Quantum Artificial Intelligence Supported Autonomous Truck Platooning

Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting each leader vehicle trajectory in the autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency and safety. Reliance on classical AI may not be efficient for this purpose as it will increase the computational burden for each truck in the platoon. However, Quantum Artificial Intelligence (AI) can be used in this scenario to enhance learning efficiency, learning capacity, and run-time improvements. This study developed and evaluated a Long Short-Term Memory Networks (LSTM) model and a hybrid quantum-classical LSTM (QLSTM) for predicting the trajectory of each leader vehicle of an autonomous truck platoon. Both the LSTM and QLSTM provided comparable results. However, Quantum-AI is more efficient in real-time management for an automated truck platoon as it requires less computational burden. The QLSTM training required less data compared to LSTM. Moreover, QLSTM also used fewer parameters compared to classical LSTM. This study also evaluated an autonomous truck platoon\u27s operational efficacy and string stability with the prediction of trajectory from both classical LSTM and QLSTM using the Intelligent Driver Model (IDM). The platoon operating with LSTM and QLSTM trajectory prediction showed comparable operational efficiency. Moreover, the platoon operating with QLSTM trajectory prediction provided better string stability compared to LSTM

Optimisation of Rail-road Level Crossing Closing Time in a Heterogenous Railway Traffic: Towards Safety Improvement - South African Case Study

The gravitation towards mobility-as-a service in railway transportation system can be achieved at low cost and effort using shared railway network. However, the problem with shared networks is the presence of the level crossings where railway and road traffic intersects. Thus, long waiting time is expected at the level crossings due to the increase in traffic volume and heterogeneity. Furthermore, safety and capacity can be severely compromised by long level crossing closing time. The emphasis of this study is to optimise the rail-road level crossing closing time in order to achieve improved safety and capacity in a heterogeneous railway network. It is imperative to note that rail-road level crossing system assumes the socio-technical and safety critical duality which often impedes improvement efforts. Therefore, thorough understanding of the factors with highest influence on the level crossing closing time is required. Henceforth, data analysis has been conducted on eight active rail-road level crossings found on the southern corridor of the Western Cape metro rail. The spatial, temporal and behavioural analysis was conducted to extract features with influence on the level crossing closing time. Convex optimisation with the objective to minimise the level crossing closing time is formulated taking into account identified features. Moreover, the objective function is constrained by the train's traction characteristics along the constituent segments of the rail-road level crossing, speed restriction and headway time. The results show that developed solution guarantees at most 53.2% and 62.46% reduction in the level crossing closing time for the zero and nonzero dwell time, respectively. Moreover, the correctness of the presented solution has been validated based on the time lost at the level crossing and railway traffic capacity consumption. Thus, presented solution has been proven to achieve at most 50% recovery of the time lost per train trip and at least 15% improvement in capacity under normal conditions. Additionally, 27% capacity improvement is achievable at peak times and can increase depending on the severity of the headway constraints. However, convex optimisation of the level crossing closing time still fall short in level crossing with nonzero dwell time due to the approximation of dwell time based on the anticipated rather than actual value

Optimal design and control of stationary electrochemical double-layer capacitors for light railways

The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%. The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26 - 27%

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more