Bus Network Research Articles

Study of Fixed Point Message Scheduling Algorithm for In-Vehicle Ethernet

With the rapid development of advanced driver assistance systems (ADASs) and autonomous driving technology, in-vehicle networks are facing huge challenges in real-time operation and data loss. Traditional vehicle bus network systems such as LIN, CAN, and FlexRay are insufficient to meet the real-time requirements of intelligent connected vehicles. In-vehicle Ethernet meets the requirements of high reliability, low electromagnetic radiation, low power consumption, bandwidth allocation, low latency, and real-time synchronization of intelligent connected vehicles. In-vehicle Ethernet has become one of the trends in the next generation of in-vehicle network architecture. This research focuses on the delay problem existing in the real-time data transmission process of in-vehicle Ethernet, and innovatively proposes a fixed point message scheduling algorithm (FPMS) based on time-sensitive network (TSN) technology. By building an experimental platform based on the CANoe simulation tool, the high-efficiency message transmission performance of the fixed point message scheduling algorithm was verified. Experimental results show that the fixed point message scheduling algorithm proposed in this study improves message transmission efficiency by 66%, laying a solid foundation for improving the real-time and reliability performance of in-vehicle Ethernet.

Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.

The economics of public transport electrification: The charging dilemma

The electrification of public transport has the potential to mitigate greenhouse gas emissions and support the electricity system, however this potential depends on how intraday charging interfaces with grid emissions and wholesale prices. Our paper explores these trade-offs by using boundary analysis to evaluate a least-cost tariff-based aggregate bus fleet charging strategy and comparing it to charging strategies that either minimise indirect greenhouse gas emissions or wholesale price exposure, based on the metropolitan Melbourne bus network. We develop a novel two-state fleet charging optimisation model, that allows us to perform a counterfactual analysis across 5 years of historical (2017–2021) and 5 years of modelled (2022–2026) changes to wholesale electricity prices and emission intensities in Australia's National Electricity Market, in response to the three charging strategies. We find that current Australian commercial tariff structures with high daytime volumetric usage and demand charges mute climate and wholesale market signals. By charging to minimise greenhouse gas emissions over bill savings, the fleet can achieve (on average) a further 33.4% reduction in indirect emissions but incurs a 103% increase in annual electricity bills. Alternatively, by charging to minimise wholesale price exposure, the fleet can achieve (on average) a 13.4% reduction in indirect emissions while incurring a 69% increase in electricity bills. Improving the temporal alignment of commercial electricity tariffs with wholesale prices should thus simultaneously reduce indirect emissions and wholesale electricity costs. This necessitates urgent tariff reforms to allow relevant wholesale price signals to pass through to commercial EV operators.

Integrated charging scheduling and operational control for an electric bus network

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.

A synthetic indicator for structuring resilient public transport operation

ABSTRACT This study introduces a synthetic resilience indicator as a unified analytical tool to evaluate public transport resilience, assessing performance changes in bus networks against random disruptions. This methodology measures the cumulative impact of disruptions, considering the network’s capacity for maintaining planned functionality (robustness and reliability) and recoverability (i.e., route diversity), factoring in network topology, passenger flow, and resource diversity. This paper analyzes the impact of disruptions at network and station levels by applying an index to six benchmark networks. Our findings reveal: (1) networks with high station connectivity maintain robustness and flow; (2) a clear link between flow reduction and alternate routes highlights route diversity’s value; (3) interplay between topological and flow-related network attributes underlines the need for an aggregated analytical approach. The proposed index can be used as an optimization goal during route network design; informing operators regarding the impact of design choices on future network properties.

Construction and Characteristics Analysis of the Xi’an Public Transport Network Considering Single-Mode and Multi-Mode Transferring

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.

Optimization on land development intensity of new industrial towns based on carrying capacity of multi-modal traffic network

ABSTRACT In the context of China’s new urbanization, the establishment of ‘new industrial towns (NITs)’ has emerged as a pivotal strategy for facilitating urban development. However, the current construction of NITs confronts numerous challenges, including issues such as unreasonable land development intensity (LDI) and overburdened transportation infrastructure. Urgent attention and improvement are required to address the coordination gaps between urban land use and transportation systems. This research explores the traffic carrying capacity (TCC) of NITs at a multi-modal super-network level, developing a bi-level programming model for LDI optimization and proposing an improved genetic algorithm (GA) to solve it. Taking the NIT in Laiwu District, Jinan, China as a case study area, the results demonstrate that our approach maximizes the utilization of space–time resources in both bus and car networks, particularly benefiting the latter during morning peak hours, thereby achieving a more balanced supply-demand traffic equilibrium.

Performance Comparison of Timing-Based Anomaly Detectors for Controller Area Network: A Reproducible Study

This work presents an experimental evaluation of the detection performance of eight different algorithms for anomaly detection on the Controller Area Network (CAN) bus of modern vehicles based on the analysis of the timing or frequency of CAN messages. This work solves the current limitations of related scientific literature, which is based on a private dataset and lacks open implementations and a detailed description of the detection algorithms. These drawbacks prevent the reproducibility of published results, making it impossible to compare a novel proposal against related work, thus hindering the advancement of science. This article solves these issues by publicly releasing implementations and labeled datasets and by describing unbiased experimental comparisons.

The Effect Pso/Abc, Abc/Bfo, Pso/Bfo and Cs/Bfo Hybrid Techniques on Power System Buses for Voltage Stability Margin Improvement

This paper presents and investigates new approaches inspired by nature to determine the optimal placement of UPFC for enhancing voltage stability margin of power system network. Voltage collapse phenomenon has remained an operational problem yet to be solved by power system engineers. To improve the voltage stability of the power system network, new optimisation techniques viz: PSO/ABC, ABC/BFO, PSO/BFO AND CS/BFO hybrids are investigated for their performance on power system buses for voltage stability improvement. The performance of the proposed hybrid methods are examined on the practical Nigerian 56 - bus network for voltage stability improvement. The simulation results has shown that ABC/BFO technique is better than BFO/PSO by 3.44 in improving voltage stability of margin of power system, gives additional improvement of 4.07, totalling 7.51% and lastly BFO/CS technique improved voltage stability margin of power system further by 5% totalling 12.51%. Also this research revealed that the smaller the gamma value the better the UPFC contribution to voltage stability enhancement. BFO/CS technique yielded the smallest gamma values of 0. 74532 and 0.830913 in both Nigerian practical network and IEEE – 14 bus network as shown in the analysis.

Reduction of Power Distribution System Losses by Using Novel Heuristic Algorithm

The Distribution Networks are the most widespread part of electrical power systems. Researcher has been trying to in the solutions to reduce high energy losses associated with them due to the low voltage level of the supply framework. Capacitors based techniques have been proposed to reduce these losses. These techniques minimize power losses, improve stability, and enhance the voltage profile of the distribution system under normal operating conditions. Finding optimal place and proper size of the capacitor is still a very hot area of research. In this research work, an innovative procedure based on the Archimedes optimization Algorithm (AOA) is presented to solve the optimal capacitor placement as well as sizing problem to improve the voltage profile and reduce the energy losses in the network. The suggested AOA based technique, is implemented on IEEE 33 bus network to find the optimal locations to place the capcitors in the distribution network. The performance of AOA is also compared with a standard model to verify the effectiveness of the scheme. By using AOA active power losses reduced upto 23.62% and reacrive losess by 21.86% respectively. The simulation results reveal that AOA based technique offer better results regarding the capacitor placement for voltage profile improvement of buses and significant reduction in active power losses as comapred to previous studies.

Overview of the production and qualification tests of the lpGBT

The Low-Power Gigabit Transceiver (lpGBT) is a radiation-tolerant ASIC used in high-energy physics experiments for multipurpose high-speed bidirectional serial links. Around 200,000 chips have been tested with a production test system capable of exercising the majority of the ASIC functionality to ensure its correct operation.Furthermore, specific individual qualification tests were carried out beyond the production tester limits, including radiation, multi-drop bus topology, inter-chip communication through different types of electrical links and characterization of jitter and stability of the recovered clocks.In this article, an overview of the production and qualification tests is given together with their results demonstrating the robustness and flexibility of the lpGBT.

Moth Flame Optimisation for optimal power flow in a power system with Static var compensator

It consequently becomes imperative in the current transmission networks to make full use of existing resources and to rapidly transition to renewable energy alternatives. Electrical energy end users also benefit from effective resource utilisation since it lowers the price they pay for electricity. In this work, we offer a multi-objective optimum power flow (OPF) for an integrated gearbox network where FACTS devices are present. The paper's originality lies in the use of a multi-objective function. Minimising voltage variation, power loss, and negative social welfare (NSW) are all part of the objective function. The abatement of loss and NSW guarantees the lowering of per-unit pricing of power at the consumer end resulting in improved customer satisfaction. The problem was fixed using a FACTS Static var compensator. The theory was tested using an IEEE 30 bus network. The objective function has been optimised using the Mouth Flame Optimisation Algorithm. Detailed presentations, comparisons, and analyses of the collected findings have been made.

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.

A tight compact quadratically constrained convex relaxation of the Optimal Power Flow problem

In this paper, we consider the Optimal Power Flow (OPF) problem which consists in determining the power production at each bus of an electric network by minimizing the production cost. Our contribution is an exact solution algorithm for the OPF problem. It consists in a spatial branch-and-bound algorithm based on a compact quadratically-constrained convex relaxation. It is computed by solving the semidefinite rank relaxation of OPF once at the root node of the algorithm. An important result is that the optimal value of our compact relaxation is equal to the rank relaxation value. Then, at every sub-nodes of our branch-and-bound, the lower bound is obtained by solving a quadratic convex problem instead of an SDP. Another contribution is that we add only O(n+m) variables that model the squares of the initial variables, where n is the number of buses in the power system and m the number of transmission lines, to construct our relaxation. Then, since the relations between the initial and auxiliary variables are non-convex, we relax them to get a quadratic convex relaxation. Finally, in our branch-and-bound algorithm, we only have to force a reduced number of equalities to prove global optimality. This quadratic convex relaxation approach is here tailored to the OPF problem, but it can address any application whose formulation is a quadratic optimization problem subject to quadratic equalities and ring constraints. Our first experiments on instances of the OPF problem show that our new algorithm Compact OPF (COPF) is more efficient than the standard solvers and other quadratic convex relaxation based methods we compare it with.

En-route charge scheduling for an electric bus network: Stochasticity and real-world practice

Bus electrification, with its high energy conversion efficiency and zero tailpipe emissions, has a significant impact on addressing the petroleum crisis and lowering carbon emissions in urban transportation. However, long charging times and uncertain operational risks hinder their wider adoption. This paper explores the robust en-route charge scheduling problem for electric buses to overcome uncertain energy consumption while ensuring charging accessibility. The problem is first described as a deterministic mixed-integer linear programming model that employs a time-of-use electricity price to balance the charging demand. Additionally, a robust counterpart model is formulated to account for stochastic energy. Numerical studies are designed to evaluate the proposed model in a real network, followed by a sensitivity analysis to investigate the impact of battery type, fleet composition, and depth of discharge on the charging schedule. The results show that the robust optimization model provides system feasibility against uncertainty with a comparable price and emissions.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Load Flow Assignments’ Definition from Day-Ahead Electricity Market Interconnection Power Flows: A Study for Transmission Networks

The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on system inertia, primary regulating energy and short-circuit power for the year 2030, characterized by a large penetration of these sources. The initial working point of the Italian transmission network has to be defined through load flow (LF) calculations before starting dynamical analyses and simulations of the power system. Terna 2030 development plan projections enable the estimation of active power generation and load for each hour of that year in each Italian market zone, as well as cross-zonal active power flows; this dataset differs from conventional LF assignments. Therefore, in order to set up a LF analysis for the characterization of the working point of the Italian transmission network, LF assignments have to be derived from the input dataset provided by Terna. For this purpose, this paper presents two methods for determining canonical LF assignments for each network bus, aligning with the available data. The methodologies are applied to a simplified model of the Italian network, but they are also valid for other transmission networks with similar topology and meet the future needs of TSOs. The methods are tested at selected hours, revealing that both approaches yield satisfactory results in terms of compliance with the hourly data provided.

Electric bus charging scheduling on a bus network

This study addresses the electric bus charging scheduling problem (EBCSP) within a bus network comprising multiple bus routes, depots, and a fleet of heterogeneous electric buses (EBs) operated by a public transport (PT) operator. The EBCSP aims to minimize total costs by concurrently determining the assignment of EBs to trips(i.e., EB-to-trip assignment) and their corresponding charging schedules. A partial charging policy allows for flexible charging durations, considering a published timetable consisting of trip tasks. We formulate the EBCSP as a mixed-integer linear programming model and a set-covering formulation. For small-scale EBCSPs, we propose a branch-and-price algorithm utilizing the set-covering formulation for exact solutions. As large-scale EBCSPs pose computational challenges, we develop an optimization-based adaptive large neighborhood search (opt-ALNS) method. The opt-ALNS method employs ALNS operators for EB-to-trip assignment and solves the remaining linear programming problem efficiently. To enhance the opt-ALNS process, we introduce a labeling method to assess solution feasibility and rebuild solutions. Moreover, we incorporate EB battery degradation effects into the proposed method. Finally, we assess the performance of the opt-ALNS method on real-life instances and compare it with the branch-and-price algorithm.

Does urban bus route assignment improve air quality?

Worldwide, one of the most important causes of mortality is air pollution. To solve this problem, governments have implemented policies to reduce on-road and industrial emissions. In this regard, the Barcelona city council and Transports Metropolitans de Barcelona (TMB) started implementing the Nova Xarxa de Bus (NXB) to redefine the bus network following the criteria of connectivity, efficiency, and rationality. This policy was implemented in seven phases from 2012 to 2018. In this context, this paper analyses the impact of this policy on Barcelona’s air quality using a dataset from 2008 to 2016. Using a difference-in-difference approach, we show that implementing these new routes increased air quality in Barcelona. Additionally, we show that pollution decreased in each phase analysed, especially in the air quality stations near the main roads. From our results, we can infer that an optimal bus route design can improve air quality in urban areas.

Design and Analysis of a Low Voltage Simulink Model (LVSM) of IEEE 57 Bus

The present power system is gaining momentum towards designing equivalent circuit models. The Ward and REI methods involve the admittance reduction method as well as being merged with the EMS model to derive boundary parameters, but these methods are limited and valid for a predefined condition. Therefore, it is required to design an equivalent circuit that adopts the real power system for analysis. In this paper, a new method is proposed to design a scaled-down power system model without changing the impedance of components. In this regard, a Low Voltage Simulink Model (LVSM) of the IEEE 57 bus network was designed in MATLAB/ Simulink so that it could be useful for laboratory model design purposes. The main objectives of this paper are to propose a mathematical procedure to scale down the network parameters and design a 3-phase LVSM of an IEEE 57 bus power system network within the Simulink platform. The performance of LVSM was analyzed with no-load, balanced load, and unbalanced load models. These simulation studies were validated and compared with the theoretical results to prove that the proposed LVSM modeling has good mathematical accuracy, robustness, and validity for practical model implementation.