Bus Lines Research Articles

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction

A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.

How to improve public environmental health by facilitating metro usage on weekend: exploring the non-linear and threshold impacts of the built environment

IntroductionThe accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage.MethodUsing multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership.ResultsResults show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10.ImplicationResearch findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

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

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

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

A machine-learning-based column generation heuristic for electric bus scheduling

Bus scheduling in public transit consists in determining a set of bus schedules to cover a set of timetabled trips at minimum cost. This planning process has evolved recently with the advent of electric buses that introduce constraints related to vehicle autonomy and battery charging process. In particular, column-generation algorithms have regained popularity for solving problems similar to the one considered in this paper, namely, the multi-depot electric vehicle scheduling problem (MDEVSP) with a piecewise linear charging function and capacitated charging stations. To tackle large-scale MDEVSP instances, we design a column generation (CG) heuristic that relies on reduced-sized networks to generate the bus schedules. The reduction is achieved by selecting a priori a subset of the arcs. Multiple selection techniques are studied: some are based on a greedy heuristic and others exploit a supervised learning algorithm relying on a graph neural network. It turns out that combining both selection types yields the best computational results. On 405 artificial instances involving between 568 and 1474 trips and generated from real bus lines in Montreal, the network reduction technique produced an average computational time reduction of 71.6% (compared to the same CG heuristic but without network reduction), while deteriorating solution cost by an average of 2.2%. On 8 larger instances containing more than 2500 trips on average, the proposed solution method also provided an average time saving of 52.5% with an average gap of 4.2% thanks to a transfer learning approach.

Examining bus service quality among clusters of commuters: the case of Maputo Metropolitan Area, Mozambique

ABSTRACT The Maputo Metropolitan Area's bus transport service is comprised of both formal and informal operations, characterized by declined service quality. To improve service quality, the Maputo Metropolitan Transport Agency is transitioning to formal operations. The study aims to determine who experiences substandard bus service and where, to inform targeted actions. Data from 8,095 bus users were analysed using cluster analysis, Sankey diagrams, and multicriteria analysis, revealing four distinct behavioural clusters (C1–C4). C1 (Non-frequent-early evening) and C4 (Non-frequent-early morning) commuters experience low and very low service quality, respectively, needing increased bus frequency and easy-to-board buses. C2 (Non-frequent-daytime) commuters enjoy high service quality but could benefit from dedicated bus lanes to further improve frequency and convenience. C3 (Frequent-short travel time) commuters experience very high service quality but require direct bus lines from the suburbs to the CBD to reduce travel costs. The study findings form a basis for targeted bus service improvements.

Machine learning-assisted microscopic public transportation simulation: Two coupling strategies

Evaluating the performance of public transportation, such as bus lines for example, is a major issue for Public Transportation operators. To be able to integrate specific and local behaviors, microscopic line simulations, modeling each buses on a daily basis, provide actual added value in terms of precision and quality. Carrying out more realistic and accurate simulations requires the use of appropriate parameters. To achieve this, machine learning models trained on real-world data can be used to feed and parameterize simulation models. To address this scientific question, it is necessary to determine how to efficiently integrate machine learning and simulation models. This study aims to couple machine learning and microscopic simulation models using various strategies, evaluate their accuracy and performance and discuss the advantages and drawbacks of each. A case study involving three bus lines was conducted, with results validated against real-world data, showing a good fit for both online and offline strategies. With the best simulation time, good accuracy and adequate travel times and bus punctuality, an offline strategy seems to stand out from other coupling strategies.

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

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

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Optimizing Customized Bus Lines Considering Users' Transfer Willingness under Cooperative and Competitive Relationship between Metro and Online Car-hailing

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct aquestionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

Beyond binary relationship: Multivariant analysis between ride-hailing and public transit based on multi-sourcing data

The impact of ride-hailing (RH) as an emerging mode of travel service on public transit (PT) systems has been confirmed. However, the current research only views the relationship between PT and RH as competition or complementation based on macro statistics and travel time differences. In fact, the relationship is beyond binary, and it is partial to take the travel time difference as the only classification factor. We constructed a Gaussian mixture model (GMM) using RH data in Xi’an, and three indicators of travel time, cost, and service quality difference were used to classify the relationship between RH and PT. To clarify the factors influencing the relationship classifications, a Multinomial logistic model (MNL) was constructed with the built environment, economic factors, and travel purpose. The results show that the RH-PT relationship can be generally classified into four classifications: Competition (26.5%), RH superiority (47.7%), PT superiority (13.6%), and Irrelevance (12.2%). Competition occurs mainly around metro stations, RH superiority mainly during working hours in outer urban areas, and PT superiority is most widely distributed in the morning peak. POI density and the number of bus lines are positively correlated with Competition, RH superiority, and PT superiority. In addition, there is significant spatial heterogeneity in the RH-PT relationship, for which we constructed a Geographically weighted regression (GWR) model to analyze it. We find that the spatial heterogeneity may stem from the spatial autocorrelation and the spatial disparities in the distribution of regression coefficients. Therefore, policymakers should formulate policies to transform competition from multiple perspectives.

A seamless bus network without external transfers using autonomous modular vehicles

Recently, autonomous modular vehicles (AMVs) capable of enabling in-motion transfer (IMT), i.e. passengers moving between vehicles while the vehicles are traveling, have been developed. IMT allows new paradigms for public transport operations that can enhance the speed, flexibility, and reliability of the system. We leverage IMT technology to propose the first network-wide seamless, stop-less bus service concept. We recently introduced the concept of a Stop-Less Autonomous Modular (SLAM) bus service, which eliminates the need for passengers to stop at each bus stop while traveling along a bus line. The current work extends this paradigm to the network level by developing a bus service framework in which multiple SLAM bus lines operating on main arterials interact by exchanging pods among each other to provide passengers with a travel experience that not only eliminates most extra passenger stops within a single line, but also eliminates external transfers, i.e. it removes the need to disembark at a bus stop to change buses when transferring from one bus line to another. The result is a high-speed service that provides a completely seamless journey from the passenger’s origin stop to destination stop anywhere in the coverage area, with integrated (i.e. internal) transfers that take place inside the vehicles themselves while they are in-motion. Our results show that while the proposed service has more stringent operating requirements than a comparable conventional bus service, it provides significantly superior performance, reducing passengers’ travel cost by up to 35% under realistic system settings.

Resilience-Based Approach to the Measurement of Headway Inconsistency

Maintaining a consistent headway is a primary goal in bus operation, but it poses a significant challenge because of fluctuating traffic conditions and uneven passenger demand. This leads to inconsistent headways and even bus bunching, resulting in negative consequences such as increased travel time and reduced vehicle capacity. Various control strategies have been developed to alleviate headway inconsistencies, but most of them have focused on whether an inconsistency occurs while overlooking its duration. This study proposes a resilience-based approach to measure headway inconsistencies considering both the depth and duration of the impacts with deviations from the optimal headway. The approach is devised to analyze the cumulative impacts of inconsistent headways comprehensively. It goes beyond addressing only extreme cases, such as bus bunching, and considers persistently uneven headways. We first identify vehicle trajectories from smart card data, then compute deviations from the center of the preceding and following vehicles as an index of impact. Using the concept of resilience, cumulative impacts are measured and their distribution is utilized to set the failure criterion. The approach is applied to 320 bus lines in Seoul to measure the deviations of headways and is used to analyze the characteristics of the bus lines and links where inconsistencies frequently occur. The proposed method enables transportation planners to identify factors that can lead to headway inconsistency, facilitating tailored and data-informed planning processes. This study provides a comprehensive framework that recognizes line-specific and link-specific factors, thereby contributing to enhancing the reliability and efficiency of public transportation systems.

Urban Infrastructure Construction Planning: Urban Public Transport Line Formulation

Urban public transport line formulation has its appeal in promoting public convenience and developing environmentally friendly cities. During the bus line planning stage, the line frequency and stop location determination is a key issue for decision makers. Our study focuses on the integrated formulation problem between line frequency and stop planning featuring multi-type vehicles. The multi-type vehicles are able to accommodate the various passenger demands at either peak hours or off-peak hours. The a priori magnitudes of user demands are investigated by drone-based technique methods in the tactical-level plan. The collected geospatial data can assist the public transport user forecast. A mixed-integer linear programming (MILP) model is proposed. The objective is to minimize the walking cost of passengers, the building cost of stops, and the operation cost of service frequency. The effectiveness of the model is validated by a real case in Nantong, China. CPLEX is used to resolve the MILP model. Yielding to the budget constraint, in high-price, medium-price, and low-price scenarios, the optimal high-quantity stop scheme can save 3.04%, 3.11%, and 3.38% in overall cost compared with the medium-quantity stop scheme, respectively; their cost savings are 8.53%, 8.70%, and 9.09% more than the costs of the low-quantity stop scheme.

A novel high-efficient lithium-ion battery serial formation system scheme based on partial power conversion

The formation of the battery is a crucial step in its manufacturing process. The existing battery formation system suffers from low efficiency and high energy consumption costs due to long energy flow paths, high DC bus line losses, and additional balancing circuit applications. Therefore, a novel high-efficient battery series formation system (BSFS) that combines partial power processing architecture (PPPA) with the modular converter is proposed to address this problem in this article. The PPPA-BSFS includes two-stage isolated sub-modules consisting of Buck-Boost converter and LLC converter with input-serial and output-parallel connection. The isolated converter only processes the serial compensation power, about 1/3 of the full power. Consequently, the converter capacity can be shrunk in the PPPA-BSFS, which can reduce the system's cost and power loss. Furthermore, the paper establishes the steady-state mathematical model and energy efficiency model of PPPA-BSFS based on analyzing the power compensation principle, and the key factors affecting the operational efficiency of PPPA-BSFS are studied. Finally, we construct the simulation models with 10 cells and 120 cells to compare and analyze energy efficiency between PPPA-BSFS and existing battery formation topology under various operating conditions. Our simulation results show that the highest operating efficiency of the PPPA-BSFS proposed is 99.37 % when 120 cells are formed normally, where there is an increment of 5.95 %, 2.36 %, and 0.85 % compared with others, respectively. The high efficiency of the PPPA-BSFS is verified, and the PPPA-BSFS shows great potential for application in battery formation scenarios.

THE CHOICE AND REASONING OF THE BUS RAPID TRANSIT SYSTEMS FOR CITY TRANSPORT

The presented research is focused on the design of a new driving cycle of the Bus Rapid Transit (BRT) for the Kyiv city. This cycle consists of a complex of two types of sections. Each of them includes various phases of the traffic. A mathematical model was specified, which serves for defining the efficiency properties indices and for regimes of bus transport. The BRT are used within the bus lines. It was found, that two articles articulated buses are the most suitable choice, the MAZ-215 among them, for exploitation. At the same time, they provide the smallest fuel consumption together with the shorter operation time on the bus line as well as higher average speed at full and half loading. The optimal driving speeds of the steady traffic are the values of 50 km/h for section up to 1 km and 60 km/h or 70 km/h for section over 1 km. It was evaluated, that depending on the steady motion speed and the load level, the driving time of the BRT on the line varies from 20.1 min to 23.4 min, which is much lower than the driving time of a trolleybus on the same line according to the traffic schedule.

Riding the Wave: The Growing Preference for Public Transit System among Customers

Public transport is essential for urban mobility, reducing traffic congestion, and minimizing environmental impact. However, inadequate public transport conditions in Karachi have shifted interest towards private vehicles. The Green Line Bus Rapid Transit (BRT) System aims to offer a modern alternative and improve the current situation. This study aims to explore the role of technological developments, best practices, and standard operating procedures (SOPs) in improving service quality and attracting people to public transportation in Karachi. A cross-sectional study and survey were conducted in Karachi to gauge residents’ perceptions and expectations of the Green Line BRT System. Data were collected from 450 participants using a non-probability sampling technique. The findings reveal that technological advancements contribute to improved service quality and are considered significant by consumers when evaluating and selecting public transport services. Improved road quality further reinforces this connection. These insights are essential for policymakers and transport authorities to develop strategies that increase the attractiveness of public transportation and ensure sustainable urban mobility. Addressing core issues with modern technological methods can improve public transport systems and inspire further research.

Surviving at a Falling Transportation Industry with the Help of Emotional Relationship with your Customers

Introduction Public intercity transportation is an example of a rapidly evolving industry. In developing countries, we observe the increasing popularity of airlines, whereas bus lines start to lose their significance. Methods The successive bankruptcy of bus companies, which have made a large amount of investment and provided significant employment, is a case worth examining. This article offers a case study from the Turkish public intercity transportation industry in which a bus company distinguishes itself by becoming a love brand with the relationship it establishes with its customers. The case shows that the emotional relationship established during the ups and downs of sectors have become insurance that protects companies during the downturn. Results To support our argument, we estimated the occupancy rates of bus companies based on characteristics such as ticket price, travel time, existence of catering service, social media interaction, and year of establishment. We found that the significant variables explaining occupancy rates are the companies' founding year and positive social media interaction frequency. Conclusion In an estimation where even the price is not a significant variable at the 0.01 level, the significance of the above-mentioned variables shows that relationship-based dynamics can be important demand determinants in a conventional transportation sector.

Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

AbstractIn order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.