scholarly journals Solving a Signalized Traffic Intersection Problem with NLP Solvers

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Teófilo Miguel M. Melo ◽  
João Luís H. Matias ◽  
M. Teresa T. Monteiro
Keyword(s):  
Queue Length ◽  
Mathematical Theory ◽  
Urban Traffic ◽  
System Model ◽  
Traffic Network ◽  
Complementarity Constraints ◽  
Objective Functions ◽  
Mathematical Programs ◽  
Software Packages ◽  
Traffic Intersection

Mathematical Programs with Complementarity Constraints (MPCC) finds many applications in areas such as engineering design, economic equilibrium, and mathematical theory itself. In this work, we consider a queuing system model resulting from a single signalized traffic intersection regulated by pretimed control in an urban traffic network. The model is formulated as an MPCC problem and may be used to ascertain the optimal cycle and the green split allocation. This MPCC problem is also formulated as its NLP equivalent reformulation. The goal of this work is to solve the problem, using both MPCC and NLP formulations, minimizing two objective functions: the average queue length over all queues and the average waiting time over the worst queue. The problem was codified in AMPL and solved using some optimization software packages.

scholarly journals BACKPRESSURE BASED TRAFFIC SIGNAL CONTROL CONSIDERING CAPACITY OF DOWNSTREAM LINKS

Transport ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 347-356
Author(s):  
Shenxue Hao ◽  
Licai Yang ◽  
Yunfeng Shi ◽  
Yajuan Guo
Keyword(s):  
Queue Length ◽  
Control Algorithm ◽  
Mesh Networks ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Network ◽  
Maximum Throughput ◽  
Urban Traffic Network

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

SIMULATION OF A SEA CARGO PORT AS A QUEUING SYSTEM IN ANYLOGIC

2020 ◽  
Vol 4 (26) ◽  
pp. 59-66
Author(s):  
A. G. Morozkov ◽  
◽  
M. R. Yazvenko ◽  
Keyword(s):  
Simulation Model ◽  
Key Words ◽  
Simulation Modeling ◽  
Queue Length ◽  
System Simulation ◽  
Queuing System ◽  
System Model ◽  
System Capacity ◽  
Mathematical Solution ◽  
Structure Simulation

The article presents simplified queuing system model of freight marine port. The article discusses the basic elements of queuing system, its mathematical solution and structure. Simulation model was created using AnyLogic to analyze an effect of system capacity on queue length. The results were analyzed and the solution for queue optimization was proposed. Key words: queuing system, simulation modeling, AnyLogic, marine port, servers, queue.

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.

Sign in / Sign up

Export Citation Format

Share Document