Search

The innovations of battery and charging technologies facilitate worldwide bus electrification, which can conserve energy and reduce emissions. The emergence of wireless power transfer technology offers the potential to alleviate bus range anxiety. This paper constructs the layout models of static wireless and centralized charging stations based on the seasonal differences associated with battery capacities and passenger flow, which aims to obtain the bus charging station layout scheme of the lowest cost. Then, the heuristic algorithm is utilized to solve the layout models as the mixed linear programming problem. This study results confirm that the combined layout of static wireless and centralized charging stations is more advantageous than a single model. Finally, the required fixed cost of supporting bus system operation is explored for future capital reserves. [ABSTRACT FROM AUTHOR]

The establishment of an evaluation model for bus operation efficiency is crucial in determining the bus routes along the rail transit line that require optimization, laying the theoretical foundation for the integration of the two networks. Firstly, a qualitative and quantitative method was used to select bus routes that have a high correlation with the rail transit system and are of research significance. Secondly, an evaluation system was constructed based on the rationality of the bus network itself and the synergy between the two networks. Finally, the Pareto optimal cross-efficiency model was employed to determine the operation efficiency of the bus routes along the rail transit line. Taking the example of Line 1 in Nanchang, 25 bus routes along the line were identified and empirically analyzed. The results indicate that the Pareto optimal cross-efficiency models score is more readily accepted by all DMUs (decision-making units). The data analysis reveales that the bus network in Nanchang exhibites good self-rationality. However, in terms of synergy between the two networks, there is room for improvement by optimizing route directions and adjusting route duplication coefficients to enhance their collaborative nature. Finally, this paper is based on the Pareto cross-efficiency mean values of both factors, the 25 bus routes were categorized into four cases, providing practical and valuable insights for the managers and decision-makers of the public transportation system. [ABSTRACT FROM AUTHOR]

Global climate change leads to frequent extreme snowfall weather, which has a significant impact on the safety and operating efficiency of urban public transportation. In order to cope with the adverse effects of extreme weather, governments should vigorously develop sustainable transportation. Since urban public transportation is a critical component of building a sustainable city, traffic management departments should quantitatively analyze the performance changes of the urban public transportation network under extreme weather conditions. Therefore, fully considering the comprehensive effects of network performance and topology to improve the robustness of urban public transportation systems requires more attention. The urban public transport network with high robustness can achieve fewer recovery costs, lower additional bus scheduling costs, and achieve the sustainable development of the public transport network. Considering the impact of travelers' travel time tolerance and in-vehicle space congestion tolerance under snowy conditions, this paper proposes a generalized-norm-based robustness evaluation model of the bus network. Example analyses are conducted using checkerboard and ring-radial topological network structures to verify the applicability of the proposed model. The results show the following: (1) In an extreme snowfall scenario, the robustness of checkerboard and ring-radiating bus networks is reduced by 38% and 39%, respectively. (2) In the checkerboard network, the central area units are always more important to the system robustness than the peripheral units, while, in the ring-radial network, the units with higher importance are all in the ring line. (3) The failure of Ring Line 5 has a great impact on both the checkerboard and ring-radial networks, causing the system robustness to decrease by 43% and 50%, respectively. [ABSTRACT FROM AUTHOR]

To improve the bus transfer efficiency and optimize the bus system, the bus transfer network was constructed based on GIS software, and the network was used to measure and analyze the transfer accessibility. Combined with Space-P model and network analysis method, the same-station transfer sub-network was constructed based on the basic information of bus route paths, stations and intersections in Chengguan District of Lhasa City. Combined with the information of bus station service scope, pedestrian. path and intersection, the different-station interchange sub-network was constructed. The two sub-networks were collaborated to realize the construction of bus transfer network based on ArcGIS, and the passenger on-board time and the transfer coefficient of bus routes were measured and analyzed based on the constructed transfer network. The results show that the constructed network can measure the passenger on-board time well, and the maximum value of passenger on-board time is 68.68 min with the minimum value of 2.00 min and the average value of passenger transfer on-board time of 29.90 min. This network can well measure the interchange coefficients to obtain 90 300 effective interchange routes with the maximum interchange coefficient of 4 (62 routes) and the minimum value of 0 (1354 routes). Using the accessibility metric model, the good measurement and analysis of bus stop time accessibility and transfer accessibility can be realized. [ABSTRACT FROM AUTHOR]

The urban public transport network is closely related to urban construction and is susceptible to external influences, especially the bus network (BN). The measurement of the changes in the performance of BN under disruptions plays an important role in the development of bus systems. This paper takes the land-use type around each bus stop to modify the standard coverage range and then combines the attractive service area of the stop and the passenger flow as the opportunity coefficient to propose an improved accessibility model. Finally, the vulnerability of the BN based on the improved accessibility model in different time periods under four disruptions is analyzed. Taking BN in the central area of Xi'an as a case study, the results show that the BN is less vulnerable when stops are associated with high land-use type attractiveness, and regions with a single land-use type have high vulnerability levels. In addition, the land-use disruption causes larger-scale network vulnerability than topological disruptions. An interesting result, opposed to common sense, is found in stops within the top 10% of topological disruption failure probabilities, i.e., the BN is the most vulnerable during the off-peak night period. This study supplements the coordinated development of public transport and land use in future planning. [ABSTRACT FROM AUTHOR]

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications. [ABSTRACT FROM AUTHOR]

Public transport today is again gaining relevance as a means of transportation in connection with the personal cars that have flooded urban spaces. The city of Almaty is no exception and since the beginning of the last decade has taken a course to organize pilot projects to create a priority for the movement of public transport. The difficulty of implementing such innovations is the public, accustomed to crossing long distances by private vehicles, and in most cases city streets are loaded from nearby agglomerations. The emergence of such a trend is directly related to the expansion of the city in breadth, i.e. from east to west, because the natural uniqueness of the urban area in the south is limited by the mountain ranges of the Trans-Ili Alatau. This paper is presented taking into account the existing initial data for the study of public transport issues through the introduction of the GTFS scientific methodology, which can give a new angle of view on the current situation with the organization of bus and trolleybus routes. This paper focuses on studying the potential of public transport in Almaty for consistent growth, because with the help of the restructuring of route networks and the creation of a priority traffic network, there is a chance to achieve an increase in capacity and an increase in the number of users. The aim of this article is to provide information about the current state of the public transport network and to discuss the potential of geographic information systems (GIS) within the urban space, which are guided by spatial analysis approaches related to the processing of General Transit Feed Specification data (GTFS), since statistical data are based on providing a complete picture of the existing transport network, and afterwards can become the basis for subsequent optimization of public transport traffic. This paper creates new perspectives for future development of public transport and restructuring of the understanding how to create public network according to necessity in Almaty city.

Further optimizing small-scale bus networks has proven to be an obstacle in linear programming over the previous decade. Data Envelopment Analysis (DEA) is a mathematical approach designed to organize a system’s inputs and outputs and determine the efficiency of its decision-making units. Existing studies show that DEA is a logical approach for network planning in larger, highly-urbanized areas, yet there remains a gap in its effectiveness in smaller, more suburban settings. To explore this inquiry, five DEA models were designed and applied to 19 bus routes operating through Delaware, USA, and their results were statistically analyzed. Findings suggest that an individual can implement DEA in virtually any environment, regardless of size and popula- tion dispersion, as this study yielded results ranging from 28% to 100% efficiency, consistent with similar studies; therefore supporting the technique’s future applicability and success.

In rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region is sufficiently large, the spatial heterogeneity in demand distribution must be considered. This paper develops continuum approximation models for optimizing a heterogeneous fixed-route feeder network in a rectangular service region next to a rail terminal. Our work enhances previous studies by: (i) optimizing heterogeneous stop spacings along with line spacings and headways; (ii) accounting for passenger boarding and alighting numbers on bus dwell times and patron transfer delays at the rail terminal; and (iii) examining the advantages of asymmetric coordination between trunk and feeder schedules in both service directions. To tackle the increased modeling complexity, we introduce a semi-analytical method that combines analytically derived properties of the optimal solution with an iterative search algorithm. Local transit agencies can readily utilize this approach to design a real fixed-route feeder system. This paper reveals many findings and insights not previously reported. For instance, integrating the heterogeneous stop spacing optimization further reduces the system cost (by 4% under specific operating conditions). The cost savings increase with demand heterogeneity but decrease with the demand rate and service region size. Choosing the layout of feeder lines where buses pick up and drop off passengers along the service region's shorter side also significantly lowers the system cost (by 6% when the service region's aspect ratio is 1 to 2). Furthermore, coordinating trunk and feeder schedules in both service directions yields an additional cost saving of up to 20%. [ABSTRACT FROM AUTHOR]

Public transport today is again gaining relevance as a means of transportation in connection with the personal cars that have flooded urban spaces. The city of Almaty is no exception and since the beginning of the last decade has taken a course to organize pilot projects to create a priority for the movement of public transport. The difficulty of implementing such innovations is the public, accustomed to crossing long distances by private vehicles, and in most cases city streets are loaded from nearby agglomerations. The emergence of such a trend is directly related to the expansion of the city in breadth, i.e. from east to west, because the natural uniqueness of the urban area in the south is limited by the mountain ranges of the Trans-Ili Alatau. This paper is presented taking into account the existing initial data for the study of public transport issues through the introduction of the GTFS scientific methodology, which can give a new angle of view on the current situation with the organization of bus and trolleybus routes. This paper focuses on studying the potential of public transport in Almaty for consistent growth, because with the help of the restructuring of route networks and the creation of a priority traffic network, there is a chance to achieve an increase in capacity and an increase in the number of users. The aim of this article is to provide information about the current state of the public transport network and to discuss the potential of geographic information systems (GIS) within the urban space, which are guided by spatial analysis approaches related to the processing of General Transit Feed Specification data (GTFS), since statistical data are based on providing a complete picture of the existing transport network, and afterwards can become the basis for subsequent optimization of public transport traffic. This paper creates new perspectives for future development of public transport and restructuring of the understanding how to create public network according to necessity in Almaty city. [ABSTRACT FROM AUTHOR]

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.

The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation. [ABSTRACT FROM AUTHOR]

This paper describes a methodology for assessing before-and-after benefits for residents in an urban area resulting from the implementation of a major bus network improvement. The new late-night bus network of the city of São Paulo, Brazil is used as a case study. A group of supply and demand performance indicators is proposed in order to evaluate changes on all network levels. Accessibility analyses are conducted to evaluate how changes in the supply side potentially impact easiness of reaching destinations, while an analysis of the demand side response is performed by comparing changes in origin and destination transit trip volumes in a three-year period using historical smart card and GPS data, as well as differences in average travel times and number of transfers per trip within the network. [ABSTRACT FROM AUTHOR]

Buses of General Company for Passenger Transport was the primary mode for public transportation in Baghdad City. This system suffers from many problems, part of which were related to bus routes, while the other part was related to the bus and its operators. These problems have a direct effect on the users of public transport buses. The objective of this study was to assess the performance of eight public transport bus routes which they represented by the Al-Tahrir bus network and adopting the level of the transit service method. Seven transit performance measures were selected in this study, such as bus travel time, hours of service during the day, service frequency (headway), total delay on the route, running speed of the bus, bus occupancy, and capacity of the route. The results of this study showed that bus routes No. (72, 36, 13, 114, 11, 30, 37, and 9) were operating at overall Level of Transit Service LOTS (D, E, E, E, E, D, E, and E) respectively, whereas the bus network (Al-Tahrir bus network) was operating at overall LOTS (E); therefore, the performance of Al-Tahrir bus network was not acceptable, and improvements were needed to increase the level of transit service of this 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. • Electric bus charging scheduling on a network has been investigated. • A tailored branch & price algorithm is designed. • An optimization-based adaptive large neighborhood search is proposed. • Experiments are performed to seek managerial insights for the operators. [ABSTRACT FROM AUTHOR]

Ningbo urban-rural fringe areas,selected for the rail transit node and conventional public traffic adaptive connection problem,considering the average daily full are theory-oriented,line passenger capacity,line network repetitive,passenger load factor and other constraints,this paper used ARCGIS software powerful storage and computing power,good ability of superposition of line network,and optimized the network topology node nonstop passenger flow detection analysis,and based on the Ningbo urban and rural integration of traffic planning and construction background,explored the Ningbo urban and rural integration of rail transit and theory-oriented and coordinate the relationship between optimization and the connection of the two forms.This paper studied the coordination and optimization mechanism of the traffic network of the urban and rural fringe of Ningbo in the aspects of station and network operation by using the accessibility method and the shortest route marking method comprehensively.Furthermore,it visualized the optimization results of rail transit network connecting Ningbo urban and rural areas.The experimental results show that the optimized traffic line network in the urban and rural fringe of Ningbo city is superior to the current strategy.This research model scheduling strategy can provide a reliable optimization scheme for the traffic control department of the urban and rural fringe of Ningbo city,and provides a scientific basis for the urban-rural fringe of other cities to make a correct traffic control policy. [ABSTRACT FROM AUTHOR]

The development of Electric Vehicle (EV) helps to ease energy crises and deduce vehicle exhaust emissions. However, it also brings a great impact on both transportation networks and power grids. There are some serious impediments in terms of energy charging to the popularization of EV, such as high deployment cost of charging stations, low charging efficiency, and voltage deviation of power grid. To address these issues, we design a new EV charging system, which levers the bus network in urban areas through the integration of OnLine Electric Vehicle (OLEV) system and Microwave Power Transfer (MPT) system. We formulate the EV route scheduling problem based on this new charging system to maximize the total residual energy subject to all EVs can arrive to their destinations before deadlines. Then, we propose an approximation algorithm, RSA, to solve the route scheduling problem. To relieve the traffic congestion, we further formulate the conflict-free EV route scheduling problem, and use the matching based algorithm, FRSA, to find the EV route schedules with the maximal residual energy. Through the extensive simulations, we demonstrate that RSA and FRSA can increase the average residual energy by 67.66% and 50.36% compared with the solution without the designed wireless charging system, respectively. Moreover, RSA reduces 22.22% of travel time and outputs 77.23% of residual energy, and FRSA can obtain 83.51% residual energy with 3.62% of extra travel time of the corresponding optimal solutions on average, respectively. [ABSTRACT FROM AUTHOR]

Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it is difficult to deploy sensors and adjust the sensor network in unknown region for dynamic disease surveillance. Second, the limited life-cycle of sensor network may cause the loss of surveillance data. Thus, it is important to provide a sustainable and robust regional disease surveillance system. Given a set of Disease surveillance Area (DsA)s and Point of disease Surveillance (PoS)s, some sensors are deployed to monitor these PoSs, and a drone collect data from the sensors as well as charge the sensors to extend their life-cycles. The drone replenish its energy by relying on the bus network. We first formulate the drone assisted regional disease surveillance problem under the constraints of life-cycle of sensors and energy of drone, and propose an approximation algorithm to find a feasible cycle of drone to minimize the traveling time cost of drone. To satisfy the diversity requirements and dynamic scalability of regional disease surveillance, we deploy one robot in each DsA instead of sensors. We further formulate the learning transferable driven regional disease surveillance problem, and propose a joint schedule algorithm of drone and robots. The results of both theoretical analysis and extensive simulations show that the proposed algorithms can reduce the total time cost by 39.71 and 48.74 percent, average waiting time by 42.00 and 50.14 percent, and increase the average accessing ratio of PoSs by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features. [ABSTRACT FROM AUTHOR]

• Resilience of metro and bus systems is affected by their interdependency relations. • The higher degree heterogeneity of nodes, the lager network resilience becomes. • Function dependency is dominating the resilience of interdependent transit networks. • Passengers matching between metro and bus have great impacts on system resilience. Owing to the pervasive interdependency among networks and the great threat of various disasters, one of the most challenging issues is the resilience evaluation of interdependent networks. Existing studies have been conducted to analyze interdependent network resilience addressing unidirectional dependency, which triggers and propagates network failures. The loss and recovery of network functionality are complicated and important under such interdependency. Ignoring the interdependency nature of different networks would lead to incomplete or incorrect results in their resilience assessment. This paper developed a resilience assessment model for interdependent transit networks under failures. Taking the interdependency relations, network topology, flow characteristics, and demand distribution into account, the proposed methodology explicitly quantifies the impacts of varying network interdependency on the resilience of interdependent networks. The approach was applied to the interdependent metro and bus networks of Xi'an, China. Results show the resilience of interdependent networks is greatly affected by node degree heterogeneity in topology. The higher the heterogeneity of interdependent nodes, the larger the network resilience becomes. The bidirectional function dependency among networks demonstrates dominant effects on the resilience of interdependent networks if one network is disrupted. If interdependent networks have homogeneous function, the resilience of the networks would be significantly improved with the increase in interdependency. The degree of flow matching between both networks plays a particularly important role in network resilience enhancement. Findings of this study would provide practical implications for the design and planning of interdependent infrastructure systems under disasters. [ABSTRACT FROM AUTHOR]

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, and low maintenance costs. It is critical for the underlying monitoring scenario (e.g. air quality, traffic state, and road roughness) to assign a limited number of sensors to a bus fleet to ensure their optimal spatial–temporal distribution. In this paper we present a trip-based sensor deployment problem, which explicitly considers timetabled trips that must be executed by the fleet while a portion of them perform sensing tasks. To address the computational challenge in large-scale instances, we design a multi-stage solution framework that decouples the spatial–temporal structures of the sensing task through line pre-selection and bi-level optimization. As a result, the computational complexity is reduced to be sub-linear w.r.t. the number of lines, rather than combinatorial w.r.t. the number of buses in existing vehicle-based approaches. A real-world case study covering 400 km 2 in central Chengdu demonstrates the effectiveness of the model in solving large-scale problems. It is found that coordinating bus scheduling and sensing tasks can substantially increase the spatial–temporal sensing coverage. We also provide a few model extensions and recommendation for practice regarding the application of this method. • Bus drive-by sensing is a promising way to survey urban environment at low costs. • Existing vehicle-based sensor deployment is a combinatorial problem. • A trip-based model considers timetabled trips while performing sensing tasks. • A multi-stage solution algorithm yields sub-linear complexity w.r.t. the number of lines. • A real-world case study reveals its effectiveness and managerial insights. [ABSTRACT FROM AUTHOR]

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies; however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to reschedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO; however, under some circumstances, the technologies are on par. This emphasises the need for a detailed analysis of the local bus network in order to make an informed procurement decision.