Travel time is one of the most desired operational variables serving as a key measure of effectiveness for evaluating the system performance of freeways and urban arterials. With accurate travel time information, decision makers, road users, and traffic engineers can make informed decisions. However, retrieving network-level travel time information has several challenges, such as traffic data c...

Title of Document: A RELIABLE TRAVEL TIME PREDICTION SYSTEM WITH SPARSELY DISTRIBUTED DETECTORS Nan Zou Directed By: Dr. Gang-Len Chang, Professor Department of Civil and Environmental Engineering Due to the increasing congestion in most urban networks, providing reliable trip times to commuters has emerged as one of the most critical challenges for all existing Advanced Traffic Information Sys...

The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First...

This paper presents an approach to estimate future travel times on a freeway using flow and occupancy data from single loop detectors and historical travel time information. The work uses linear regression with stepwise variable selection method and more advanced tree based methods. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out ...

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [1], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We pr...

We present a method to predict the time thatwill be needed to traverse a certain stretch of freeway whendeparture is at a certain time in the future. The predic-tion is done on the basis of the current traffic situation incombination with historical data.We argue that, for our purpose, the current situation of astretch of freeway is well summarized by the ‘current status...

Introduction: The medical literature has identified a variety of health risks associated with travel. Risks depend on the susceptibility of the traveler, the specifics of the destination, the mode of transport, and on chance events. Ill-prepared travelers who underestimate travel risks may encounter a variety of health problems. In order to eventually increase the capability of...