5,015 research outputs found
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
Towards Building Deep Networks with Bayesian Factor Graphs
We propose a Multi-Layer Network based on the Bayesian framework of the
Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional
lattice. The Latent Variable Model (LVM) is the basic building block of a
quadtree hierarchy built on top of a bottom layer of random variables that
represent pixels of an image, a feature map, or more generally a collection of
spatially distributed discrete variables. The multi-layer architecture
implements a hierarchical data representation that, via belief propagation, can
be used for learning and inference. Typical uses are pattern completion,
correction and classification. The FGrn paradigm provides great flexibility and
modularity and appears as a promising candidate for building deep networks: the
system can be easily extended by introducing new and different (in cardinality
and in type) variables. Prior knowledge, or supervised information, can be
introduced at different scales. The FGrn paradigm provides a handy way for
building all kinds of architectures by interconnecting only three types of
units: Single Input Single Output (SISO) blocks, Sources and Replicators. The
network is designed like a circuit diagram and the belief messages flow
bidirectionally in the whole system. The learning algorithms operate only
locally within each block. The framework is demonstrated in this paper in a
three-layer structure applied to images extracted from a standard data set.Comment: Submitted for journal publicatio
Machine Learning Approaches for Traffic Flow Forecasting
Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions.
The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data.
In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used.
The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning.
The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
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
Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data
Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domainsβ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate
Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data
An accurate and reliable forecasting method of the subway passenger flow provides the operators with more valuable reference to make decisions, especially in reducing energy consumption and controlling potential risks. However, due to the non-recurrence and inconsistency of large events (such as sports games, concerts or urban marathons), predicting passenger flow under large events has become a very challenging task. This paper proposes a method for extracting event-related information from websites and constructing a multi-step station-level passenger flow prediction model called DeepSPE (Deep Learning for Subway Passenger Flow Forecasting under Events). Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of website data in subway passenger flow forecasting under events
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν 건μ€ν경곡νλΆ,2020. 2. κ³ μΉμ.Urban traffic flows are characterized by complexity. Due to this complexity, limitations arise when using models that have commonly been using to estimate the speed of arterial road networks. This study analyzes the characteristics of the speed data collected by the probe vehicle method in links on the urban traffic flow, presents the limitations of existing models, and develops a modified recurrent neural network model as a solution to these limitations. In order to complement the limitations of existing models, this study focused on the interrupted flow characteristics of urban traffic. Through data analysis, we verified the separation of platoons and high-frequency transitions as phenomena in interrupted flow. Using these phenomena, this study presents a two-step model using the characteristics of each platoon and the selected dropout method that applies traffic conditions separately. In addition, we have developed an active imputation method to deal with frequent missing data in data collection effectively. The developed model not only showed high accuracy on average, but it also improved the accuracy of certain states, which is the limitation of the existing models, increased the correlation between the estimated value and the estimated target value, and properly learned the periodicity of the data.λμκ΅ν΅λ₯λ 볡μ‘μ±μ λ΄μ¬νκ³ μλ€. μ΄ λ³΅μ‘μ±μΌλ‘ μΈν΄, μΌλ°μ μΌλ‘ μ§μκ° κ°μ λλ‘ λ€νΈμν¬μ μλλ₯Ό μΆμ νλ λͺ¨νλ€μ μ¬μ©ν κ²½μ° μ¬λ¬κ°μ§ νκ³μ μ΄ λ°μνκ² λλ€. λ³Έ μ°κ΅¬λ λμκ΅ν΅λ₯ μμ λ§ν¬μμ νλ‘λΈ μ°¨λ λ°©μμΌλ‘ μμ§λ μλμλ£μ νΉμ±μ λΆμνκ³ , κΈ°μ‘΄ λͺ¨νμ νκ³μ μ μ μνκ³ , μ΄λ¬ν νκ³μ μ λν ν΄λ²μΌλ‘μ λ³νλ μνν μ κ²½λ§ λͺ¨νμ κ°λ°νμλ€. λͺ¨ν κ°λ°μ μμ΄, κΈ°μ‘΄ λͺ¨νμ νκ³μ μ 보μνκΈ° μν΄, λ³Έ μ°κ΅¬μμλ λμκ΅ν΅λ₯μ λ¨μλ₯μ νΉμ§μ μ£Όλͺ©νμλ€. μλ£ λΆμμ ν΅ν΄, λ³Έ μ°κ΅¬μμλ λ¨μλ₯μμ λνλλ νμμΌλ‘μ μ°¨λκ΅°μ λΆλ¦¬μ λμ λΉλμ μ μ΄μν λ°μμ νμΈνμλ€. ν΄λΉ νμλ€μ μ΄μ©νμ¬, λ³Έ μ°κ΅¬μμλ κ° μ°¨λκ΅°μ νΉμ§μ μ΄μ©ν μ΄μ©ν 2λ¨κ³ λͺ¨νκ³Ό, κ΅ν΅ μνλ₯Ό λΆλ¦¬νμ¬ μ μ©νλ μ νμ λλ‘μμ λ°©μμ μ μνμλ€. μΆκ°μ μΌλ‘, μλ£μ μμ§μ μμ΄ λΉλ°νλ κ²°μΈ‘ λ°μ΄ν°λ₯Ό ν¨κ³Όμ μΌλ‘ λ€λ£¨κΈ° μν λ₯λμ λ체 λ°©μμ κ°λ°νμλ€. κ°λ° λͺ¨νμ νκ· μ μΌλ‘ λμ μ νλλ₯Ό λ³΄μΌ λΏ μλλΌ, κΈ°μ‘΄ λͺ¨νλ€μ νκ³μ μΈ νΉμ μν©μ λν μ νλλ₯Ό μ κ³ νκ³ μΆμ κ°κ³Ό μΆμ λμκ°μ μκ΄κ΄κ³λ₯Ό λμ΄λ©°, μλ£μ μ£ΌκΈ°μ±μ μ μ νκ² νμ΅ν μ μμλ€.Chapter 1. Introduction 1
1.1. Study Background and Purpose 1
1.2. Research Scope and Procedure 8
Chapter 2. Literature Review 11
2.1. Data Estimation 11
2.2. Traffic State Handling 17
2.3. Originality of This Study 20
Chapter 3. Data Collection and Analysis 22
3.1. Terminology 22
3.2. Data Collection 23
3.3. Data Analysis 26
Chapter 4. Model Development 54
4.1. Basic Concept of the Model 54
4.2. Model Development 58
Chapter 5. Result and Findings 72
5.1. Estimation Accuracy of Developed Models 72
5.2. Correlation Analysis of Developed Model 77
5.3. Periodicity Analysis for Developed Models 81
5.4. Accuracy Analysis by Traffic State 86
5.5. Summary of the Result 92
Chapter 6. Conclusion 94
6.1. Summary 94
6.2. Limitation of the Study 95
6.3. Applications and Future Research 96
Appendix 98
Bibliography 119Docto
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