Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here since taxi requests in the future are ...

1 Taxi is certainly the most popular type of on-demand transportation service in urban areas because taxi 2 dispatching systems offer more and better services in terms of shorter wait times and travel convenience. 3 However, a shortage of taxicabs has always been critical in many urban contexts especially during peak 4 hours and taxi has great potential to maximize its efficiency by employing s...

Taxi service is an indispensable part of public transport in modern cities. However, due to its decentralized operation mode, taxi services in many cities are inefficient. Besides, the decentralized nature also poses significant challenges to analyzing and regulating taxi services. State of the art computational methods for optimizing taxi market efficiency suffer from two important limitations...

The traditional taxi prediction methods model the taxi trajectory as a sequence of spatial points. It cannot represent two-dimensional spatial relationships between trajectory points. Therefore, many methods transform the taxi GPS trajectory into a two-dimensional image, and express the spatial correlations by trajectory image. However, the trajectory image may have noise and sparsity according...

This study compared two hypothetical transit scenarios with the current bus transit system for serving the transit passengers of Ann Arbor, Michigan, for a typical fall weekday in 2013. One scenario consists of an automated taxi system that allows only one rider at a time, and the other consists of a similar automated taxi system that allows ridesharing for up to four passengers. The two automa...

Accurate estimation of taxi-out time in the presence of uncertainties in the National Airspace System (NAS) is essential for the development of a more efficient air traffic management system. The dynamic nature of operations in the NAS indicates that traditional regression methods characterized by constant parameters would be inadequate to capture variations in taxi-out time across a day. In th...

It has been estimated that over 60 thousand licensed taxis in the Great Taipei area are not occupied over 70 percent of driving time on average. However, the taxi company, TaiwanTaxi, indicates that even in rush hour, there are customers whose requests are not satisfied. The demand and supply are not paired, causing not only customers wait too long for a cab, but also taxi drivers waste time an...

During spatial navigation, lesion and functional imaging studies suggest that the right hemisphere has a unique functional role. However, studies of direct human brain recordings have not reported interhemisphere differences in navigation-related oscillatory activity. We investigated this apparent discrepancy using intracranial electroencephalographic recordings from 24 neurosurgical patients p...

Incorporating skills in reinforcement learning methods results in accelerate agents learning performance. The key problem of automatic skill discovery is to find subgoal states and create skills to reach them. Among the proposed algorithms, those based on graph centrality measures have achieved precise results. In this paper we propose a new graph centrality measure for identifying subgoal stat...

Taxi delay is the largest of all aviation movement delays. However, taxi-out delays have not received attention equal to that focused on airborne delays because taxi-out delays often result from downstream problems. Also, until recently, there was no practical means of tracking surface movements. New surface surveillance technology will revolutionize surface management by providing data for pla...