Passenger Cost Research Articles

Modelling reservation strategies for managing peak-hour stranding on an oversaturated metro line

Metro is the main travel model for urban commuters in many metropolises around the world. During peak hours, large numbers of passengers pour into metro stations for rail services, but some are unable to board the trains in time and left stranded on the platform or even queuing outside the stations. The trip reservation (TR) strategy, where passengers preplan their trips and reserve their entry time to the stations. This paper develops an entry reservation strategy (ERS) to optimize the commuter flow during peak hours, and construct a multi-objective passenger flow joint optimization model based on many-to-many passenger demand to minimize the total trip cost of passengers at reservation station and the number of stranded passengers at intermediate stations. The passenger flow optimization problem is formulated as a mixed-integer non-linear programming (MINLP) model. We design an iterative sequential search algorithm combined with the GUROBI solver to obtain the parameters of the optimal ERS and the passenger flow distribution in the metro system after disaggregated reformulation of the complex constraints of the model. We also demonstrate the accuracy and effectiveness of the proposed method with two experiments – an illustrative example and a large-scale case study of Beijing Metro. The results of Beijing Metro experiment show that the joint optimization model with entry reservation strategy (JO-ERS) reduces the number of stranded passengers by 88.46 % compared with the original passenger flow from the AFC.

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

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

Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers

This study examines a ride-hailing platform that employs a mixed fleet of autonomous vehicles (AVs) and human drivers to offer on-demand ride-hailing services across a network over a finite planning horizon. In the mixed fleet, AVs have a fixed fleet size and are centrally managed by the platform, while human drivers can dynamically enter and exit from the ride-hailing market based on the prospective earning opportunities at various time periods. We introduce a game-theoretic model that encapsulates the strategic behavior of human drivers, passengers, and the ride-hailing platform. In this model, passengers choose between ride-hailing and alternative transportation options by considering fares and waiting times. Human drivers schedule their working hours in response to potential earnings and strategically relocate to maximize passenger pickups, while the platform adjusts pricing and AV repositioning strategies to maximize its profit. The solution is characterized as a Nash equilibrium within a two-player game, with one player representing the ride-hailing platform, and the other as a virtual representative of the collective human drivers. To address the non-convex nature of the game, we employ a convex relaxation technique to ascertain a near-optimal solution framed as an ϵ-Nash equilibrium, with ϵ accurately characterized. The proposed model and solution algorithm are validated in a case study for Manhattan, New York City. The simulation results underscore that the distribution of human drivers is closely aligned with the immediate, minute-to-minute fluctuations in passenger demand, enabling them to effectively meet the surge during peak demand windows. Conversely, the deployment patterns of AVs are more attuned to the extended, daily cycles of passenger movements throughout different city zones, from daytime to nighttime. We further compare the mixed fleet model with scenarios where the platform exclusively utilizes human drivers or AVs, respectively. The comparison show that the mixed fleet model excels in balancing the supply and demand over space and time, thereby leading to shorter wait times and reduced travel cost for ride-hailing passengers, as well as improved profit for the ride-hailing platform.

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.

Transport capacity optimization for high-speed rail network considering flexible train composition and additional capacity pool

To balance the limited capacity and constantly changing demand in the high-speed rail network, a transport capacity optimization problem is investigated by adjusting train composition through uncoupling/coupling operations at the origin station, scheduling additional trains from the capacity pool, and optimizing the seat allocation among ODs. To make joint decisions, a two-stage stochastic programming model is formulated to minimize both operating cost and penalty cost for unsatisfied passengers, while incorporating various constraints such as flow conservation, loading capacity, flexible composition, etc. The two-stage model is linearized into a mixed-integer linear programming model, which can be effectively solved for small networks but is still intractable to solve for realistic networks. Therefore, a Lagrangian relaxation algorithm is proposed integrating parallel computing and sub-gradient method. Finally, two sets of case tests, including a toy network and a real-world case of a high-speed rail network in China, are implemented to show the performance of the proposed approach. The results illustrate that the parallel heuristic algorithm could generate a high-quality solution with a slight discrepancy from the optimal solution, while achieving a commendable computation time. Furthermore, the findings emphasize the advantages of applying flexible train composition and additional capacity pool in the high-speed railway system for cost-saving and supply–demand matching.

An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs.

ON THE ISSUE OF DETERMINING THE PRODUCTION COST OF THE CAPITAL RENEWAL OF PASSENGER CARS

In recent years, the inventory of passenger cars has decreased significantly due to the exclusion from service of the ones that have already exhausted their service life. The wear and tear of the passenger rolling stock of Ukrzaliznytsia JSC is approaching 90%. Repair and maintenance costs for outdated railcars account for over half of the industry’s expenses. In conditions of an acute shortage of newly built cars, practically the only way to maintain the operability of the car fleet is to overhaul passenger cars that have exhausted their service life. At the same time, based on the results of operations in different regions, the railway cars have different technical conditions. Also, different car repair enterprises use differing methods for calculating the cost of repairs. It leads to a significant scatter of calculation results. The article is devoted to the issue of creating a generalised methodological approach to determining the production cost of passenger cars overhaul. We have classified production costs and determined which expenses relate to the direct costs of car repair production, the auxiliary production costs, and general production costs. Next, we formulated the components of each of these groups. The article considers the main aspects influencing the formation of costs, particularly labour costs and social contributions, material costs, fuel, electricity, repairs, and equipment depreciation. It also takes into account the cost of return expenses. The proposed scientific approach allows accounting for the actual service life and level of wear of the passenger car, its type, and design features. We carried out an analysis of the factors affecting the production cost of repairing passenger cars. It makes it possible to determine rational technical and organisational solutions to reduce the cost of overhauling passenger cars that have exhausted their service life. The stated provisions have a practical orientation and can be used at the car repair enterprises of the Passenger Company branch of JSC Ukrzaliznytsia. Keywords: passenger car, repair, resource, wear and tear, production cost, expenses.

Evolution of the revenue/ expenditure structure and passenger cost coverage ratio of regular passenger transport between 2007 and 2022

This article presents a general methodology for aggregating the actual costs and revenues of public transport in Hungary, which allows to calculate a realistic passenger (user) cost coverage ratio for the main players in the domestic market (MÁV-Volán Group, GYSEV, BKK). After an international perspective and a presentation of the specificities of the domestic market, it is concluded that the cost coverage ratio for fixed rail transport is significantly lower than for public transport services on road. The main reason for this is that fixed rail infrastructure is expensive to develop and operate and is mainly used by public passenger transport. The ridership ratio is significantly reduced by significant investments, and this has been analysed both on a time series and average basis. We present the timetable, tariff changes and major investments that have had an impact on ridership rates, typically only to a small extent. Our analysis is financially oriented and only touches on positive externalities, social benefits and the general government balance

Optimization of ticket pricing for high-speed railway considering full competitive environment

The rapid development of high-speed railways (HSR) worldwide has provided a fast, convenient, safe, and comfortable mode of transportation. It is also an important public transportation tool for green travel, contributing to the reduction of carbon emissions. HSR has a strong competitive advantage in long-distance passenger transportation, but it faces significant competition from the aviation industry. Airlines attract passengers with discounted fares and differentiated pricing strategies, apart from shorter travel times. Therefore, it is necessary for railway enterprises to enhance their competitiveness through differentiated pricing methods for HSR. In this study, we focus on optimizing a long-distance origin-destination (OD) pair and creating a competitive market environment by considering all operating trains and flights for the selected OD pair. We aim to improve the realism of the study by considering passengers’ fully rational travel choice behavior. To reduce the complexity of variables, passengers are grouped based on their expected departure time and income level. A mixed integer linear programing model is developed to address the problem, aiming to minimize overall travel costs for passengers while ensuring that the income of the railway enterprise does not fall below a specified value. By setting this specific value, the maximum revenue of HSR train tickets can be obtained.

Study on Optimization of Departure Interval of Pure Electric Bus in Airport Area

As a zero-emission, pollution-free and pure green public transport, pure electric bus has received more and more attention from society and enterprises. At present, most urban public transport lines in China have basically realized pure electric bus. With the popularization of pure electric bus, the operation and management of pure electric bus in specific areas are required. How to reasonably set the departure time interval, configure the number of vehicles and reduce the charging cost while meeting the travel needs of passengers and the enterprise cost as low as possible has become a hot issue in the research of pure electric bus operation management. In this paper, from the perspective of system cost, a pure electric bus departure interval optimization model is constructed, which considers the comprehensive cost of passengers and enterprises to be minimum. The load factor constraint and departure interval are considered as the main constraints, and the optimal departure interval is obtained.

Network design with route planning for battery electric high-speed passenger vessel services

This paper studies the Zero Emission passenger Vessel Service Network Design Problem (ZEVSNDP) in order to investigate how technical and economic challenges related to diffusion of battery electric vessels can be alleviated by appropriate planning of services. The ZEVSNDP considers decisions that are strategic (i.e., vessel fleet and charging locations), tactical (i.e., routes, whether to omit servicing ports, fleet deployment, and operating frequencies), as well as operational (i.e., passenger flow, sailing speeds, and scheduling decisions). A novel Mixed Integer Programming (MIP) model considering operator and passenger costs is proposed for the ZEVSNDP. Since the MIP model cannot be solved to optimality by a commercial solver except for tiny instances, we implement a heuristic Decomposition Based (DB) solution method. The DB solution method is applied to a real complex passenger vessel service in Florø, Norway, as well as two other test instances focusing on short-range transport and dense markets, respectively. Except for the short-range test instance, abatement costs (i.e., the costs of removing CO2 emissions by introducing battery electric vessels) are found to be significant. This is attributed to limited reach and time used for charging of battery electric vessels. Routes should consequently accommodate range limitations: omitting ports from the current route can be a cost-effective strategy when the cost of alternative transport for the passengers is moderate.

Understanding market competition between transportation network companies using big data

As in a typical two-sided market, the competition between transportation network companies (TNCs) can lead to market fragmentation and loss of matching efficiency between passengers and drivers, whereas a monopoly market may result in the dominant TNC abusing its market power. Therefore, whether to encourage or discourage competition between TNCs is a debatable question for cities. Prior studies explored this question mostly through mathematical equilibrium models, but few have comprehensively investigated it based on empirical analysis using real-world data. To fill this gap, this study proposes a framework to measure and analyze the competition between TNCs using the most accessible ride-hailing trip data. Specifically, using an interpretable machine learning model, we investigate how TNCs’ pricing and wage strategies influence their market shares and how competition intensity affects passenger cost and driver income. The results based on large-scale trip records from four TNCs in New York City show that the pricing strategy is more influential than the wage strategy on the market shares and competition intensity. Instead of the top TNC, it is the strategies of challenger TNCs (with sizeable but not the biggest market shares) that affect the competition more. Both the passenger cost and driver income can benefit from competition even after considering the potential loss of matching efficiency, while TNCs’ profits shrink with growing competition intensity. These findings suggest that cities should encourage competition between TNCs, yet within a limit. They add empirical evidence to prior studies and provide implications for regulating TNC competition.

Optimal planning of an urban ferry service operated with zero emission technology

While passenger-only ferries can be an effective instrument in mitigating road congestion in urban areas, they are among the most polluting modes of transportation. This paper studies technical and economic feasibilities of a battery-powered high-speed ferry service in Oslo, Norway. An urban ferry planner problem that minimizes ferry operator and passenger costs and external costs of road transport subject to strategic (fleet selection and infrastructure location), tactical (service frequency) and operational (vessel speed) decisions is proposed. While the results show that zero emission technologies can pass the cost-benefit test for a short-range service, competitiveness hinges on energy costs and capacities and on the performance of the existing service. Counterfactual scenarios show substantial cost reductions from altering the current ferry route. Anticipated increase in external costs of road transport from closing the ferry service is also much smaller than the system costs of maintaining the urban ferry connection.

A line planning approach based on time-varying demand for high-speed rail under the combined operation of periodic and aperiodic services

Passenger demand for high-speed rail systems is highly fluctuating in temporal and spatial throughout the day. In order to effectively improve passenger satisfaction and service level for high-speed rail, the formulation of line plan must make the distribution of the trains’ capacities be meet with the time-varying demand of passengers. The aperiodic line plan can well meet this demand since it is more flexible with different stop patterns and various headways. However, the irregular arrival/departure times are not convenient for travel planning. We propose a new type of line plan that would combine both the regularity of the periodic line plan and the flexibility of the aperiodic ones. An optimization method of high-speed railway line plan based on a combination of periodic and aperiodic operation is structured, which can well satisfy the temporal-spatial distributions of passenger demands in different time periods. The main challenge is to coordinate the numbers of periodic and aperiodic trains, and their temporal-spatial distributions on the rail network considering the passengers’ train-choice decisions. Based on generating a set of candidate routes and constructing a passenger travel network, we propose a model to optimize the numbers of both periodic and aperiodic trains, and their paths, stops, approximate arrival and departure times at each visited stations for minimizing the total operation cost of trains and the total generalized travel cost of passengers. Then, a simulated annealing algorithm is designed to efficiently solve our proposed optimization model. Finally, numerical experiments based on the Shanghai-Nanjing intercity railway in China verify the effectiveness and practicability of the optimization method.

Optimization of smart bus scheduling based on dynamic speed limit of load rate

Aiming at the problem that fixed speed limit and static regular scheduling of single-type bus cannot meet the actual passenger flow demand with uneven distribution of time and space. By designing a dynamic speed limit rule of full load rate, based on the integrated scheduling of multiple types of bus models, taking the total passenger cost and bus scheduling cost as the overall optimization objective, and considering various constraints comprehensively, OBLESGA (Opposition-based learning elite strategy genetic algorithm) was designed to solve the problem, and the data of Beijing 563 route downlink was taken as an example for simulation experiment. The results show that OBLESGA can improve the convergence rate and solution accuracy of GA. At the same time, the optimized scheduling can reduce the cost of passenger congestion by 86.61 percent and the cost of passenger waiting times by 40.65 percent compared to traditional scheduling, while only increasing operating costs by 26.57 percent.

Data-driven distributionally robust timetabling and dynamic-capacity allocation for automated bus systems with modular vehicles

The collaborative design of the timetable and dynamic-capacity allocation plan of emerging modular vehicles (MVs) is a promising solution to the mismatch between supply and demand in public transportation studies; however, such efforts are subject to high-level dynamics and uncertainty inherent in operating environments. In this study, we focus on the timetabling and dynamic-capacity allocation problem of MVs within the context of distributionally robust optimization under time-dependent demand uncertainty. The dynamic capacity refers to the number of modular units (MUs) comprising an MV can be potentially changed at different times and stops. A Wasserstein distance-based ambiguity set with a time-dependent and station-wise perturbation parameter is adopted to incorporate all potential distributions within a 1-Wasserstein distance for addressing the uncertainty of passenger demand. Further, a data-driven distributionally robust optimization model that considers time-varying capacity is formulated to minimize passenger waiting costs and dispatching costs of operators over all possible demand distributions within the ambiguity set. Subsequently, an expansion that allows for flexible formations of MVs assigned to each trip at each stop is proposed, and this results in more customized operational plans driven by the passenger demand. To improve the computational efficiency of realistic problems, we design a customized integer L-shaped method to exactly solve the models, which incorporates a class of valid equalities to further speed up the computation. The effectiveness of the proposed approaches in reducing the costs for both passengers and operators compared with the practical fixed-capacity operations is verified by real-world case studies based on the operating data of Beijing Bus Line 468. Furthermore, the superiority of the distributionally robust optimization method in comparison to the stochastic programming and the robust optimization approaches is demonstrated.

Artificial Intelligence Assisted Intelligent Adjustment Method for Urban Rail Transit Train Operation

<p>The operation of intercity rail transit has greatly relieved the pressure of urban traffic. In order to improve the operation quality and passenger carrying capacity, the scheduling strategy of urban rail needs to be timely adjusted according to the passenger flow and other disturbing factors, especially the traffic control problems brought by the outbreak of the epidemic. In this paper, according to the epidemic situation and the characteristics of peak passenger flow in the morning and evening, an optimization model is designed to minimize the travel cost of passengers and the daily cost of the urban rail operation company. The optimal solution of the model is found through the reinforcement learning algorithm. Finally, based on the parameters of Shijiazhuang Metro, the optimal train scheduling scheme is obtained through simulation, which verifies the effectiveness of the research method in this paper.</p> <p> </p>

Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time

This study presents a biobjective hub-and-spoke (HS) network design model for the global air passenger networks. The model explores the tradeoff between the total airline cost (airline preference) and lost time cost for passengers (user preference) as the model’s objectives. Most previous studies have focused on airline objectives and established HS networks based on the viewpoint of airlines, despite the importance of passenger objectives. Poor passenger service and inconvenience and dissatisfaction may lead to network breakdown. The major criteria for passenger dissatisfaction in HS networks are schedule and trip delays caused by nondirect flights. These delays (the lost time cost for passengers) are multiplied by the passengers’ value of time (VOT) and minimized as one of the model’s objectives. Another objective that is minimized is the transportation costs of the airline depending on the services provided (short-, medium-, and large-haul flights). The model is solved in a case study (Iranian Aeronautics Network) that is applied to the well-known yearbook of tourism statistics data. Pareto frontier was found for all candidate airports. Also, the number of aircraft required (short-, medium-, and large-haul), as well as the average load factor for different types of aircraft in various weights of the first objective (airline costs), was presented. The results of Pareto frontier indicated that Imam Khomeini International Airport should be selected as the global hub airport for Iran international flight network. Otherwise, Shiraz International Airport and Tabriz International Airport (as the first alternative), as well as Isfahan International Airport and Mashhad International Airport (as the second alternative), would be the best choices. The weight of the first objective (airline costs) seems to be between 0.7 to 1, a practical and logical weight that can reduce passenger costs (as the second objective) by 20% on average by adding only 15 long-haul, 40 medium-haul, and 37 short-haul aircraft to the airline’s fleet. Also, in this range, the average load factor for medium- and long-haul aircrafts is greater than 0.9, which seems to be ideal.

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling.

Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely considered in the rolling stock scheduling. An integrated optimization solution is proposed which considers the line plan, timetable, and rolling stock schedule. Candidate service routes are generated according to the layout of the turn-back stations. Considering the constraints of operation and passenger flow demand, an integer nonlinear programming model is established to minimize the cost of operation and passenger waiting time. The model complexity is analyzed and based on its decomposability a deterministic search algorithm is designed. Taking Chongqing Metro Line 3 in China as an example to verify the effectiveness of the proposed model and algorithm. Compared with the train operation plan based on manual experience and compiled by stages, the integrated optimization model can better improve the quality of train operation plan.

Analyzing the trade-off between minimizing travel times and reducing monetary costs for users in the transit network design

The efficiency of the planning process in public transport is represented through different measures, which are practically impossible to optimize simultaneously. This study defines a bi-objective optimization problem for the transit network design to analyze the trade-off between minimization of travel times and reducing monetary costs for passengers (which was not addressed in the literature) while considering a hard constraint for operational costs. Indeed, the minimization of monetary costs for passengers is relevant in transport systems without a complete integrated fare system, where passengers may pay for each trip-leg; thus, modeling monetary costs for users is essential when referring to the system’s accessibility and route choice. To achieve our goal, we implement an epsilon-constraint algorithm capable of obtaining high-quality approximations of the Pareto front for benchmark instances in hours of computational time, which is reasonable for strategic planning problems. Numerical results show that the conflict between both objectives is evident, and it is possible to identify the more useful lines to optimize each objective, leading to relevant information for the decision-making process. Finally, we perform a sensitivity analysis on the budget parameter of our optimization problem, showing the classic trade-off between the operational costs and the level of service in terms of travel time and monetary cost.