Related papers: Traffic Incident Analysis on Urban Arterials Using…

We present a novel approach to understand vehicular traffic jams by studying a simple model, Elementary Cellular Automaton Rule 184 (ECA 184). Using key traffic observables, such as the total delay and relaxation time, as well as…

This paper proposes a graph-based approach to representing spatio-temporal trajectory data that allows an effective visualization and characterization of city-wide traffic dynamics. With the advance of sensor, mobile, and Internet of Things…

This paper addresses the problem of short-term traffic prediction for signalized traffic operations management. Specifically, we focus on predicting sensor states in high-resolution (second-by-second). This contrasts with traditional…

To time-efficiently and stably acquire the intensity information for phase retrieval under a coherent illumination, we leverage an event-based vision sensor (EVS) that can detect changes in logarithmic intensity at the pixel level with a…

In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specific sequence of situations that occur in the video, as for…

Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality is highly useful. In this paper, we propose the deep learning based cost-effective real-time…

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements…

Full-field traffic state information (i.e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways. However, incomplete traffic information tends to be directly collected…

Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other…

Due to the stochastic nature of events, predicting the duration of a traffic incident presents a formidable challenge. Accurate duration estimation can result in substantial advantages for commuters in selecting optimal routes and for…

Heavy hitters and frequency measurements are fundamental in many networking applications such as load balancing, QoS, and network security. This paper considers a generalized sliding window model that supports frequency and heavy hitters…

In this paper, we propose an ETA model (Estimated Time of Arrival) that leverages an attention mechanism over historical road speed patterns. As autonomous driving and intelligent transportation systems become increasingly prevalent, the…

While inferring common actor states (such as position or velocity) is an important and well-explored task of the perception system aboard a self-driving vehicle (SDV), it may not always provide sufficient information to the SDV. This is…

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex…

With rapidly increasing of the land development density along suburban arterials, much more irregular signal spacing appeared on suburban arterials, and high access density is commonly observed on suburban arterials. These issues tend to…

Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise,…

A wide variety of sensor technologies are recently being adopted for traffic monitoring applications. Since most of these technologies rely on wired infrastructure, the installation and maintenance costs limit the deployment of the traffic…

Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection…

With the rapid development of urbanization, the boom of vehicle numbers has resulted in serious traffic accidents, which led to casualties and huge economic losses. The ability to predict the risk of traffic accident is important in the…

Introducing Internet traffic anomaly detection mechanism based on large deviations results for empirical measures. Using past traffic traces we characterize network traffic during various time-of-day intervals, assuming that it is…