Passenger Travel Time Research Articles

A capacity adaption optimization model for supernormal operation of urban rail transit networks

In response to urban rail transit capacity shortage, a capacity adaption optimization model for the new mixed train operation mode is proposed in the supernormal operation of urban rail transit networks. First, the characteristics of express-local train and long-short route slow train are analyzed to obtain the main influencing factors of capacity adaption. Moreover, various train types are simultaneously classified and recombined in the adaption train operation mode to maximize transportation capacity for passengers. A nonlinear mixed-integer programming model is established to maximize the total number of trains, improving train load rate and reducing the total passenger travel time. In addition, the actual case of Beijing Metro Line 1 is solved to demonstrate the feasibility and effectiveness of the proposed model. The optimal passenger travel time-saving rates of the morning and evening peak hours are 12.1% and 11.5%, respectively. The optimal utilization rates of capacity in the morning and evening peak hours are 79.5% and 84.5%, respectively. Meanwhile, the optimal load factor within the range of 0.7–0.9 would benefit the utilization of train resources and the passengers’ service level. The optimized results show that the proposed capacity adaption optimization model benefits for high service level and practical significance and rationality for operators.

Analysis on the Evaluation of Airport Intelligence Level and Its Obstacle Diagnosis Based on DPSIR-TOPSIS Model

Combined with the actual situation of smart airport construction, five listed airports in China are used as research objects, and based on the Driving Force-Pressure-State-Impact-Response (DPSIR) conceptual model, 24 indicators were selected to form the evaluation system. The Analytic Hierarchy Process (AHP) method and entropy weight method were used to assign comprehensive weights to the indicators, and then, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was introduced to evaluate the level of intelligence, combined with the coupling coordination model to analyze the relationship between the two subsystems, by introducing the obstacle model to diagnose and analyze the influencing factors; finally, based on the analysis results, optimization suggestions are provided for the airport intelligence level. The results show that the intelligence level of each airport is between high (IV) and average (II). The pairwise coupling degree of each subsystem is higher than 0.91 and presents a benign coupling. The airport intelligence level is mainly hindered by the proportion of nonaviation revenue, the average travel time of passengers, and the regional fiscal revenue growth rate. The research results are of great significance to the construction of smart airport and the development of regional economy and transportation.

Research on temporal and spatial distribution characteristics of passenger flow of Daxing airport line based on automatic fare collection data

AbstractThe airport line plays an important role in the airport landside traffic connection, which greatly relieves the urban road pressure. Studying the characteristics and laws of the time and space distribution of the airport line passenger flow will provide basic support for the airport line's capacity configuration, passenger flow control, and air‐track coordinated operation, and will also provide quantitative basis for the layout of urban terminal buildings. This paper proposes a method to analyse the temporal and spatial distribution characteristics of airport line passenger flow based on AFC card swiping data. By mining and extracting the travel behaviour data of subway airport line air passengers, the distribution characteristics of airport line passenger flow are analysed and visualized from two aspects of passenger travel time and space. Finally, a case study is carried out based on the actual data of Daxing airport express. The results show that: (1) the travel time of departure and return passengers is concentrated in 60–90 min; (2) The departure and arrival points of passengers are concentrated within the Fourth Ring Road, and a large number of air passenger flows are gathered and evacuated at hubs such as Beijing South Railway Station and Beijing West Railway Station.

Optimization of New Energy Public Transportation Network Based on Ant Colony Algorithm and Low-Carbon Concept

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Optimization of urban rail transit station spacing for minimizing passenger travel time

Urban rail transit, regarded as the backbone of urban public transportation, can effectively solve the problems of urban traffic congestion and automobile exhaust pollution. However, due to improper planning, unreasonable station location often appears, which seriously affects the service access and mobility of rail transit. In order to improve the station spacing and passenger transport efficiency, a calculation model of station spacing for minimizing passenger travel time was established. In order to enhance the adaptability of the model in different cities, the grid road network and radial road network are considered when establishing the model. The results show that the station spacing is related to the passenger access/egress speed, the passenger travel distance, the maximum train running speed, the train stop time and the train acceleration. The influence of these parameters on the station spacing is obtained through the control variate method, and there are indeed obvious differences under different road network conditions. The research results can provide theoretical reference for station planning of new lines. Since the station spacing of the operated lines cannot be changed, this study can provide reference for skip-stop operation.

An iterative heuristic for passenger-centric train timetabling with integrated adaption times

In this paper we present a method to construct a periodic timetable from a tactical planning perspective. We aim at constructing a timetable that is feasible with respect to infrastructure constraints and minimizes total perceived passenger travel time. In addition to in-train and transfer times, our notion of perceived passenger time includes the adaption time (waiting time at the origin station). Adaption time minimization allows us to avoid strict frequency regularity constraints and, at the same time, to ensure regular connections between passengers’ origins and destinations. The combination of adaption time minimization and infrastructure constraints satisfaction makes the problem very challenging.The described periodic timetabling problem can be modelled as an extension of a Periodic Event Scheduling Problem (PESP) formulation, but requires huge computing times if it is directly solved by a general-purpose solver for instances of realistic size. In this paper, we propose a heuristic approach consisting of two phases that are executed iteratively. First, we solve a mixed-integer linear program to determine an ideal timetable that minimizes the total perceived passenger travel time but neglects infrastructure constraints. Then, a Lagrangian-based heuristic makes the timetable feasible with respect to infrastructure constraints by modifying train departure and arrival times as little as possible. The obtained feasible timetable is then evaluated to compute the resulting total perceived passenger travel time, and a feedback is sent to the Lagrangian-based heuristic so as to possibly improve the obtained timetable from the passenger perspective, while still respecting infrastructure constraints. We illustrate the proposed iterative heuristic approach on real-life instances of Netherlands Railways and compare it to a benchmark approach, showing that it finds a feasible timetable very close to the ideal one.

Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach

In terms of urban rail transit network vulnerability, most studies have focused on the changes of network topology and functional characteristics under disruptive events while ignoring the impacts of cascading failures on network performance. In this study, an improved Coupled Map Lattices model is proposed to address the evolution process of cascading failures on rail transit network. Particularly, the model quantifies important factors of cascading failures integrating quantitative failure spreading mode, anti-risk ability of stations, and perturbation index. The rail transit network vulnerability is then modeled addressing numbers of failed stations, degree of affected passengers, and loss of passenger travel time as a result of cascading failures. The methodology is applied to the metro network of Shanghai, China. Results show that the propagation of network cascading failure depends on the station's anti-risk ability, geographical location, and type of adjacent stations. Stations located in the urban center area are more susceptible to failures of adjacent stations. Furthermore, higher affected passenger volume and station coupling strength would lead to more serious and rapid spreading of failures, and thus more vulnerable of the metro system. However, the anti-risk ability of stations could effectively resist the spread of failures in the early stage, and stations with higher topology redundancy demonstrate stronger anti-risk ability.

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.

Simulation Analysis on Benefits of Introducing Meeting Points Into On-Demand Shared Mobility Services

We investigate the benefits of introducing meeting points (MPs) into on-demand shared mobility services (OSMS). OSMS generally provide door-to-door transportation between passengers’ origins and destinations. In addition, services using specific pick-up/drop-off locations, which are called as MPs, are also deployed. The operational efficiency of OSMS could be improved by asking passengers to walk to an MP. Previous studies have found that the introduction of MPs can improve operational efficiency in terms of the number of vehicles, the vehicle kilometers traveled (VKT), and the rejection rate of passengers’ requests. However, the travel time of passengers (i.e. passenger convenience) has not been sufficiently explored. The introduction of MPs may reduce the average travel time of passengers by reducing the number of detours. We conduct a static analysis under simple settings, and a simulation analysis using actual road network data. The effects of the introducing MPs are evaluated via various configurations such as MP locations, the number of passengers’ requests, the number of vehicles, and vehicle capacity. Results showed that the introduction of MPs reduces VKT as well as average travel time of passengers when the number of demands is greater than 200 per hour in the same conditions on service provider.

Coordinating feeder and collector public transit lines for efficient MaaS services

Coordinating the schedules of feeder and collector public transit lines can reduce the passenger travel times. With advances in smart mobility, mobility-as-a-service (MaaS) schemes allow passengers to book a combined ticket for all their trip legs. This detailed information about the origins and destinations of door-to-door trips offers the opportunity to coordinate the schedules of public transport lines to reduce the passenger travel times. In this study, we model the coordination problem of feeder and collector lines by explicitly considering the regularity of the feeder lines and the transfer times of passengers. The coordination problem is modeled as a nonlinear non-convex problem and reformulated to an easy-to-solve convex optimization problem. Because of the travel times uncertainty, we also introduce a stochastic optimization formulation based on the sample average approximation approach. We test the performance of our approach in a case study with feeder and collector lines in Singapore showing an improvement potential of 5%–10% in passenger travel times. • A novel, easy-to-solve model for the coordination of feeder and collector transit lines. • Explicit consideration of door-to-door travel times for efficient MaaS services. • Improvement potential of 5%–10% even under uncertain demand conditions.

Novel model for integrated demand-responsive transit service considering rail transit schedule.

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.

Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning

As travel efficiency matters to the work productivity of cities, shortening passengers' travel time for metros is therefore a pressing need. To this end, we study a strategy by dynamically scheduling dwell time for trains. Developing such a strategy is challenging because of three aspects: 1) Optimizing the average travel time of passengers needs to properly balance passengers' waiting time at platforms and journey time on trains, as well as considering long-term impacts; 2) Capturing dynamic spatio-temporal (ST) correlations of incoming passengers for metro stations is difficult; and 3) For each train, the dwell time scheduling is affected by other trains, which is hard to measure. To tackle these challenges, we propose a novel deep neural network, entitled AutoDwell. Specifically, AutoDwell optimizes the long-term rewards of dwell time settings in terms of passengers' waiting and journey time by a reinforcement learning framework. Next, AutoDwell employs gated recurrent units and graph attention networks to extract the ST correlations of the passenger flows among metro stations. Moreover, attention mechanisms are leveraged in AutoDwell for capturing the interactions between the trains. Extensive experiments on two real-world datasets demonstrate the superior performance of AutoDwell over several baselines, capable of saving passengers' travel time significantly.

Data-Driven Bus Route Optimization Algorithm Under Sudden Interruption of Public Transport

With the continuous expansion of urban traffic operation scale, public transport emergencies occur from time to time, causing serious traffic jams and potential safety hazards in a short time. In view of this, a new adaptive bus route optimization strategy based on the emergency demand responsive public transport is proposed. Firstly, in order to improve the fine-grained passenger carrying capacity of emergency demand responsive public transport and build a clustering model of passenger information, this paper proposes an adaptive clustering algorithm, which considers the main influencing factors such as vehicle capacity, passenger travel time window and the number of stations visited. Aiming at minimizing the cost of vehicle operation and passenger traffic, a multi-objective optimization model of emergency bus route is constructed based on Vehicle Routing Problems with Time Windows (VRPTW) to ensure the operation efficiency of emergency bus. Secondly, a Modified Adaptive Large Neighborhood Search with Nearest Vehicle Dispatch (NVD) algorithm (MALNSN) is proposed, which is an extension of the Adaptive Large Neighborhood Search algorithm (ALNS), by improving the generation rules of initial solutions with NVD and operator selection strategy with Modified Choice Function (MCF), and the effectiveness of algorithm is analyzed according to the Solomon benchmark. The average gain of the proposed MALNSN algorithm is 17.11% higher than that of the original algorithm. Finally, based on the actual road network, experiments are carried out to compare the proposed algorithm with the representative algorithms. The experimental results show that the MALNSN algorithm proposed in this paper can not only ensure the stability of the algorithm, but also formulate a reasonable route optimization strategy in a shorter time, effectively reducing the consumption of transport capacity resources, improving the operation efficiency of public transport and increasing the accessibility of public transport. The theoretical analysis was consistent with the experimental results.

КОЭФФИЦИЕНТ ЭТАЛОННОСТИ ПРОСТРАНСТВЕННО-ГЕОМЕТРИЧЕСКИХ ХАРАКТЕРИСТИК МАРШРУТА

The coefficient for assessing the optimality of the route by the criterion of minimum passenger travel time - the coefficient of reference of spatial-geometric characteristics of the route has been developed. Formulas for calculating the coefficient for the entire route, as well as for a single line, and for a section consisting of several lines are proposed.

Approbation of the methodology for determining the time of passenger movement on the inner-city and suburban route

Presents the results of testing the methodology for determining the passenger's travel time according to the "door-to-door" scheme on intra-city and suburban routes using hovercrafts. The introduction presents the results of the analysis of a number of research works of domestic and foreign scientists in the field of passenger transportation by hovercraft. The analysis showed that studies of passenger travel taking into account such factors as distance and time according to the "door-to-door" scheme on intra-city and suburban routes in riverine regions by buses and hovercraft do not occur, which means that the chosen research topic is relevant. The methods section summarizes the basics of calculations and initial data for testing the methodology for determining the time spent by passengers on a trip using a hovercraft. The results section provides graphical data as a result of testing the methodology. The opinion of the authors of the article on the use of alternative travel options by a passenger on intra-city and suburban routes by road and (or) river transport is put up for discussion. In the final part of the article, it is determined that this method of determining the travel time of a passenger on intra-city, and in comparison, may be of interest both for passengers and transport organizations.

Skip‐stop operation plan for urban rail transit considering bounded rationality of passengers

Reasonably optimizing the urban rail transit stop plan from the perspective of bounded rational cognition of passengers regarding their travel choices is an important way to improve riding environment, transportation capacity, and service quality. Based on the analysis of the characteristics of passengers’ bounded rational choice behaviour, starting from two aspects of travel time and congestion, the prospect theory is applied to describe optimization goals: maximum travel time savings and maximum congestion costs savings. Subsequently, an urban rail transit stop plan optimization model is constructed, and a non-dominated sorting genetic algorithm with the elite strategy is designed to solve the model, and stop plans based on the calculated Pareto solution set are selected. A case study of an urban rail transit with 13 station nodes verifies the research conclusion as follows: compared with the traditional station stopping plan, the skip-stop plan has better passenger flow adaptability, in which the average travel time of passengers is shortened by 22.57 min at most, and the congestion degree is reduced by 10.93% at most. Based on this, the impact of different factors on the stop plan, such as the target weight, passenger flow, and behavioural parameters, is further discussed.

Joint optimisation of regular and demand-responsive transit services

This study aims to jointly optimise regular and demand responsive transit (DRT) services, which can offer opportunities for leveraging on their respective advantages. An optimisation model with the objective of minimising the total travel time of passengers and the total fleet size is proposed. The terminal bus stops of regular bus lines, the service area of the DRT, and the fleet size of both regular and DRT are optimised simultaneously. A rule-based optimisation preparation step is added to the proposed model to obtain a reasonable design scheme and to reduce the computational load. The model is solved using a tailored boundary-start-based two-step heuristic algorithm. The performance of the mixed network is affected by the preference of the decision maker and the operation mode adopted for the DRT service. A reduction in the operational level of the DRT results in a considerable increase in the travel time of DRT passengers.

Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line

A combination of express and local trains (E/L mode) is generally used to operate a suburban rail service, it can meet the rapid and direct service needs of long-distance travelers as well the needs of short-distance travelers. Generally, a stop plan is the core of the E/L mode. A stop plan optimization model in E/L mode, which aims to minimize the total passenger travel time and the number of operating trains during the peak period with the safe headway and departure frequency constraints, is proposed in this study. Meanwhile, an algorithm based on a genetic algorithm is designed to solve the proposed model. A case study of the Jiangjin Line, a suburban railway in Chongqing, China, is carried out. The results show the efficiency and feasibility of the proposed method. The calculation results also show that the total passenger travel time under E/L mode with the overtaking condition is significantly reduced compared with the all-stops (AS) mode and E/L mode without overtaking condition. The superiority of the E/L mode can be enhanced by reducing the dwell time at stations and adopting the overtaking condition.

Resilience assessment of an urban rail transit network: A case study of Chengdu subway

Existing studies seldom consider network structure and passenger travel demand jointly, and certain impractical assumptions are generally considered for assessing the resilience of an urban rail transit (URT) network. To address the abovementioned limitations, we have proposed a performance indicator called the demand–impedance (DI) indicator, in which demand and impedance are reflected by passenger trips and travel time. By considering effective travel paths (ETPs) and passengers’ path choice behavior, we have proposed a node centrality called effective path betweenness (EPB) by modifying the betweenness centrality (BC) to evaluate the importance of stations. The performance curve of a URT network during the attack and repair processes is depicted using the DI indicator, and a modified resilience metric is formulated by referring to the resilience triangle. The model application in the Chengdu subway network demonstrates that the correlation coefficient between the EPB and BC of stations is 0.901, which indicates that stations with a higher EPB are inclined to have a higher BC. The Chengdu subway network demonstrates a higher resilience under random disturbances than it does under malicious disturbances. Disturbance duration, passengers’ tolerance time, and rescue ability on the Chengdu subway network significantly affect its resilience. Several practical suggestions involving the management of disturbances, shortening the emergency response time, providing passenger services, and improving emergency rescue ability are provided for managing the Chengdu subway system under disturbances.

Improving service regularity for high-frequency bus services with rescheduling and bus holding

In high-frequency bus services, maintaining the service regularity is a critical issue. The service regularity is directly related to the excessive waiting times (EWT) of passengers at bus stops. In a regular service, the EWT is minimized resulting in even headways between consecutive buses of the same line. In this study, we propose the combined use of rescheduling and bus holding to improve passengers' excessive waiting times. We model the dynamic rescheduling and bus holding problem as an integer nonlinear program (INLP) and we prove its NP-hardness. Our model considers the constraints of the original timetable – an issue that is usually neglected from most dynamic control methods. Given the NP-hardness of our mathematical program, we introduce a problem-specific heuristic to explore efficiently the solution space. The convergence rate of the proposed heuristic is tested against other solution methods, including simulated annealing with linear cooling, hill climbing and branch and bound with multi-start sequential quadratic programming. In addition, simulations with the use of actual operational data from a major bus operator in Asia Pacific demonstrate an up to 35% potential EWT improvement for a minor increase of 6% to the travel times of onboard passengers. • Novel model for the periodic optimization of bus operations. • Combined rescheduling and bus holding decisions. • Up to 35% improvement of the average passenger waiting times. • Proof of NP-Hardness and development of problem-specific heuristics.