24 research outputs found
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
Metro origin-destination prediction is a crucial yet challenging time-series
analysis task in intelligent transportation systems, which aims to accurately
forecast two specific types of cross-station ridership, i.e.,
Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete
OD matrices of previous time intervals can not be obtained immediately in
online metro systems, and conventional methods only used limited information to
forecast the future OD and DO ridership separately. In this work, we proposed a
novel neural network module termed Heterogeneous Information Aggregation
Machine (HIAM), which fully exploits heterogeneous information of historical
data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices)
to jointly learn the evolutionary patterns of OD and DO ridership.
Specifically, an OD modeling branch estimates the potential destinations of
unfinished orders explicitly to complement the information of incomplete OD
matrices, while a DO modeling branch takes DO matrices as input to capture the
spatial-temporal distribution of DO ridership. Moreover, a Dual Information
Transformer is introduced to propagate the mutual information among OD features
and DO features for modeling the OD-DO causality and correlation. Based on the
proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD
and DO ridership simultaneously. Extensive experiments conducted on two
large-scale benchmarks demonstrate the effectiveness of our method for online
metro origin-destination prediction
Forecasting public transport ridership: Management of information systems using CNN and LSTM architectures
This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings
Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM
Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station
A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials
Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials
IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation
During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture