Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups—for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.

Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world.

This paper presents a detailed design of an on-grid PV system that meets the electrical needs of a typical domestic building in the southern corner (i.e. Khulna) of Bangladesh. The system comprising of the photovoltaic array to capture solar energy, a power converter to change over between AC and DC, grid connection and lead acid battery to store energy. The modelling is completed by assessing the required load, choosing and deciding the proper specifications of the components associated with the system. Different factors, for example, the geographic area, atmospheric condition, solar irradiance and load consumption upon which the whole work depends are all considered.  The cost optimization of the system is performed as per the system’s net present cost, cost of energy, operating expense and initial capital. Additionally, an efficient algorithm to manage the system energy along with power flow is proposed. The techno-economic analysis of the proposed system is performed by using HOMER simulation software. Simulated results indicate that the proposed model meet the load demand and show tasteful execution.

This paper focuses on research works of control engineering field and aims at impenetrable security system especially in case of medication, jewelry, documents & others valuable items and mandatorily in the higher intelligence agency. Here, a developed security system with automatic sensing is introduced by the use of both Radio frequency identification (RFID) card tagging system and fingerprint sensing biometric security system to maintain the valid access of a person to a secured place. RFID reader and fingerprint sensing device work as a locker of the security and RFID tag and a validly ratified finger is considered as the key of the locker. In case of access granted entity, door bar gets opened with a servo mechanism system connected with door bar. On the contrary, no action is taken as cavalcade if the entity is considered invalid in the sensing system. These knock out the necessity for keeping track of keys or remembering a combination of password or pin. A prototype of the security system is also designed and the performance of it is tested. The satisfactory results of its performance show the validity of the system and indicate a better solution for the future security system.

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