Methods for Modeling Urban Road Traffic Using Timed Automata

2020 ◽  
pp. 97-107 ◽  
Author(s):  
Camelia Avram ◽  
Jose Machado ◽  
Adina Aştilean
Keyword(s):  
Road Traffic ◽  
Timed Automata ◽  
Urban Road

scholarly journals An Innovative Approach for Modelling Urban Road Traffic Using Timed Automata and Formal Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Eduardo Valente ◽  
Camelia Avram ◽  
José Machado ◽  
Adina Astilean
Keyword(s):  
Cellular Automata ◽  
Traffic Control ◽  
Road Traffic ◽  
Timed Automata ◽  
Point Of View ◽  
Urban Road ◽  
Analysis Techniques ◽  
Huge Challenge ◽  
Over Time

Modelling and implementing adequate controllers for urban road traffic control constitute a huge challenge nowadays because of the complexity of systems, as well as possible scenarios and configurations, in each road in a city. A series of issues related to modelling these behaviours are common to arise when using formalisms, tools, and computation machines to perform complex calculations and limitations. This paper presents a formal, flexible, and adaptable approach, with no limitations, from the scientific point of view. For this purpose, modelling formalisms (cellular automata and timed automata) and analysis techniques (simulation and formal verification) are proposed to reach the main goals of modelling complex and adaptable behaviours in urban road traffic with multiple over time changeable configurations. A case study is presented, in order to illustrate the approach and demonstrate in detail the unlimited application of the presented approach.

Influence ranking of road segments in urban road traffic networks

Computing ◽  
2020 ◽  
Vol 102 (11) ◽  
pp. 2333-2360
Author(s):  
Tarique Anwar ◽  
Chengfei Liu ◽  
Hai L. Vu ◽  
Md. Saiful Islam ◽  
Dongjin Yu ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Networks ◽  
Urban Road ◽  
Road Segments ◽  
Road Traffic Networks

The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

2014 ◽  
Vol 694 ◽  
pp. 80-84
Author(s):  
Xiao Tong Yin ◽  
Chao Qun Ma ◽  
Liang Peng Qu
Keyword(s):  
Road Traffic ◽  
Velocity Estimation ◽  
Least Square ◽  
Single Type ◽  
Estimation Model ◽  
Average Speed ◽  
Urban Road ◽  
Traffic State ◽  
Floating Car Data ◽  
Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

scholarly journals Should We Expect a Disastrous Fire Accident in an Urban Road Tunnel? Literature Data Review and a Case Study for Selected Tunnels in Poland

2021 ◽  
Vol 13 (11) ◽  
pp. 6172
Author(s):  
Krystian Szewczyński ◽  
Aleksander Król ◽  
Małgorzata Król
Keyword(s):  
Time Horizon ◽  
Road Traffic ◽  
Traffic Intensity ◽  
Road Tunnel ◽  
Urban Road ◽  
Road Tunnels ◽  
High Uncertainty ◽  
Fire Outbreak ◽  
Fire Accident

Urban road tunnels are a reasonable remedy for inconvenience due to congested road traffic. However, they bring specific threats, especially those related to the possibility of fire outbreak. This work is a case study for selected urban road tunnels. Considering tunnel specificity, road traffic intensity, and structure and based on the literature data for vehicle fire probability, the chances of a fire accident were estimated for selected tunnels in Poland. It was shown that low power tunnel fires could be expected in the 10–20-year time horizon. Although such threats cannot be disregarded, tunnel systems are designed to cope with them. The chances of a disastrous fire accident were estimated as well. Such events can occur when an HGV with flammable goods or a tanker are involved. Such accidents are fortunately very rare, but, on the other hand, that is the reason why the available data are scanty and burdened with high uncertainty. Therefore, a discussion on the reliability of the obtained results is also provided.

scholarly journals The impact of courier-, express- and parcel (CEP) service providers on urban road traffic: The case of Vienna

2021 ◽  
Vol 9 ◽  
pp. 100278
Author(s):  
Sebastian Kummer ◽  
Marko Hribernik ◽  
David M. Herold ◽  
Jasmin Mikl ◽  
Mario Dobrovnik ◽  
...  
Keyword(s):  
Service Providers ◽  
Road Traffic ◽  
Urban Road ◽  
The Impact

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

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