Bus Operation Research Articles

An integrated model of electric bus energy consumption and optimised depot charging

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

Decarbonizing Public Transportation: A Multi-Criteria Comparative Analysis of Battery Electric Buses and Fuel Cell Electric Buses

To combat global warming, many industrialized countries have announced plans to ban vehicles powered by fossil fuel in the near future. In alignment with this global initiative, many countries across the globe are committed to decarbonizing their public transportation sector, which significantly contributes to increased greenhouse gas emissions. A promising strategy to achieve this goal is the adoption of electric buses, specifically battery electric buses and fuel cell electric buses. Each technology offers distinct advantages and drawbacks, making the decision-making process complex. This research aims to answer two critical questions: What is the optimal choice for decarbonizing the bus transportation sector—electric battery buses or fuel cell electric buses? And what are the best energy carrier pathways for charging or refueling these buses? We propose a methodological framework based on multi-criteria decision-making to address these questions comprehensively. This framework utilizes the entropy weighting and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) methodologies to rank alternative bus technologies along with energy carrier pathways. The framework evaluates a range of criteria, including economic viability, energy demand, and environmental aspects. To illustrate the framework, we considered Qatar as a case study. Our results indicate that, with respect to economic viability and energy consumption, the operation of battery electric buses is favored over fuel cell electric buses, regardless of the energy pathway utilized during both the energy production and bus operation phases. However, from an environmental perspective, operating both bus alternatives using energy from green sources provides superior performance compared to when these buses are powered by natural gas sources.

Breaking through the Key Challenges to Further Electrification of Public Transport in Beijing

In recent years, with the improvement of people's awareness of environmental protection and the increasingly serious problem of urban traffic congestion, the Beijing Municipal Government has actively promoted the development of green transportation, among which electric buses, as a low-carbon, environmentally friendly and energy-saving mode of transportation, are regarded as an important measure to improve air quality and reduce pollution emissions, which has attracted much attention. As electric buses grow, so do the operational challenges of the promoted electric buses, including high initial investment costs, inadequate charging infrastructure, technical limitations in terms of vehicle range and charging speed, and a lack of more supportive policies and regulations. This paper summarizes and analyzes the current situation of electric buses in Beijing. And proposes to solve the challenges encountered in the operation of electric buses by implementing targeted strategies. For example, optimize government financial subsidy measures and implement diversified financial support; The government supports companies to carry out technological innovation and invest in R&D to enhance electric vehicle technology; further promote the construction of charging infrastructure and improve the level of charging services; Effectively deploy battery recycling; promote the integrated development of intelligent vehicles and networking, and accelerate the construction of intelligent public transportation systems; strengthening international cooperation and resource sharing; and the development of a comprehensive policy to promote and facilitate the electrification of public transport in Beijing. Inject new impetus into Beijing's sustainable urban development.

A simulation-based optimization approach for the recharging scheduling problem of electric buses

This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO) The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.

Identification of Risk Factors for Bus Operation Based on Bayesian Network

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Bayesian inference for link travel time correlation of a bus route

Estimation of link travel time correlation of a bus route is essential to many bus operation applications. Link travel time on a bus route could exhibit complex correlation structures, such as long-range correlations, negative correlations, and time-varying correlations. This paper develops a Bayesian Gaussian model to estimate the link travel time correlation matrix of a bus route using smart-card-like data. Our method overcomes the small-sample-size problem in correlation matrix estimation by borrowing/integrating those incomplete observations from other bus routes. We first conduct a synthetic experiment and results show that the proposed method produces an accurate estimation for correlations with credible intervals. Next, we perform experiments on a real-world bus route with in-out-stop record data; results show that both local and long-range correlations exist on this bus route. Finally, we demonstrate an application of using the estimated covariance matrix to make probabilistic forecasting of link and trip travel time.

Research on charging scheduling optimization of pure electric buses considering peak-valley time-of-use electricity prices and battery loss costs

In recent years, with the accelerated progress of pure electric buses in my country, the charging scheduling and operation plan of pure electric buses urgently needs to be rationalized and scientific. This study takes the charging scheduling operation in the daily operation of pure electric buses as the starting point, and effectively describes the battery loss cost function by analyzing the optimal fluctuation range of the state of charge of the power battery of pure electric buses. Combined with the analysis of the characteristics of peak and valley time-of-use electricity prices during the charging period, the problem is refined into a pure electric bus charging scheduling decision with controllable operation completion, and the parallel machine scheduling theory is used to characterize and solve the mathematical optimization model, with the aim of minimizing the operating cost of bus companies. At the same time, an immune optimization algorithm based on random key encoding and a greedy algorithm based on the idea of avoiding peaks and filling valleys are developed to solve large-scale problems. In addition, the effectiveness of the algorithm and the applicability of the method are verified through the real bus network in Shanghai.

Collaborative optimization of multi-modal transport solutions for urban-rural bus routes.

Faced with the land use heterogeneity and trip distribution non-equilibrium along the corridor connecting urban district and rural area, the operation characteristics of the all-stop bus, the express bus and the shuttle bus running on the transportation corridor should be given full consideration, as well as the tradeoff between passenger trip cost and bus operation cost during the peak-hour period. In order to realize the reliable decision of bus resources configuration for urban-rural public transportation, a bi-objective programming model of multi-modal urban-rural bus operation scheme is established, where the decision variables include the departure frequency of different bus modals, the terminals of shuttle buses, and the stop scheme of express buses, under the constraints of departure interval, passenger volume, vehicle occupancy, section capacity usage and modal competition. To validate the feasibility of proposed optimization model, an urban-rural bus corridor from Rural Jasmine Ecological Park in Hanjiang District to West Xiangyang Bridge in Songqiao Town was chosen as a study case, and the operation organization scheme of three bus modals were solved by the mathematical solver Lingo using mixed integer programming. Meanwhile, the scheme differences under different subobjective weights were discussed, taking into account the uncertain passenger demand and operation supply. The result shows that the increase of passenger trip cost weight will increase the departure frequency of all three bus modals, while the increase of operation cost weight will decrease the total operating vehicles, but the stop frequency at middle stations for express buses will increase accordingly. Compared to the current operation scheme, the optimized scheme can greatly enhance the section capacity usage at a slight sacrifice of passenger trip time with fewer bus vehicles.

Reinforcement Learning for Transit Signal Priority with Priority Factor

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios.

Analyzing School Bus Electrification in Richmond, Virginia

School buses are an essential component of the transportation infrastructure, serving as a lifeline for students across the globe. However, the widespread use of diesel school buses has raised concerns about the health impact on millions of students exposed to harmful emissions daily. Recognizing this issue, school districts worldwide are urgently seeking cleaner energy alternatives. Electric school buses emerge as an environmentally friendly and sustainable option, fostering a healthier environment for both students and communities. However, school bus electrification faces the challenges of high upfront cost, cumbersome charging management, and constraints from power grids. To help school bus operators address those challenges, this study presents a data-driven analysis for school bus electrification. This study considered a real-world school bus system in Richmond, VA, and developed a mathematical programming model to analyze the system design, charging strategies, and charging load profiles for the electrification scenario. The study evaluated different charging strategies based on model outcomes, aiming to optimize efficiency and effectiveness. Ultimately, this research generated electric school bus charging demand profiles under various scenarios, shedding light on the feasibility and implications of transitioning to electric-powered school buses.

Weathering the wait: Temperature impacts on school bus delays

Transportation systems are growingly subject to extreme weather events induced by climate change. Yet, delays in operating public transportation systems due to adverse weather conditions are not well understood. In this study, we quantify the impact of temperature shocks on school bus delays in New York City during 2015–2019. We find that low temperatures lead to significant school bus delays regarding the frequency and delay time. Our heterogeneity analysis further shows that increased school bus delay time in cold weather is primarily driven by Curb-to-School buses (for students with special education and needs) and school bus operations in the mornings. Our findings provide new evidence on how temperature shocks affect transportation systems and highlight the need for adaptive strategies and planning that can aid school buses in better responding to adverse weather conditions.

Evaluation of the Impacts of On-Demand Bus Services Using Traffic Simulation

This paper uses smart card data from Melbourne’s public transport network to model and evaluate the impacts of a flexible on-demand transport system. On-demand transport is an emerging mode of urban passenger transport that relies on meeting passenger demand for travel using dynamic and flexible scheduling using shared vehicles. Initially, a simulation model was developed to replicate existing fixed-schedule bus performance and was then extended to incorporate on-demand transport services within the same network. The simulation results were used to undertake a comparative analysis which included reliability, service quality, operational efficiency, network-wide effectiveness, and environmental impacts. The results showed that on-demand buses reduced average passenger trip time by 30%, increased vehicle occupancy rates from 8% to over 50%, and reduced emissions per passenger by over 70% on an average weekday compared to fixed-schedule buses. This study also offers insights for successful on-demand transport implementation, promoting urban sustainability. It also outlines future research directions, particularly the need for accurate short-term passenger demand prediction to improve service provision and passenger experience.

Determinants of paratransit feeder service provision using AI-synthesized revealed preference data: a paratransit drivers’ perspective on PT reform using machine learning

ABSTRACT Although there is a wealth of research on passengers’ perception of public transport services and their willingness to use them, only a few have examined paratransit drivers’ intention to provide service. In this study, we have proposed using combined forward sequential feature selection with random forest classifier (FSFS-RFC) and Artificial Neural Network (ANN) to investigate the factors influencing drivers’ intention to provide feeder services to a dedicated bus lane system in Freetown. Focus group discussion was held with heads of various public transport unions, and a questionnaire survey was administered to a sample of 1124 paratransit drivers. The proposed FSFS-RFC and ANN algorithms revealed consistent results with schemes for fleet renewal, provision of coordinated paratransit feeder terminals, guaranteed hours of bus operation and increase in daily profit as the most significant determinants. The results imply that the algorithms can learn from AI-synthesized data on paratransit drivers’ operations for efficient planning.

Who's behind the wheel? Work and health backgrounds of new transit bus operators.

Investigate new bus operators' (n = 293) occupational and health backgrounds to inform how transit authorities can support their future health and job success. New bus operators completed surveys and direct measurements that addressed demographics, work history, and 10 health risk factors. Participants averaged 42.76 years of age and were predominantly male (73.5%). Many (45.7%) came from minority backgrounds and most (66.3%) had no prior commercial driving experience. Transportation and material moving occupations were operators' most common prior jobs followed by protective service and sales and related occupations. Study-specific criteria classified operators as having low (49.5%), medium (37.9%), and high (12.6%) health risk levels. About half of the sample had medium-to-high health risks and most lacked commercial driving experience. Such information may help employers proactively support new bus operators' health and job success.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

Assessment of transit bus electricity consumption using a random parameters approach

The shift towards sustainable transportation emphasizes the crucial role of electric transit buses in promoting cleaner and more efficient public transit systems. This study conducts an explanatory analysis of electric bus electricity consumption, with a focus on the impacts of detailed driver behavior and vehicle operation. A random parameters ordered probit model is applied to an electric bus dataset collected from Guangzhou, China. Findings indicate that some factors exhibit consistent impacts on electricity consumption across bus fleets, such as the acceleration rate at bus stops and the number of stops induced by traffic conditions. However, variability exists within the bus fleet regarding the effects of other factors, such as the average speed and handbrake usage. These results underscore the need for targeted strategies, such as maintenance, route optimization, driver training, and policy adjustments in bus operations, to decrease electricity consumption and improve the efficiency of public transit systems.

Tripartite evolutionary game and simulation analysis of electric bus charging facility sharing under the governmental reward and punishment mechanism

Electric vehicles (EVs) are environmentally friendly with the features of low noise levels and zero tailpipe emissions. However, limited charging facilities have restricted the large-scale development of EVs, and electric bus charging facility sharing is a promising solution to this issue. While reducing charging time and improving facility utilization, this measure involves the interests of the government, bus operators, and electric car owners, requiring a systemic analysis of their interactions. Therefore, this study constructs a tripartite evolutionary game model under the governmental reward and punishment mechanism. The model examines the strategic decision-making process, influential factors, and evolutionary stability of three players, followed by simulation analysis. The results show that: (1) The evolutionary system may exhibit three stable states: (0,0,0), (1,0,0) and (1,1,1), and enhance the initial ratio of the three stakeholders will help evolve towards the ideal state. (2) Increasing rewards and penalties can both be meaningful, when penalties exceed rewards, the measures can be more effective. (3) Bus operators are more sensitive to incentive and penalty measures than electric car owners, and governments should pay more attention to them in the implementation process. The findings can provide references for the policy formulation on electric bus charging facility sharing.

Differential Pricing Strategies for Airport Shuttles: A Study of Shanghai Based on Customized Bus Ticketing Data

Airport shuttle buses, as a specialized form of bus service, serve as an economical, efficient, and sustainable transportation option for air travelers. In contrast to conventional bus services, airport shuttle bus operations exhibit more pronounced market-oriented characteristics, striving to balance extensive public transport coverage with the optimization of corporate profitability. Although these services outperform regular bus transit in terms of efficiency, they incur higher operational costs. However, existing studies on enhancing profitability and optimizing resource allocation for airport shuttle buses are inadequate. This study proposes a differential pricing strategy based on historical ticketing data. Initially, we analyze the characteristics of orders, users, and reservations within the context of customized bus operations. Leveraging the differences among various groups, we employ clustering techniques to classify seat grades and segment users. Based on the clustering outcomes, we determine distinct price elasticity values for each segment. As the strategies are developed based on seat grades, booking time, and user travel patterns, the numerical experiments indicate that the proposed differentiated pricing strategy can increase the revenue of customized public transport services by at least 41%. This strategy not only enhances the efficiency of resource allocation and service accessibility but also makes the service more financially sustainable.