Optimization driven cellular automata for traffic flow prediction at signalized intersections

2021 ◽  
Vol 40 (1) ◽  
pp. 1547-1566
Author(s):  
Shuang You ◽  
Yaping Zhou
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Fitness Function ◽  
Traffic Density ◽  
Traffic Flows ◽  
Average Speed ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Ca Model ◽  
Signalised Intersection

The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed.

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

2021 ◽  
pp. 036119812110111
Author(s):  
Hong Gao ◽  
Zengjie Wang ◽  
Zhenjun Yan ◽  
Zhaoyuan Yu ◽  
Wen Luo ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Moving Average ◽  
Three Dimensional ◽  
Prediction Method ◽  
Tensor Decomposition ◽  
New Method ◽  
Traffic Flows ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatial Locations

Predicting entry-traffic flows synchronously could enable inferences about the changing trends and spatial structure of dynamic traffic flows in an expressway network. This research develops a synchronized entry-traffic flow prediction method for regional expressway systems. The new method first organizes numerous entry-traffic flows as a three-dimensional (time slots, spatial locations, and vehicle types) tensor, then applies tensor decomposition to extract their temporally changing features. After forecasting the temporally changing features, predicted values of entry-traffic flows can be calculated synchronously by tensor reconstruction. Data from hourly entry-traffic flows involving nine vehicle types and 201 spatial locations in a regional expressway system of China are used to discuss the performance of this new method. The results show that the new method could obtain prediction results with high overall accuracy. Comparative experiments indicate that the new method and existing methods (autoregressive integrated moving average, or ARIMA, and Holt-Winters) could generate prediction results with similar accuracy. However, the proposed method has the advantage of reducing the number of time series that need to be handled in the prediction of numerous entry-traffic flows for regional expressway systems. This method might be helpful for administrators to guide and manage vehicles so that they enter the expressway system effectively.

Prediction of traffic flow with small time granularity at intersection based on probabilistic network

2020 ◽  
Vol 39 (2) ◽  
pp. 1659-1670
Author(s):  
Wenbin Xiao ◽  
Shunying Zhu ◽  
Qiucheng Chen
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Small Time ◽  
Traffic Density ◽  
Flow Section ◽  
Flow State ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Probabilistic Network ◽  
Flow Prediction

In order to overcome the inaccuracy of current research results of traffic flow prediction, this paper proposes a prediction method for traffic flow with small time granularity at intersection based on probability network. This method takes one minute as time granularity, collects traffic data such as cross-section flow, section traffic flow velocity data, traffic density, road occupancy, section delay and steering ratio by using RFID technology, and analyzes and processes the data. By introducing Bayesian network in probabilistic network and combining K-nearest neighbor method, historical data and predicted traffic flow state are classified to realize the prediction of traffic flow with small time granularity at intersections. The experimental results show that this method has high prediction accuracy and reliability, and is a feasible traffic flow prediction method.

Highway Short-Term Traffic Flow Prediction with Traffic Flows from Multi Entry Stations

2020 ◽  
Author(s):  
Housong Ruan ◽  
BangYu Wu ◽  
Bin Li ◽  
Zhu Chen ◽  
Han Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic Flow Prediction with Genetic Network Programming (GNP)

2009 ◽  
Vol 13 (6) ◽  
pp. 713-725 ◽  
Author(s):  
Huiyu Zhou ◽  
◽  
Shingo Mabu ◽  
Wei Wei ◽  
Kaoru Shimada ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Fitness Function ◽  
Memory Function ◽  
Genetic Network ◽  
Network Programming ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Adaptive Penalty ◽  
Sharing Function ◽  
Genetic Network Programming

In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models forN-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.

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