Biomedical subjects

Anurag Pande

Publications and source records attributed to Anurag Pande.

3 recordsLinked to original sources

PubMed2006

Assessment of freeway traffic parameters leading to lane-change related collisions.

This study aims at 'predicting' the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes.

Accidents, Traffic↗

PubMed2005

Identifying crash propensity using specific traffic speed conditions.

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.

Accidents, Traffic↗

PubMed2005

Phenolic contents and antioxidant activity of some food and medicinal plants.

To identify promising sources of antioxidants, some food and medicinal plants were studied for total phenolic contents and antioxidant activity. The leaves, bark and fruits of Terminalia arjuna, Terminalia bellerica, Terminalia chebula and Terminalia muelleri, the leaves and fruits of Phyllanthus emblica, and the seeds of Syzygium cumini were found to have high total phenolic contents (72.0-167.2 mg/g) and high antioxidant activity (69.6-90.6%). Leaves of Eucalyptusglobulus were a rich source of rutin, Moringa oleifera for kaempferol, aerial parts of Centella asiatica for quercetin, fruits of T. bellerica and T. chebula for gallic acid, and bark of T. arjuna, leaves and fruits of T. bellerica and bark, leaves and fruits of T. muelleri for ellagic acid.

Antioxidants↗