Bus Operation Research Articles

Characterizing spatiotemporal patterns of bus bunching frequency on a bus route network: A case study of Taipei city

Bus bunching is a critical issue in bus operation management. This study investigated the impacts of potential factors affecting bus bunching on a city-wide bus route network, using Taipei City as an example with its 267 bus routes and 4741 bus stops. Frequency of bus bunching was measured on an hourly basis using Automatic Vehicle Location data. Multilevel count models based on Conway-Maxwell Poisson distributions were developed, and the spatiotemporal distributions of random intercepts and residuals were examined. The results showed that the effects of bus lane design, bus route network and operation, and traffic characteristics were consistent with those reported in prior studies. However, a quadratic relationship between bus route length, best measured by number of stops, and bus bunching was identified, which explains the conflicts between the theories of bus bunching propagation and self-repair. Bus route design often faces a dilemma between extending route length to collect additional passengers and reducing in-vehicle travel time to enhance service quality. The findings of the study could be a useful reference for bus route design to minimize bus bunching.

Optimal deployment of dynamic wireless charging infrastructure for battery electric buses – a case study in Auckland, New Zealand

ABSTRACT Inductive power transfer (IPT) systems offer a promising solution to multiple major hurdles associated with electric vehicles, such as the lack of widespread charging infrastructure, driving range anxiety, and significant charging time. With the aid of IPT systems, dynamic wireless charging enables electric vehicles to be charged when running over the IPT power pads. The objective of this study is to minimise the overall cost of charging infrastructure by optimising the deployment of IPT transmitters and the battery size required to support charging for electric buses. A mixed-integer linear programming model is proposed and solved using the commercial solver Gurobi. Bus route information and the characteristics of electric buses, such as weight, drag coefficient and frontal area, are used as input to run the traffic simulation in SUMO to get time-varying velocity and acceleration data during bus operations. A bus route in Auckland, New Zealand, is used as a case study in the presented numerical experiments. A strong correlation between the velocity of the bus and the state of charge is identified. The largest cost component is the charging pad, followed by the battery.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Performance evaluation of bus stops on arterial roads in Hanoi city

The operations of critical bus stops on arterial roads substantially influence the facility bus capacity and the bus quality of service in terms of schedule reliability. The aim of this article is to employ the methodology in the Transit Capacity Quality of Service Manual (TCQSM) to better understand the performance of key bus stops on arterial roads in Hanoi city. The obtained results shown that the capacities of most examined bus stops (Bus Stops 02, 03, and 04) are considerably lower than the actual demands of buses on the considered roads, and increasing the number of loading areas at bus stops is one of the feasible solutions to effectively improve the capacity of bus stops in Hanoi city. The results achieved provide urban transport management agencies and public transport practitioners in Vietnam with a valuable tool for bus stop capacity measurement and additional information for bus quality of service improvement

Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example

In order to enhance passenger willingness to choose buses for commuting and to reduce the operating costs and tailpipe emissions of bus companies, a bus scheduling model is established. The model aims to minimize the sum of the operating costs of the bus company, the costs associated with the loss of passengers’ interest, and the cost of tailpipe emissions. It considers constraints such as maintaining an average load factor of the buses above 60%, ensuring a departure interval of greater than 5 min during non-peak hours and less than 30 min during peak hours, and limiting the maximum number of buses allocated to a route. The passenger flow is divided into peak hours and nonpeak hours according to the survey of passenger flow during each period of a bus operation on a route in Nanjing City, China. A genetic algorithm is employed to solve the proposed bus scheduling model and determine the total costs during peak and non-peak hours. After designing the parameters of the genetic algorithm, optimal departure intervals and bus numbers for a day’s operation cycle on a given route are calculated using a weighting method.

Green-Powered Electric Public Mobility: Integrating Urban and Interurban Routes—A Case Study Analysis

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analyzed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic (PV) plant distributed on some modular shelters which serves to power the same routes, is carried out. The resulting system has a power of the order of 190–200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated.

Coping with unforeseen circumstances: the COVID-19 crisis and bus contract renegotiation in Sweden

In principle, competitively tendered contracts in Swedish public bus transport are not supposed to be renegotiated during the contract period. In practice, it happens quite frequently. The need for renegotiations became evident during the COVID-19 crisis. This external shock to the system was unusually strong. Demand for bus services dropped abruptly, affecting revenues of procuring authorities and, depending on contract type, also bus operators. To make the situation even worse, costs typically remained constant, as frequent services were supposed to be upheld to avoid the spreading of the virus in crowded buses. Contract renegotiations during the COVID-19 crisis were clearly caused by unexpected exogenous events. Contracts with passenger incentives, linking a substantial part of compensation to the number of passengers, have been more likely to be renegotiated than gross-cost contracts, which were easier to adjust to the drop in demand. Typically, renegotiations were triggered by operators. While being a pragmatically rational response to an extreme situation, the renegotiations and their outcome may not in all cases have been compliant with the legal framework for public procurements, even when taking into account the exemptions made possible by the framework. In cases where passenger incentive contracts have been transformed into gross-cost contracts it could be argued that the nature of the contract has been changed. A new tender should then probably have been performed instead, although this has not been tried in court. The COVID-19 crisis could have long-term impact on contracts and procurements in the industry, affecting how effective competition will be in future tenders. For now, gross-cost contracts seem to be the norm, and it is possible that passenger-incentive contracts will become less common overall. One way to handle unexpected events with profound impact could be to include so-called threat-points that trigger renegotiation of a contract.

Prediction of full-scale crashworthiness performance based on component-level testing: application to bus structures

The growing interest in vehicle crashworthiness necessitates efficient methods to predict full-scale crash performance from component-level testing. This study introduces a framework for predicting vehicle crashworthiness using component-level tests. The framework employs a scaling procedure incorporating factors like total energy, deformation mode, and vehicle dimensions. The feasibility of this method was validated both experimentally and numerically in a study on the rollover performance of paratransit buses, utilizing two decades of data from full-scale and component testing of six buses. Wall-to-floor connections, prone to plastic deformation during rollovers, were tested for component bending. A rollover performance index was developed as a scaling procedure, establishing a clear correlation between full-scale and component tests. Additionally, extensive finite element simulations confirmed the framework applicability to numerical simulations, demonstrating its practical use in vehicle crashworthiness evaluations to foster resource-efficient design and reduce dependence on full-scale tests.

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.

Methods and Techniques Supporting Energy and Media Savings in Maintenance of Public Transport Buses—State of the Art and Recommendations

In the article, the authors discussed the topic of energy and media savings in a public transport company. The article is of a review nature, referring to 100 sources, including scientific papers, books, conference proceedings, and websites. In the first part, a detailed literature review on environmental protection problems in road transport and methods of solving them was conducted. Subsequently, the authors reviewed the literature content on maintenance as a pro-environmental activity in transport companies. The great accent was paid to the problem of saving energy and media in the maintenance of public transport buses. Based on the literature and knowledge, the authors proposed the possibilities of conducting a rational method of managing the operation and maintenance of buses from the point of view of environmental protection, based on the strategy of predictive bus maintenance.

Collaborative vehicle-crew scheduling for multiple routes with a mixed fleet of electric and fuel buses

Since some fuel buses have not yet reached their retirement age, many cities still have a mix of fuel buses and electric buses (EBs) operating. This study focuses on multiple bus routes with fuel-electric mixed fleet. To address operation problems arising from separately scheduling vehicles and crews (known as the two-stage method) of each route, we propose a collaborative approach to optimize the vehicle dispatching plan and crew scheduling plan. Initially, we establish a nonlinear programming model by minimizing the overall operating costs of bus companies, encompassing vehicle purchase costs, EB charging costs, fuel bus refueling costs, and driver labor costs. Next, we devise an improved simulated annealing algorithm to solve the model. The proposed method is validated through a case study involving three real-world routes. A comparison with the traditional two-stage method reveals promising outcomes. Results indicate that the proposed method can decrease the number of vehicle changes by 21.43 %, driver labor costs by 2.04 %, and daily operating costs for the bus company by 0.82 %.

Reliability modeling of [formula omitted]-out-of-[formula omitted]: [formula omitted] balanced systems with common bus performance sharing

This study examines the reliability of k-out-of-n: F balanced systems incorporating common bus performance sharing. The system comprises m sectors, with each sector containing n components. Each component has multiple performance levels to fulfill random demands. All components are interconnected through a common bus, enabling the surplus performance from certain components to be transmitted to deficient components via the bus. The system remains balanced as long as the difference in the number of functional components between any two sectors does not exceed a threshold d. A sector fails when the number of nonfunctional components reaches k, and the entire system fails if either Kf sectors fail or the system becomes unbalanced, whichever occurs first. Existing studies primarily focus on forcing-down functional components or activating standby components as the primary means of maintaining balance, which often leads to component wastage. However, performance sharing can also achieve rebalancing in practice. This study contributes by developing reliability models for performance sharing in balanced systems and proposing algorithms to determine the optimal performance sharing policy maximizing system reliability. The universal generating function is utilized to calculate the balance probability and system reliability. Furthermore, a case study involving a wing system in a Solar-powered Unmanned Aerial Vehicle is presented to illustrate the effectiveness of the proposed methodology.

Life cycle thinking-based analysis of diesel and electric-powered buses for Canadian transit systems

Increasing greenhouse gas emissions from the conventional fleet of diesel buses has made Canadian transit agencies explore low-emission alternative fuels. Despite electric buses showing great potential to reduce emissions during their operational phase, the transformation from diesel buses to electric buses would require in-depth analysis pertaining to their economic and social implications. Published literature highlights the importance of developing a comprehensive framework that considers multiple decision parameters over a life cycle perspective for analyzing different fuel options to replace the existing fleet of diesel buses. This paper assesses the triple-bottom-line sustainability of diesel and electric buses in different regions of Canada. Moreover, a framework is proposed to incorporate multiple decision criteria (life cycle environmental, economic, and social impacts) over different perspectives to make the best decisions for transitioning the diesel bus fleet. The results showed that the environmental performance of electric buses highly depended on the electricity grid mix. Despite diesel buses having a low cost of production compared to electric buses, most provinces showed a low life cycle operational cost for electric buses. Electric buses’ life cycle social impacts are high during their production stage, whereas diesel buses have the highest social impacts during their operational phase. Overall, electric buses have a high sustainability performance in all provinces and territories in Canada except Nunavut. The proposed framework and findings can aid policymakers and planners in implementing electric buses for public transit systems in Canada and beyond.

New perspective of environmental impact research: predicting bus exhaust emissions using the ETSformer based on collaborative perception

Accurate prediction of urban traffic exhaust emissions plays a crucial role in controlling motor vehicle exhaust pollution. Urban public transport vehicles often operate in stop-and-go traffic conditions, emitting significant exhaust pollution. Existing approaches primarily focus on assessing vehicle emissions in the past or present, which may not adequately address long-term planning needs. To enhance the accuracy of bus exhaust emissions prediction, we introduce a novel time series transformer architecture named ETSformer for forecasting future vehicle emissions. This framework integrates exponential smoothing principles to refine the transformer-based time series prediction model, utilizing historical pollutant emissions data to forecast future exhaust emissions. The experiment collects emissions data and driving status information from public transport vehicles in Zhenjiang City, Jiangsu Province, considering factors such as vehicle runtime, route, and operational state to standardize response variables and enhance model precision and consistency. Experimental results demonstrate a determination coefficient (R2) of 70.7% and a mean square error (MSE) of 41.1% for exhaust gas prediction, signifying improved accuracy and efficiency compared to other models. Applying the exhaust gas prediction model to bus operations enables advanced forecasting of future exhaust emissions, addressing current challenges and potentially reducing Carbon Dioxide (CO2) emissions by over 3% during bus operations.

ВЛИЯНИЕ УСЛОВИЙ ХРАНЕНИЯ АВТОБУСОВ В КРЫТЫХ ОТАПЛИВАЕМЫХ СТОЯНКАХ НА ИНТЕНСИВНОСТЬ КОРРОЗИОННОГО ИЗНАШИВАНИЯ КУЗОВОВ

The efficiency of the rolling stock of road transport is determined by both the efficiency of organizing transport work and the cost of ensuring the operability of vehicles. One of the current problems with the operation of buses is the corrosive destruction of their bearing structure, which leads to both a reduction in the operating time of vehicles and an increase in the operating costs associated with the overhaul of bodies. During the work, it was revealed that the main reasons for the intensive corrosion destruction of bus bodies are insufficient corrosion resistance of metals that are used in the manufacture of the body frame. The main places of occurrence of body corrosion foci are: wheel arches; rear and lower parts of the bus, which are more affected by adverse road conditions; upper and lower parts of window openings in case of seal leakage. Studies were conducted on the temperature and relative humidity of the indoor heated bus parking area in winter. Based on the results of the study, dependencies were obtained: air temperature in the indoor parking room from ambient temperature, relative air humidity in the indoor parking room from relative ambient air humidity, relative air humidity in the indoor parking room and ambient temperature. The nature of air exchange between the parking room and the environment has been established. According to the results of the study, factors contributing to the intensive corrosive destruction of bus bodies were established.

Optimization of electric bus dispatching interval considering stochastic traffic conditions

ABSTRACT Due to their efficient and environmentally friendly features, electric buses have become the key to solving urban transportation and environmental problems. However, existing studies primarily concentrate on optimizing schedules under fixed operational scenarios. This paper proposes a scheduling optimization approach to investigate the impact of stochastic traffic conditions on electric bus operations. First, a static dispatching interval optimization model is established, balancing the interests of the service and operation sides by measuring the passenger and company costs. Then, stochastic model formulas are designed considering three stochastic traffic conditions. Finally, a probabilistic evolution model is developed to improve the genetic algorithm. Based on the bus operation data in Harbin, we validate the model’s performance in enhancing operational benefits. Results reveal that the impact of stochasticity is nonlinear and more significant on passenger travel quality. Furthermore, retrofitting buses and upgrading routes are beneficial to reducing costs and improving travel experience.

Determination of GPS sampling interval for trip energy consumption estimation of electric buses: Analysis of real-world data

This paper analyzes the effect of GPS sampling interval on the performance of the longitudinal dynamic model for estimating electric bus trip energy consumption based on real-world operational data. The performance of the estimation model under different sampling intervals is primarily evaluated by the MAE, RMSE, MAPE, and probability distribution functions. It is observed that the relationships between sampling intervals and the MAE, RMSE, and MAPE are all roughly S-shaped. The estimation accuracy is similar when the sampling interval is less than or equal to 13 s. The probability distribution functions of residuals are no longer stably consistent with that of the observed trip energy consumption when the sampling interval is larger than 16 s. In addition, the adaptability of the estimation model under different sampling intervals is analyzed from the perspective of bus operation management. Results indicate that the threshold value of the sampling interval is 16 s at the current battery rated capacity of 162.3 kWh. The threshold value of the sampling interval will become smaller with the decrease in battery rated capacity and increase in daily operation mileage.

An assessment of the energy performance of battery-electric buses in hot environments

Battery Electric Buses (BEBs) are drawing increasing interest as a viable option for sustainable heavy-duty vehicles with reduced emissions. However, their widespread adoption is facing several obstacles, including significant operational limitations engendered by their driving range and the availability of charging infrastructure. Addressing these challenges requires a comprehensive evaluation of BEBs’ energy consumption (EC), accounting for factors like charging requirements, mileage, and battery specifications. This work analyzes the actual EC of four BEB types in Qatar through a real-world testing campaign involving an extensive testing program assessing the EC of the various subsystems, i.e., heating, ventilation, and air conditioning, driveline, and auxiliary, under diverse operating conditions using on-board telematics. In addition, a forecasting simulation model is developed to account for Qatar’s distinctive weather and climate conditions, providing a useful comparison with the real-world case study. Specifically, the study examines how the region’s soaring temperatures will impact on BEBs’ driving range, while the comparative analysis presents a fascinating exploration of the disparities between the actual on-road scenario and the meticulously simulated environment.

Engine Oil Degradation in the Real-World Bus Fleet Test Based on Two Consecutive Operational Intervals

The literature on the subject and the results of numerous research experiments indicate that single replacement cycles do not reflect the actual state of oil quality in the context of its degradation. Monitoring the operational quality of the oil in several successive stages allows for a more accurate diagnosis of the optimal oil change time. Therefore, it was decided to investigate the relationship between two consecutive periods of changing the operating oil in an engine. Comparative tests of seven buses included in the fleet were carried out. An important division criterion was taken into account—the operation of city and intercity buses. The HDXRF instrumental chemical analysis method was used to determine changes in the content of abrasive metals, and additives in engine oils. Additionally, the oxidation, nitration, sulfonation, and soot content were assessed using infrared spectroscopy (FTIR) based on the ASTM E2414-10 standard and kinematic viscosity at 40 °C and 100 °C using a Stabinger viscometer according to ASTM D7042. The course of these changes was analyzed in terms of their dynamics. The comparative study aimed to identify trends and sources of differences between the tested oils, as well as to demonstrate the number of exceedances of limit values for the selected parameters.

Utilizing electric bus depots for public Charging: Operation strategies and benefit analysis

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs’ charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running.