Dynamic Travel Time Prediction Models for Buses Using Only GPS Data

Dynamic travel time prediction using data clustering and genetic programming

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upp...

Dynamic Travel Time Prediction using Pattern Recognition

21 Travel-time information is an essential part of Advanced Traveler Information Systems (ATISs) 22 and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the 23 prediction of travel times. From the perspective of travelers such information may assist in 24 making better route choice and departure time decisions. For transportation agencies these data 25 provide cr...

Developing travel time estimation methods using sparse GPS data

Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data

We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimen...

Dynamic Freeway Travel Time Prediction Using Probe Vehicle Data: Link-based vs. Path-based

Short-term travel time prediction is very important to the real time travelers’ information and route guidance system. Various methodologies have been developed for dynamic travel time prediction. However, most of the existing studies presume that path travel time is the simple addition of travel times on the consisting links. Through simulation, it is shown that, under recurrent traffic condit...