Designing Emergency Vehicle Detection System with Wireless Bluetooth Based on Arduino

Congestion is a problem that arises because of the many vehicles on the road. One of the bottlenecks that is often encountered is at a crossroads. From several vehicles, there are emergency vehicles that must be prioritized. Therefore, this research develops a system using Bluetooth technology that gives priority to emergency vehicles to cross the junction. This system works is the first Bluetooth module before the junction with a distance of 500 meters detecting it with autopairing and autoconnect on the Android Bluetooth that has been made. This system uses Arduino Mega as its data processor to make the traffic lights output freeze to the green light, then the traffic light will return to normal when the second Bluetooth module after the junction with a distance of 100 meters detects Android Bluetooth. The results of the study are very helpful when the lane is jammed, but for empty lines this system has a maximum limit at speeds not exceeding 81.43 km/h because the detection distance limit of the Bluetooth connection is only 50 meter

Network Maintenance and Capacity Management with Applications in Transportation

abstract: This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities. This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule. Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201