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...

Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful...

This paper presents an approach to estimate bus route and network travel times using micro-simulation. This can be used in predicting the effectiveness of bus route designs using some network traffic measures or indicators. The used indicators are average network traffic intensity, posted speeds, route length, frequency of bus operation, and average passenger loadings (boarding and alighting). ...

Understanding mechanisms underlie the individual’s daily time allocations is very important to understand the variability of individual’s time-space constraints and to forecast his/her daily activity participation. At most of previous studies, activity time allocation was viewed as allocating a continuous quantity (daily time budget) into multiple discrete alternatives (i.e. various activities ...

Intelligent techniques derived from knowledge-based engineering and related computing paradigms have provided useful concepts and tools to undertake a variety of real-world problems. These systems mimic the analytical and learning capabilities of the human brain. They harness the benefits of knowledge and intelligence to form an integrated framework for problem solving. In this special issue, a...

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring safety. Existing prediction models primarily rely on path-finding algorithms corresponding distance/speed relationships to calculate time. However, these overlook the complex nature of time, which influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, perso...