25,481 research outputs found
Combining Stream Mining and Neural Networks for Short Term Delay Prediction
The systems monitoring the location of public transport vehicles rely on
wireless transmission. The location readings from GPS-based devices are
received with some latency caused by periodical data transmission and temporal
problems preventing data transmission. This negatively affects identification
of delayed vehicles. The primary objective of the work is to propose short term
hybrid delay prediction method. The method relies on adaptive selection of
Hoeffding trees, being stream classification technique and multilayer
perceptrons. In this way, the hybrid method proposed in this study provides
anytime predictions and eliminates the need to collect extensive training data
before any predictions can be made. Moreover, the use of neural networks
increases the accuracy of the predictions compared with the use of Hoeffding
trees only
A Review of Traffic Signal Control.
The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project
Transport poverty meets the digital divide : accessibility and connectivity in rural communities
Peer reviewedPublisher PD
Smart Bike Sharing System to make the City even Smarter
These last years with the growing population in the smart city demands an
efficient transportation sharing (bike sharing) system for developing the smart
city. The Bike sharing as we know is affordable, easily accessible and reliable
mode of transportation. But an efficient bike sharing capable of not only
sharing bike also provides information regarding the availability of bike per
station, route business, time/day-wise bike schedule. The embedded sensors are
able to opportunistically communicate through wireless communication with
stations when available, providing real-time data about tours/minutes, speed,
effort, rhythm, etc. We have been based on our study analysis data to predict
regarding the bike's available at stations, bike schedule, a location of the
nearest hub where a bike is available etc., reduce the user time and effort
Microsimulation models incorporating both demand and supply dynamics
There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework.
The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makersâ decisions and individual vehicle movements across the network. To achieve this it represents directly individual driversâ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the spaceâtime trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic
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