In recent decades, transportation planning researchers have used diverse types of machine
learning (ML) algorithms to research a wide range of topics. This review paper starts with a …

In the field of transportation, data analysis is probably the most important and widely used
research tool available. In the data analysis universe, there are two 'schools of thought'; the …

This paper presents a hybrid approach to automatic incident detection (AID) in transportation
systems. It combines time series analysis (TSA) and machine learning (ML) techniques in …

Detecting incidents on urban streets or arterials using loop detector data is quite
challenging. The pattern of the incident could be quite similar to non-incident cases as …

Work or study purpose trips usually make up most intercity trips during peak hours. Being at
a destination at a specific time is one of the characteristics of such trips, and the occurrence …

This paper analyzes the characteristics of vehicle breakdown duration and the relationship
between the duration and vehicle type, time, location, and reporting mechanisms. Two …

Quick and reliable automatic detection of traffic accidents is of paramount importance to
save human lives in transportation systems. However, automatically detecting when …

Algorithms for automatic incident detection (AID) detect traffic incidents on the basis of traffic
flow measurements. There are two important steps in an AID algorithm: traffic flow feature …

Traffic accident detection is an important component in Intelligent Transportation System
(ITS). Compared with freeway, urban accident detection is more complicated. The mean …

Abstract Automatic Incident Detection Algorithms (AIDA) have been part of freeway
management system software from the beginnings of ITS deployment. These algorithms …