Bus Type Research Articles

Reconfiguration of a Bus Chassis Module Using the Digital Expression for Connectivity between Module Interfaces

Modular design for a bus chassis is a practical solution to address different bus types and their differing powertrains. Physics interfaces are used to connect interacting subsystems in bus chassis modules. There are few studies focusing on the criteria for assessing interface connectivity, with significantly non-standard characteristics, between bus chassis modules. This study proposes criteria for assessing the interface connectivity in bus chassis modules in accordance with the digital expression of the module interface. Criteria for judging the compatibility of the connection between bus chassis modules are proposed and modular reconfiguration methods are provided, based on the criteria. Reconfiguration of the process matrix is constructed to indicate changes in position, shape, and size after the component is modified, based on the connectivity between modules. Finally, the proposed method is applied to the reconfiguration and replacement of the front suspension frame module of a bus chassis. The results demonstrated and proved the effectiveness of this method.

Application of Spearman’s Method for the Analysis of the Causes of Long-Term Post-Failure Downtime of City Buses

The aim of this study was to analyze the impact of the categories of bus downtime caused by the necessity of their repair on the availability indicator. A retrospective study covering 12 years of bus operation was carried using the selected municipal transport company as an example. The share of the selected categories of downtime in the total time of vehicle downtime was analyzed. The Spearman’s rank correlation coefficient was used for the analysis. The research covered seven makes and types of buses and six categories of downtime. A ranking of the downtime categories was developed according to the criterion of the statistical dispersion of stoppage time as a measure of their unpredictability. It was shown that categorized unavailability was strongly dependent on the rank of downtime and loosely dependent on the age of buses. In practical applications, the test results may be useful in improving the fleet maintenance processes, selecting the make of new buses or development of models for safety margin availability.

Passenger flow prediction in bus transportation system using deep learning.

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

Universal Transparent Artificial Neural Network‐Based Fault Section Diagnosis Models for Power Systems

AbstractFault section diagnosis (FSD) is significant for the power system dispatching. Artificial neural network (ANN)‐based FSD method has strong fault tolerance but it looks like a black box and lacks the interpretability to the diagnosis outputs. In addition, when the topology of power systems changes, the ANN structure needs to be reconstructed and retrained, and thus has low adaptive capability. In order to tackle these challenges, in this paper, an ANN‐based FSD method by constructing universal transparent diagnosis models is proposed. The diagnosis models are constructed for the transmission line, transformer, and bus types rather than for a specific power system section. They can express the logical relations among sections, protective relays (PRs) and circuit breakers (CBs) clearly and intuitively. In addition, fuzzy values are used to model the uncertainties of PRs and CBs, and to determine the inputs of diagnosis models. Furthermore, the differential evolution algorithm is employed to optimize the network parameters of diagnosis models. The proposed method is verified on the IEEE 30‐bus test system and an actual local power system in Jilin Province of China.

Pathways to achieve future CO2 emission reduction targets for bus transit networks

Apart from electric vehicles, emissions targets for 2025 and 2030 in the heavy-duty transportation sector could be achieved with hybrid powertrains. Moreover, alternatives such as the use of synthetic or e-fuels may also offer a feasible path for transport decarbonization. This work explores different pathways to reduce CO₂ emissions considering the city of Valencia as a case study. The 10 most used bus lines operating in the city are evaluated using their GPS based vehicle speed information with 0D GT Suite simulations. First, the hybridization level for the share of buses was varied from 0 to 100% and the number of different bus types operating in each line was optimised for minimum CO2. Next, the battery and E-motor sizing is optimised for each bus line. Further, an assessment was done assuming 100% electrified fleet, with the 2030 and 2050 electricity generation CO2 footprint projections. Moreover, the potential of e-fuels in the current fleet is also evaluated. The results show that to meet the 2050 target, 100% electrified fleet (with 2050 electricity mix) as well as using e-fuels (generated from renewable sources) in the current fleet are feasible options. However, the e-fuel pathway is more economical than 100% electric fleet.

Economic, Ecological and Social Analysis Based on DEA and MCDA for the Management of the Madrid Urban Public Transportation System

The aim of this research is to help public transport managers to make decisions on the type of buses that should compose their public transport fleet, taking into account economic, environmental and social criteria from the point of view of sustainability. This paper fills a knowledge gap by including the social dimension of sustainability in addition to the economic and environmental dimensions. The original nature of this study lies in analyzing complementarities in the structuring of an efficiency and multicriteria problem. Our research analyzes Madrid public bus system data; the problem is structured in a comparative way between two analytical methods, a Data Envelopment Analysis (DEA) and an ELimination Et Choice Translating REality (ELECTRE) III. Our research results show that two main groups of vehicles could play a part in part the theoretical solution. The main conclusions of this research are that (a) plug-in and induction electric vehicles are not comparable to GNC and diesel–hybrid vehicles in terms of cost, pollution and service; and (b) the ELECTRE III model provides more information in solving this problem than the DEA model.

Behavior and Non-Behavior Predictors of Seat Belt Use amongst Iranian Bus Passengers

Background: No behavior as much as the use of a seatbelt affects the reduction of the severity of injuries caused by driving accidents. So far, there has not been sufficient evidence of predictive factors regarding the use of seatbelts in bus passengers. The purpose of this study was to determine the predictors of seatbelt use in passengers of intercity buses. Methods: In this cross-sectional study, 458 passengers of intercity bus terminals participated. The sampling method was multistage in this study. To collect data, a researcher-made questionnaire whose validity and reliability were confirmed was used. The collected data were analyzed using SPSS software version 22 and logistic regression test. Results: The study population included 47% of men and 53% of women with an average age of 28.35 ± 6.1. Police stations, high bus speeds, night, and inappropriate weather conditions were among the situations in which the use of a seatbelts was more on the bus. The results of the logistic regression test in the third model showed that perceived barriers, law and supervision, subjective norms, environment, and bus type predict the use of seat belts significantly (P <0.000). Conclusions: It seems that in addition to interventions designed to increase the knowledge and attitude of passengers and enhance the use of seatbelts on the bus, a special look is also needed at important people who influence the behavior of the people (subjective norms).

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity.

Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.

ENGINEERING AND PLANNING SOLUTIONS OF BUS STOPS OF TRANSPORT ON THE EXAMPLE OF KYIV

The purpose of the study is to conduct an analysis of foreign and domestic experience regarding planning decisions of bus stops and to identify their influence on the formation of transport interchanges. The article presents the generalized results of the current state of engineering and planning decisions of bus stops in the city of Kyiv and reveals the factors of influence of spatial planning decisions on the formation of transport and transfer nodes in the urban environment. The areas of improvement and development of the functional-spatial organization of solid waste are considered. The article is aware of its purpose, namely, it presents the generalized results of the state of planning decisions of bus stops in the city of Kyiv, determines the influencing factors on the formation of transport interchanges. Analysis of the current development of bus stop planning solutions in the capital made it possible to form a classification and distinguish types of bus stops according to four classification features: functions, volume of passenger traffic, location of bus stops, and degree of modality. The study of the spatial configuration of solid waste on the basis of bus stops made it possible to identify the following types of solid waste: the solid waste zone is located at an intersection; the solid waste zone is formed at a T-shaped intersection; the solid waste zone, which consists of stopping points, placed sequentially; solid waste zone, which contains stopping points located in parallel; combined type of solid waste. The analysis and research of the current engineering and planning solutions of bus stops in the city of Kyiv revealed that the formation of solid waste in the urban environment mainly takes place on the basis of railway stations, airports, bus stations, which receive and send external passenger flows directed in city.

Trendovi i perspektive primene autobusa na električni pogon u gradovima Evrope i sveta

The electric bus (E-bus) is a new concept of environmentally friendly and energy-efficient bus subsystem of public urban transport, which is increasingly present in many cities around the world. The introduction of this type of bus has a steady upward trend and is a major alternative to conventional buses. The paper gives a brief chronology of the most important events related to the application and introduction of electric buses around the world, the current state of the number of vehicles in operation, an overview of the electric bus market, and future directions of further development and implementation of this new concept in public transport, with a special emphasis on Europe.

Interior Heating and Its Influence on Electric Bus Consumption

This paper focuses on the statistical evaluation of various operating characteristics of electric buses. The data obtained for statistical evaluation come from practice. In this paper, we focus on electricity consumption—an important aspect of electric bus operation. The ambient temperature significantly affects electricity consumption. In this paper, we use applied mathematics—correlation analysis, we accurately identify the effect of temperature on the consumption of the electric bus. Our next goal was to define the relationship between the loss of energy from the battery and driving power. We used regression analysis to describe this relation. Our article also includes an example of the practical use of ANOVA analysis in identifying a statistically significant effect of a particular vehicle on average consumption. We also show results from previous research and compare two different types of electric buses in operation.

APPLICATION OF PHOTOMETRY TO UNDERSTAND THE BEHAVIOUR OF GEOSTATIONARY OBJECTS ON ORBIT

One of the challenges of satellite characterization is the ability to not only determine the spacecraft or-bit, but also satellite operating status, orientation, size, bus type, and material properties. Positional observations allow us to determine and/or update orbital elements of satellites, but they do not afford an insight into the behaviour of a satellite in orbit. The article discusses the results of solving the inverse problem of astrophysics. How you can use photometric, astrometric information about a satellite, its lighting conditions, supplemented by additional information, to understand the behavior of a satellite in orbit. The results are shown using examples of four satellites in geostationary orbit. An algorithm for calculating the photometric and dynamic characteristics of geostationary objects is provided.

Towards a sustainable road transportation system in urban areas: A case study of Owerri, Nigeria

Urban Areas in almost all cases develop outwards from the centre. Public facilities (schools, offices, markets) are usually located at about the centre while residential abodes radiate outwards. Such development pattern necessitates road transportation movement to the centre. This paper examines strategies that can be adopted to achieve the necessary movement in a sustainable manner for these urban areas, with Owerri Nigeria as a case study. A field study was conducted to ascertain the total passenger requirement, number and mix of passenger vehicles as well as measure three main road transport-induced air pollutants. The result of the field work showed existing commuter vehicles mix of 56.2:63.7: 19.6:1.6:1 for salons, wagons, mini-buses, coaster buses and big buses respectively, of a total of 85,950 vehicles. The survey also revealed ambient air pollutants level higher than the recommended standards. A new model was developed to achieve a vehicles remix of 10:33:53: 14:1 of same vehicle types and reduction in traffic volume and target air pollutants. The analyses showed that mini-buses and coaster buses have advantage over salon cars, wagon vehicles and big buses in terms of traffic congestions and pollutants release into the environment. The two bus types have least pollutants release per passenger carried. An optimal vehicle remix, which gives higher priority to these buses has been shown to reduce congestion by 40%, Carbon monoxide by 40%, Nitrogen Dioxide by 50% and Methane by 50%. Based on the findings, it is recommended that vehicular remix of 10:33:53:14:1, for salon: wagon: mini-buses: coaster buses: big buses be adopted for Owerri commuters’ transportation need. Some policy measures were put forward to help achieve this. The measures include an outright ban on use of low-passenger-carrying vehicles for commercial purposes.

Management of road transport-induced air pollution in urban areas: A case study of Owerri, Nigeria

Environmental concerns have continued to be on the increase as human activities soar. One such environmental concern is air pollution. A major contributor to air pollution is the road transportation activities. This paper sets out to provide a management strategy for air pollution owing to road transportation in urban areas, with Owerri Nigeria as a case study. A field study was conducted in Owerri to ascertain the total passenger requirement, number and mix of passenger vehicles as well as measure three main road transport-induced air pollutants at five locations in the city. The result of the field work showed existing commuter vehicles mix of 56.2:63.7: 19.6:1.6:1 of salons, wagons, mini-buses, coaster buses and big buses respectively, of a total of 85,950 vehicles and air ambient pollutants level higher than the recommended standards. A new model was developed to achieve a remix of 10:33:53: 14:1 of same vehicle types and reduction in traffic volume and target air pollutants. The analyses show that mini-buses and coaster buses have advantage over salon cars, wagon vehicles and big buses in terms of traffic congestions and pollutants release into the environment. The two bus types could be said to have least pollutants release per passenger carried. An optimal vehicle remix, which gives higher priority to these buses have been shown to reduce congestion by 40%, Carbon monoxide by 40%, Nitrogen Dioxide by 50% and Methane by 50%. It therefore recommends that vehicular remix of 10:33:53: 14:1, for salon: wagon: mini-buses: coaster buses: big buses be adopted for Owerri commuters’ transportation need. It concludes that governments should adopt economic instruments embedded in a “push and pull” strategy, leveraging on disincentive and incentive measures to skew road transportation to the use of mini and coaster buses as a deliberate means of reducing air pollution in cities.

Assessment of the Economic Efficiency of the Operation of Low-Emission and Zero-Emission Vehicles in Public Transport in the Countries of the Visegrad Group

Transport is one of the key sectors of the European economy. However, the intensive development of transport caused negative effects in the form of an increase in the emission of harmful substances. The particularly dramatic situation took place in the V4 countries. This made it necessary to implement solutions reducing emissions in transport, including passenger transport. Such activities can be implemented in the field of implementation of low-emission and zero-emission vehicles for use. That is why the European Union and the governments of the Visegrad Group countries have developed numerous recommendations, communications, laws, and strategies that order carriers to implement low- and zero-emission mobility. Therefore, transport organizers and communication operators faced the choice of the type of buses. From an economic point of view, each entrepreneur is guided by the economic efficiency of the vehicles used. Hence, the main aim of the article was to conduct an economic evaluation of the operational efficiency of ecological vehicles. As more than 70% of vehicles in use in the European Union are still diesel driven, the economic efficiency assessment was also made for vehicles with traditional diesel drive. To conduct the research, the method of calculating the total cost of ownership of vehicles in operation was used. As a result of the research, it was found that electric buses are the cheapest in the entire period of use (15 years), and then those powered by CNG. On the other hand, the cost of using hydrogen buses is the highest. This is due to the high purchase prices of these vehicles. However, the EU, as well as the governments of individual countries, support enterprises and communication operators, by offering them financing for investments. The impact of the forecasted fuel and energy prices and the planned inflation on operating costs was also examined. In this case, the analyses showed that the forecasted changes in fuel and energy prices, as well as the expected inflation, will significantly affect the costs of vehicle operation and the economic efficiency of using various types of drives. These changes will have a positive impact on the implementation of zero-emission vehicles into exploitation. Based on the analyses, it was found that in 2035 hydrogen buses will have the lowest operating costs.

A power flow adjustment strategy based on weak bus identification and new bus type conversion

The power flow solvability is greatly challenged by the growing pressure on power grid induced by the rapid development of economy. The approaches of restoring power flow solvability should be profoundly studied. Most of adopted power flow adjustment methods are based on manual interference which require extensive human resources with inefficiency. Thus, a power flow adjustment strategy based on a weak bus identification and a new bus type conversion is proposed. A weak bus identification index is defined and expressed when power flow has no solution. We propose a novel bus type and a concept of the power shortage to restore power flow solvability. The generator action set is determined by calculating the left eigenvector of the Jacobian matrix. At last, we establish a linear programming model to determine the generator output adjustment. Numerical results are obtained in 3 test systems and an actual system in China. Compared with other methods, the proposed method is verified to have a higher success rate of restoring power flow solvability and has robustness in various scenarios.

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organisational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customised factors that may help measure and offer practical solutions for decreasing the level of bus drivers' MWL in real-world road driving. Practitioner summary Mental workload is affected by several contributing factors. Depending on the working context, some of these contributing factors have a more significant influence on the level of the experienced MWL. Therefore, the main factors influencing the MWL of BRT and conventional bus drivers were assessed in their real-life environment. Abbreviations: MWL: mental work load; BRT: bus rapid transit; CB: conventional bus; DALI: driving activity load index; NASA-TLX: NASA task load index; SWAT: subjective workload assessment technique; EEG: electroencephalography electrocardiogram; fNIRS: functional magnetic resonance imaging; ITS: intelligent transportation systems; AVL: automated vehicle location

Influence of bus stop land use characteristics on passenger waiting time satisfaction ‐ A case study in Guangzhou

Researchers usually conduct a questionnaire survey at bus stops to obtain data regarding the satisfaction of bus passengers with waiting times. The results are affected by many factors. Among them, the land use of the bus stops was proved to have an important impact on the survey results. The main contribution of this paper is the introduction of propensity score matching (PSM) into the evaluation of passenger waiting time satisfaction. By eliminating interference factors, this paper can quantify the impact of the various land use types of bus stops. On this basis, a method to modify the survey results of passenger waiting time satisfaction is proposed. This paper takes data pertaining to passenger satisfaction with bus service quality in Guangzhou City in 2018–2019 as an example, and the findings include that: different land use types of sites have different effects (positive or negative) on passenger waiting time scores. Also, matching propensity scores can balance the distribution of covariates between the treatment and control groups, effectively excluding the interference of other factors. After correcting the original rating results, this study found that 61.84% of the routes' waiting time ratings were overestimated. This finding indicates that data correction is necessary to accurately identify passenger waiting time ratings. The waiting time satisfaction of people using residential stations can best reflect the actual level. Therefore, it is suggested that stations with residential land use in each administrative area should be taken as representatives to conduct a waiting satisfaction survey.

Efficiency evaluation of the microgrid for selection of common bus using copula function-based efficiency curves of the converters

Due to the increase in load growth, construction of renewable energy-based microgrids (MG) has been seen as the most economical and viable solution in the recent past. It leads to the need for feasibility investigation at the planning and design stage for appropriate selection of common bus (CB) type. One of the critical factors to determine the suitable CB is efficiency analysis. This paper proposes an efficiency evaluation method by using the copula function-based efficiency curves of the converters. The degree of association between percentage loading and efficiency of the converters is defined using best-fitted copula estimation, and a large number of iterations are performed using Monte Carlo simulations to generate the efficiency for each time instant. A variety of scenarios are simulated to determine when and which type of CB is most favourable. The efficiency of the MG is strongly dependent on the converter efficiency, number of converters in the MG, and seasonal variations, both in generation and load demand. The results indicate that DC CB has superior performance with the net efficiency gain ranging from 8.26% to 5.63% in all scenarios considered in this study. Moreover, DC CB is most advantageous in the MG with the DC load only. It will bring greater advantages in the future with the continuous development of renewable energy-based MGs and the increasing penetration of DC loads.

Emission modeling for new-energy buses in real-world driving with a deep learning-based approach

Nowadays, new energy bus is gradually replacing those with diesel engines with its better environmental protection characteristics. As one of the main types of new energy buses, liquefied natural gas (LNG) bus still emits a large number of pollutants as well as greenhouse gases. Consequently, the primary objective of this study is to investigate how the deep learning approach contributes to emission prediction by leveraging emission datasets of LNG buses. This study uses datasets of LNG buses measured in Zhenjiang, China to illustrate the procedure. A deep learning framework based on the gated recurrent unit (GRU) capturing both time dependence and external factors including speed, acceleration, dynamic passenger load, and road grade is proposed for real-time emission rates modeling. The results suggest that the predicted values are very close to the true values except that the estimation errors are relatively large when the measured emission rates are near 0. Moreover, one commonly used statistical model and two state-of-art machine learning models are selected as benchmark methods to compare with the proposed model for the emission modeling tasks. The comparative analyses suggest that the proposed GRU-based emission model outperforms the benchmark approaches for different kinds of emission prediction tasks in terms of higher prediction accuracy and training efficiency. The results indicate that the proposed model has a good ability to predict exhaust emissions for LNG buses in real-world driving, which can potentially guide transportation and environmental protection engineers to select appropriate methods for monitoring urban traffic emissions.