Abstract-This paper proposes a novel anomaly detection and classification algorithm that combines the spatiotemporal changes in the variability of microscopic traffic variables, namely relative speed, inter-vehicle time gap and lane changing. When applied to real-world scenarios, the proposed algorithm can use the variances of statistics of microscopic traffic variables to detect and classify traffic anomalies. Based on a simulation environment, it is shown that with minimum prior knowledge and partial availability of microscopic traffic information from as few as 20% of vehicle population, the proposed algorithm can still achieve 100% detection rates and very low false alarm rates which outperforms previous algorithms monitoring loop detectors that are ideally placed at locations where anomalies originate.
In a fast-paced environment of today society, safety issue related to driving is considered a second priority in contrast to travelling from one place to another in the shortest possible time. This often leads to possible accidents. In order to reduce road traffic accidents, one domain which requires to be focused on is driving behaviour. This paper proposes three algorithms which detect driving events using motion sensors embedded on a smartphone since it is easily accessible and widely available in the market. More importantly, the proposed algorithms classify whether or not these events are aggressive based on raw data from various on board sensors on a smartphone. In addition, one of the outstanding features of the proposed algorithm is the ability to fine tune and adjust its sensitivity level to suit any given application domain appropriately. Initial experimental results reveal that the pattern matching algorithm outperforms the rule-based algorithm for driving events in both lateral and longitudinal movements where a high percentage of detection rate has been obtained for 11 out of 12 types of driving events. In addition, a trade-off between the detection rate and false alarm rate has been demonstrated under a range of algorithm settings in order to illustrate the proposed algorithm's flexibility.
Abstract-This study investigates mobility patterns in microcellular wireless networks, based on measurements from the 802.11-based system that blankets the Carnegie Mellon University campus. We characterize the distribution of dwell time, which is the length of time that a mobile device remains in a cell until the next handoff, and sign-on interarrival time, which is the length of time between successive sign-ons from the same mobile device. Many researchers have assumed that these distributions are exponential, but our results based on empirical analysis show that dwell time and sign-on interarrival time can be accurately described using heavy-tailed arithmetic distributions that have infinite mean and variance. We also show that the number of handoffs per sign-on can be modeled accurately with a heavy-tailed distribution.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.