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This research investigated accessibility and connectivity of a public transport network calculated under two equally popular analytical approaches, namely the topological network analysis and the temporal network analysis, using the bus network in Ho Chi Minh City (HCMC), Vietnam as a case study. Specifically, we presented an apple-to-apple comparison of the two approaches to answer the question “What is the degree of discrepancy and/or loss of information, if any, in analyses using the topological network approach compared to those from the supposedly more computationally demanding temporal network approach?”. Stop-based accessibility metrics as well as measures of most-used infrastructure (bus stops and bus routes) were calculated, compared, and discussed for both approaches. In calculating shortest path between stop pairs, we adapted the multi-criteria profile connection scan algorithm for paths with earliest departure earliest arrival and minimal transfers. In representing the HCMC bus network, which had 253 directed routes servicing 4350 stops, the number of nodes and the number of edges in the temporal network over a 24-hour period were a few orders of magnitude larger than those in the topological network. Computing the shortest path between all stop pairs in the temporal network over a 3-hour period took at least an order of magnitude longer than that in the topological network. While results of network accessibility and most-used infrastructure from both representations of the bus network shared some qualitative similarities, they clearly demonstrated disadvantages of the topological network approach in capturing the temporal heterogeneity of a public transport network operation.

Bus transfers (access) at rapid-transit stations represent more than one-third of total boardings in large, dispersed, polycentric cities in the U.S. Yet, many station-level access-mode statistics and person-trip surveys often ommitt bus access mode or confound it with walk access. The abscence of this key statistic in official survey reports results in an incomplete understanding of multimodal interactions and may mislead planners and decision-makers in crafting effective policies geared to increase rapid-transit ridership and performance. Study of eight large transit systems and further analysis of the Los Angeles case study, with its detailed 2012 bus-rail on-board survey, were conducted to improve our understanding and planning of bus/rapid-transit interactions in polycentric cities. In order to evaluate scope, share, and impact of bus feeder routes on station boardings, the author indexed bus access events in the original survey database to estimate the number of bus transfers (access) at station-level. The socio-economic profile of riders that accessed rapid transit via bus was also rendered and trip-origin spatial distributions by access mode was also identified and tabulated. In addition, the influence of different bus feeder services (e.g. local, rapid, express) on rapid-transit station boardings was assesed in multivariate generalized linear models. Descriptive and inferential statistics results indicate that in Los Angeles, >30% of patrons are estimated to access rapid-transit via a variety of bus services; bus network connectivity is highly significant in explaining rapid transit boardings with large effects comparable to key Transit Oriented Development (TOD) factors; and notable variance in the magnitude of effects exists depending on bus service type. Station-level boarding and bus-transfers predictive equations derived from multivariate regressions such as the ones illustrated in this paper, may be used for sketch planning purposes in direct-demand models. A more nuanced, periodic, and detailed compilation of statistics related to bus transfers at station-level (as well as that of other emerging and potentially disrupting access modes) would provide a better understanding and planning of large multi-modal transit systems in the U.S. It would also bring to light an important, but under-reported, set of users.

Drawing on complex network theory, this paper combines bus network and subway network into a multi-subnet composited complex network (MSCCN) of urban public transport (UPT). Then, the cascading failure of the MSCCN nodes and edges was modelled, and the passenger flow transfer rules were established under node and edge failures. Considering the impact of extreme weather on the UPT, a synthetic operator was created to measure the robustness of UPT MSCCN, in the light of network topology and passenger flow, and used to quantify the robustness of UPT MSCCN under extreme weather. Finally, a UPT MSCCN was set up for Qingdao, China, and subject to cascading failure simulation. The simulation results reveal how MSCCN robustness changes with different variables. The research findings help to optimize the response of the MSCCN to extreme weather.

The paper proposes a conceptual model of an integrated solution for urban logistics and passenger flows to reduce the number of fossil combustion powered commercial vehicles traveling within city, solely for goods transportation, thus contributing to reduce negative effects of urban logistics activities, namely pollution, noise, traffic congestion and accidents. For this solution, a bus network is used to combine the cargo flow with the passenger flow within the cities. After a literature review, a user-centered approach supported by interviews with key stakeholders was followed to define the concept, followed by solution modelling using the Unified Modeling Language (UML) notation. Thus, the high-level requirements will be presented based on a use-cases diagram and the data model based on a class diagram. Also, the automatic order check-in and check-out process on the bus is designed, using Industry 4.0 and IoT principles. This process is relevant since it speeds up the handling of orders in transshipments, reduces the possibility of human error, while at the same time enhancing control and promoting real-time knowledge of the delivery status of orders. The results of this study expand the knowledge in an area with scarce literature. In practical terms this study presents valuable contributions, since it specifies a solution for urban logistics that can have environmental and social impact in the context of urban areas.

Rapid urban growth and rising mobility demand in megacities having a population of more than 10 million people in emerging economies call for a consistent and comprehensive urban mobility policy framework. Implementing sustainable urban mobility policies in these cities is complicated by fragmented governance structures, conflicting policy priorities and lack of effective financing mechanisms. The city of Istanbul, with a population of nearly 15 million people, has been grappling with rapidly increasing urban mobility problems such as chronic traffic congestion, increasing motorization, overcrowded public transport services, air and noise pollution. Although major infrastructure investments such as new metro and tram lines, a bus rapid transit (BRT) line and capacity increases in public bus network have been undertaken in recent years, the objective of achieving a modal shift from car use to more sustainable transport modes such as public transport, cycling and walking is far from being realized. The focus on megaprojects such as Marmaray, Eurasia Tunnel and Third Bridge further complicate the urban mobility landscape by bringing about conflicting policy priorities. In this paper, a comprehensive review and analysis of Istanbul’s urban transport policies and strategies are scrutinized under the framework of sustainable urban mobility paradigm to better understand the mobility predicament which have long stifled the quality of life of Istanbulites. The challenges are identified, recommendations are proposed, and prospects are put forward as to how to achieve sustainable urban mobility in Istanbul.

Real-time information system about bus arrival and departure times at bus stops is one of the key elements of high-quality public transport. To provide accurate prediction of bus arrival at the bus stop the information of current position of the bus and bus travel time to the target stop are required. This paper investigates the bus velocity during different time periods and the impact of the bus network data model on the real-time prediction of arrival times at bus stops. The proposed model classifies bus travel times into time periods with respect to the historical data and the data model of the bus network. Discussion of four types of data model is presented, such as: a data model defined by bus stops and junctions of the roads, a data model defined only by bus stops, a data model which addresses the individual parts of the network in relation to the potential barriers that affect the travel speed of buses, and a data model with fixed-length links of the bus network. The analysis is performed in two different environments, the city of Ljubljana and the city of Maribor. The travel times are classified according to the average travel speed into four time periods: morning, afternoon, weekend and off-peak periods. Simulations showed that both the data model of the bus network and classifying runs into time periods affect the accuracy of predictions of bus arrival times at bus stops.

The electrification of city bus systems is an increasing trend, with many cities replacing their diesel buses with battery electric buses (BEBs). Due to limited battery capacities, and to random battery discharge rates—which are affected by weather, road and traffic conditions—BEBs often need daytime charging to support their operation for a whole day. The deployment of charging infrastructures, as well as the number of stand-by buses available, has a significant effect on the operational efficiency of electric bus systems. In this work, a stochastic integer program has been developed to jointly optimise charging station locations and bus fleet size under random bus charging demand, considering time-of-use electricity tariffs. The stochastic program is first approximated by its sample average and is solved by a customised Lagrangian relaxation approach. The applicability of the model and solution algorithm is demonstrated by applications to a series of hypothetical grid networks and to a real-world Melbourne City bus network. Managerial insights are also presented.

This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator’s revenue. The CB passenger’s travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.

The paper presents modeling, control and analysis of an urban public transport network. First, a centralized system description is given, built up from the dynamics of individual buses and bus stops. Aiming to minimize three conflicting goals (equidistant headways, timetable adherence, and minimizing passenger waiting times), a reference tracking model predictive controller formulated based on the piecewise-affine system model. The closed-loop system is analyzed with three methods. Numerical simulations on a simple experimental network showed that the temporal evolution of headways and passenger numbers could maintain their periodicity with the help of velocity control. With the help of randomized simulation scenarios, sensitivity of the system is analyzed. Finally, infeasible regions for the bus network control was sought using by formulating an explicit model predictive controller.

Overcrowding in public transport (PT) is one of the major issues and should be improved in order to make the PT system more attractive in megacities. However, most of the mathematical models do not consider the cost of crowding discomfort faced by the users in the total cost minimization objective function while determining the optimum frequency of PT systems. This work develops a mathematical model that explicitly considers this aspect. The work first develops an exponential function of crowding multiplier with load factor and then uses it to determine the cost of crowding discomfort. The work is applied to a real-life network of 34 bus routes of Delhi, India. Lastly, the work also presents sensitivity analysis with the value of waiting time and travel demand and provides policy and planning implications for the Delhi bus network.

To create a sustainable urban transportation environment, transportation authorities often implement incentive strategies to attract new public transportation users. Various studies have shown that increasing bus revenue vehicle-kilometers (BK) is a key factor for increasing bus ridership (BR). In this situation, BK affects BR. However, an increase in BR may lead to an increase in BK. Then, BR affects BK. The existence and directions of causal effects between BK and BR are the research questions addressed in this study. Since 2007, the Taichung City Government has been adding and adjusting bus routes to build a dense bus network. In 2011, it implemented a free fare bus policy. BR grew from ca. 2 million per month in 2007 to 10 million per month in 2015. Moreover, BK increased by ca. 3 times during that same period. This provides an opportunity to understand the causal effects between BK and BR. Extended convergent cross mapping (ECCM) can be used to assess the causal relationship between two variables without modelling. We adopted this method to distinguish the influences of BK and BR. According to the results, increasing BK attracted more public transit passengers during the period 2007 to 2010. However, there was no effect of BR on BK. After implementation of free fare bus policy in 2011, increases in BK led to increases in BR and increases in BR drove incremental growth of BK. The result was a virtuous cycle of mutual influence between BK and BR. In conclusion, increasing BK, such as route length or number of scheduled runs, can promote the usage of public transportation. Moreover, authorities can implement free fare bus policy to attract potential users and force incremental growth of BK.

Interconnected microgrids can enable mutual power support among microgrids (MGs) and improves the utilization of renewable energy sources, especially for CCHP-based (Combined Cooling, Heating, and Power) Microgrid Cluster (MGC). To preserve information privacy and achieve scheduling independence of microgrids, the problem of multi-area economic and environmental dispatch in CCHP plus MGC can be computed by distributed algorithm framework, i.e., generalized benders decomposition (GBD), optimal condition decomposition (OCD) and auxiliary problem principle (APP), respectively for interconnected topology and bus topology. Moreover, chance constrained programming (CCP) is added to address the uncertainty factors of renewable energy, cooling, heating, and electrical loads. A consensus-based distributed fair cost allocation algorithm is proposed to make a comparison with the condition of adding selfish constraints and independent operation, so that guaranteeing the stability of economic coalition of MGC. A case study with four networked CCHP microgrids in two kinds of topology is tested to demonstrate the effectiveness of the proposed approach in summer scenario. In conclusion, distributed algorithms will have a prospective application on MGC as the result of the necessity from different entities in the future.

This paper describes the detailed modeling and simulation of an aerogenerators system based on DFIG using RT-LAB simulator. A stator flux oriented vector is applied to decouple the control of active and reactive powers generated by the DFIG, and other controllers are needed to keep the DC-link voltage and grid currents at desired values. In order to adjust appropriately the pitch angle and keep the wind power at rated value, a pitch control and MPPT are involved. The model of the studied was developed first in the MATLAB ® , then, it was rebuilt on RT-LAB to test its performance. Simulation results validate the capability of the platform to achieve rapid simulation of complex power systems. At the end, the details of the simulation results are presented, discussed, and concluded.

Before enduring a substantial network reform in a system, it is of utmost importance to understand if, how, and why the users’ perception of the system will be affected. In the PT literature, we find evidence about what drives users’ satisfaction. Still, past studies have utilised data that has been mostly retrieved from user surveys during periods where the network and its services were quite stable, or during an economic downturn, in contrast to facing a structural network transformation. Barcelona public bus company (TMB) reformed into a transfer based-network, the NXB (Nova Xarxa de Bus) since 2012. This reform allows discerning which operative, travel, and sociodemographic characteristics affect the users’ satisfaction and their priorities, during a major network reform. We use a three-year customer satisfaction survey (n = 12,511), for our study. Employing a structural equation (SEM) approach, with both numeric and ordinal Probit models, we assess whether critical variables from the bus network reform affect users’ satisfaction significantly. Following a two step-approach, initially an SEM-MIMIC model for the whole population, then a Multi-Group Analysis (MGA), we appraise for different satisfaction models across subpopulations. We propose a reflective latent variable to measure the socioeconomic status (SES), which resulted in significant and negative estimates towards satisfaction, as expected, in all models. Our most important result confirms that users value reliability over other latent satisfaction constructs. Besides the satisfaction constructs, users give an additional relevance to the NXB lines, indicating that intrinsically the users perceive added-value from these lines. Most of the transfers between buses and other transport modes do not affect bus-users satisfaction. Our results imply that efficiently designed transfer-based networks may be a suitable solution from users’ perspective.

International audience; We introduce a new perspective to improve the reliability of a network, which aims at finding cost-effective portfolios of Prognostics and Health Management (PHM) systems to be installed throughout the network. To do this, we estimate the reliability of the single network element equipped with a PHM system, whose prognostic performance is measured in terms of the − α λ performance, false positive and false negative metrics. Then, we apply genetic algorithms for finding the portfolios of PHM systems to be installed on the network elements, which are optimal with respect to cost and a global reliability efficiency index of the network. The workbench case study of the IEEE 14 bus network is considered as application.

Revenue insufficiency is a major problem in market designs built on unit commitment and marginal pricing based remuneration schemes. Varied financial mechanisms and market clearing procedures have been adopted by different electricity markets to address this problem. But these schemes are based on deterministic market clearing algorithms. To the best of the authors’ knowledge, there has been no work done in ensuring revenue sufficiency in stochastic market clearing algorithms in the manner adopted in the proposed work. With the increasing penetration of renewable sources of energy, it has become imperative to adopt market equilibrium solutions that consider the dynamism of renewable sources of energy along with a guarantee of revenue sufficiency. This paper presents a novel stochastic market clearing procedure wherein the mathematical formulation explicitly includes the marginal pricing scheme into its modeling features. Recourse approach has been adopted to include the uncertainties associated with renewable sources of the system into the market clearing strategy. The entire problem has been formulated as a mixed integer linear programming (MILP) problem with marginal pricing being a part of the unknown decision variables. Dual variables of a security constrained economic dispatch problem is used for implementing revenue constrained stochastic market clearing model. The proposed model is validated using a three bus network with wind penetration that has wind forecast presented as scenarios over a time horizon of four hours.

Urban public transport operations in peak periods are characterized by highly uneven demand distributions and scarcity of resources. In this work, we propose a rule-based method for systematically generating and integrating alternative lining options, such as short-turning and interlining lines, into the frequency and resource allocation problem by considering the dual objective of (a) reducing passenger waiting times at stops and (b) reducing operational costs. The bus allocation problem for existing and short-turning/interlining lines is modeled as a combinatorial, constrained and multi-objective optimization problem that has an exponential computational complexity and a large set of decision variables due to the additional set of short-turning/interlining options. This constrained optimization problem is approximated with an unconstrained one with the use of exterior point penalties and is solved with a Genetic Algorithm (GA) meta-heuristic. The modeling approach is applied to the bus network of The Hague with the use of General Transit Feed Specification (GTFS) data and Automated Fare Collection (AFC) data from 24 weekdays. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs with the addition of only a few short-turning and interlining options.

Public transit systems enable metropolitan areas to develop and be sustained by providing mobility to people. At the same time, they can function as tools to alleviate the social and spatial exclusion of deprived groups or communities. Recently, equity has become a crucial element in transportation planning, yet it has not received sufficient attention. This study examined transit equity and the changes in it resulting from a major expansion of the transit network in a metropolitan area. Three stages of transit service provision were defined based on the actual network expansion plans. Transit equity at the global level was measured using the concept of the Gini index. The resultant Gini indices indicated that transit equality has improved and will continue to improve with the provision of additional subway lines. This was followed by a statistical analysis to determine which groups or areas were more likely to benefit from the expansion. When only a linear subway line was added to the bus network, transit dependent groups did not appear to benefit from the increased transit supply. However, with the addition of another circular subway line, females and young people were found to have a significant and positive association with the increased transit supply, indicating a vertically fairer distribution of resources.

Electromobility in public bus systems is growing rapidly and experiencing a fundamental transformation in their infrastructure and operations. The dilemma of limited driving range and charging time of battery electric buses (BEBs) hinders their adoption. A novel approach to address BEB limitations is to utilize dynamic wireless charging (DWC) technology that allows buses to charge while in motion. This paper aims to analyze robust strategic planning of DWC and BEB fleet scheduling based on a real bus network at Binghamton University. The problem is first formulated as a new deterministic mixed-integer linear programming model to simultaneously optimize both the charging planning problem and fleet scheduling problem in an integrated fashion. To address the uncertainty of energy demand and charging time, a robust counterpart model (RCM) has been derived. To increase RCM flexibility, the battery status variable is formulated in a cumulative form. Dependent and independent budget uncertainty sets have been developed to control the robustness. A sensitivity analysis has been conducted to study the system behavior in response to different charging types, auxiliary energy demand, depth of discharge, charging options at terminals, battery degradation, and electricity cost. The deterministic model shows that eight homogeneous BEBs are required to operate on the selected routes with a battery capacity of 54.01 kWh and a total cost of $3,636,347. The results show that joint planning of charging infrastructure and fleet scheduling can save 19.2% of total cost compared to disjoint planning. The RCM results in 10 BEBs to ensure feasiblility against uncertainty.

Bus controllers are an essential part of the London bus network. Although the bus driver is the individual directly in control of the vehicle, inputs from both the controller and driver influence operations. Currently, little research has focused on the dynamic between these parties, and how it works in the day-to-day operation of a bus. In the current study, data was collected across focus groups with controllers to understand the controller-driver relationship from the controller perspective. The objectives of the research were to: investigate interactions and working relationships between bus controllers and drivers in London and to explore the effect of controller/driver relationships on workload, stress and fatigue. It is clear that the working relationship between controllers and drivers is a challenging one, with both parties often misunderstanding the role of the other. This is made worse by the nature of communication via radio including poor quality audio leading to difficult interactions. All of the participating controllers expressed being overloaded with work, leading to feelings of stress and fatigue, with shift work and irregular hours being discussed as a cause of controller fatigue. Any steps taken to improve the difficult working relationship between drivers and controllers can be seen as beneficial because they could improve efficiency, worker wellbeing, and possibly safety.

Bus services can be seen as a way to reduce traffic congestion where they can encourage a mode shift from car. However, they can also generate negative effects on traffic flow due to stop-start operations at bus stops. This paper aims to assess the net impact of bus operations on traffic congestion in Melbourne. The methodology used to achieve this aim comprised of three main stages. First, a primary survey was conducted to determine the mode shift from bus to car when buses are unavailable. This figure was used to estimate the positive impact of buses on relieving congestion. Second, the negative impact of buses was investigated by considering the effect of bus stop operations on vehicle traffic flow using microsimulation. Finally, the net effect was estimated by contrasting congestion measures determined from a traditional four step model between two scenarios: ‘with bus’ and ‘without bus’. The results indicated that Melbourne’s bus network contributes to reduce the number of severely congested road links by approximately 10% and total delay on the road network by around 3%. The highest congestion relief impact was found in inner Melbourne with a 7% decrease in vehicle time travelled and total delay, and 16% decrease in the number of heavily congested road links. In inner areas, the level of congestion is relatively high so the mode shift from car to bus, even if not as high as middle and outer areas, have a significant effect on relieving traffic congestion. Areas for future research are suggested such as investigating the long-term effect of buses on traffic congestion.

An increasing number of cities are transitioning from fossil fuel-powered buses for public transport to battery electric buses. Evaluating the energy demand of buses has become an important prerequisite for the planning and deployment of large electric bus fleets and the required charging infrastructure. A number of state-of-the-art approaches to determining the energy requirements of electric buses use individual specific energy demand values or rely on standard driving cycles, though these do not consider local bus route characteristics. Others require high-resolution measurements of the vehicles’ driving profiles, which is impractical for large bus fleets. This paper presents a longitudinal dynamics model to calculate the energy demand for electric buses. The model is designed to be easily applied to large bus networks using real data sources that are commonly available to bus transit operators. This data can be derived from low-resolution data collected from day-to-day operations, where only the arrival and departure time of the buses at each bus stop are available. This approach offers a practical alternative to state-of-the-art methods and requires no high-resolution velocity profiles, which are difficult to obtain, while still taking into account the details of the operational characteristics of the transportation network under consideration. The application of the model is demonstrated in a case study to electrify the complete public bus network in Singapore. The results showed that the heterogeneity of driving conditions observed in a large network leads to a high variance in energy requirements between different bus lines and at different times of day. This confirms the need to take the characteristics of each individual bus route into account. In the case-study, a fully electric public bus fleet would require about 1.4 GWh per day for revenue service, which is about one per cent of Singapore’s daily electricity demand. Another finding is that 50% of the bus lines require less than 40 kWh per terminus-to-terminus journey, which indicates a good potential for fast opportunity charging during layover time. The results of the model should serve as the basis for further studies into battery sizes, charging strategies and charging infrastructure requirements.

The urban transport sector offers a noteworthy potential for the reduction of national greenhouse gas emissions as well as local pollutant emissions such as nitrogen oxides and particulate matter if electric drive systems are increasingly used. Owing to the fact that electric busses are still in the development phase, higher investment costs have evolved for public transport providers. Hence, decision making about where to introduce electric bus lines is mainly characterized by economic as well as technological considerations. The integration of local or regional ecological aspects is often neglected. An interdisciplinary approach was applied to the bus network of an urban public transport provider. By combining spatial-analytical techniques and statistical methods, the local environmental relief potential of electric busses has been evaluated. The results show that due to their specific line characteristics and the frequency of service, two bus lines out of 28 are particularly suitable for the introduction of electromobility in Dresden, Germany. The presented scientific work contributes to the extension of environmental assessments and decision making tools by including the spatial dimension of environmental impacts. It increases the practical relevance, especially for management decisions of political and entrepreneurial stakeholders by providing a sensible decision basis for local or regional infrastructure projects.

User satisfaction is a key indicator of public service quality, especially for those services considered basic necessities. The conceptualization and measurement of transport service quality—a fundamental determinant of demand—poses challenges for conducting economic analyses and designing mobility policies. Several operating companies are involved in the transport sector. In this paper, the bus network of the metropolitan area of Granada, Spain, is taken as a case study. The aim is to design a model of overall satisfaction based on level of satisfaction using a specific set of factors that take into account the individual characteristics of users and the differential effect of using different bus lines. A combined method using nonlinear principal component analysis (NLPCA) and a logit multilevel model (LMLM) in two steps is applied to a satisfaction survey conducted by the Metropolitan Transport Consortium of Granada (Consorcio de Transporte Metropolitano del area de Granada) in 2013. The survey shows that even though customers within the metropolitan area of Granada are satisfied with the service received (67.26%), the level of satisfaction is not equal for all bus lines, with the perceived quality of some lines being above or below the average. This differential effect is due to different reasons, including the technical and functional performance of the operating companies, commercial speed and length and type of route, among others. Both the operators and the public administration need to focus their attention on these lines in order to design economic policy measures to improve bus lines with below standard compliance.

Public transit systems with efficient designs and operating plans can reduce both total costs and greenhouse gas (GHG) emissions. Total costs are a sum of user cost and agency cost. In previous work, we explored in various hypothetical city scenarios possible tradeoffs between GHG emissions and total costs for optimally designed transit systems. However, most current transit systems were not designed to minimize total costs. This study aims at finding the potential emission impact when optimizing the total costs of such a system, the MUNI bus network for the city of San Francisco. The continuum approximation (CA) method is used to obtain a stylized representation of the network. Total costs and emissions are derived as functions of system attributes such as headways and spacing. We analyze the optimal total costs and emissions and the corresponding attributes. Our result shows that a significant reduction in GHG emissions is achieved when total costs are reduced simultaneously. The cost-optimal MUNI bus system has a total cost of 0.15 billion $/year and emits 1 680 metric tons of greenhouse gases. These figures amount to about half of the cost and a third of the emissions in the current MUNI bus system. The optimal system has a lower spatial availability but a higher temporal availability of bus service than the current system, which highlights the potential benefits of providing more frequent express bus services.

Hub stations and important lines play key roles in transfers between stations. In this paper, a node failure model is proposed to identify hub stations. In the model, we introduce two new indicators called neighborhood degree ratio and transfer index to evaluate the importance of stations, which consider neighborhood stations’ degree of station and the initial transfer times between stations. Moreover, line accessibility is developed to measure the importance of lines in the bus network. Xiamen bus network in 2016 is utilized to test the model. The results show that the two introduced indicators are more effective to identify hub stations compared with traditional complex network indicators such as degree, clustering coefficient and betweenness.

The absence of effective access to opportunities and services is a key contributor to poor socio-economic and health outcomes in underserved neighborhoods in many cities. The city of Columbus, Ohio, USA is attempting to enhance residents' accessibility by providing new public transit services. These new services include a major Transit System Redesign (TSR) of the conventional bus network and the introduction of a new bus rapid transit, named CMAX. Using a high-resolution space-time accessibility measure, we analyze whether these new public transit services will change residents' accessibility to job and healthcare in an underserved neighborhood of Columbus. Also, we assess whether enhancing the CMAX service to reduce delays (e.g., reserved lane, off-board payment system) will improve accessibility. The high-resolution space-time accessibility measure in this study uses published public transit schedules via the General Transit Feed Specification (GTFS). We use multiple departure times during a day to account for the temporal fluctuations of accessibility based on the transit schedule changes. We also consider the operating hours of job opportunities and healthcare services. Results suggest that the TSR yields ambiguous benefits for accessibility to jobs and healthcare. However, the new CMAX service and its potential upgrades lead to a substantial increase in both job and healthcare accessibility. The results can be used for city officials and urban planners to evaluate the effectiveness of public transit innovations in improving accessibility.

In order to characterize the structural features of public transportation systems (PTSs) in mountain cities, this paper systematically examines the robustness of urban transportation networks in Chongqing, a famous inland mountain city in China, and reveals the community feature of mobility patterns of residents. First, according to the transportation information (i.e., bus route data and metro route data) in Chongqing, three static network models, i.e., a bus network, a metro network, and a bus-metro transit network, are proposed based on the spaces L and P. More specifically, as a multiplex network, the bus-metro transit network is modeled for depicting the coupled structure between the bus network and the metro network. By analyzing the attack tolerance of transportation networks under the condition of different attack patterns, we find that the inter-layer coupling between the bus network and the metro network can improve the robustness of an urban transportation system. Furthermore, the community structure of mobility patterns of residents is revealed based on passenger information (i.e., smart-card data) of the metro system, which is caused by the regionalization of residential areas in a mountain city. Experimental studies above can provide guidance for the design and optimization of a PTS.

A recent issue of increasing public focus is the need for robust, sustainable and climate friendly power systems that are intelligent, reliable and green. The intermittent nature of renewable energy generation and the associated power electronic inverters creates a number of potential challenges in integrating large-scale renewable energy (RE) into the grid that affects power quality of the distribution network. Therefore, this study initially, investigates the potential technical impacts in particular voltage regulation, active and reactive power variations, transformer loading and current and voltage harmonics causes with RE integration. Then, to reduce the level of impacts observed, STATCOM and energy storage system (both optimised) were integrated into the network that ensures a smooth power supply to the customers. As a case study, the Berserker Street Feeder, Frenchville Substation under Rockhampton distribution network, Central Queensland, Australia has been considered. Similar analyses also carried out with the IEEE 13 bus network to investigate the potential technical challenges of RE integration and identify suitable mitigation measures. Results shows that integration of both optimised STATCOM and energy storage enhances the overall power quality of the power network as it improves voltage regulation, power distribution, and transformer utilisation and reduce total harmonic distortion of the power network.

In this paper, we establish panel models to examine the relationships between the annual investment in the public transport sector (AIP) and bus network facility scale, and between AIP and the land area for urban roads in different tiers of Chinese cities. The empirical study uses panel data of 160 Chinese cities and a time span over 2000–2015. To capture the characteristics of the panel models, including the individual effects, variable intercepts, and coefficient values, different regression functions are adopted for the relevant analysis. As for these regression functions, seven explanatory variables are selected. These variables are: the length of operated bus transit routes (LR), the total number of annual passengers trips (PT), the land area for urban roads (LA), the density of urban road networks (ND), the resident population (POP), the gross domestic production (GDP), and the value-added tax (TAX). On the other hand, the explained variable in these regression models is the AIP of each city. According to model results, it is found that AIP is positively related to LR and GDP, and negatively to PT, LA, and TAX. However, the effects of ND and POP are negative on the increase of AIPs of mega-cities and medium-size cities. It could also be found that the increase in bus infrastructure investment caused by the expansion of land area for urban roads is not negligible, especially in small cities. When the LR changes greatly, LA should be considered as a more stable factor to predict the AIP in mega-cities. Based on the findings, related implications for the policy-making on annual budget allocations for different tiers of cities are listed.

Designing appropriate quality incentives in contracting out public bus services is essential to ensure that cost savings are not pursued at the expense of deteriorating quality. This study investigates a practical incentive design problem that links additional rewards and penalties to quality indicators of bus services. A leader–follower game model is proposed in which the transit authority (leader) determines the quality baseline of each bus service as well as the marginal reward for outperformance and penalty for underperformance. Subject to a given amount of budget, the transit authority has to guarantee a minimum profit margin for each transit operator so that they have enough incentives to improve the service quality. As a response, the profit-maximizing transit operators (followers) determine the level of service quality by weighing the marginal revenue and cost involved. A tailored solution approach is devised by exploiting the model properties. Motivated by Singapore’s recent trial, a case study is presented to demonstrate how the transit authority can determine the performance baseline as well as the reward-penalty levels for a large bus network and evaluate the amount of public funds needed to achieve a targeted quality outcome. Our analysis also cautions that quality deterioration may surprisingly occur in a few bus services after introducing the incentive scheme. This may be attributed to an unreasonably strict quality baseline and/or an inefficient operator running that service. It is thus suggested that the authority should figure out the real contributing factors and fine-tune the incentive scheme accordingly.

Critical infrastructure networks such as transportation networks, power grids, internet, etc. play a crucial role in the economic development of a country and also in the well-being of its citizens. These systems are however vulnerable to failures due to natural disasters, component aging, terrorist attacks and so on and the resulting service disruptions may result in debilitating impacts on the whole society. Hence, it is crucial to understand how the failure of the components in such a network affects the performance and integrity of the whole network. In order to investigate this, the paper studies the effect of random as well as intentional removal of components (nodes) on the functioning of critical infrastructure networks. The paper also proposes a flow redistribution mechanism incorporating geographic proximities to distribute the load on the failed or removed nodes. Two vulnerability indicators: network efficiency and accessibility have been used to measure the failure consequences in the network after node removals. The proposed framework for vulnerability analysis has been applied to a real world urban transit rail network, a bus network and a combined rapid transit-bus network to test its validity. The results from the study can help to gain foresight in safety management and can thus help to protect the system before failure/harm occurs.

Many transit network layouts require the operation of multiple routes along a common transit segment in the busiest area of the city. At some points, these routes branch out to provide spatial coverage to the city periphery. These schemes allow more efficient deployment of resources at the expense of introducing more complexity into the system operation. This study aims to determine the effect of the branched layout of a corridor, demand distribution and traffic lights on the total cost of the system as well as its service regularity. The transit corridor is operated by buses, although it can be generalized for other transit modes. An operational model to estimate the travel and waiting time of users and the performance of buses on each route segment is developed. The model considers the stochastic effect of the passenger arrivals at stops and vehicle acceleration–deceleration rates. An optimization procedure to determine the optimal headway and relative synchronization of routes, which minimizes the total cost incurred by transit agencies and users or the headway variations in the common route segment, is introduced. Furthermore, a performance evaluation of bus control strategies based on a combination of holding points and green extensions at traffic signals in the H10 cross-town corridor of Barcelona’s new bus network is presented.

Due to the adoption of the Innovative Clean Transit (ICT) regulation, California is transitioning its public transit buses from fossil fuel to zero-emission buses (ZEBs). Due to required high upfront investment for battery electric buses (BEBs), transit agencies must make planning decisions with a system-wide optimization mindset. This study presents a tool to investigate the optimal split between depot charging and opportunity charging for transit networks. A case study was performed on the Unitrans transit system in Davis, California, and it was found that 35 buses would be sufficient to provide the existing level of service over the bus network, which is approximately the same number of buses that Unitrans is currently using to serve those routes. The sensitivity of the relationship between the energy use of BEBs and the deployment decisions was investigated. Decreasing the energy use of the buses by removing the probabilistic effects of drive cycle aggression decreased the maximum required number of buses from 73 to 45 and the interquartile range (IQR) of the number of buses from 10 to 2. The results indicate that a BEB fleet is more sensitive to these changes than a fossil fuel fleet. This relationship needs to be more strongly considered in planning BEB deployments.

In this paper, allocation of hybrid photovoltaic panels, wind turbines and battery storage (PV/WT/BA) system in distribution network is presented aimed active losses cost minimization, voltage profile enhancement and minimizing power purchased from the hybrid system by the network. Meta-heuristic improved whale optimizer algorithm (IWOA) is used to determine the optimal location and size of the PV/WT/BA system components as decision variables. The conventional WOA is inspired by social behavior and the hunting of humpback whales and in this study its performance is improved by using crossover and mutation operators of differential evolution (DE) method to avoid getting caught in local optimal and reinforcement to achieve global optimal. The methodology is implemented on IEEE 33 bus network considering seasonal variations. The results indicated that optimal determination of the decision variables in the network minimizes the active losses cost, voltage deviations and cost of power purchased from the hybrid system by the network using IWOA. The superiority of the IWOA is confirmed in benchmark test functions solution with very competitive results and also better results in statistic analysis and higher convergence speed and accuracy in comparison with the WOA, DE and particle swarm optimization (PSO). Also the results showed that the highest losses and better voltage are related to the summer and the lowest values are obtained in autumn season. The solar panels only participate in energy generation in spring and summer seasons, cost of power purchased by the network is highest in summer and lowest in autumn season. Moreover, the network is placed in stable current and voltage condition and the network power losses and voltage deviations are minimized.

Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

This paper proposes an integrated methodological framework to design a spatially heterogeneous bus route network and time-dependent service headways to serve travel demand that varies over time and space. Travelers choose to use either the transit mode or the driving mode (as well as travel paths in the corresponding modal network) that minimizes their equilibrium travel cost. In addition, transit routes involve dedicated bus lanes that occupy part of the city streets and affect the capacity reserved for private cars. Hence, roadway congestion depends on the transit route design, and its dynamic evolution is described by regional macroscopic fundamental diagrams. The proposed modeling framework consists of two parts: a bus network optimization module based on continuum approximation that produces optimal headways and local route spacing, and a dynamic aggregated network model that determines route choice, mode split, and user equilibrium conditions. An iterative solution algorithm is developed to solve the integrated model. Numerical experiments are used to demonstrate the applicability of the proposed modeling framework, and to conduct a careful analysis on the influence of the demand pattern on the transit network design, roadway congestion, and the overall system performance.

Photovoltaic project with Battery energy storage system (BESS) provide a sustainable and low-cost fuel technology to world and would help to meet world countries decarbonization target. This paper presents a multi-objective based methodology for BESS allocation in distribution networks. In proposed work, objective function combines various benefits from energy arbitrage, energy losses reduction, reduction in transmission access fee and environmental emission and reduction in investment costs of BESS. The new node voltage sensitivity analysis strategy utilized in initial step to recognize and visualize the prominent nodes in system and additionally decreases the search space for genetic algorithm (GA). Battery charge/discharge function is formulated for optimal allocation and every day charge/discharge of BESS. The proposed method is tested on IEEE 33- bus network. The results of this method shows reduction in energy losses, environmental emission and improvement in system parameter are also observed.

Wireless Rechargeable Sensor Network (WRSN) is largely used in monitoring of environment and traffic, video surveillance and medical care, etc., and helps to improve the quality of urban life. However, it is challenging to provide the sustainable energy for sensors deployed in buildings, soil or other places, where it is hard to harvest the energy from environment. To address this issue, we design a new wireless charging system, which levers the bus network assisted drone in urban areas. We formulate the drone scheduling problem based on this new wireless charging system to minimize the total time cost of drone subject to all sensors can be charged under the energy constraint of drone. Then, we propose an approximation algorithm DSA for the energy tightened drone scheduling problem. To make the tasks of WRSN sustainable, we further formulate the drone scheduling problem with deadlines of sensors, and present the approximation algorithm DDSA to find the drone schedule with the maximal number of sensors charged by the drone before deadlines. Through the extensive simulations, we demonstrate that DSA can reduce the total time cost by 84.83% compared with Greedy Replenished Energy algorithm, and uses at most 5.98 times of the total time cost of optimal solution on average. Then, we also demonstrate that DDSA can increase the survival rate of sensors by 51.95% compared with Deadline Greedy Replenished Energy algorithm, and can obtain 77.54% survival rate of optimal solution on average.

We study patterns of entry and exit in the German interurban bus industry in the first three years after its deregulation in January 2013. Using a comprehensive data set of all firm and route entries and exits, we find that the industry grew much quicker than originally expected – with particularly a few new entrants being most successful in quickly extending their route networks from regional to national coverage. Although the clear majority of routes is operated on a monopoly basis, competition does play a key role on routes with a sufficiently large base of (potential) customers. From a spatial perspective, three years after deregulation, the entire interurban bus network connects 60 percent of all 644 larger German cities – with the intensity of entry being dependent on the number of inhabitants, average income, the share of under 24 years old and the presence of intermodal competition by intercity railway services.

With the recent rapid improvement of wireless communication technology, wireless network gains increasing interest as a train backbone in order to provide the passenger service as well as the original real-time control duties. To use the wireless network as a train backbone, however, the existing standard protocol, IEC Std. 61375 Train Communication Network(TCN), needs to be modified to reflect the wireless-specific characteristics because it was originally designed for the wired network such as switched Ethernet. The train topology discovery protocol(TTDP) that provides automatic network reconfiguration is one of the key TCN protocols to be modified to support a wireless train bus. This paper proposes a modified topology discovery protocol that can be used for a train-wide wireless LAN. Proposed protocol finds the bus topology based on the radio signal strength with maintaining the existing standards and controls the transmit signal power to avoid overhearing problem in wireless network. The time bound to build network topology using the proposed protocol is also estimated by computer simulation.

Bus operations, due to their unstable nature, are inefficient when left uncontrolled with respect to retaining headways. Irregularities such as bus bunching lead to loss of time and decrease bus service quality. Development of bus transport system management schemes to avoid bus bunching and improve performance are of high importance, and has thus been the focus of many works in the public transport systems literature. Motivated by the importance of developing bus control strategies for improving performance, and specifically by the lack of a detailed but computationally efficient mathematical model describing bus transport system dynamics in the literature (which can facilitate model-based control design), we propose a mixed logical dynamical model of a single loop bus transport system, which involves both continuous (e.g., bus positions) and binary (e.g., the state of a bus regarding whether it is holding at a certain stop or not) states. Furthermore, we develop a hybrid model predictive control scheme with actuation via bus speeds, which can regularize headways and improve bus service quality. Performance of the predictive controller is evaluated via simulation experiments using the proposed model, where the passenger demands and maximum bus speeds are extracted from data collected from the bus network of the city of Fribourg. Results indicate the potential of the proposed controller in avoiding bus bunching and decreasing passenger travel times.

This paper presents the reconfiguration of the distribution network in the presence of distributed generations (DGs) by considering two bus types i.e., P bus and PQV bus (remotely voltage controlled bus). The ‘P’ bus is represented by active power specification only whereas the PQV bus is one whose voltage is remotely controlled by the P bus. A methodology is proposed to select the P bus for controlling the voltage magnitude of remotely located PQV bus. A sensitivity analysis approach is used for selecting the buses for the placement of DGs operating at unity power factor. The placement of DGs is done in two ways i.e., non-sequential placement and sequential placement of DG in a distribution network. Genetic algorithm (GA) technique is used for the optimization of DGs followed by network reconfiguration. The objective function for network reconfiguration in this paper is considered to be real power loss reduction. Effectiveness of the proposed method is demonstrated through examples of 33 bus and 69 bus distribution networks.

Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a 'source/destination' pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory, works well yet suffers from outliers. To overcome the outliers problem, we use Machine Learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.

This paper presents an enhanced differential evolution algorithm (DE) for solving the directional overcurrent relay (DOCR) coordination problem. Comparisons among different versions of DE for coordination of DOCRs in larger and interconnected power systems are presented. The evaluation criteria consist of fitness value, number of violation and standard deviation. The outstanding DE algorithm has been enhanced in four aspects: dial reduction scheme, elitism and mediocrity scheme, mutation index renew scheme, and population reduction scheme. The enhanced-DE (eDE) shown best result in terms of execution time, result quality, robustness and convergence ability compared to the others analyzed in this research. The IEEE 14 and 57 bus systems are used to validate the proposed algorithm. A significant comparison result between eDE and DE on the IEEE 57 bus network are: execution time in seconds 364 vs. 2040, fitness value 1.933 vs. 7.91, and standard deviation of fitness value 0.018 vs. 5.52. Results are averaged values in 50 runs.

The purpose of this paper is to model and evaluate the performances of a bus network using Colored Petri Nets (CPN) and (max, +) Algebra. Our main contribution is to describe CPN with (max, +) equations in order to evaluate buses timetables and also boarding, disembarking and waiting times of passengers. Furthermore, the influence of buses limited capacity on passengers waiting times is studied. Simulation results will illustrate the applicability of the proposed methodology.

User satisfaction is one of the key factors in running a successful city bus service from both passengers’ and operators’ perspective. To improve the facilities provided by public buses, a performance analysis is essential. Conventional empirical models, such as generalized linear mode (GLM) and multiple regression models have been used in several studies to predict bus performance characterizing parameters and overall performance of bus network system. This paper proposes a methodology for evaluating the overall performance provided by the bus network system at different operating conditions(viz. Office Days, Weekend Holidays, Festive Days, Rainy Days) prevailing in Dhaka Metropolitan city. A Structural Equation Model (SEM) is used to reveal the observed and unobserved performance defining parameters and their relationship with the overall performance of bus network system. Under the study, performance of the public buses has been identified by processing field survey data according to a suitable mathematical procedure. For this purpose, a total of 400 passengers of buses were interviewed with a predetermined structured questionnaire at different operating conditions to know their experience, level of satisfaction and opinion about the existing service as well as their expectations. Some attention-grabbing results have been obtained. Variation of parameters significance at different operating conditions has been shown. Three latent parameters have been found representing the overall bus network performance. And among the three of them roadway geometry obtains the highest weightage on overall bus network performance. Among the 14 observed parameters, number of private car obtains the highest weightage. However, during rainy days drainage system gets the highest weightage. This study also generates a ranking of the parameters that influence the overall performance of bus network system at different operating conditions. The study findings can be utilized by the city transportation officials to improve overall bus network performance to attract new passengers and retaining the current ones.

Arterial coordinated control and transit signal priority are effective ways to alleviate traffic congestion. Transit vehicles must enter the bus stops for passenger boarding and alighting. The research of transit signal priority should consider the impact of bus stop locations under the condition of arterial coordinated control. The objective of this paper is to analyze and optimize the bus stops layout under the conditions of coordinated control. The restrictions of relocated bus stops on public transportation planning were explained, and the impact of relocated bus stop on bus trajectory and arterial coordinated control was analyzed. The simulated optimization method was applied to optimizing bus stops layout when compared with the mathematical optimization method. Then, the arterial simulation model with ten signal controls and eight bus stops was built, and typical layouts of bus stops were planned. One bus stop at one link was defined as “single” layout, near- and far-side bus stops at the same link was defined as “pairing” layout. Single layout for some bus stops and pairing layout for others defined as “mixed” layout. Simulation experiments of each layout at the same coordinated control and traffic volume were carried out, and performance index such as delay was evaluated. The simulation result shown that delay of all bus stops at pairing layout was less than all at single or mixed layout. Furthermore, the ideal layout module composed with three bus stops at some segments was proposed, and suggestion of relocated bus stops based on ideal layout module was presented.

As the basic travel service for urban transit, bus services carry the majority of urban passengers. The characterisation of urban residents' transit trips can provide a first-hand reference for the evaluation, management and planning of public transport. Over the past two decades, data from smart cards have become a new source of travel survey data, providing more comprehensive spatial-temporal information about urban public transport trips. In this paper, a multi-step methodology for mining smart card data is developed to analyse the spatial-temporal characteristics of bus travel demand. Using the bus network in Guangzhou, China, as a case study, a smart card dataset is first processed to quantitatively estimate the travel demand at the bus stop level. The term ‘bus service coverage’ is introduced to map the bus travel demand from bus stops to regions. This dataset is used to create heat maps that visualise the regional distribution of bus travel demand. To identify the distribution patterns of bus travel demand, two-dimensional principal component analysis and principal component analysis are applied to extract the features of the heat maps, and the Gaussian mixture model is used for the feature clustering. The proposed methodology visually reveals the spatial-temporal patterns of bus travel demand and provides a practical set of visual analytics for transit trip characterisation.

Traditional irrigation system in developing countries led to disorganized growth in agricultural load which is creating stress on grid by increasing losses. Therefore, to reduce the effective peak loads of the annual seasons 11 kV rural feeders emanating from Jaipur city_Dis-com, having 33 kV grid sub-station named as Jamwa-Ramgarh is selected for study. Test system is a model of two hundred & thirty bus network planned as per actual connection topology. Case study is carried for 33 kV GSS encircling 230 bus network by estimating and fulfilling VAR requirements with adding solar generation too. The relative study of system with & without solar integration has been simulated. Different loading scenarios (50, 70 & 90 %) are taken care as per agriculture season requirements. Feeder loss identification, network loading level, transformers loading level of the test system are studied with real time data. Lastly detailed researched outputs of savings and earnings to be obtained from solar and VAR integrated model of test system is presented.