A traffic management system for controlling traffic flow in an area, comprising: plural sensors positioned at respective junctions in the area, forming a network of linked nodes; software to process the sensor signals to derive vehicle signatures, each signature being indicative of a particular vehicle detected at a particular node at a particular time; software to process the signature data to track the routes taken by vehicles traveling across the network by matching the signature of each vehicle across plural nodes and derive vehicle statistics at various time periods, in accordance with the routes taken by the vehicles over the time periods, the statistics comprising: vehicle numbers at each node from a particular approach direction; the probability of those vehicle numbers exiting from each node and later arriving at other nodes in the network from a particular approach direction where said other nodes include the nodes immediately linked to each node and nodes further downstream from each node, wherein the probability is in the form of a probability matrix; and average journey time for the vehicles to traverse each link between nodes; artificial intelligence (Al) trained on the historical vehicle statistics arranged to predict traffic arriving at plural nodes from particular directions at a future time based on receiving a current count of vehicles numbers detected at nodes in the network, wherein the AI is trained to learn traffic patterns and predict the probability matrix and wherein the prediction of traffic arriving at a particular downstream node from a particular direction is based on combining the current vehicle numbers with the probabilities from the probability matrix that those vehicle numbers will take a route to arrive at the particular downstream node from the particular direction and using the average journey times of detected vehicles to calculate a predicted arrival time for the vehicles at that downstream node, wherein the calculation is performed for each upstream node in the network to build up a profile of expected number of vehicles arriving at the downstream node at various times from each direction, and wherein the profile is built up for each node in the network; and based on the prediction, determining a control plan for the traffic lights at the junctions to optimise the flow of traffic ahead of the predicted arrival of the vehicles at the nodes corresponding to those junctions and transmitting control signals to change the traffic light timing to implement the plan to the traffic lights at the junctions. plural sensors positioned at respective junctions in the area, forming a network of linked nodes; software to process the sensor signals to derive vehicle signatures, each signature being indicative of a particular vehicle detected at a particular node at a particular time; software to process the signature data to track the routes taken by vehicles traveling across the network by matching the signature of each vehicle across plural nodes and derive vehicle statistics at various time periods, in accordance with the routes taken by the vehicles over the time periods, the statistics comprising: vehicle numbers at each node from a particular approach direction; the probability of those vehicle numbers exiting from each node and later arriving at other nodes in the network from a particular approach direction where said other nodes include the nodes immediately linked to each node and nodes further downstream from each node, wherein the probability is in the form of a probability matrix; and average journey time for the vehicles to traverse each link between nodes; vehicle numbers at each node from a particular approach direction; the probability of those vehicle numbers exiting from each node and later arriving at other nodes in the network from a particular approach direction where said other nodes include the nodes immediately linked to each node and nodes further downstream from each node, wherein the probability is in the form of a probability matrix; and average journey time for the vehicles to traverse each link between nodes; artificial intelligence (Al) trained on the historical vehicle statistics arranged to predict traffic arriving at plural nodes from particular directions at a future time based on receiving a current count of vehicles numbers detected at nodes in the network, wherein the AI is trained to learn traffic patterns and predict the probability matrix and wherein the prediction of traffic arriving at a particular downstream node from a particular direction is based on combining the current vehicle numbers with the probabilities from the probability matrix that those vehicle numbers will take a route to arrive at the particular downstream node from the particular direction and using the average journey times of detected vehicles to calculate a predicted arrival time for the vehicles at that downstream node, wherein the calculation is performed for each upstream node in the network to build up a profile of expected number of vehicles arriving at the downstream node at various times from each direction, and wherein the profile is built up for each node in the network; and based on the prediction, determining a control plan for the traffic lights at the junctions to optimise the flow of traffic ahead of the predicted arrival of the vehicles at the nodes corresponding to those junctions and transmitting control signals to change the traffic light timing to implement the plan to the traffic lights at the junctions.