1,782 research outputs found
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
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
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
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
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