Probabilistic models of freeway safety performance using traffic flow data as predictors

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

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Alternative sources of navigation and traffic flow data

ITS Sensors and Architectures for Traffic Management and Connected Vehicles ◽

10.1201/9781315206905-10 ◽

2017 ◽

pp. 221-252

Author(s):

Lawrence A. Klein

Keyword(s):

Traffic Flow ◽

Flow Data ◽

Alternative Sources

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Modeling Traffic Flows with Fluid Flow Model

International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) ◽

10.34010/injiiscom.v1i1.4860 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Paulus Setiawan Suryadjaja ◽

◽

Maclaurin Hutagalung ◽

Herman Yoseph Sutarto ◽

◽

...

Keyword(s):

Fluid Flow ◽

Traffic Flow ◽

Flow Model ◽

Intelligent Transportation System ◽

Macroscopic Model ◽

Chain Process ◽

Data Traffic ◽

Flow Data ◽

Order Markov Chain ◽

Parameter Values

This Research presents a macroscopic model of traffic flow as the basis for making Intelligent Transportation System (ITS). The data used for modeling is The number of passing vehicles per three minutes. The traffic flow model created in The form of Fluid Flow Model (FFM). The parameters in The model are obtained by mixture Gaussian distribution approach. The distribution consists of two Gaussian distributions, each representing the mode of traffic flow. In The distribution, intermode shifting process is illustrated by the first-order Markov chain process. The parameters values are estimated using The Expectation-maximization (EM) algorithm. After The required parameter values are obtained, traffic flow is estimated using the Observation and transition-basedmost likely estimates Tracking Particle Filter (OTPF). To Examine the accuracy of the model has been made, the model estimation results are compared with the actual traffic flow data. Traffic flow data is collected on Monday 20 September 2017 at 06.00 to 10.00 on DipatiukurRoad, Bandung. The proposed model has accuracy with MAPE value below 10%, or falls into highly accurate categories

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Analysis of Freeway Secondary Crashes in Different Traffic Flow States by Three-Phase Traffic Theory

Journal of Advanced Transportation ◽

10.1155/2020/8890351 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Bo Yang ◽

Yao Wu ◽

Weihua Zhang

Keyword(s):

Traffic Flow ◽

Safety Performance ◽

Crash Risk ◽

Contributing Factors ◽

Crash Data ◽

Conditional Logit ◽

Three Phase ◽

Secondary Crashes ◽

Traffic Theory ◽

Flow States

The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different.

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SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽

10.3846/16484142.2013.818057 ◽

2013 ◽

Vol 30 (4) ◽

pp. 397-405 ◽

Cited By ~ 37

Author(s):

Kranti Kumar ◽

Manoranjan Parida ◽

Vinod Kumar Katiyar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Congestion ◽

Traffic Volume ◽

Flow Data ◽

Highway Traffic ◽

Short Term ◽

Artificial Neural ◽

Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

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