Improving the travel time prediction accuracy for trucks based on historical GPS data

Route planning is an important part for companies that transport goods between different locations. To optimize the route planning, it is important that the travel time predictions come close to reality. A too tight schedule would lead to pressure to the driver, late fees and reputation damage, but a too loose schedule would result in wasted...

Advanced in-car route advice: Improvement of the Superroute-app urban travel time prediction

The Superroute-app, part of the Praktijkproef Amsterdam (PPA) provides commuters with an elaborate and personalized route advice and travel time prediction in the Amsterdam area. On urban roads the travel time prediction from the ARS Traffic & Transport Technology route planner is used, which is based on free flow travel times. To improve the...

An Online Learning Approach to Eliminate Bus Bunching in Real-time

Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable...

Evaluating the Added-Value of Online Bus Arrival Prediction Schemes

Online predictions of bus arrival times have the potential to reduce the uncertainty associated with bus operations. By better anticipating future conditions, online predictions can reduce perceived and actual passenger travel times as well as facilitate more proactive decision making by service providers. Even though considerable research...

A hybrid scheme for real-time prediction of bus trajectories

The uncertainty associated with public transport services can be partially counteracted by developing real-time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical...

Bayesian Data Assimilation for Improved Modeling of Road Traffic

This thesis deals with the optimal use of existing models that predict certain phenomena of the road traffic system. Such models are extensively used in Advanced Traffic Information Systems (ATIS), Dynamic Traffic Management (DTM) or Model Predictive Control (MPC) approaches in order to improve the traffic system. As road traffic is the result...

Travel time prediction for urban networks

Online Learning Solutions for Freeway Travel Time Prediction

Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and—if nothing else—on the reliability...