Article

Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing

Department of Civil and Environmental Engineering, Imperial College, Exhibition Road, SW7 2BU London, UK
Transportation Research Part B Methodological (Impact Factor: 2.94). 05/2004; 38(4):329-342. DOI: 10.1016/S0191-2615(03)00015-8
Source: RePEc

ABSTRACT The genetic algorithm approach to solve traffic signal control and traffic assignment problem is used to tackle the optimisation of signal timings with stochastic user equilibrium link flows. Signal timing is defined by the common network cycle time, the green time for each signal stage, and the offsets between the junctions. The system performance index is defined as the sum of a weighted linear combination of delay and number of stops per unit time for all traffic streams, which is evaluated by the traffic model of TRANSYT [User guide to TRANSYT, version 8, TRRL Report LR888, Transport and Road Research Laboratory, Crowthorne, 1980]. Stochastic user equilibrium assignment is formulated as an equivalent minimisation problem and solved by way of the Path Flow Estimator (PFE). The objective function adopted is the network performance index (PI) and its use for the Genetic Algorithm (GA) is the inversion of the network PI, called the fitness function. By integrating the genetic algorithms, traffic assignment and traffic control, the GATRANSPFE (Genetic Algorithm, TRANSYT and the PFE), solves the equilibrium network design problem. The performance of the GATRANSPFE is illustrated and compared with mutually consistent (MC) solution using numerical example. The computation results show that the GA approach is efficient and much simpler than previous heuristic algorithm. Furthermore, results from the test road network have shown that the values of the performance index were significantly improved relative to the MC.

2 Bookmarks
 · 
236 Views
  • Source
    Artificial Intelligence Applications to Critical Transportation Issues, Circular E-C168 01/2012: chapter Application area 5: Infrastructure Design and Construction: pages 121-133; Transportation Research Board.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This study aims to solve dynamic user Equilibrium Network Design Problem (ENDP) with dynamic network loading profiles using modified Reinforcement Learning (RL) approach. The bi-level programming technique is used to solve the problem. At the lower level of the problem, the dynamic User Equilibrium (UE) link flows are obtained by simulation based Dynamic Traffic Assignment (DTA) model with DynusT and signal timings are obtained at the upper level by modified RL method. The system Performance Index (PI) is defined as the sum of a weighted linear combination of delay and number of stops per unit time for all traffic streams, which is evaluated by the traffic model of TRANSYT-7F. Q-learning, a model-free approach, is one of the RL methods. The modified RL method is actually based on Q-learning. By integrating the modified RL method, traffic assignment and traffic control, the modified REinforcement Learning TRANSYT-7F DynusT (RELTRAD) model is proposed to solve the dynamic ENDP. The objective function of the proposed RELTRAD is total network PI. The model is tested on the medium sized Allsop and Charlesworth's network. Two scenarios, related to various dynamic network loading profiles, are proposed for numerical application. Encouraging results are obtained. Results showed that the RELTRAD model effectively optimizes the signal timings and values of the network PI. The RELTRAD model improves to the network PI from the initial value to the final value as 65% and 67% for loading profile 1 and 2, respectively.
    Procedia - Social and Behavioral Sciences 02/2014; 111:38-47. DOI:10.1016/j.sbspro.2014.01.036
  • [Show abstract] [Hide abstract]
    ABSTRACT: Traffic congestion is a challenging problem in the present scenario where we are enjoying the conveniences of automobiles every day and want faster transportation. This problem is increasing exponentially day by day so to deal with this problem we devise an adaptive traffic signal controller (TSC) as traditional traffic signal controllers are inefficient in dealing with increasing demands of growing traffic. This controller uses neural network (NN) and Genetic Algorithm (GA) to adapt the traffic signal timings according to the congestion. NN takes signal timings as input and gives the queue length as output. GA is further applied to get the optimized green signal timing at its output, which is capable of reducing the queue length and overall delay. The performance of proposed model is also compared with fixed time TSC and an already existing adaptive TSC and a significant improvement were observed.
    2014 Recent Advances in Engineering and Computational Sciences (RAECS); 03/2014

Full-text (2 Sources)

Download
325 Downloads
Available from
May 31, 2014