scholarly journals Exploiting witness for traffic simulation

2014 ◽  
Vol 1 (1) ◽  
pp. 12-24
Author(s):  
Azhar Ismail ◽  
Muhammad Latif ◽  
Mian Awai
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Waiting Times ◽  
Simulation Software ◽  
Peak Time ◽  
Traffic Lights ◽  
Traffic Intersection ◽  
Flow Through ◽  
Alternative Scenarios

Traffic congestion in urban cities is an increasing problem. Not only does it lead to an increase in pollution, but the time spent waiting in traffic queues wastes valuable time in addition to causing frustration. A system that can control and manage traffic efficiently is one way that this issue can be reduced.A specific road traffic intersection in South Manchester, UK, was selected for investigation as it experiences high levels of traffic flow through it during the evening peak time. This has led to large queues and long waiting times due to the fixed timings of the traffic lights. This paper explores strategies to better control the traffic flow through it. A model of the selected traffic junction has been built using Witness simulation software. Data for this junction has been obtained partially from observations and mostly from traffic surveys enabling a simulation of the traffic flow. Analysing the results allowed two alternative scenarios to be developed and simulated. Results from one of the scenarios showed noticeable reductions in the average queue waiting times at the traffic junction.

OPTIMISING TRAFFIC FLOW AT A SIGNALISED INTERSECTION USING SIMULATION

2019 ◽  
Vol 4 (2) ◽  
pp. 261
Author(s):  
Muhammad Rozi Malim ◽  
Faridah Abdul Halim ◽  
Sherey Sufreney Abd Rahman
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Simulation Software ◽  
Traffic Signal ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
Light Management ◽  
Optimal Setting ◽  
Pedestrian Crossing

Traffic signal lights system is a signalling device located an intersection or pedestrian crossing to control the movement of traffic. The timing of traffic signal lights has attracted many researchers to study the problems involving traffic light management and looking for an inexpensive and effective solution that requires inexpensive changes in the infrastructures. A simple traffic lights system uses a pre-timed control setting based on the latest traffic data, and the setting could be manually changed. It is a common type of signal control and sometimes the setting was not correctly configured with the traffic data, thus leading to congestion at an intersection. Many mathematical strategies were applied to get an optimal setting. This study aims to model the traffic flow at Persiaran Kayangan and Persiaran Permai Intersection, Section 7, Shah Alam, as the case study, by using AnyLogic simulation software. The model was used to determine the best timings of traffic green lights that minimise the average time at the intersection and reduce traffic congestion. The findings showed that the best timings of traffic green lights for four directions at the intersection are 120 seconds, 75 seconds, 130 seconds and 100 seconds, respectively. These timings of green lights produced the lowest average time at the intersection (55.65 seconds).

scholarly journals Urban Wasteful Transport and Its Estimation Methods

2018 ◽  
Vol 10 (12) ◽  
pp. 4562 ◽  
Author(s):  
Xiangyang Cao ◽  
Bingzhong Zhou ◽  
Qiang Tang ◽  
Jiaqi Li ◽  
Donghui Shi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Transport Theory ◽  
Road Traffic ◽  
Urban Traffic ◽  
Flow Density ◽  
Urban Road ◽  
Road Capacity ◽  
Density Relationship

The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

Discussion on Urban Road Traffic Congestion Algorithm for Automatically Determining

2014 ◽  
Vol 926-930 ◽  
pp. 3790-3793
Author(s):  
Yu Bo Dong
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Signal Control ◽  
Transportation Engineering ◽  
Urban Road ◽  
Traffic Incident ◽  
Road Congestion ◽  
Road Traffic Congestion

Compared with the expressway, most of the traffic flow in urban road network can be denoted as interrupted traffic flow. Based on the current employed equipment for traffic flow collection and traffic signal control in urban roads, different types of traffic flow in urban roads are analyzed with the traffic flow arrival/departure model in transportation engineering. Mathematical models complying with traffic flow changes are utilized to match the traffic flow in both entry and exit road blocks, thus, enabled the automatic detection of traffic incident. This algorithm provides a measurement for the automatic judgment of urban road congestion and the expansion utility of intelligent transportation facilities in urban areas.

Simulation Research of Flow and Speed on Urban Expressway Interchange Entrance

2013 ◽  
Vol 779-780 ◽  
pp. 796-799
Author(s):  
Liang Wang ◽  
Yu Jie Wang ◽  
Ling Yu Wang
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Service Level ◽  
Simulation Software ◽  
Simulation Research ◽  
Main Road ◽  
Microscopic Traffic Simulation ◽  
Urban Expressway ◽  
Traffic Capacity ◽  
And Control

The urban expressway overpass entrance is an important node of the urban road system. Traffic jams often happen at entrance. The characteristics of urban expressway entrance and the advantages of the microscopic traffic simulation were combined to analyze capacity of entrance. VISSIM simulation software was used and the validity of the simulation model was verified. The influence of the main road traffic flow and the desired speed of entrance to flow and speed of the urban expressway entrance were analyzed through simulation experiment. On the whole, traffic capacity of urban expressway entrance reduces with the increase of traffic flow on the main road. The higher the desired speed is, the faster traffic capacity reduces. The increase of speed and control of main road traffic flow is of great significance for improving the capacity and service level of expressway.

scholarly journals INTELLIGENT TRAFFIC CONTROL DEVICE MODEL USING AD HOC NETWORK

2019 ◽  
Vol 01 (02) ◽  
pp. 68-76
Author(s):  
Robert Bestak
Keyword(s):  
Traffic Flow ◽  
Automobile Industry ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Communication Systems ◽  
Ad Hoc ◽  
Road Traffic ◽  
Control Device ◽  
Device Model

The advancements in the technologies related to the wireless communication systems has made the vehicular adhoc networks prominent area of research in the automobile industry. The absolute volume of road traffic affects the safety, convenience and the efficiency of the traffic flow in the urban areas. So the paper scopes in developing an intelligent traffic control device model using the adhoc network to ameliorate the traffic flow. The proposed system enhances the convenience in travel by gathering the information of the vehicles along with the density of the vehicles and the movement of the vehicles on road. The device is modelled using the MATLAB and examined over the traffic flow on the peak hours as well as the normal hours and the holidays to understand its intelligent traffic control. The results obtained shows that the performance improvement in optimizing the traffic congestion through the proposed method is better compared to the existing methodologies used in traffic controlling.

scholarly journals An Intelligent Fuzzy Logic Based Traffic Controller

2020 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Cycle Time ◽  
Traffic Congestion ◽  
Carbon Dioxide Emissions ◽  
Road Traffic ◽  
Green Light ◽  
Traffic Lights ◽  
Road Congestion ◽  
Light Duration ◽  
Matlab Programming

Increasing road congestion, travel time, number of accidents, carbon dioxide emissions, and fuel consumption are some of the consequences of growth in the vehicle population. Therefore, intelligent traffic controllers are required to solve road traffic congestion problems. The results of prevalent methods, including preset cycle time controller and vehicle-actuated controller, indicated that they do not effectively perform at traffic peak moments. Therefore, due to the deficiency of common methods, fuzzy logic based traffic signal controllers have attracted a lot of attention among researchers. In this article, a fuzzy logic based algorithm for 4-way intersections is proposed and it consists of two main stages for sorting the phase and determining the green light duration. The proposed system is simulated in the MATLAB programming environment and the performance of the designed controller and a conventional controller is compared for some of the presumed conditions. The results of applying the proposed system indicate that this algorithm has a better performance in different traffic conditions in contrast to a preset cycle time controller and it can reduce the number of vehicles behind traffic lights at intersections and the waiting time of passengers.

Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

2018 ◽  
Vol 22 (5) ◽  
pp. 704-710 ◽  
Author(s):  
Robert Kerwin C. Billones ◽  
◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Volume Estimation ◽  
Public Utility ◽  
Scene Analysis ◽  
Volume Distribution ◽  
Distribution Analysis ◽  
Traffic Scene Analysis

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

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