## 2 Related Work
### Current Analysis of Journey Times  
In [31], the authors considered an adaptive genetic algorithm for reducing the average waiting time at traffic lights. In this work, they used SUMO for the implementation. The genetic algorithm required a number of iterations and, hence, there are issues around scalability and its use in real-time systems. In [32], the authors looked at improving [PERSON]'s algorithm for vehicular routes in urban areas, in which two different Dijkstra systems are combined: the first is for distance and the second is for traffic. This method was used between two paths and there are scalability issues for heavy traffic systems. In [33], the authors looked at how we can use VANET network traffic to predict traffic flow on the road. The system uses two algorithms. The first is random forest (RF), which is a machine learning algorithm, to analyse the network traffic, and the second is the gated recurrent unit (GRU) algorithm, which is a deep learning algorithm, to analyse the road traffic. The system did improve the overall traffic, but there are scalability issues and it may not be suitable for real-time traffic situations. In [34], the authors attempted to analyse and manage regional traffic flows using a traffic classifier and a genetic algorithm. There are scalability issues but the results could be used to drive a mathematical model. Finally, in [35], the authors tried to optimise a multi-junction traffic light control system using a genetic algorithm and by classifying the traffic as light, medium, heavy or very heavy. The algorithm shows improvement but cannot adapt to the changing nature of traffic. A summary of these systems is shown below in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Paper** & **Type of Model** & **Analysis of Links** & **Analysis of Links** & **Journey Time** & **Evaluation** \\ \hline Adaptive Genetic for & Adaptive & No & Single & No & Uses several iterations \\ Reducing Average & Genetic & & & & \\ Waiting Time for & Algorithm & & & & therefore \\ Road Traffic & & & & & scalability \\ Signals [31] & & & & & issues \\ \hline Implementation of & Uses two types of & Working & No & No & Scalability \\ Diskstra Algorithm & Dijkstra algorithms & between & & & issues for \\ in Vehicle Routing & based on & two paths & & & large \\ to Improve & distance & & & traffic \\ Traffic Issues in & and traffic & & & systems \\ Urban Areas [32] & & & & & \\ \hline Network Traffic & Random Forest- & No & No & No & Scalability \\ Prediction Model & used to analyse & & & & issues. \\ Considering Road & the VANET traffic. & & & & May not be \\ Traffic Parameters & Gated Recurrent & & & & suitable for \\ Using AI Methods & used to analyse the & & & real-time \\ in VANET [33] & road traffic & & & traffic \\ \hline Time-allocation & Uses a traffic & Regional & No & No & Results could \\ Optimization of & Classifier followed & Links & & & be used \\ Regional Traffic & by Genetic Algorithm & & & to drive a \\ Computer Control & with cross sectional & & & mathematical \\ Based on & analysis & & & model. \\ Distributed & & & & Scalability \\ Algorithm [34] & & & & issues \\ \hline Optimization of & Genetic Algorithm & No & Multiple & No & Algorithm shows \\ Multi-Junction & using light, medium, & & Junctions & & improvement but \\ Traffic Light & heavy or very & & & & cannot adapt to \\ Control Using the & heavy traffic & & & to the changing \\ Classic Genetic & & & & nature of traffic. \\ Algorithm [35] & & & & & \\ \hline Developing traffic & Based on Queuing & Yes & Multiple & Yes & High \\ predictions from & theory and looks & & Junctions & & Accuracy \\ Source to & at new models & & & & \\ Destination using & for delays at & & & & \\ Stochastic & links and & & & & \\ Modelling & junctions & & & \\ (Our Model) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current Analysis of Journey Times.