Passenger Travel Time Research Articles

A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem

<abstract> <p>We proposed a multi-objective optimization framework for green demand responsive airport shuttle scheduling, which simultaneously aims at assigning demand points to selected stops and routing airport shuttles to visit these stops in their overlapping time windows to transport all passengers from their homes or workplaces to the airport. Our objectives were to minimize total travel time for passengers, the punishment expense of violating the time-window as well as carbon emissions for all shuttles. Since such issues belongs to the NP-problem, a two-stage Multi-objective ant lion optimizer (MOALO)-based algorithm incorporating dynamic programming search method was developed to acquire the optimal scheduling schemes. Finally, a case study of airport shuttle service in Tianjin Airport, China, was used to demonstrate the validity of the model and algorithm.</p> </abstract>

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.

Integrated Robust Optimization of Scheduling and Signal Timing for Bus Rapid Transit

The unbalanced nature of passenger demand and the uncertainty of delay at intersections are considered critical factors in the efficiency of public transportation operations. Considering the controllable characteristics of the bus rapid transit system (BRT), an integrated optimization model that includes multi-mode transit scheduling and signal timing is proposed. The robust optimization model copes with the fluctuations in passenger demand and bus operation. A non-linear programming is developed to minimize the total passenger travel time and optimize the transit departure time, dwelling strategies at stations, and signal timings at intersections. The constraints on the signal control, degree of saturation, transit scheduling, and passenger waiting time at stations are intended to reflect real traffic conditions. A case study and extensive sensitivity analyses were conducted to evaluate the performance of the proposed model. The results show that the proposed integrated model can adjust the BRT dwelling strategies based on different passenger demands and coordinate transit departure time and signal timing adjustment to mitigate the passenger delay. Furthermore, experimental results demonstrate improvements of 5.3% and 8.1% in the mean and maximum values of passenger travel time, respectively, using the proposed model, compared to those obtained using the conventional operation strategy.

Influence of in-vehicle crowding on passenger travel time value: Insights from bus transit in Shanghai, China

In-vehicle crowding is an external cost imposed on passengers by public transport. It is necessary to quantify the crowding discomfort to measure its influence on passengers’ travel choice. This study presents a stated preference survey embedded with revealed preference to investigate the influence of in-vehicle crowding on the travel choice behavior. The in-vehicle crowding degree is measured as standing density. Considering the in-vehicle environment and the subjective perception of passengers, the classification criteria for the in-vehicle crowding degree of bus transit are put forward. Then, the mixed logit model is used to establish the utility function of bus passenger travel choice behavior under different in-vehicle crowding degrees, and the value of travel time is calculated. Also, the functional relationship between in-vehicle crowding and travel time value is identified. In addition, by converting in-vehicle crowding into time, the travel impedance model considering in-vehicle crowding and the crowding value of time multipliers are defined. A study of Shanghai bus transit shows that when the standing density is higher than 7.5 passengers per square meter, bus passengers are willing to exchange 12.83 Chinese Yuan RMB for 1-hour travel time saving, and the perceived in-vehicle time is twice as long as the actual in-vehicle time. This study provides a reference for improving the attraction of bus transit and easing the decline in bus rides.

A Model of a Limited-Stop and Local Mixed Bus Service with Increased Fares for the Limited-Stop Service

This study designs a limited-stop and local mixed bus service from the perspectives of both transit agencies and passengers based on a proposed optimization model. Close attention was paid to the increase in fares for the limited-stop service in the proposed model to offset the loss in bus fare. The objective function was to minimize the passenger travel time and bus agency costs; the decision variables were the stops for the limited-stop service and the headways of the limited-stop and local services; the constraints included the range of fare increases for the limited-stop service and other factors. The Lingo software and the genetic algorithm were compared in solving the proposed model from the aspects of solving time and results. Because the solutions for the model within the planning time and the influence of the global optimal solution upon parametric analysis were stable, the Lingo software was more suitable for the proposed model. Evaluations of the total costs for the mixed service and local-only service were carried out for different bus corridor lengths. The ratio of the fares for the limited-stop service to the fares for the local service must be within the appropriate range in order to take advantage of the limited-stop service. When the stop spacing and the bus corridor distance were longer, the mixed service was more competitive and noticeable.

An Integrated Multi-Objective Optimization for Dynamic Airport Shuttle Bus Location, Route Design and Departure Frequency Setting Problem.

An airport shuttle bus (ASB), as an environmentally friendly mode of green transportation, is an effective way to solve the "first/last mile" of aviation passengers, which can attract a higher passenger transfer from private cars to public transport, thereby reducing emissions of carbon dioxide and other polluting gases. This study presents a multi-objective mixed-integer linear programming for ASB services in a dynamic environment. Taking into account time-varying demand and travel time characteristics in different periods, the proposed model provides a comprehensive framework that simultaneously advises passengers to join the bus at the nearest bus stations, designs routes for transporting them from these selected stations through the airport, and computes their departure frequencies in multiple periods. The primary objective is to optimize both the total ride time and waiting time for all passengers. The secondary objective is to optimize the total transfer distance of all passengers simultaneously. Given the Non-Deterministic Polynomial (NP) hardness of this problem, a two-stage multi-objective heuristic approach based on the non-dominated sorting genetic algorithm (NSGA-II) is combined with a dynamic programming search method and further advanced to obtain the Pareto-optimal solutions of the proposed model within a reasonable time. Finally, the proposed model and algorithm feasibility are proved by a test example of designing a shuttle bus route and schedule at Tianjin Airport, China. The results show that the total passenger travel time of the presented model is markedly reduced by 1.21% compared with the conventional model.

Modeling Limited-Stop Bus Corridor Services with Fare Payment Mode Choice and Trip Purpose Consideration.

This paper proposes a novel model for optimizing limited-stop bus corridor services with consideration of varied payment modes and different trip purposes. In the proposed model, the bus dwell time at a stop is dependent on the fare payment modes and the number of passengers getting off and waiting at the stop while those with the similar trip purpose are grouped into one user class. Given an origin-destination (OD) passenger trip matrix and a set of candidate bus lines serving a corridor, the proposed model is to minimize the total social cost that consists of the cost to the bus operator and the cost to the passengers. In the formulation of the optimization problem, a weighting parameter is adopted to balance the operator cost and the passenger cost. Numerical examples are presented to illustrate the importance of considering passenger flow impacts on bus (and passenger) travel times in the proposed model. We also investigate effects on the optimal limited-stop services (e.g., short-turn, skip-stop, and express) taking into account the choice of fare payment modes (e.g., on-board fare collection including payment by cash, magnetic strip or smart card, off-board fare collection) and different values of travel time due to passenger trip purposes. It is shown that the off-board payment mode would be more efficient in a high-demand corridor, that more passengers prefer to express and skip-stop services rather than normal regular services in the four collection systems, and that different limited-stop service plans should be used for different periods of the day in response to temporal variation in OD passenger travel patterns. The intellectual merit of this paper is not the seemingly obvious conclusions but that the proposed model can handle the problem of limited-stop bus corridor service design with the consideration of fare payment mode choice and trip purposes.

Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events

Railway traffic management focuses on regulating train movements and delivering improved service quality to passengers; however, such efforts are subject to many uncertainties in terms of disruptions and passenger demand on a rail transit line. In contrast to most existing studies, which focus on the rescheduling of passenger timetables in a deterministic framework, this study proposes a two-stage stochastic optimization model for allocating backup rolling stocks (BRS) to storage lines to reschedule the timetable and serve passengers delayed by disruptions. The first stage is an assignment problem to determine the optimal plan for the allocation of BRS to storage lines to achieve a good trade-off between the investment cost for the BRS and the expected travel time of delayed passengers across different stochastic scenarios. The second stage is explicitly formulated as a network flow model to optimize the timetable of the delayed trains on the tracks and the BRS from the storage lines such that the passenger travel time is minimized under each stochastic scenario. To improve the efficiency of convergence, we develop an improved L-shaped method with several accelerating techniques. Among these, we show that the classical integer L-shaped cut can be tightened given the property of the second-stage problem, which can also be generalized to other two-stage integer stochastic programs. Real-world case studies based on historical data from the Beijing metro verify the effectiveness of the proposed approach in reducing the travel time for passengers. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This research was supported by the National Natural Science Foundation of China [Grants 71621001, 71825004, and 71901016]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1233 ] or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at [ http://dx.doi.org/10.5281/zenodo.6892548 ].

Estimation and simulation for route choice proportion in multi-period urban rail transit control systems

As we all known, estimating the proportion of passenger route choice is of great significance in almost every aspect of urban rail transit control system, including passenger allocation, fare clearing and flow control strategies. Existing researches only pay attention to the route choice through travel time, but usually ignore the influence in different periods of the day. Therefore, this paper proposes a novel estimation method for the proportion of passenger route choice in different periods. Firstly, by introducing the normalized value of passenger flow and the standard coefficient of peak passenger flow, the train operation time is divided into peak and flat periods. Secondly, the travel time distribution of each route can be obtained by estimating the expected value and standard deviation of passenger travel time in each different period. The Naïve Bayes algorithm is further employed to realize the identification of the proportion of passenger route choices. Finally, this proposed algorithm is applied to Hangzhou Metro. The result shows that by using the segmented estimation, the error can be reduced by more than 60% compared with the whole-day experiment, which indicates the superiority of the method.

An Efficient Train Timetable Scheduling Approach With Regenerative-Energy Supplementation Strategy Responding to Potential Power Interruptions

The timetable of a metro system is essential for trains running safely and efficiently. For the design of a timetable, the potential power interruptions of energy supplies from the substations to the trains should be borne in mind. In case potential power interruptions occur, the backup running plan should be completed responsively to accommodate the passenger and energy demands In this study, we propose an energy supplementation strategy utilizing regenerative energy from decelerating trains in the event of power interruptions and develop a tri-objective optimization model incorporating passenger travel time, potential power interruption, and energy consumption. Particularly, for calculating regenerative energy utilization, we suggest a many-to-many energy allocation mechanism between decelerating and accelerating trains based on the real-time energy demands and supplies. A heuristic algorithm is developed to obtain a regular and cyclic timetable for minimizing passenger travel time, the potential power interruptions, and energy consumption. The suggested model and algorithm are tested based on the smart-card data collected from a bidirectional metro line in Beijing (China). The results show that the suggested approach significantly improves energy efficiency, reduces passenger waiting time, and decreases power interruption risks, compared with the currently used scheduling method.

Modeling of cross line operation of urban rail transit trains based on passenger travel time

Cross line operation mode of urban rail transit can not only meet diversified travel needs of passengers and improve efficiency of passenger travel, but also shorten the time and space distance between suburban and urban core areas. In this paper, the travel time cost of different types of passengers is analyzed on the basis of considering the train selection probability for passengers to choose cross train or transfer. An optimization model of cross line operation organization is established with the objective function of minimizing the total travel time cost of passengers. The model is solved by genetic algorithm. The scheme of metro Line Ⅰ of city S crossing into Line Ⅱ is used as an example to verify the feasibility of the model, and the optimal train density under different train load rates is obtained. This study can provide a reference for the cross line operation organization of urban rail transit.

Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.

Optimization of Train Operation Planning with Full-Length and Short-Turn Routes of Virtual Coupling Trains

Virtual coupling uses wireless communication instead of mechanical coupling to ensure that trains are easily reconnected or disconnected. This technology can shorten the interval time between trains, give full play to the carrying capacity of lines, and improve the service level of urban rail transit. This paper optimizes the train operation plan with full-length and short-turn routes of virtual coupling trains by establishing a two-level optimization model. The upper model is used to minimize passenger travel time and enterprise operation cost, and the lower model to optimize the equilibrium of train load rate on short-turn routes. Meanwhile, a method based on the genetic algorithm is designed to solve the model. A case study of the Metro Line M has been carried out. The results can verify the efficiency and feasibility of the proposed method. The full-length and short-turn routes of virtual coupling trains can effectively reduce passenger travel time, enterprise operating cost and the number of vehicles, and improve the average load factor of the trains. Finally, sensitivity analyses are performed using three parameters which include departure frequency of the full-length train and short-turn train, starting and terminal station of short-turn route, and number of marshalled vehicles of the full-length train and short-turn train.

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak

After the widespread impact of the COVID-19 pandemic, all public transport, including urban rail transit, inevitably adopted a vigorous physical-distancing policy to prevent the disease from spreading among passengers. Adoption of this measure resulted in a substantial reduction in train service capability and required control of the risk contact exposure duration. Thus, this paper proposes the Skip-Stop Strategy Patterns (3S–P) decision-support model to incorporate social distancing constraints in train operations. The 3S–P model is a two-stage, multi-objective optimization model for scheduling train skip-stop patterns to satisfy the study's two main objectives of minimizing the average passenger travel time and unserved passengers. In the proposed model, the first optimization identifies the optimal train skip-stop patterns, while the second assigns these patterns to establish an hourly train schedule. The paper's case study uses data from the Bangkok Mass Transit System (BTS) SkyTrain Silom Line in Bangkok, Thailand and considers the 0.5, 1, 1.5, and 2 m social distancing schemes. The results reveal that the optimal train skip-stop patterns are superior to the all-stop alternative with, on average, a 13.4% faster travel time at the same level of unserved passengers. Furthermore, the non-dominated schedules from the second optimization decrease the numbers of unserved passengers given equal average passenger travel times.

A Multi-Objective Optimization Model for the Intercity Railway Train Operation Plan: The Case of Beijing-Xiong’an ICR

For intercity railway transportation enterprises, a reasonable intercity train operation plan is not only the foundation of the intercity railway operation organization, but also the key to the sustainable development of the intercity railway (ICR). In this paper, taking into account the economic benefits of railway transportation enterprises and the social benefits of passenger travel, an optimization model is established with the intercity railway train operation plan as the research object. The model aims to minimize the operating cost of railway transportation enterprises and minimize the travel time of passengers, and considers constraints such as passenger seat utilization, passenger flow, train frequency, and stops. It is a multi-objective optimization model that accumulates two objectives by introducing the passenger time value coefficient. According to the characteristics of the model, a genetic algorithm is designed to solve the model. Taking the Beijing-Xiong’an Intercity Railway (BXICR) as an example, the “smart business card” of China’s high-speed railway, two scenarios of passenger time value are designed, and the optimized train operation plan is obtained according to the existing OD passenger flow data, which verifies the effectiveness of the model and algorithm. The results show that compared with the original train operation plan, the number of stops per train of the optimized train operation plan under the two passenger time value scenarios decreased by 8.8% and 14.9%, the operating cost of the enterprise decreased by 7.7% and 1.6%, the travel time of passengers decreased by 0.7% and 1.5%, respectively. Under the condition of meeting the demand of passenger flow, the optimized train operation plan can effectively reduce the operating cost of enterprises and save the travel time of passengers, which is conducive to the sustainable development of intercity railways.

Intelligent Bus Operation Optimization by Integrating Cases and Data Driven Based on Business Chain and Enhanced Quantum Genetic Algorithm

Intelligent public transport systems are a key direction for the study of intelligent transportation systems (ITSs), and they perform the following functions: location and tracking, aided navigation, dispatch and command, and dynamic information release; these systems also help travelers determine optimal routes. This paper mainly studies intelligent bus operation optimization by integrating cases and data from business chains, including the optimization of public transport vehicle scheduling according to the characteristics of vehicle scheduling, considers the interests of passengers and public transport companies, and adopts a true-value encoding method with the departure time as the variable goal optimization. In addition, this paper builds a model for the intelligent scheduling problem in public transport based on an enhanced quantum genetic algorithm (EQGA) to find the optimal timetable. This model sets the minimum waiting time cost of passengers and the maximum interests of public transport companies as the goals, restricts the departure interval and two adjacent intervals, and constrains the load factor of passengers. Moreover, based on the analysis of public transport travel characteristics and passenger flow data, this paper evaluates the efficiency of public transport operation, reasonably analyzes the utilization of public transport resources and the travel time of public transport passengers, and thoroughly studies the public transport operation system. This paper selects actual bus line data for empirical analysis, and the empirical results show that the proposed algorithm and model can meet the requirements of many aspects and provides good intelligence, applicability, and optimization.

The Influence of the Resident Subsidy on Regional Carrier Economies and the Environment in the Canary Interisland Air Traffic Network

The existence of aviation subsidies can exacerbate congestion in airports that operate near their maximum threshold. One of the main operational issues in airports, that is, aircraft taxiing operations, could become more difficult and, therefore, produce negative effects on airport capacity management. Air carrier economies, touristic mobility (leisure travel), and the environment could also be negatively affected. Greenhouse gas emissions, passenger travel time, and air carriers’ fuel costs, for example, might rise due to aircrafts’ increased taxiing time. This paper seeks to analyse the impact of regional aviation subsidies for regional hub airports in the Canary Island air traffic network; specifically, the effect it has on airports’ capacity in terms of LTO (landing and taking off) cycle delays and its repercussions for passenger travel time, air carrier economics, and the environment. Therefore, to quantify the extent that the increased traffic from subsidies cause airport ground operations to delay flights, the disturbance between aircraft categories in landing and taking off operations must be considered. The disturbance between aircrafts will be evaluated by employing a simple model of landing intervals considering two aircraft categories (i.e., ATRs-72, ATRs-42, and B737/A320 aircraft families). The above-mentioned methodology provides an approximation of the average processing rate for take-off using the ultimate capacity concept for a variety of aircrafts landing on a single runway of the airport. As a result, it must be noted that there is a potential economic and environmental impact associated with the implementation of the subsidy increase for residents’ interisland travel and from the Canary Islands to the mainland. There is also a negative effect on runways for both airports due to the capacity constraints. These impacts, however, have to be balanced with the social and economic benefits that each regional inhabitant derives from the subsidy in terms of improvement of their mobility for any purpose.

Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.

To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers’ OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.

Bus Scheduling of Overlapping Routes Based on the Combination of All-Stop and Stop-Skipping Services

Developing public transportation is an effective way to alleviate traffic congestion and reduce fossil fuel consumption. However, the result of ineffective public transport planning can be that some bus lines have long overlapping intervals, and share many common stops between them, which causes a significant waste of resources. The current bus scheduling method does not consider the imbalance of passenger flow between different stops or combine the overlapping interval with transit mixed scheduling, including all-stop and stop-skipping services. Therefore, based on the analysis of the characteristics of overlapping interval, this paper proposes a combined bus scheduling method, which includes both all-stop and stop-skipping bus services. The method is a deterministic method applied in the scheduling stage of transit planning. The method optimizes scheduling strategies by minimizing total passenger travel time, including the waiting time and the in-vehicle travel time, and the line frequency of each bus service is taken as the decision variable. Then a genetic algorithm is designed to find the optimal solution. The model is verified by two bus lines in Changchun city. The experimental results showed that the proposed model could reduce total passenger travel time by 7.71% compared with the existing scheduling method. The combined bus scheduling method can supplement the research on bus scheduling of overlapping routes and provide suggestions for operators.

Machine identification method of subway service quality based on smart card data

Abstract A new automatic evaluation method of subway service quality based on metro smart card data is proposed suitable for three different levels: station pair, railway line and subway network, which has merits of overcoming the previous lagging and subjective evaluation in the system of ‘questionnaire survey plus evaluation method’. First, passengers' travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station's operating characteristics at the different periods. Second, the classification is verified by K-means cluster analysis and K-S tests. Third, the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies. Finally, the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example. The new automated evaluation method of subway service quality is suitable for online and offline application. Highlights • A new machine identification method based on travel time distribution is proposed for estimating the service quality. • The proposed model employs the K-means clustering method to distinguish passenger's travel time distribution into different modes. • Characteristics of the different modes of passenger travel time distribution reflect different service levels of the urban subway system. • The method provides automatically identification of service levels for urban subway operations.