BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

Abstract

This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV.

Machine Learning Use-Cases in C-ITS Applications

“…In their simulation, the proposed approach could reduce the travelling distance by 3 times with negligible computing time [122]. In their another publication [123], a Bat Algorithm-Based Convolutional Neural Network (BA-CNN) is proposed. Bat is one of the most powerful optimisation algorithm that is inspired by the echolocation behavior of bats proposed by Yang and Gandomi [124].…”

“…Bat is one of the most powerful optimisation algorithm that is inspired by the echolocation behavior of bats proposed by Yang and Gandomi [124]. The BA-CNN is very effective and can outperform existing methodologies [123].…”

“…The research reported in this paper is part of project no. BME-NVA-02, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from [109] 2020 Congestion detection using public cameras CNN Gatto and Forster [110] 2021 Congestion detection using audio features RF Leung et al [111] 2020 Trolley bus delay prediction Fuzzy-logic Wu et al [112] 2019 Urban railway delay prediction C-LSTM+Encoder-Decoder Zhao et al [116] 2019 Real-time traffic forecasting t-GCN Zhao et al [119] 2019 Routing management Q-Learning Hussein et al [122] 2022 Ambulance vehicle routing DNN Hussein et al [123] 2022 Ambulance vehicle routing BA-CNN Nallaperume et al [125] 2019 Smart traffic control Q-learning Hussein [122] investigates how machine learning can help find the shortest travelling time for ambulance vehicles (AV). In their first ML based approach, they developed a neural network based model that can suggest optimal travelling path based on information about the accident, the injured patients and on position describers.…”