Insights from computational modelling and simulation towards promoting public health among African countries

One of the problems associated with some African countries is the increasing trend of road mortality as a result of road fatalities. This has been a major concern. The negative impacts of these on public health cannot be underestimated. An issue of concern is the high record of casualties being recorded on an annual basis as a result of over-speeding, overtaking at dangerous bends, alcohol influence and non-chalant attitude of drivers to driving. The aim of this research is to explore and adapt the knowledge of finite state algorithm, modeling and simulation to design and implement a novel prototype of an advanced traffic light system towards promoting public health among African countries. Here, we specify and built a model of an advanced wireless traffic control system, which will help complement existing traffic control systems among African countries. This prototype is named Advanced Wireless Traffic Control System (WPDTCS). We developed this model using an event-driven programming approach. The technical details of the model were based on knowledge adapted from the Finite State Automation Transition algorithm. It is expected that the AWTCS will promote the evolution of teaching in modeling, simulation, public safety by offering trainees an advanced pedagogical product. It will also permit to strengthen the collaboration of knowledge from the fields of Computer Science, Public health, and Electrical Engineering. Keywords: public health, public safety, modelling , simulation, pr

Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high

Optimization of traffic light control system of an intersection using fuzzy inference system

This paper considers an automated static road traffic control system of an intersection for the purpose of minimizing the effects of traffic jam and hence its attendant consequences such as prolonged waiting time, emission of toxic hydrocarbons from automobiles, etc. Using real-time road traffic data, a dynamic round-robin allocation of right-of-way to road users based on fuzzy inference system (FIS) was implemented as a decision support tool. The static phase scheduling algorithm for traffic light systems was used as a benchmark to measure the performance of our technique which is based on dynamic phase scheduling algorithm. The performance comparison records a significant improvement of about 65.35% in average waiting time. This clearly demonstrates the efficacy and potential of our solution strategy to address the traffic scheduling problem.Keywords: Fuzzy Logic; Traffic Control Systems; Dynamic Phase Scheduling; Static Phase Scheduling, Fuzzy Set

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page

Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end  collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed