4,554 research outputs found
Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs
In this paper, we propose machine learning solutions to predict the time of
future trips and the possible distance the vehicle will travel. For this
prediction task, we develop and investigate four methods. In the first method,
we use long short-term memory (LSTM)-based structures specifically designed to
handle multi-dimensional historical data of trip time and distances
simultaneously. Using it, we predict the future trip time and forecast the
distance a vehicle will travel by concatenating the outputs of LSTM networks
through fully connected layers. The second method uses attention-based LSTM
networks (At-LSTM) to perform the same tasks. The third method utilizes two
LSTM networks in parallel, one for forecasting the time of the trip and the
other for predicting the distance. The output of each LSTM is then concatenated
through fully connected layers. Finally, the last model is based on two
parallel At-LSTMs, where similarly, each At-LSTM predicts time and distance
separately through fully connected layers. Among the proposed methods, the most
advanced one, i.e., parallel At-LSTM, predicts the next trip's distance and
time with 3.99% error margin where it is 23.89% better than LSTM, the first
method. We also propose TimeSHAP as an explainability method for understanding
how the networks perform learning and model the sequence of information
Comparison of Machine Learning and Statistical Approaches for Predicting Travel Times in the Oklahoma Highway System
Traffic management systems play a vital role in supporting the smooth flow of traffic in road networks. By accurately predicting travel time, a traffic condition parameter that is extensively used in such systems, we can significantly improve the efficiency of these systems, decision-makers, and travelers. In this work, we use a dataset from the Oklahoma Department of Transportation to compare the accuracy of statistical and machine learning approaches to predicting travel time.
We establish baseline accuracy by constructing a traditional statistical model using the seasonal autoregressive integrated moving average (SARIMA) approach. We compare this baseline to two machine learning models: one-dimensional convolutional neural networks (1-D CNNs) and long short-term memory (LSTM) networks. Our results show that our 1-D CNN and LSTM models have better performance than the statistical model. As an example, in a 4-step architecture (a model structure that simultaneously predicts travel time four periods ahead), the median root means squared relative error (RMSRE) scores for our LSTM and 1-D CNN models are 0.060 and 0.063, respectively. These compare to the median RMSRE score of 0.12 for the corresponding 4-step SARIMA model. The results also indicate that the machine learning approaches have significantly lower computation time compared to SARIMA. In addition, the 1-D CNN model has the least error variance across all architectures and among all modeling methods. Finally, the 1-D CNN approach is more consistent in terms of prediction error across the experimented architectures compared to the LSTM appraoch. Therefore, based on the results, we highly recommend using machine learning approaches, specifically, 1-D CNNs, for estimating travel time in roadway systems and for other similar time-series prediction problems
Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control
Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life
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
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
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