The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints / rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.

Intelligent traffic light control using distributed multi-agent Q learning

An Inverted Ant Colony Optimization approach to traffic

Traffic congestion in the urban area occurs more frequent than the past due to rapidly increasing on road vehicle usage rates. It could seriously hinder the development of urban area if a well management system has not being established. These scenarios necessitate the development of advance traffic management systems to increase the performance of signalized intersection. Traffic signal timing management (TSTM) system which comprise of genetic algorithm based optimization is proposed. Using a proper TSTM system, network traffic flow can be improved with considerably less cost than other infrastructural improvements. The proposed genetic algorithm based optimization approach allows signal timing parameters such as offset, cycle time, green split and phase sequence to be optimized with objective of minimum delay and better traffic fluency. The proposed GATSTM system has the ability to handle and manage the dynamic changes of the traffic networks condition by calibrating the system parameters accordingly.

Multiple intersections traffic signal timing optimization with genetic algorithm

The increased urbanization and the drive for realizing smart cities have motivated extensive research and development in the realm of Intelligent Transportation Systems (ITS) in order to deal with the increased traffic intensity. Yet, the scope of ITS is not limited to vehicles, as pedestrians are special road users that play an important role in affecting traffic, road infrastructure, and vehicle design. Pedestrians are deemed to be the most vulnerable road users and are the major sufferers of road-incident fatalities and injuries each year. Therefore, quite a few studies have focused on pedestrian’s support and safety. On the other hand, due to imperceptible behavior, a pedestrian may also negatively impact traffic efficiency and can thus be viewed as an obstacle for fully realizing the advantages of ITS. In addition, more issues related to pedestrians have been raised with the emergence of Autonomous Vehicles (AVs). In this paper, we discuss different issues related to pedestrians as road users. Furthermore, we provide a comprehensive survey and classification of the different solutions of pedestrian protection. Finally, we highlight technical gaps and point out possible future research directions.

Pedestrian support in intelligent transportation systems: challenges, solutions and open issues

Vehicular traffic noise prediction using soft computing approach.

Summary The increase in urban traffic has resulted in traffic congestions, long travel times and increase hazards to pedestrians due to inefficient traffic light controls. These scenarios necessitate the use of new methods in the design of traffic light control for vehicles and pedestrian crossings. In a conventional traffic light controller, the traffic lights change at constant cycle times which are clearly not optimal. The preset cycle time regardless of the dynamic traffic load only adds to the problem. It would be more feasible and sensible to pass more vehicles at the green interval if there are fewer vehicles waiting behind the red lights or vice versa. We apply the genetic algorithm technology in the traffic control system and pedestrian crossing to provide intelligent green interval responses based on dynamic traffic load inputs, thereby overcoming the inefficiencies of the conventional traffic controllers. We apply such technology to a four-way, two-lane junction based on two sets of parameters: vehicles and pedestrians queues behind a red light and number of vehicles and pedestrians that passes through a green light. The algorithms dynamically optimize the red and green times to control the flow of both the vehicles and the pedestrians. To represent a typical traffic flow system, we use the Cellular Automata for modeling vehicular motion behind the traffic lights. We developed an algorithm to model the situation of a four-way two-lane junction based on this technology. We compare the performance between the genetic algorithms controller and a conventional fixed time controller and the results show that the genetic algorithms controller performs better than the fixed-time controller.

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Jalan Kajang-Puchong

Papers

The Use of Genetic Algorithm for Traffic Light and Pedestrian Crossing Control