8,798 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
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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 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
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
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