31 research outputs found
Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network
Ride-hailing services are growing rapidly and becoming one of the most
disruptive technologies in the transportation realm. Accurate prediction of
ride-hailing trip demand not only enables cities to better understand people's
activity patterns, but also helps ride-hailing companies and drivers make
informed decisions to reduce deadheading vehicle miles traveled, traffic
congestion, and energy consumption. In this study, a convolutional neural
network (CNN)-based deep learning model is proposed for multi-step ride-hailing
demand prediction using the trip request data in Chengdu, China, offered by
DiDi Chuxing. The CNN model is capable of accurately predicting the
ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu
for every 10 minutes. Compared with another deep learning model based on long
short-term memory, the CNN model is 30% faster for the training and predicting
process. The proposed model can also be easily extended to make multi-step
predictions, which would benefit the on-demand shared autonomous vehicles
applications and fleet operators in terms of supply-demand rebalancing. The
prediction error attenuation analysis shows that the accuracy stays acceptable
as the model predicts more steps
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
One Model Fits All: Cross-Region Taxi-Demand Forecasting
The growing demand for ride-hailing services has led to an increasing need
for accurate taxi demand prediction. Existing systems are limited to specific
regions, lacking generalizability to unseen areas. This paper presents a novel
taxi demand forecasting system that leverages a graph neural network to capture
spatial dependencies and patterns in urban environments. Additionally, the
proposed system employs a region-neutral approach, enabling it to train a model
that can be applied to any region, including unseen regions. To achieve this,
the framework incorporates the power of Variational Autoencoder to disentangle
the input features into region-specific and region-neutral components. The
region-neutral features facilitate cross-region taxi demand predictions,
allowing the model to generalize well across different urban areas.
Experimental results demonstrate the effectiveness of the proposed system in
accurately forecasting taxi demand, even in previously unobserved regions, thus
showcasing its potential for optimizing taxi services and improving
transportation efficiency on a broader scale.Comment: Accepted to The 31st ACM International Conference on Advances in
Geographic Information Systems(SIGSPATIAL '23) as a short paper in the
Research, Systems and Industrial Experience Papers trac
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Urban dispersal events are processes where an unusually large number of
people leave the same area in a short period. Early prediction of dispersal
events is important in mitigating congestion and safety risks and making better
dispatching decisions for taxi and ride-sharing fleets. Existing work mostly
focuses on predicting taxi demand in the near future by learning patterns from
historical data. However, they fail in case of abnormality because dispersal
events with abnormally high demand are non-repetitive and violate common
assumptions such as smoothness in demand change over time. Instead, in this
paper we argue that dispersal events follow a complex pattern of trips and
other related features in the past, which can be used to predict such events.
Therefore, we formulate the dispersal event prediction problem as a survival
analysis problem. We propose a two-stage framework (DILSA), where a deep
learning model combined with survival analysis is developed to predict the
probability of a dispersal event and its demand volume. We conduct extensive
case studies and experiments on the NYC Yellow taxi dataset from 2014-2016.
Results show that DILSA can predict events in the next 5 hours with F1-score of
0.7 and with average time error of 18 minutes. It is orders of magnitude better
than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was
the misspelled author name in the meta-data field. Author name was corrected
from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and
remained unchange
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Machine learning models are omnipresent for predictions on big data. One
challenge of deployed models is the change of the data over time, a phenomenon
called concept drift. If not handled correctly, a concept drift can lead to
significant mispredictions. We explore a novel approach for concept drift
handling, which depicts a strategy to switch between the application of simple
and complex machine learning models for regression tasks. We assume that the
approach plays out the individual strengths of each model, switching to the
simpler model if a drift occurs and switching back to the complex model for
typical situations. We instantiate the approach on a real-world data set of
taxi demand in New York City, which is prone to multiple drifts, e.g. the
weather phenomena of blizzards, resulting in a sudden decrease of taxi demand.
We are able to show that our suggested approach outperforms all regarded
baselines significantly