A Novel Traffic Flow Data Imputation Method for Traffic State Identification and Prediction Based on Spatio-Temporal Transportation Big Data

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Wei Li ◽  
Jianming Hu ◽  
Zuo Zhang ◽  
Yi Zhang
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Imputation Method ◽  
Data Imputation ◽  
Flow Data ◽  
Traffic State ◽  
State Identification ◽  
Spatio Temporal

scholarly journals Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-pu Lu ◽  
Zhi-yuan Sun ◽  
Wen-cong Qu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Flow ◽  
Optimization Model ◽  
Bilevel Optimization ◽  
Data Driven ◽  
Stage Model ◽  
Flow State ◽  
Real Time Traffic ◽  
State Identification

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

scholarly journals Tracking the spread of novel coronavirus (2019-nCoV) based on big data

2020 ◽  
Author(s):  
Xumao Zhao ◽  
Xiang Liu ◽  
Xinhai Li
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Linear Models ◽  
Early Stage ◽  
Local Population ◽  
Control Measures ◽  
The Novel ◽  
Flow Data ◽  
Novel Coronavirus ◽  
Local Transmission

AbstractThe novel coronavirus (2019-nCoV) appeared in Wuhan in late 2019 have infected 34,598 people, and killed 723 among them until 8th February 2020. The new virus has spread to at least 316 cities (until 1st February 2020) in China. We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China. We found the contribution of air passengers from Wuhan was decreasing gradually, but the effect of local population was increasing, indicating the trend of local transmission. However, the increase of local transmission is slow during the early stage of novel coronavirus, due to the super strict control measures carried out by government agents and communities.

Missing data imputation for traffic flow speed using spatio-temporal cokriging

2018 ◽  
Vol 88 ◽  
pp. 124-139 ◽  
Author(s):  
Bumjoon Bae ◽  
Hyun Kim ◽  
Hyeonsup Lim ◽  
Yuandong Liu ◽  
Lee D. Han ◽  
...  
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Flow Speed ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatio Temporal

scholarly journals A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Yiliang Zeng ◽  
Jinhui Lan ◽  
Bin Ran ◽  
Yaoliang Jiang
Keyword(s):  
Traffic Flow ◽  
Incomplete Data ◽  
Road Traffic ◽  
Flow Speed ◽  
State Assessment ◽  
Assessment System ◽  
Flow Data ◽  
The Road ◽  
Traffic State ◽  
Probe Vehicle

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

An Identification Method of Traffic State Inequality for Urban Roads

2014 ◽  
Vol 505-506 ◽  
pp. 979-984
Author(s):  
Feng Jie Fu ◽  
Qi Feng Lou ◽  
Bin Wang ◽  
Dian Hai Wang
Keyword(s):  
Traffic Flow ◽  
Flow Data ◽  
Simulation Data ◽  
Traffic State ◽  
Identification Method ◽  
Calculation System

Traffic state inequality coefficient, an index characterizing traffic state inequality for urban roads, is presented based on traffic state indexes of intersections and links calculated by traffic flow data collected from fixed traffic detectors. And then it establishes the calculation system for traffic state inequality coefficient of intersections, links and networks and determines the ways to classify traffic state inequality. After that, a case study is conducted using Vissim simulation data reproducing the process and results of the identification.

Spatio-temporal knn prediction of traffic state based on statistical features in neighbouring roads

2021 ◽  
pp. 1-15
Author(s):  
Bagus Priambodo ◽  
Azlina Ahmad ◽  
Rabiah Abdul Kadir
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Nonlinear Models ◽  
Ripple Effect ◽  
Traffic Condition ◽  
Traffic State ◽  
Road Congestion ◽  
Spatio Temporal ◽  
Occurrence Matrix

Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only.

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