scholarly journals Context-Aware Link Embedding with Reachability and Flow Centrality Analysis for Accurate Speed Prediction for Large-Scale Traffic Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1800
Author(s):  
Chanjae Lee ◽  
Young Yoon
Keyword(s):  
Traffic Flow ◽  
Large Scale ◽  
Time Windows ◽  
Time Of Day ◽  
Feature Representation ◽  
Traffic Networks ◽  
Speed Prediction ◽  
External Conditions ◽  
Traffic Speed ◽  
Spatio Temporal

This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks. We first analyze how traffic flows in and out of every link through the lowest cost reachable paths. We aggregate the traffic flow conditions of the links on every hop of the inbound and outbound reachable paths to represent the traffic flow dynamics. We compute a new measure called traffic flow centrality (i.e., the Z value) for every link to capture the inherently complex mechanism of the traffic links influencing each other in terms of traffic speed. We combine the features regarding the traffic flow centrality with the external conditions around the links, such as climate and time of day information. We model how these features change over time with recurrent neural networks and infer traffic speed at the subsequent time windows. Our feature representation of the traffic flow for every link remains invariant even when the traffic network changes. Furthermore, we can handle traffic networks with thousands of links. The experiments with the traffic networks in the Seoul metropolitan area in South Korea reveal that our unique ways of embedding the comprehensive spatio-temporal features of links outperform existing solutions.

scholarly journals Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

2020 ◽  
Vol 2674 (4) ◽  
pp. 459-470 ◽  
Author(s):  
Ruimin Ke ◽  
Wan Li ◽  
Zhiyong Cui ◽  
Yinhai Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Data Science ◽  
Prediction Models ◽  
Rapid Development ◽  
Traffic Volume ◽  
Speed Prediction ◽  
Traffic Speed ◽  
The Impact

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

SIMPLIFIED DYNAMICS AND OPTIMIZATION OF LARGE SCALE TRAFFIC NETWORKS

2004 ◽  
Vol 14 (04) ◽  
pp. 579-601 ◽  
Author(s):  
MICHAEL HERTY ◽  
AXEL KLAR
Keyword(s):  
Differential Equations ◽  
Traffic Flow ◽  
Large Scale ◽  
Dynamic Models ◽  
Optimization Problems ◽  
Network Models ◽  
Traffic Networks ◽  
Algebraic Models ◽  
Simplified Models ◽  
Large Scale Networks

Simplified dynamic models for traffic flow on networks are derived from network models based on partial differential equations. We obtain simplified models of different complexity like models based on ordinary differential equations or algebraic models. Optimization problems for all models are investigated. Analytical and numerical properties are studied and comparisons are given for simple traffic situations. Finally, the simplified models are used to optimize large scale networks.

Route guidance during evacuations integrated with perimeter control strategy in large-scale mixed traffic flow networks

2018 ◽  
Vol 29 (11) ◽  
pp. 1850112 ◽  
Author(s):  
Mianfang Liu ◽  
Dongchu Han ◽  
Dongmei Li ◽  
Ming Wang
Keyword(s):  
Traffic Flow ◽  
Control Strategy ◽  
Traffic Management ◽  
Large Scale ◽  
Route Choice ◽  
Heterogeneous Traffic ◽  
Route Guidance ◽  
Traffic Networks ◽  
Mixed Traffic ◽  
Mixed Flow

Recent efficient monitoring and traffic management of large-scale mixed traffic networks still remain a challenge for both traffic researchers and practitioners. The difficulty in modeling route guidance evacuation of pedestrian-vehicle mixed flow lies in mixed flow and uneven or heterogeneous network flow. Existing studies have demonstrated that multi-region control can display different layers of traffic state measurement and control, and incorporate heterogeneity effect in the large-scale network dynamics. The optimal perimeter control can manipulate the percentages of flows that transfer between two regions, offering real-time traffic management strategies to improve the network performance. However, the effect of route guidance evacuation integrated with perimeter control strategies in case of heterogeneous traffic networks is still unexplored. The paper advances in this direction by firstly extending route choice behavior aggregation with perimeter control. For an evacuation study, we consider a campus and its surrounding traffic network that can be classified into two types of networks: the first includes emergency areas that involve a large number of evacuees, and the second includes roads that lead to different destinations. The second network consists of some regions with different evacuation directions. Based on the configuration, this paper proposes a route evacuation guidance control strategy that addresses traffic flow first assignment between regions by controlling perimeter flow with the help of Macroscopic fundamental diagram (MFD) representation and to guide evacuates’ route choice at intersections by LOGIT model in regions. In addition, comparison results show that the proposed route guidance strategy has considerable potential to improve performances and equilibrium conditions (i.e. system optimum and user equilibrium) on the overall network.

scholarly journals A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30050-30057 ◽  
Author(s):  
Kun Niu ◽  
Huiyang Zhang ◽  
Tong Zhou ◽  
Cheng Cheng ◽  
Chao Wang
Keyword(s):  
Speed Prediction ◽  
Temporal Model ◽  
Traffic Speed ◽  
Spatio Temporal

scholarly journals Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

2020 ◽  
Vol 11 (1) ◽  
pp. 315
Author(s):  
Milan Simunek ◽  
Zdenek Smutny
Keyword(s):  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Absolute Error ◽  
Transportation Systems ◽  
Traffic Information ◽  
Ensemble Model ◽  
Temporal Data ◽  
Speed Prediction ◽  
Traffic Speed

Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.

scholarly journals Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

2014 ◽  
Vol 15 (2) ◽  
pp. 794-804 ◽  
Author(s):  
Muhammad Tayyab Asif ◽  
Justin Dauwels ◽  
Chong Yang Goh ◽  
Ali Oran ◽  
Esmail Fathi ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatiotemporal Patterns ◽  
Speed Prediction ◽  
Traffic Speed

scholarly journals Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6402
Author(s):  
Duanyang Liu ◽  
Xinbo Xu ◽  
Wei Xu ◽  
Bingqian Zhu
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Flow Data ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Speed Prediction ◽  
Traffic Speed ◽  
Real World Datasets ◽  
Relationship Of

Traffic speed prediction plays an important role in intelligent transportation systems, and many approaches have been proposed over recent decades. In recent years, methods using graph convolutional networks (GCNs) have been more promising, which can extract the spatiality of traffic networks and achieve a better prediction performance than others. However, these methods only use inaccurate historical data of traffic speed to forecast, which decreases the prediction accuracy to a certain degree. Moreover, they ignore the influence of dynamic traffic on spatial relationships and merely consider the static spatial dependency. In this paper, we present a novel graph convolutional network model called FSTGCN to solve these problems, where the model adopts the full convolutional structure and avoids repeated iterations. Specifically, because traffic flow has a mapping relationship with traffic speed and its values are more exact, we fused historical traffic flow data into the forecasting model in order to reduce the prediction error. Meanwhile, we analyzed the covariance relationship of the traffic flow between road segments and designed the dynamic adjacency matrix, which can capture the dynamic spatial correlation of the traffic network. Lastly, we conducted experiments on two real-world datasets and prove that our model can outperform state-of-the-art traffic speed prediction.

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