Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

Yan, Zheng; Li, Yanran; Own, Chung-Ming; Meng, Zhaopeng; Gao, Mengya

doi:10.1177/1550147718769784

Cited by 13 publications

(11 citation statements)

References 20 publications

(29 reference statements)

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“…Nevertheless, how to apply the dynamic strategy of traffic lights to improve traffic flow is not considered. In the research [ 32 ], the authors propose an advanced dynamic traffic network prediction and navigation model. Compared with the traditional shortest path algorithm which considers the static network as the core, the first part of this guiding method considers the potential traffic jam, gives the optimal driving suggestion for different times of day, designs the equilibrium Markov chain model, and this method is used to dispatch vehicles to alleviate urban congestion.…”

Section: Efficiencymentioning

confidence: 99%

Efficient and Safe Strategies for Intersection Management: A Review

Wang

Guo

Yang

2021

Sensors

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Intersection management is a sophisticated event in the intelligent transportation system due to a variety of behavior for traffic participants. This paper primarily overviews recent studies on the scenes of intersection, aiming at improving the efficiency or guaranteeing the safety when vehicles pass the crossing. These studies are respectively surveyed from the perspectives of efficiency and safety. Firstly, recent contributions to efficiency-oriented, intersection management overviews from four scenes, including congestion avoidance, green light optimized speed advisory (GLOSA), trajectory planning, and emergency vehicle priority preemption control. Furthermore, the studies on intersection collision detection and abnormal information warning are surveyed in the safety category. The corresponding algorithms for velocity and route management presented in the surveyed works are discussed.

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Section: Efficiencymentioning

confidence: 99%

Efficient and Safe Strategies for Intersection Management: A Review

Wang

Guo

Yang

2021

Sensors

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“…Sometimes, a user wishes for a route with less traffic [11], [12] while another time shortest path is preferred. Nevertheless, the main goal is still pattern extraction and mining [3], [6], [11], [13], [14], grouping similar routes and trajectories [15], [16], route prediction [7], [8], [17] and hot path mining [18], [19] including pattern mining [3], [6], [13], [14], trajectory clustering [15], [16], hot route discovery [18], [19], trajectory prediction [7], [8], [17] etc. Nevertheless, none of the afore-mentioned research approaches the challenge of discovering the most popular routes from one given location to another based on defined user constraints and preferences, and in many cases, uses simulated data.…”

Section: Related Workmentioning

confidence: 99%

A Stochastic Approach Towards Travel Route Optimization and Recommendation Based on Users Constraints Using Markov Chain

et al. 2019

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Accurate analysis of tourist movement is essential for a country to devise sustainable policies for promoting and growing tourism. From the activities of tourists and the spots they visit, the amount of revenue generated for a particular region can be predicted. However, the tourist preferences evolve and vary from one user to another, and thus, a tourist spot favorite for one set of users is not preferred by another set of users. This paper aims to design and implement a novel application to recommend an optimal travel route based on user constraints. The user constraints can be the maximum time, distance, and popularity of a particular place. The real data are collected from the Wi-Fi routers installed at different tourist spots of Jeju Island, South Korea. We apply a Markov chain model to predict the popularity of different places on the short-and long-term bases. The popularity index alongside user constraints is provided to find optimal routes. A responsive web-based prototype is developed to collect user constraints, and, in response, recommends optimal routes using the Google Maps directory services. The results indicate the difference between the short-and long-term popularities to prove the effectiveness of the Markov chains in forecasting long-term behavior. The system is made responsive for all sizes of screens to make it uniformly serviceable on mobile phones. The accuracy of the system is computed based on the historical data and the recommendation system, and it is ascertained to fall between 95% and 100% all the time. Furthermore, the results are compared with popular state-of-the-art methods, and they are found to be significantly better than that in the long-term location prediction.

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“…The huge number of cars has become very challenging in terms of the efficient operation of urban road traffic networks and causes traffic congestion. Road traffic congestion in many cities around the world is very serious, especially in metropolitan cities [1]. There have been a lot of research on the prediction of urban road traffic congestion and traffic management [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Zhang

et al. 2020

Algorithms

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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

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Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

Cited by 13 publications

References 20 publications

Efficient and Safe Strategies for Intersection Management: A Review

Efficient and Safe Strategies for Intersection Management: A Review

A Stochastic Approach Towards Travel Route Optimization and Recommendation Based on Users Constraints Using Markov Chain

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

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