Bus Lines Research Articles

Spatial Modeling of Travel Demand Accounting for Multicollinearity and Different Sampling Strategies: A Stop‐Level Case Study

Stop‐level ridership data serve as a basis for various studies toward increasing bus patronage and promoting sustainable land use planning. To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop‐level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop‐level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop‐level ridership variable, thus enhancing the goodness‐of‐fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well‐spread in the geographic space might be preferred to accurately estimate missing stop‐level ridership data. In addition to solve the lack of stop‐level ridership data, supporting a reliable bus system planning, the proposed method indicates what predictors should be addressed by policymakers to stimulate a transit‐oriented development. The method can be successfully applied to other travel demand variables facing a lack of data such as traffic volume in road segments and mode choice at the household level.

A study on reduction of route bus service and population along bus line in local urban area

A study on reduction of route bus service and population along bus line in local urban area

A microscopic public transportation simulation framework based on machine learning

The evaluation of performance of public transportation, such as bus lines for example, is a major issue for operators. To be able to integrate specific and local behaviors, microscopic simulations of the lines, modelling each buses on a daily basis, brings an actual added value in terms of precision and quality. A scientific deadlock then appears regarding the parameterization of the simulation model. In order to be able to gather relevant performance indicators on a potential evolution of the configuration of the line, validated and modifiable simulation models need to be developed. This study aims at proposing a model development methodology based on a multi-agent simulation framework and data inputs extracted by a hybrid approach combining machine learning (ML) trained on actual bus data to predict travel times and probabilistic distributions to accurately estimate travel time variability. It also aims to propose a two-step validation framework that exhibits the performance of the obtained model on a case study based on actual data. The results of the proposed approach are validated by a real case study of three bus lines, including a number of simulation scenarios, to study the impacts of bus recovery time and bus control strategies on bus punctuality. The results obtained show that proposed hybrid approach combining ML with probabilistic distributions outperforms probabilistic distributions on average. Overall, the results show a good fit with the actual Key Performance Indicator (KPI) used by bus operators.

Stability-Constrained Contingency Analysis for Modern Power Systems

Modern power systems comprise diverse nonlinear components, and an increasingly large number of low inertia renewable power sources necessitate the modernization of conventional power system security measures. Contingency analysis (CA), a routine process for power system operators, ensures grid security under unforeseen circumstances by identifying potential issues and enabling proactive measures for uninterrupted power flow. Electromechanical oscillations (EMOs) in the power system that are a threat to stability must be regularly monitored and mitigated. An online hierarchical EMO index integrating time and frequency response analysis can be utilized for system stability assessment. The integration of an EMO index threshold into contingency analysis is presented in this paper to enhance system security. This new approach is referred to as the stability-constrained contingency analysis (SCCA). Typical results for a modified two-area, four-machine power system with large solar photovoltaic plants simulated on a real-time digital simulator (RTDS) are presented. These results demonstrate that SCCA flags potential issues that can arise from EMOs for certain contingencies, whereas traditional CA does not, as it solely considers bus voltage limits and line ratings.

Optimal control of electric automated buses in intercity lines: scenario-based analysis of the Pareto-optimal solutions

This paper deals with the optimal control of electric and automated buses that have to follow an intercity line, where some stops are equipped with a flash-charging infrastructure to charge the batteries, while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the roads of the bus line, and to minimize two objectives related to the deviations from the bus timetable and the lack of energy, at the end of the bus trip, with respect to a desired final energy level. The resulting optimal control problem is a multi-objective linear quadratic problem with both continuous and integer variables. In this paper, we propose and apply two methods based on lexicographic ordering aimed at determining the Pareto-optimal solutions of the control problem in twelve scenarios inspired by a real case.

Valuation of Bus Characteristics in Urban Area: Al-Najaf as a Case Study

Increased population, the rapid growth of vehicles owned, and quick land-use shifts have resulted in an abnormal rise in trips generated and directed to and from the activities area; the occurrence of traffic congestion in different segments of the transportation network leads to extended travel time, resulting in inconvenience and discontent among individuals. It has become necessary to provide alternative transportation systems, as city residents need faster, safer, and more comfortable transportation systems. This study aims to evaluate current public transport in Najaf city and propose a bus line based on international standards as an alternative transport means in al-Najaf city and its effectiveness. Sustainable performance indicators were evaluated based on previous studies, such as traffic congestion, travel time, sustainability, and reliability. Where Traffic flow rates indicate high congestion streets, as flow rates reached 6228 (Veh. /hr.) at Karbala-Najaf Street. The more increase in flow rates the more defect in the network, and the journey time increases reached 30 minutes at Kufa-Najaf Street (8,5) km. The data of traffic mix from most of urban roads have been determined. The results of studies stated that Public The forms of transportation in the city of Najaf are generally represented by several routes starting from the Najaf internal garage and passing through small buses. These routes do not cover the entire city and the actual need for people. The percentage of transport buses and minibuses is about 0.8% and 10%, respectively. The public transport in the city is not managed by any policies and has no schedule times for travels with no considerations for passengers' comfort which indicate that public transport sector in the city of Najaf is significantly poor.

Line Congestion Management in Modern Power Systems: A Case Study of Pakistan

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large‐scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO2 emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large‐scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system’s secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind‐integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real‐time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight‐bus and five‐machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units.

Modelling bus-based substitution capabilities for metro systems using bipartite graphs

Abstract A disruption of metro services can have a negative impact in the performance of a city’s transportation system and hinder mobility needs of travellers. Investigating the vulnerability of metro systems is required for planning mitigation actions, such as bus substitution services. This study develops a model, which consists of a bipartite graph and its projection to represent the bus substitution capabilities for metro networks. The proposed methodology effectively identifies significant substitution elements (bus lines), evaluates the robustness of alternative options in terms of both connectedness and connectivity, and suggests effective strategies for enhancing bus line capacity to improve network robustness. By applying the methodology to a real-world metro network, valuable insights are gained regarding important bus lines and substitution robustness. Study findings suggest that approaches based on the weighted degree exhibit the greatest effectiveness when it comes to connectivity and the overall efficiency of the network. These findings can assist public transport operators in proactively managing disruptions and improving their services.

Evaluation of energy consumption and electric range of battery electric busses for application to public transportation

Vehicle fleet electrification is the main target of the following decades, as a measure to decarbonize the transport sector. Electric urban busses consist of a solution for upgrading the bus fleet aiming to an improvement of urban areas and city centres air quality. To electrify the bus fleet at the city of Athens, Greece, the responsible organization, Athens Urban Transport Organization S.A. (OASA), organized a pilot program for using battery electric buses (BEB) in existing bus lines. The goal of the pilot program was to evaluate the energy consumption of the selected busses under real operation scenarios. The main target of this study is to describe the combined experimental and simulation methodology followed for the evaluation of the BEBs’ . The experimental part of the study is based on the monitoring of the BEBs under real operation for a specific bus line. During the operation of the vehicles, energy consumption along with environmental conditions and A/C usage were monitored. The simulation models were used to predict the energy consumption under different driving conditions and quantify the impact of operating parameters on energy consumption. Experimental results showed that the average daily energy consumption ranged between 96 kWh/km and 220 kWh/km, values strongly related to ambient temperature. Simulations highlighted that A/C usage can lead to two times higher energy consumption for the same route and load. Finally, the expected electric range of buses considered in the study calculated between 130 km and 170 km for the selected line, load equivalent of 25 passengers and 7 kW of A/C consumption.

A bilinear interpolation model for estimating commercial vehicles' fuel consumption and exhaust emissions

In this paper, an estimation model based on bilinear interpolation of data from the EMEP/EEA air pollutant emission inventory guidebook database regarding fuel consumption and exhaust emissions of commercial vehicles in different operating conditions is developed. Depending on vehicle's average speed, road slope and load factor, the model determines the fuel consumption and emissions for the observed vehicle type, category and emission standard. In order to validate the model, recordings on actual commercial vehicles were made in real operating conditions on two urban public transport bus lines in Belgrade. Comparison of the estimated and actual results from vehicle operation substantiate satisfactory validation of the model, making it suitable for logistics managers in operational and tactical planning as easy to use decision making tool allowing better sustainability and lower environmental footprint.

Bus Line Shift Behaviour: Evidence of Influential Factors based on Smart Card Data

This paper aims to analyse the factors that influence the behaviour in relation to bus line shift with the support of large-scale smart card datasets, focussing on a case study of the public transport network in Belo Horizonte, Brazil. We developed a binary discrete choice model to assess the factors affecting the changes of the bus line, including the time of day, peak periods, day of the week, number of trips taken by users, type of user, and destination of the trip. We address some research gaps identified in the recent literature by examining factors that influence user behaviour in addition to uncovering urban mobility patterns. The results of a Logit choice model are discussed considering the attributes of the service and of the journey to support evidence-based policy making for adapting bus service design, drawing insights from extensive smart card dataset analysis. Our findings indicate that users are generally inclined to bus line shifts than using the same lines, with such changes occurring more frequently during late hours and inter-peak periods compared to the morning and afternoon peak hours. Additionally, regular users are more likely to change lines than occasional users, and trips with discounts and smart card usage for transfers on trips home tend to involve different lines. Several policy measures can be considered to mitigate passenger discomfort associated with changing bus lines and to reduce operational costs arising from multiple lines serving the same locations. These include investing in promotional efforts to enhance the image of specific bus lines, prioritising certain lines during the day to minimise travel times and retain passengers on the same bus, adjusting line frequencies to reduce the need for transfers, and offering additional incentives to frequent users. Implementing discounts in corridors with shorter travel times can also encourage passengers to remain on the same buses.

Routing and charging optimization for electric bus operations

The transition to alternative energy sources and the adoption of on-demand operating modes in urban bus systems are crucial steps towards reducing carbon footprints and improving public transit services. This paper presents a two-phase approach for the collaborative optimization of charging schedules and passenger services, aimed at enhancing the operation of on-demand electric bus systems. First, we propose a label-setting dynamic programming algorithm that enables the efficient generation of bus-trips for each bus line in response to passenger requests. Second, we introduce a time–space network optimization model that facilitates integrated multiple bus-trip planning for the transit network, involving multiple bus lines and charging spots. The model selects bus-trips from various time–space arcs, which represent passenger carrying, bus deployment, and bus charging activities. To validate the effectiveness of our approach, we conduct a case study using real-world data from bus lines in Beijing, China. Computational results demonstrate that our approach can handle on-demand electric bus operations within minutes of solution time, efficiently serving over 2,000 passengers. Practically, our approach achieves a notable reduction in average transit time and effectively reduces the waste of public transit resources. The proposed approach can serve as a beneficial tool for decision-makers and operators seeking to enhance the performance and environmental impact of their electric bus systems.

What can be done with today’s budget and demand? Scenarios of rural public transport automation in Mühlwald (South Tyrol)

Rural public transport is typically limited in its coverage, frequency and service period. This is often linked to a low and dispersed demand, which makes the provision of competitive transport services often financially unsustainable. Autonomous vehicles (AVs) might change this condition, allowing for an upgrade of public transport in rural areas. Nevertheless, current studies are mostly focused on urban areas, while the potential for rural applications remains underexplored. This paper contributes to this research line by developing a set of so-called AV scenarios for a potential upgrade of public transport in the rural study area of Mühlwald (South Tyrol, Italy). These scenarios are designed following three core principles. First, line-based and on-demand applications of AVs are not only individually tested, but even combined over space and time in different manners to exploit their synergy. Second, the performances of the scenarios are quantified by assuming the today´s agency cost budget, public-transport demand and peak-hour system capacity to be fixed parameters to comply with. Third, the uncertainties regarding the impacts of AVs on the agency costs are taken into account by defining optimistic, neutral and pessimistic variants of each scenario. Results indicate that the full replacement of current bus lines with a system of on-demand shared taxis might provide the highest performance improvements with the same budget as today, but only with a much bigger fleet. At the same time, the combination of bus lines and on-demand services over time (and space) might provide similarly competitive results while keeping the needed fleet size, service distance and service time relatively low. With this study, policy makers may get insights into the potential improvements of rural public transport that might be initially obtained with different uses of AVs, given a fixed agency cost budget and demand.

Prioritization of crowdsourcing models for last-mile delivery using fuzzy Sugeno–Weber framework

Modern technologies provide new opportunities for the industry but raise the expectations of the customers as well. This is valid also for the postal, courier, and logistics industries. The biggest challenge for the companies in the field is to improve the efficiency of the final phase in the transfer of shipments – last-mile delivery. A contemporary solution relates to collaboration between delivery companies and citizens who are willing to perform the delivery tasks as an additional activity to their established job or routine. Such a concept is known as crowdsourcing. There are several approaches within crowdsourcing, for example, transportation by car, utilizing public transport, etc. To make an appropriate choice, multiple criteria should be considered. This paper aims to propose an original methodology for solving the explained multi-criteria decision-making problem. The proposed framework is based on the application of the Sugeno–Weber nonlinear functions in a fuzzy environment. The application of the fuzzy Sugeno–Weber weighted assessment methodology showed that the proposed methodology has adaptability, a high degree of generalization, and stability of results. This enables the applicability of the methodology to various tasks from a broad spectrum of areas, which represents an additional value. A real-life case study is provided to solve the prioritization of crowdsourcing models in suburban municipalities of Belgrade, Serbia. The available alternatives and the most important criteria for the crowdsourced delivery model are identified. All criteria are divided into four groups, which define the dimensions of sustainability, including the technical aspect. The results of the conducted research indicate a high level of convenience in using one’s car or daily bus lines for crowdsourced delivery to optimize the entire process of shipment transfer in the observed territory.

Planning a zero-emission mixed-fleet public bus system with minimal life cycle cost

AbstractThe variety of available technology options for the operation of zero-emission bus systems gives rise to the problem of finding an optimal technology decision for bus operators. Among others, overnight charging, opportunity charging and hydrogen-based technology options are frequently pursued technological solutions. As their operating conditions are strongly influenced by the urban context, an optimal technology decision is far from trivial. In this paper, we propose an Integer Linear Programming (ILP) based optimization model that is built upon a broad input database, which allows a customized adaption to local circumstances. The ultimate goal is to determine an optimal technology decision for each bus line, considering its combined effects on charging and vehicle scheduling as well as infrastructural design. To this end, we develop technology-specific network representations for five distinct technologies. These networks can be viewed individually or as a multi-layered graph, which represents the input for the optimal technology mix. The proposed optimization framework is applied to a real-world instance with more than 4.000 timetabled trips. To study the sensitivity of solutions, parameter changes are tested in a comprehensive scenario design. The subsequent analysis produces valuable managerial insights for the bus operator and highlights the decisive role of certain planning assumptions. The results of our computations reveal that the deployment of a mixed fleet can indeed lead to financial benefits. The comparison of single technology system solutions provides a further basis for decision making and demonstrates relative superiorities between different technologies.

Transport supply management on regular intercity bus lines

Introduction. Intercity public transport ensures the satisfaction of mobility not only for long-distance passengers, but also for residents of small settlements located on a regular line. There are often no other types of regular public transport between such settlements. The growing demand for intercity bus transportation increases the need for efficient transportation design in order to provide competitive public transport services and minimize the costs of carriers.Materials and methods. The article presents a mathematical model of transportation planning on an intercity regular bus line passing through settlements with different numbers of inhabitants. The objective function is aimed at forming a transport supply that satisfies potential demand, for which the gravitational method is used to determine. The analysis of the unevenness of passenger flows by months of the year, days of the week and flights shows that on most flights the capacity of the rolling stock is not used efficiently enough.Results. By comparing the potential number of passengers on flights with the actual one, it is shown that flexible regulation of transport supply taking into account demand will significantly improve the quality and efficiency of public transport services.Discussion and conclusion. Based on the data for 2022 of the booking system serving the regular bus line, it is shown that with the introduction of flexible regulation of the transport offer, the expected increase in passenger turnover will be about 25%.

Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current

Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation.

Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers

Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.

Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression

Metro systems provide a mass and sustainable mobility option for urban populations. Drawing on the ridership data in 2019 released by the Shanghai Municipal Transportation Commission, we examine the spatially varying effects of station-level factors measured with different metro-station catchments (MSCs) on the daily and hourly ridership through multiscale geographically weighted regression (MGWR) models. The results indicate that independent variables measured with relatively small and non-overlapping MSCs can better explain the spatial variations in metro ridership. Regarding the important determinants, land use intensity (LUI) demonstrates positive effects diminishing from the core to the peripheral areas, transfer lines (TL) also exhibits positive effects that decrease from southwest to northeast. The effects of road density (RD) and building density (BD) are negative in central urban areas but positive in suburbs, which is the opposite of bus lines (BL). Additionally, the explanatory variables have significantly different ranges of influence, and the range of influence for a single variable also varies across models that use ridership at different times (e.g., weekdays vs. non-weekdays, the morning/evening peak vs. non-peak hours) as dependent variables. This study expands the knowledge of the spatial variations in the relationship between metro ridership and the geographical determinants. The findings can provide empirical evidence and implications for urban planners in formulating context-specific policies to improve the usage of metro systems.

Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture Model

Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems: it can help passengers make informed decisions on departure time, route choice, and even transport mode choice, and it also support transit operators on tasks such as crew/vehicle scheduling and timetabling. However, most existing approaches in bus travel time forecasting are based on deterministic models that provide only point estimation. To this end, we develop in this paper a Bayesian probabilistic model for forecasting bus travel time and estimated time of arrival (ETA). To characterize the strong dependencies/interactions between consecutive buses, we concatenate the link travel time vectors and the headway vector from a pair of two adjacent buses as a new augmented variable and model it with a mixture of constrained multivariate Gaussian distributions. This approach can naturally capture the interactions between adjacent buses (e.g., correlated speed and smooth variation of headway), handle missing values in data, and depict the multimodality in bus travel time distributions. Next, we assume different periods in a day share the same set of Gaussian components, and we use time-varying mixing coefficients to characterize the systematic temporal variations in bus operation. For model inference, we develop an efficient Markov chain Monte Carlo (MCMC) algorithm to obtain the posterior distributions of model parameters and make probabilistic forecasting. We test the proposed model using the data from two bus lines in Guangzhou, China. Results show that our approach significantly outperforms baseline models that overlook bus-to-bus interactions, in terms of both predictive means and distributions. Besides forecasting, the parameters of the proposed model contain rich information for understanding/improving the bus service, for example, analyzing link travel time and headway correlation using covariance matrices and understanding time-varying patterns of bus fleet operation from the mixing coefficients. Funding: This research is supported in part by the Fonds de Recherche du Quebec-Societe et Culture (FRQSC) under the NSFC-FRQSC Research Program on Smart Cities and Big Data, the Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Teams grants, and the Natural Sciences and Engineering Research Council (NSERC) of Canada. X. Chen acknowledges funding support from the China Scholarship Council (CSC). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0214 .