Passenger Travel Cost Research Articles

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.

Railway line planning with passenger routing: Direct-service network representations and a two-phase solution approach

The railway line planning problem (LPP) plays a crucial role in determining the quality of services provided to passengers, as well as operation costs borne by railway companies. In periodic railway LPPs, it is common to consider passenger transfers between train lines to realize a general passenger travel cost setting in the railway system. While detecting passenger transfers requires incorporating passenger routing into mathematical formulations, thereby significantly complicating the problem. Studies on transfer-included LPPs are generally based on the Change&Go network that is constructed based on a pre-given line pool, which however is usually non-exhaustive due to computational intractability. To efficiently include passenger transfers in large-scale railway LPPs, this paper proposes a novel extended direct-service network representation of LPP, where lines are dynamically generated within the optimization process, and part of passenger transfers between lines can be precisely captured without the need for explicit modeling of passengers’ distribution on specific lines. A two-phase solution approach based on the representation is designed. The first phase formulates LPP with part of transfers as a path-based service network design model, solved using a branch-price-and-cut algorithm. The second phase conducts a neighborhood search around the first-phase solution to seek better ones when considering all passenger transfers. Numerical results showcase the good performance of the two-phase solution approach. It delivers optimal solutions in 18 out of 24 test instances for a small case and achieves optimality gaps within 2.85% across all small instances. The large case study of China’s Shandong high-speed railway network whose line pool size reaches millions demonstrates the scalability of the approach and its advantage over the traditional Change&Go method with partial line pools and an exact model developed in the paper.

Integrated Optimization of Train Diagrams and Rolling Stock Circulation with Full-Length and Short-Turn Routes of Virtual Coupling Trains in Urban Rail Transit

The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.

Scheduling shared passenger and freight transport for an underground logistics system

With the introduction of freight transportation into the passenger transit network, underground city logistics are regarded as a desirable alternative to address the challenges due to truck movements in urban freight transport. Furthermore, some metro lines suffer from low-capacity utilization because of the unbalanced demand, which provides the potential to expand the extra capacity for freight transportation. Therefore, this study integrates the train unit scheduling problem with combined transportation of passengers and freight during off-peak hours. The objective is to fully utilize the remaining capacity by not only integrating passenger and freight flows but also allowing a flexible composition such that the capacity can better meet the demand. The composition of a trip is defined as its state. A three-dimensional space–time–state network is constructed to capture the composition transitions and passenger/freight trajectories. The problem is formulated as an integer linear programming model to minimize the weighted sum of train unit operational, passenger travel, and freight travel costs. Utilizing the problem-specific characteristics, a constrained-gap-based branch-and-bound approach is developed to efficiently solve the model. The worst bound for each objective is guaranteed by introducing two gaps in the passenger- and freight-related objectives. The nodes with estimated lower bounds exceeding the designated gaps are pruned. A beam search procedure is also included to further reduce the computational complexity. The developed algorithm is tested on real-life instances from the Beijing Metro Network. The results provide insights into the benefits and applicable scenarios for integrating passenger and freight transportation. Moreover, we demonstrate that the number of train units should be carefully determined considering the tradeoff between passenger service quality and freight demand volume.

A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands

In this study, an effective travel cost (ETC) traffic equilibrium model is proposed for multi-modal networks with uncertain demands. The multi-modal networks are transformed into supernetworks and travel demands are assumed to be closed intervals. Passenger flows and travel costs are also formulated as closed intervals to capture the effects of uncertain demands. The ETC concept is introduced and regarded as a choice criterion to develop an equilibrium model which captures travellers’ travel mode and route choice behavior under interval travel costs. The model is formulated as a variational inequality problem, and the method of successive average algorithm is adapted to interval mathematics to obtain the results in the form of interval variables. Illustrative examples are also presented to demonstrate the model’s characteristics and its differences from the traditional equilibrium model, in which the expected travel cost is regarded as the choice criterion.

Optimising Electric Bus Departure Interval Considering Stochastic Traffic Conditions

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management.

Bi-objective optimization of last-train timetabling with multimodal coordination in urban transportation

When urban rail transit (URT) does not provide 24-hour services, passengers who travel at late night may not be able to reach their destinations with only URT trains. As a result, passengers have to find alternative transport means, or combine URT trains with other transport services to fulfill their journeys. This paper investigates the integrated optimization of last train timetabling and bridging service design with consideration of passenger path choices. Two bridging services are considered: taxis and buses. Based on pre-constructed path sets, a bi-objective mixed-integer nonlinear programming (MINLP) model is developed, aiming at minimizing total passenger travel time and total passenger travel cost. To reduce the model scale and improve solution efficiency, three path dominance principles are proposed to remove redundant passenger paths without loss of optimality. An adaptive iterative algorithm is designed to obtain the Pareto frontier curve. The proposed model and solution methods are demonstrated on the Chengdu URT network. Results indicate that passenger travel costs and travel times can be significantly reduced by the integrated optimization. It also provides passengers with a safer night travel environment due to the reduction in passenger travel times in taxis.

Multi-depot vehicle scheduling with multiple vehicle types on overlapped bus routes

In China, overlapped routes from multiple bus lines are widespread. Some buses are overcrowded while others have few passengers. The goal of this study is to reduce onboard passenger crowding without wasting bus capacity. This paper examines an extended multiple-depot vehicle scheduling problem (VSP) with multiple vehicle types. First, a bi-objective integer programming model was created. The trade-off between operating cost and passenger travel cost was established and the crowding cost due to different vehicle sizes was especially considered. Then, the capacity restraint incremental assignment algorithm and a vehicle scheduling method with multiple vehicle types were developed to find Pareto-optimal solutions that considered vehicle capacity constraints. Finally, the proposed methodology was validated using a real-world transit network in Beijing, China. The optimization results from about 1.18 million bus card swiping records show that our model and solution algorithm can effectively reduce the passenger crowding cost and avoid the waste of transport capacity.

Bi-objective bus scheduling optimization with passenger perception in mind

With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs. Improving the classical Genetic Algorithm (GA) by adaptively determining the crossover probability and mutation probability of the algorithm. We use an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the Dual-CBSOM. Taking Qingdao city as an example for optimization, the constructed A_DPGA is compared with the classical GA and Adaptive Genetic Algorithm (AGA). By solving the arithmetic example, we get the optimal solution that can reduce the overall objective function value by 2.3%, improve the bus operation cost by 4.0%, and reduce the passenger travel cost by 6.3%. The conclusions show that the Dual_CBSOM built can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost. It is demonstrated that the A_DPGA built in this research has faster convergence and better optimization results.

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.

IMMUNER: Integrated Multimodal Mobility Under Network Disruptions

Targeting at improving door-to-door accessibility, efficiency and reducing environmental impact, recent decades have witnessed vigorous development of multimodal transport. Coupled through passenger transfer, the failure of one mode, however, is likely to cause cascading disruptions to the complete system. Therefore, to maintain safe and efficient operations, the integrated recovery of the multimodal transportation system becomes an important, timely topic, given increasingly complex schedule interactions. This study proposes a mathematical model to handle the integrated recovery problem of multimodal transportation network by minimizing the passenger travel cost and mode recovery cost under failures of multiple critical infrastructures. For a case study on air/HSR interaction in China, our model incorporates decisions in terms of aircraft recovery, railway timetable rescheduling, and passenger routing with realistic constraints (e.g. max travel time, max transfer time). A column-and-row generation based heuristic solution algorithm is developed in order to solve the complex model with given computational budgets. Results reveal that real-world test disruption scenarios can be solved within 20 minutes with a small relaxation gap. A significant amount of passenger traveling time can be reduced in contrast to a sequential recovery approach with independent reaction decisions for each transport mode. This work contributes towards the development of support tools for integrated disruption management of multimodal transportation.

A Schedule-Based Model for Passenger-Oriented Train Planning With Operating Cost and Capacity Constraints

In the planning stage, train operators design timetables to serve passenger trips and a train circulation plan to support these timetables. These designs consider not only operating costs but also passenger convenience. In this study, we developed an optimization model for a new problem that focuses on timetabling and train-unit scheduling while also considering passenger itinerary choices in a schedule-based train system. This optimization model minimizes passenger travel costs within the constraints of a limited budget available for operating costs. The model is solved by an iterative heuristic that simulates the interaction between train operations and passenger itinerary choices. The heuristic solves the timetabling and train-unit scheduling problem using a decomposition approach to increase computational efficiency, while passenger loading is solved by a user-equilibrium passenger assignment model. An example based on the high-speed railway network in southern China was used to demonstrate the effectiveness of the proposed model and method.

Analysis of Competition Relationship of Multiairport Regional Airport Market Based on Deep Learning

At present, China’s civil aviation industry is in a period of rapid development. The passenger traffic is increasing year by year. The contradiction between the demand and supply of flight resources is becoming more and more prominent, and the competition between airports is becoming more and more fierce. Driven by local development and the country’s overall planning, there are more and more multiairport areas, so how to analyze the airport market competition in multiairport areas needs to be further realized. This paper constructs a two-dimensional Hotelling model based on flight frequency and passenger travel cost to analyze the internal mechanism of market competition among multiple airports. It is assumed that, in a multiairport area, two airports are distributed at both ends of a linear straight line, and the other airport is in between, and each airport provides displacement services connecting the same destination airport. The research shows that, (1) with the increase of the flight frequency of the airports at both ends, its equilibrium profit shows a trend of decreasing first and then increasing; the difference of the flight frequencies of the airports at both ends affects the equilibrium profit of the middle airport. (2) With the gradual increase of flight frequency, the reduction of passenger travel cost will make the equilibrium market share and equilibrium profit show a trend of first decreasing and then increasing. (3) Taking the Yangtze River Delta regional airport as an example, it is found after verification that increasing flight frequency or reducing passenger travel costs can increase the balanced profit and market share of Hangzhou Xiaoshan Airport and Nanjing Lukou Airport; increasing flight frequency can improve the balanced profit and balanced market share of Sunan Shuofang Airport.

A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention

Because the strategy of stopping bus lines during an epidemic can negatively impact residents, this study proposes a bus passenger flow control model to optimize the safety of and access to bus transport. The information interaction environment can provide a means for the two-way regulation of buses and passengers. In this model, passengers first request their pick-up and drop-off location, and then the bus feeds back information on whether it accepts the request. Through this method, passenger flow control can be realized through complete information interaction. The study aimed to establish a multi-objective function that minimizes the weighted total cost of the safety cost, the passenger travel cost, and the bus travel cost during an epidemic. The constraints were the full load and riding rates of urban buses in peak periods under the condition of epidemic prevention and control. The results showed that, in the morning peak period, the passenger flow control scheme reduced the passenger infection probability by 17.89%, compared with no passenger flow control scheme. The weighted total cost of the epidemic safety cost, the passenger travel cost, and the bus operation cost was reduced by 8.04%. The optimization effect of the passenger flow control scheme of this model is good, and not only reduces the probability of passengers being infected, but also meets the requirements of epidemic prevention and the travel needs of residents.

Research on the combinatorial optimization of EBs departure interval and vehicle configuration based on uncertain bi-level programming

ABSTRACT Based on uncertainty theory, this paper proposed a vehicle scheduling method considering the dynamic departure interval and vehicle configuration of electric buses (EBs). An uncertain bi-level programming model (UBPM) is established, which takes the total cost of passenger travel (CP) as the upper and total cost of EBs (CB) as the lower. A chance constrained programming model (CCPM) based on the randomness of passenger waiting time and the uncertainty of interstation running time is proposed as the upper model. With a certain confidence level of service level as constraints, the goal is to minimize the total cost of passenger travel. Then, an expected value model (EVM) based on the fluctuation of energy consumption is proposed as the lower model. Taking the number of EBs as the constraint condition, the goal was to minimize the energy consumption of EBs. Finally, a practical bus route is taken as an example to verify the effectiveness of the proposed method. The results demonstrated that the optimal scheduling plan considering the uncertain variables can reduce the passenger travel cost. Collaborative optimization of EBs vehicle configuration can reduce energy consumption, delay, and the number of EBs.

Evaluation of Public Welfare Level of Urban Rail Transit considering Operation Management

Evaluating the public welfare level of the urban rail transit systems has not only great significance for the government to provide fair and reasonable subsidies but for the better operation and management of urban rail transit enterprises. An evaluation index system composed of 3 criterion-level indicators and 12 subindexes has been established in this paper. The 3 criterion-level indicators conclude service level, social benefit, and policy loss which are all affected by operation management. Besides, the subjective and objective comprehensive weighting method combined with the analytic hierarchy process method and the entropy weight method is proposed to calculate the index-level weights. Furtherly, the grey correlation-TOPSIS comprehensive evaluation method was designed to calculate the comprehensive evaluation value of the public welfare level of each city. To verify the effectiveness of the proposed method, urban rail transit systems in 16 Chinese cities are studied as a case study. The results show that (1) the three indicators of passenger travel cost (25.69%), the increase in housing prices around urban rail transit(10.74%), and operating cost ratio(9.95%) are more likely to affect the evaluation of public welfare level of urban rail transit. (2) the level of public welfare in different cities is not balanced. The cities with a relatively high level of public welfare relative closeness exceeding 0.5 include Shanghai, Beijing, Shenzhen, Guangzhou, Suzhou, Wuhan, Nanjing, Wuxi, and Dalian. (3) Both GDP and urban population are positively correlated with the relative closeness of social benefit and service level. (4) The level of public welfare can be improved by reducing the fare price and improving the service level, such as increasing the network density, reducing the departure interval, and increasing the average speed.

Optimization for Feeder Bus Route Model Design with Station Transfer

To fully take the advantages of conventional bus and subway, and to maximize the overall feeder efficiency of the public transport system, the topic of feeder bus route optimization is studied in this paper. Considering the origin destination demand of passenger flow between subway stations and bus stations and transfer characteristics, the objective function is established with the minimum sum of bus operation cost and passenger travel cost. Taking into account the integrity of the feeder bus route, the rationality of the route, the route capacity and the station transfer factors, the constraints of the optimization model are constructed. Based on the idea of the genetic algorithm, the solution algorithm of the optimization model is developed. The genetic algorithm and enumeration algorithm are used to solve the optimization of the feeder bus route in this case, and the accuracy and efficiency of the solution are analyzed. The influence of the number of feeder bus routes on the system in the case network is compared and discussed. We compare and analyze the differences between the original bus network and the feeder bus network in terms of bus operation cost, passenger flow demand and total passenger travel cost. The research shows that the model and algorithm can find the approximate optimal solution of the feeder bus network scheme related to the subway through fewer iterations. The number of routes in the model has little impact on the whole feeder system, and the optimization scheme using five routes is effective and reasonable in this paper. Compared with the existing bus network, the optimization scheme has obvious advantages in improving the passenger-carrying rate, reducing the per capita travel cost and improving the overall operation efficiency of the system.

Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data

The aim of this study was to explore the bus operating state of the city bus passenger corridor, taking the minimum bus operating cost and passenger travel cost as the objective function, taking passenger flow demand and operating income as the constraint, and considering the average speed change of the bus line in the bus corridor at different times. This paper proposes a dynamic optimization model of bus route schedule based on bus Integrated Circuit Card (IC Card) data. The optimization variable is the departure frequency of the candidate lines. To solve the model, a dynamic departure interval optimization method based on improved Genetic Algorithm (GA) was designed under different decision preferences. The method includes the calibration of generalized cost functions for passengers and bus companies and grasps the characteristics of bus operating speed changes and the design of departure strategies under different decision preferences. The validity and applicability of the proposed method are verified by a numerical example. We mainly carried out the following work: (1) Dynamic analysis of the time dimension of the bus departure interval takes into account the changes in passenger time characteristics during peak periods. (2) Seven schemes of weight ratio of passenger waiting time cost and bus operation cost were designed, and the departure intervals with different benefit orientations of passengers and operators were discussed, respectively, so as to select the corresponding departure schemes for decision makers under different decision preferences. The results show that (1) the total cost of the 7 different weighting schemes is lower than the actual value by 6.90% to 18.20%; (2) when decision makers need to bias the weight to the bus company, the weight ratio α : β between passengers and bus company is 0.25 : 0.75 which works best. The frequency of departures has been reduced by 6, and at the same time, the total optimized cost is reduced by 18.2%; (3) when decision makers need to bias the weight to the passengers, the weight ratio α : β between the passengers and bus company is 0.75 : 0.25 which works best. The frequency of departures has been increased by 19, and at the same time, the total optimized cost is reduced by 17.7%; and (4) when decision makers consider passengers and bus companies equally, the weight ratio α : β between passengers and bus companies is 0.5 : 0.5, the optimization cost is the closest to the actual cost, the optimization cost is reduced by 6.9%, and the frequency of departures has been increased by 5. The results show that the model in this paper provides a new idea for the information mining of bus routes in the research based on the bus IC Card data and provides an effective tool for the management of different operation decision preferences.

Research on Route Deviation Transit Operation Scheduling—A Case Study in Suburb No. 5 Road of Harbin

Route deviation transit is a flexible “door-to-door” service method that combines the efficiency of conventional public transport modes and the flexibility of demand response modes, meeting the travel needs of people with low travel density and special groups. In this paper, the minimum value of the sum of vehicle operating cost and passenger travel cost was the optimal goal, and the RDT multi-vehicle operation scheduling model was constructed. Taking the available relaxation time as the control parameter of the RDT system and considering the insertion process of the random travel demand of the passengers during the operation process, we used a heuristic search algorithm to solve the scheduling model. This paper took Suburb No. 5 Road of Harbin as an example, using MATLAB to simulate the RDT operation scheduling model to verify the stability and feasibility of the RDT system under different demands. The results showed that under different demand conditions, the system indicators such as passenger travel time, waiting time, and vehicle mileage in the RDT system fluctuated very little, and the system performance was relatively stable. Under the same demand conditions, the per capita cost of the RDT system was 5.9% to 10.8% less than that of the conventional bus system. When the demand ρ is 20~40 person/hour, the RDT system is more effective than the conventional bus for the 5 bus in the suburbs of Harbin.

A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.