A probabilistic estimation of traffic congestion using Bayesian network
TL;DR: The case study results showed that the proposed BN models could quantify the probable congestion level in terms of a probability for each state in a variable, at the presence of different combinations of prior variables’ state.
About: This article is published in Measurement.The article was published on 2021-04-01. It has received 25 citations till now. The article focuses on the topics: Probabilistic logic & Bayesian network.
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TL;DR: In this paper, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in which the noise algorithm was employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster.
Abstract: With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.
115 citations
01 Sep 2021
TL;DR: In this article, the authors present a survey on the recent advances of UAVs and their roles in current and future transportation systems and highlight the challenges and opportunities of integrating UAV towards future intelligent and resilient transportation systems.
Abstract: The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.
34 citations
TL;DR: In this paper, a Long Short-Term Memory (LSTM)-based correlated traffic data prediction framework was proposed for two different real-time traffic datasets, which can help control traffic congestion and ensure a more robust traffic management system in the future.
Abstract: Correlated traffic data refers to a collection of time series recorded simultaneously in different regions throughout the same transportation network route. Due to the presence of both temporal and spatial correlation properties between multiple time series data, accurate time series prediction becomes challenging. The accuracy of the traffic data prediction helps mitigating traffic congestion and establish a robust traffic management system. When a prediction algorithm fails to consider the correlations present in the dataset, the accuracy of the prediction results reduces. To overcome the prediction shortcomings, this study proposes a Long Short-Term Memory (LSTM)-based correlated traffic data prediction (LSTM-CTP) framework. The proposed LSTM-CTP framework was employed for two different real-time traffic datasets. These datasets were initially preprocessed to capture both temporal and spatial trends and the correlations between the collected data series. By employing LSTM, temporal and spatial trends were predicted. Further, the Kalman-filter approach was employed to obtain the final prediction by aggregating the temporal and spatial trend predictions. The performance of the proposed LSTM-CTP was evaluated using different performance metrics and compared with different time-series prediction algorithms. The proposed framework showed substantial improvements in prediction results compared to the other algorithms. Overall, the proposed LSTM-CTP framework can help control traffic congestion and ensure a more robust traffic management system in the future.
15 citations
01 Feb 2022
TL;DR: In this paper , a Long Short-Term Memory (LSTM)-based correlated traffic data prediction framework was proposed for two different real-time traffic datasets, which can help control traffic congestion and ensure a more robust traffic management system in the future.
Abstract: Correlated traffic data refers to a collection of time series recorded simultaneously in different regions throughout the same transportation network route. Due to the presence of both temporal and spatial correlation properties between multiple time series data, accurate time series prediction becomes challenging. The accuracy of the traffic data prediction helps mitigating traffic congestion and establish a robust traffic management system. When a prediction algorithm fails to consider the correlations present in the dataset, the accuracy of the prediction results reduces. To overcome the prediction shortcomings, this study proposes a Long Short-Term Memory (LSTM)-based correlated traffic data prediction (LSTM-CTP) framework. The proposed LSTM-CTP framework was employed for two different real-time traffic datasets. These datasets were initially preprocessed to capture both temporal and spatial trends and the correlations between the collected data series. By employing LSTM, temporal and spatial trends were predicted. Further, the Kalman-filter approach was employed to obtain the final prediction by aggregating the temporal and spatial trend predictions. The performance of the proposed LSTM-CTP was evaluated using different performance metrics and compared with different time-series prediction algorithms. The proposed framework showed substantial improvements in prediction results compared to the other algorithms. Overall, the proposed LSTM-CTP framework can help control traffic congestion and ensure a more robust traffic management system in the future.
15 citations
TL;DR: In this article, the authors investigated the relationship of roads in a neighboring area based on the similarity of traffic condition and identified roads with high relationship with other neighbouring roads by extracting spatial and temporal features using traffic state clustering.
Abstract: Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighboring roads. When congestion occurs, it will propagate through the road network due to increasing traffic flow. One of the complexities of traffic congestion is unpredictability, thus it is difficult to represent traffic flows by numerical equations. One possible approach is to use the spatial historical data of traffic flow and relate them with traffic condition (congestion or clear) using statistical approach. Studies on traffic flow propagation generally involves visualization with real time GPS trajectory data to help analyze traffic flow propagation using human vision. Our research focuses on traffic flow pattern based on data from sensors without having information about the connected roads. We study spatial and temporal factors that influence traffic flow near a congested road in a neighboring area. Hence, our study investigates the relationship of roads in a neighboring area based on the similarity of traffic condition. Roads with high relationship with other neighbouring roads are identified by extracting spatial and temporal features using traffic state clustering. Grey level of co-occurrence matrix (GLCM) is utilized with spectral clustering to cluster road segments that have the same duration of road congestion in terms of day and time intervals. The emission probability is then calculated for prediction of traffic state impact of road congestion in neighboring area using Hidden Markov Model (HMM). We proposed HMM together with our clustering method to predict traffic state impact of road congestion. The experimental results show that the accuracy of prediction using the proposed HMM achieve 89%.
9 citations
References
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01 Jan 2001TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Abstract: Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.
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01 Jan 2007
TL;DR: In this paper, the authors examine a graphical representation of uncertain knowledge called a Bayesian network, which is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation.
Abstract: We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by combining domain knowledge with statistical data.
1,600 citations
TL;DR: The pros and cons of the use of Bayesian networks especially in the context of environmental modelling and management are summarised.
966 citations
01 Jan 2008
TL;DR: In this paper, the authors discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models, including techniques for learning with incomplete data.
Abstract: A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.
717 citations
01 May 2005
TL;DR: The 2005 Urban Mobility Report shows that the current pace of transportation improvement, however, is not sufficient to keep pace with even a slow growth in travel demands in most major urban areas as mentioned in this paper.
Abstract: Congestion continues to grow in America's urban areas. Despite a slow growth in jobs and travel in 2003, congestion caused 3.7 billion hours of travel delay and 2.3 billion gallons of wasted fuel, an increase of 79 million hours and 69 million gallons from 2002 to a total cost of more than $63 billion. The solutions to this problem will require commitment by the public and by national, state and local officials to increase investment levels and identify projects, programs and policies that can achieve mobility goals. The 2005 Urban Mobility Report shows that the current pace of transportation improvement, however, is not sufficient to keep pace with even a slow growth in travel demands in most major urban areas.
561 citations