed Qualitative and Quantitative Study

ABSTRACT Purpose To evaluate learning experience and clinical outcomes after intervention. Design A mixed study. Methods A multiple cohort sample (n=75) of community nurses underwent an SP simulation. A survey and 12- and 24-month incident data were used to evaluate the learning experience and clinical outcomes. Findings The nurses were highly satisfied with their learning. In addition, their ability to identify near-misses and the presence of incidents showed a statistically significant improvement (p < .05). Conclusions SP simulation effectively improved learning experience and clinical outcomes regardless of nurses’ experience. Clinical Evidence Simulation learning can have a positive impact on clinical outcomes.

Development of Online Scalable Approach for Identifying Secondary Crashes

Secondary crashes are some of the most critical incidents occurring on highways. Such crashes can induce extra traffic delays and affect highway safety performance. Transportation agencies are interested in understanding the mechanism of the occurrence of secondary crashes and implementing appropriate countermeasures. However, no well-established procedure identifies secondary crashes; this deficiency in turn impedes the possibility of investigating the underlying mechanism of their occurrence. The intent of this study was to develop an online scalable approach for helping to identify secondary crashes for the large number of highways with insufficient traffic surveillance units to collect the continuous traffic data required to classify such crashes accurately. The developed approach consisted of two major components: (a) acquisition of open source traffic data and (b) identification of secondary crashes through the use of these data. Unlike existing approaches based on static thresholds, queuing models, or infrastructure-based sensor data, the developed approach took advantage of various open-source data to identify traffic conditions in the presence of incidents. This study proposed to develop virtual sensors collecting traffic data from private traffic information providers such as Bing Maps, Google Maps, and MapQuest. The availability of such data greatly expands the ability of transportation agencies to cover more highways without installing infrastructure sensors. The virtual-sensor output provides the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm is described step by step to provide a readily deployable approach for transportation agencies interested in identifying secondary crashes on their highway networks.

Quantum-Frequency Algorithm for Automated Identification of Traffic Patterns

Knowledge of the normal traffic flow pattern is required for a number of transportation applications. Traditionally, the simple historic average has been considered as the best way to derive the traffic pattern. However, this method may often be significantly biased by the presence of incidents. One solution to avoid this bias is through visual inspection of the data by experts. The experts could identify anomalies caused by incidents and thereby identify the underlying normal traffic patterns. Three main challenges of this approach are (a) the bias introduced because of subjectivity, (b) the additional time required to analyze the data manually, and (c) the increasing sizes of the available traffic data sets. To address these challenges and also to exploit the potential of information technology, new data analysis tools are essential. In this research, a new tool, the quantum-frequency algorithm, was developed. This algorithm can aid in the automated identification of traffic flow patterns from large data sets. The paper presents the algorithm along with its theoretical basis. Finally, in the case study presented in the paper, the algorithm was able to identify a reasonable traffic pattern automatically from a large set of archived data. When compared with the historic average, it was found that the pattern identified by the quantum-frequency algorithm resulted in 39% lower cumulative deviation from the pattern identified manually by experts.

Learning Systems for Predicting Experiential Travel Times in the Presence of Incidents: Insights and Lessons Learned

Implementation of the intelligent transportation systems program has resulted in a wealth of readily available data describing key transportation system parameters for many of the nation's roadways. However, the development of systems that can process the data to support effective decision making aimed at improving travel conditions has lagged behind the development of monitoring and data recording devices. A study investigated the feasibility of developing an artificial neural network (ANN) model to predict experiential travel time under transient traffic conditions, including accidents, by using readily available data from roadway sensors in real time. Computational experiments were performed to investigate the impact of several factors on the quality of the ANN's predictions: ( a) the time lag of the input data, ( b) the input variables, and ( c) the temporal resolution of the input variables. With the correct input data set and network parameters, the ANNs were able to predict reasonable values for experiential travel time. The study shows that the length of the time lag did not have a statistically significant effect on ANN performance, that speed appears to be the most influential input variable, and that varying the resolution of the input data produced similar errors for both higher and lower resolutions.