9,603 research outputs found
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
Trip Prediction by Leveraging Trip Histories from Neighboring Users
We propose a novel approach for trip prediction by analyzing user's trip
histories. We augment users' (self-) trip histories by adding 'similar' trips
from other users, which could be informative and useful for predicting future
trips for a given user. This also helps to cope with noisy or sparse trip
histories, where the self-history by itself does not provide a reliable
prediction of future trips. We show empirical evidence that by enriching the
users' trip histories with additional trips, one can improve the prediction
error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This
real-world dataset is collected from public transportation ticket validations
in the city of Nancy, France. Our prediction tool is a central component of a
trip simulator system designed to analyze the functionality of public
transportation in the city of Nancy
Advances in Public Transport Platform for the Development of Sustainability Cities
Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency
Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport
Delays and disruptions are commonplace in public transportation. An important tool to limit the impact of severely delayed vehicles is the use of short turns, where a planned trip is shortened in order to be able to resume the following trip in the opposite direction as close to the schedule as possible. Short turns have different effects on passengers: some suffer additional delays and have to reschedule their route, while others can benefit from them. Dispatchers therefore need decision support in order to use short turns only if the overall delay of all affected passengers is positively influenced. In this paper, we study the planning of short turns based on passenger flows. We propose a simulation framework which can be used to decide upon single short turns in real time. An experimental study with a scientific model (LinTim) of an entire public transport system for the German city of Stuttgart including busses, trams, and local trains shows that we can solve these problems on average within few milliseconds. Based on features of the current delay scenario and the passenger flow we use machine learning to classify cases where short turns are beneficial. Depending on how many features are used, we reach a correct classification rate of more than 93% (full feature set) and 90% (partial feature set) using random forests. Since precise passenger flows are often not available in urban public transportation, our machine learning approach has the great advantage of working with significantly less detailed passenger information
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