Passenger Demand Research Articles

Connecting sustainability: harnessing digital infrastructure for green economic efficiency

ABSTRACT This research investigates the influence of ‘Broadband China’ pilot (BroadbandCP) policies on the green economic efficiency (GEE) in Chinese urban areas, utilizing urban data from 2006 to 2021. The outcomes of this study demonstrate a substantial augmentation in the GEE as a direct outcome of the implementation of BroadbandCP policies. Rigorous testing confirms the positive causal impact of BroadbandCP policies on the GEE. Further examination reveals that urban areas with higher levels of passenger demand and a larger population of internet users experience more significant enhancements in the GEE. Mechanism analysis suggests that BroadbandCP policies bolster the GEE by stimulating upgrades in industrial structure and facilitating advancements in green technology.

Spatio-Temporal Evolution and Influencing Factors of Supply–Demand Coupling and Coordination in Civil Aviation Passenger Transport

The interaction between supply and demand of civil aviation passenger transport serves as an important reference for airport planning, transportation structure optimization and dynamic matching of supply and demand. Based on the panel data of 31 province-level administrative divisions (excluding Hong Kong, Macao, and Taiwan) in China spanning from 2004 to 2019, this study employs the entropy-weighted TOPSIS method to evaluate the supply level and demand level of civil aviation passenger transport. On this foundation, this study uses the modified coupling coordination degree model to measure the coupling coordination degree of civil aviation passenger transport supply and demand, further analyzes the spatio-temporal evolution characteristics of coupling coordination degree and employs the double fixed-effect model to investigate the influencing factors. The results indicate that China’s civil aviation passenger supply, demand and supply and demand coupling coordination degree are increasing year by year. The level of provincial supply and demand coupling coordination has risen to the upper-middle level, and has shown significant spatial differences. Economically developed regions demonstrate a higher level of coordination. Economic development, urbanization rate and the degree of opening to the outside world have a positive impact on the coupling and coordinated development.

Applying BIM and pedestrian simulation for architectural flow line optimization in subway stations: an empirical analysis at Chongqing Ranjiaba Station

ABSTRACT In China’s new urbanization, urban rail transit has developed rapidly. Multiline subway stations in Chongqing face issues such as high passenger flow and intersecting pedestrian flows, leading to congestion, which affects station safety and service quality. This study aims to optimize subway station architectural flow lines, defined as pathways facilitating pedestrian movement within stations, to meet passenger demands, improve service quality, and contribute to smart city development. Combining BIM technology with pedestrian simulation, this research uses quantitative data analysis to optimize subway station flow lines under non-emergency conditions, a scenario rarely addressed in existing studies that focus on emergency evacuations, and proposes optimization strategies. We constructed an optimization framework based on BIM and pedestrian simulation and conducted an empirical study at Chongqing Ranjiaba Station to verify its feasibility. This research systematically examines subway station entry, exit, and transfer systems through literature reviews, surveys, simulations, and case studies. The analysis, within the context of smart city development and transit-oriented design, identifies issues in station layout and pedestrian flow, proposing and validating optimized design schemes. The study introduces a BIM-based framework for optimizing subway station flow lines, demonstrating improvements in pedestrian safety, operational efficiency, and user experience, offering practical solutions.

Optimizing Modular Vehicle Public Transportation Services with Short-Turning Strategy and Decoupling/Coupling Operations

In public transportation systems, the passenger demand during peak hours is characterized by over-saturation at intermediate stops and directional imbalances, and the traditional single scheduling strategy and fixed capacity cannot solve the contradiction between the demand and capacity mismatch. In order to accurately match demand and capacity, this paper proposes a method to optimize the service of a public transportation system by using a short-turning strategy combined with decoupled/coupled operation of modular vehicles (MVs). The short-turning strategy is used to alleviate the heavy passenger flow at intermediate stations, and the decoupling/coupling operations of MVs are employed to flexibly adjust the capacity levels in different directions. Considering urban space limitations, depots for storing modular units (MUs) are only set up at the starting and ending stations of bidirectional lines. MVs can not only adjust the departure capacity at the starting station but also consider whether to decouple/couple at turnaround stations for short-turning trips to achieve a more effective supply–demand match, with the decoupled/coupled MUs being deadheaded from or provided by the depot. We formulated this problem as an integer nonlinear programming (INLP) model, jointly optimizing the departure intervals of each trip, the capacity of MVs, the turnaround scheme for short-turning trips, and the decoupling/coupling scheme for MVs at turnaround stations, with the aim of minimizing passenger waiting time costs and vehicle operating costs. To facilitate a solution, we equivalently transformed some nonlinear terms in the model, which was then solved by the commercial solver Gurobi. The numerical study shows that, compared with the traditional full-length strategy combined with conventional buses, the model proposed in this paper can reduce the total system cost by about 19.59%. In particular, it can achieve precise matching between passenger demand and transport capacity, thereby reducing the passenger waiting time cost by about 29.99%. Compared with the full-length strategy combined with MVs, the total system cost is also reduced by about 14.65%. The research results contribute to enhancing the service quality and efficiency of public transportation systems, which is of great significance to the sustainable development of these systems.

Formulation and solution framework for real‐time railway traffic management with demand prediction

AbstractRecent transport policies increasingly promote shifts towards rail travel aiming at a more sustainable transportation system. This shift is hampered by widespread unexpected perturbations in operations, resulting in perceived poor punctuality and reliability. When prevention of such perturbations is not feasible, traffic management must mitigate their effects, resolving arising conflicts to restore regular train operations and minimize delay. Current practice generally includes the assessment of railway performance in terms of train delays, but the quality of service to passengers is rarely explicitly accounted for. A railway traffic management framework is proposed that accounts for both passenger and train delays. To do so, a predictive optimization framework is proposed, integrating a demand prediction module, a passenger demand assignment module and a traffic management module. The first dynamically predicts future origin‐destination passenger flows using linear regression on real‐time observed smart card data. Then, the demand assignment module links predicted passengers to specific train paths, given a railway schedule. Finally, the traffic management module optimizes train scheduling and routing in real time, under the combined objective of minimizing train and passenger delays. The methodology is validated and benchmarked against equivalent passenger agnostic traffic management on a case study of the Copenhagen suburban railway network. The results show that it is possible to take into account passenger perspective in railway traffic management, without reducing the railway system efficiency compared to classic approaches.

Life Cycle Cost Analysis of Connecting Tracks for Expansion of High-Speed Rail Network

New rail lines have been constructed to accommodate the growing demand for high-speed train passengers. In this regard, life cycle cost (LLC) analysis is an essential decision-support tool for evaluating the feasibility of track construction plans for high-speed rail lines. In the construction sequence for connecting tracks, a turnout track can be installed at the connection point between two lines after the existing track has been demolished. The LLC varies depending on the type of track components. Therefore, this study aims to compare and analyze the LCC results of different connecting track scenarios in high-speed rail lines. The total construction cost for designing the existing and new connecting tracks with a concrete track bed was found to be between 484 and 561 million KRW (for turnout No. 18.5–46), which is approximately 230 million KRW higher than that of a gravel track bed. However, the LLC of the ballast track exceeded that of the slab track after seven years of operation. A connecting track comprising a slab and high-speed turnout can reduce the LCC value.

Assessing the social costs of public transport in a mixed traffic environment with endogenous demand

In reality, public transport (PT) passenger demand levels are influenced by internal factors rather than external ones, as they are shaped by the performance of public transport services such as price, service frequency and travel time. This paper develops a calculation process for PT endogenous demand with respect to social costs in motorcycle-dominated mixed transport systems, based on the total social cost of public transport in previous research. The incremental elasticity analysis is used to estimate the endogenous passenger demand for dedicated PT technologies. A case study of Quang Trung – Tran Phu – Nguyen Trai corridor in Hanoi is presented, highlighting the incremental elasticity analysis (IEA) of PT modes, including conventional buses,bus rapid transit (BRT), monorail and urban rail transit (URT), with a focus on passenger waiting and invehicle times. The findings reveal that conventional buses are most cost-effective for daily demands below 31,000 passengers per direction per day (pdd), while BRT is preferable for demands ranging from 31,000 to 55,000 pdd. The Monorail emerges as the most efficient option for demand between 55,000 and 165,000 pdd, with Urban Rail Transit (URT) becoming optimal when demand exceeds 165,000 pdd. These insights provide urban transport planners and policymakers with valuable guidance for strategic decision-making regarding new PT projects in mixed transport environments with a dominance of motorcycles

Subway Multi-Station Coordinated Dynamic Control Method Considering Transfer Inbound Passenger Flow

The prominent contradiction between passenger demand and capacity in rush hours at subway stations causes inconveniences to travel and even leads to safety risks. Existing research on the cooperative control of passenger flow at stations mostly focuses on a single direction, rarely considering transfer passenger flow control. This study formulated a coordinated dynamic control strategy for multiple stations in both directions as a deterministic mathematical programming model to optimise the crowded passenger flow. The optimisation objectives were set as the warning levels of crowded passenger flow and the detention time of all passengers. The constraints included limitations on station service capacity, train capacity, and the number of people boarding trains. Additionally, considering separate control over the transfer inbound passenger flow at transfer stations, an upward- and downward-direction coordinated dynamic control model was constructed. Numerical experiments based on real-world data from the Nanjing Metro Line 1 were conducted to investigate the effectiveness of the proposed cooperative control scheme and evaluate its performance.

A Train Timetable Optimization Method Considering Multi-Strategies for the Tidal Passenger Flow Phenomenon

The rapid growth of cities and their populations in recent years has resulted in significant tidal passenger flow characteristics, primarily manifested in the imbalance of passenger numbers in both directions. This imbalance often leads to a shortage of train capacity in one direction and an inefficient use of capacity in the other. To accommodate the tidal passenger flow demand of urban rail transit, this paper proposes a timetable optimization method that combines multiple strategies, aimed at reducing operating costs and enhancing the quality of passenger service. The multi-strategy optimization method primarily involves two key strategies: the unpaired operation strategy and the express/local train operation strategy, both of which can flexibly adapt to time-varying passenger demand. Based on the decision variables of headway, running time between stations, and dwell time, a mixed integer linear programming model (MILP) is established. Taking the Shanghai Suburban Railway airport link line as an example, simulations under different passenger demands are realized to illustrate the effectiveness and correctness of the proposed multi-strategy method and model. The results demonstrate that the multi-strategy optimization method achieves a 38.59% reduction in total costs for both the operator and the passengers, and effectively alleviates train congestion.

Dynamic pricing optimization for high-speed railway based on passenger flow assignment.

In order to improve the operation efficiency and market competitiveness, how to optimize the ticket pricing strategy of high-speed railway to match the dynamic supply-demand relationship was an urgent problem to be studied. Taking differentiated passenger demand and supply trains as the research object, the space-time service network based on train timetable was constructed. The generalized cost formula and travel utility formula of passenger travel were proposed, which contained economy, rapidity, convenience, comfort, and route correlation cost. A multi-objective dynamic pricing model was proposed. The model aimed at maximize the corporate revenue and maximize passenger travel benefit, and was solved by large neighborhood search heuristic algorithm and path size logit assignment based on capacity constraint-passenger flow increment accurate algorithm. Based on real data, the Shandong circular high-speed railway case compared the average total revenue under different ticket price adjustment ranges and the ticket price for different classes of trains under different OD levels. The case proved the practicability of dynamic pricing adjustment strategy considering train classification, which could provide a reference for the ticket price management of high-speed railway.

Modeling Passengers’ Reserved Time Before High-Speed Rail Departure

The pre-departure reserved time (PDRV) for high-speed railway (HSR) passengers, which encompasses all the time between passengers leaving their origin and the departure of the HSR train they are going to take, is a crucial factor in planning intercity travel. Understanding how passengers select their PDRV is not only important for developing effective strategies to improve HSR efficiency but also for optimizing the integration between HSR hubs and urban transportation networks. However, analyzing passenger choice behavior regarding PDRV is complex due to numerous influencing factors. Despite this, few studies have explored how HSR passengers make their PDRV choices. This paper, using Nanjingnan Railway Station as a case study, presents a novel investigation into the PDRV choice behavior of HSR passengers. An integrated latent class model (LCM) and ordered probit model (OPM) are applied to identify the factors affecting passengers’ PDRV choices. The sample data are segmented based on individual characteristics using the LCM, and OPM models are then constructed for each segment to analyze PDRV choice behavior. The results reveal that several factors—such as travel purpose, the number of times passengers used HSR at Nanjingnan Station in the previous year, the duration of HSR travel, the number of companions, feeder trip duration, and departure time—significantly impact PDRV choices. The integrated LCM and OPM approach also uncovers choice heterogeneity among different passenger groups. These insights can serve as a valuable reference for forecasting HSR passenger demand and for designing integrated HSR hubs and urban transport systems.

Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data

This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.

Last train schedule optimization for metro systems considering minimum adjustment cost under elastic passenger demand

ABSTRACT Optimizing the last train schedule is challenged by the elastic nature of passenger flow and the immutability of the schedule once announced to the public. The elastic passenger demand is first mathematically described, and an optimization method is proposed to minimize the adjustment to the last trains while maximizing the accessible passengers within the metro network. Linearization techniques are then employed to transform it into a linear programming form. The proposed method is verified through a real-world case study, which indicate an 8.4% increase in accessible passenger flow on a weekday with adjustments required for only 3 lines and 10 stations’ last train times. In a special event scenario, a 9.8% is achieved with adjustments to 2 lines and 8 stations’ last train times. The results suggest that for existing metro systems, judiciously adjusting the last train times can effectively enhance the overall level of nighttime operational service.

Analysis of the Station Passenger Capacity: The Case Study of Kocaeli Tram Line

Public transportation systems play a crucial role in facilitating mobility in bustling urban areas and alleviating traffic congestion. Among various urban rail system types, trams have emerged as environmentally friendly, low carbon emission, and high-capacity transportation vehicles. Effective use of tram lines necessitates accurate capacity planning. The aim of this study was to assess whether the station passenger capacity of the tram serving Kocaeli is adequate for peak hour travel demands. This study initially provides information on tram system criteria, capacity types, and components. Subsequently, details about the line, vehicle specifications, dwell times, peak hours, train and passenger numbers, and their correlation with stations are discussed. Various calculation techniques are explored to estimate station passenger capacity, with analytical methods based on 2022 data employed in this study. The analysis reveals that the tram line operates close to its station passenger capacity, particularly during peak hours, and may exceed capacity on special occasions. It is anticipated that the station capacity of this line, which serves the city center route, may struggle to accommodate passenger demand in the near future. Consequently, suggestions for enhancing line and station capacity are proposed to ensure that passengers have a comfortable waiting experience.

Greenhouse gas emission driving factors and reduction strategies for operational phase of urban rail transit in China

AbstractTo promote the sustainable development of urban rail transit (URT) in China, it is necessary to quantitatively analyze the greenhouse gas (GHG) emissions and its internal driving factors. Based on the LMDI algorithm, this research constructs an assessment model and makes a quantitative analysis of internal driving factors for GHG emissions in the operational phase of the URT system in China. According to the official investigated data, the GHG emission of URT in the operational phase is between 600 × 104 and 2000 × 104 tons of carbon dioxide equivalent (CO2e) per year from 2012 to 2023. The results of the driving factors decomposition showed that the passenger demand intensity was the key and positive factor affecting carbon emissions in the operational phase and the contribution rate was 12.93%–60.81% from 2013 to 2023. Therefore, it is necessary to formulate targeted policies in terms of management and technical means to reduce the average traveling distance demanded by passengers and reasonably control the scale of the line network for GHG reduction in the Chinese URT system.

Research on the Optimization of Urban–Rural Passenger and Postal Integration Operation Scheduling Based on Uncertainty Theory

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.

Short-Term Passenger Flow Prediction in Urban Rail Transit based on Hybrid Deep Learning Models

Urban rail transit systems are essential for efficient, reliable, and environmentally friendly transportation in modern cities. Correct and effective short-term passenger flow prediction is crucial for optimizing operational efficiency, enhancing service quality, and ensuring passenger safety. Traditional prediction methods often fail to capture the complex, non-linear, and dynamic patterns in urban rail transit systems. This study uses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict short-term passenger flow in urban rail transit. By integrating the strengths of CNN in capturing spatial features and LSTM in modeling temporal dependencies, the hybrid model aims to improve prediction accuracy. Using historical data from Hangzhou Metro, the study demonstrates the model's effectiveness in predicting passenger flow, which reasonably has a good fit. Additionally, models with different convolutional layers show different performances. These improved predictions offer valuable insights for transit authorities, enabling them to make more informed decisions regarding train scheduling, resource allocation, and emergency response planning. By anticipating passenger demand more accurately, authorities can optimize the deployment of trains, reduce waiting times, enhance passenger comfort, and improve overall service reliability.

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

Joint optimization of overbooking and seat allocation for high-speed railways considering stochastic demand.

To mitigate empty seat loss caused by random passenger no-show behavior, this study extends seat allocation to joint optimization of overbooking and seat allocation for high-speed railways (HSR). Assuming that stochastic passenger demand follows a specific distribution and considering various constraints, including train capacity, demand, and denied boarding rate constraints, a nonlinear stochastic programming model for joint optimization of overbooking and seat allocation for HSR is constructed with the aim of maximizing railway expected revenue. To solve this optimization model, a multi-level optimization algorithm is designed. Based on the sampling averaging approximation method, demand scenarios and passenger no-show scenarios are generated and the optimization problem is decomposed, including the joint optimization of overbooking and seat allocation under a single demand scenario, and the ticket adjustment under other demand scenarios. For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. The results demonstrate that the proposed joint optimization method effectively addresses the randomness of passenger demand and no-show behavior, thereby improving HSR expected revenue and making up for the empty seat loss resulting from passenger no-show behavior.

EFFICIENCY OF URBAN PASSENGER TRANSPORT BY BUS: AN APPLICATION TO THE CASE OF CUENCA-ECUADOR

Objective: The study aims to analyze the technical and scale efficiency of public transportation in Cuenca, Ecuador, using Data Envelopment Analysis (DEA). It evaluates both supply and demand aspects. Theoretical Framework: DEA measures the efficiency of decision-making units (DMUs) in using inputs to produce outputs. It's widely used to assess public transportation efficiency by comparing resource usage and service outcomes. Method: The study analyzes 37 DMUs, considering kilometers traveled, number of frequencies, and total fleet as inputs, and operating costs and passenger demand as outputs. Results and Discussion: DEA_Demand showed a 90% technical efficiency and 45% scale efficiency. DEA_Offer showed 94% and 96% efficiency, respectively, highlighting the potential for adjustments in both approaches. Research Implications: The findings help policymakers optimize resource allocation and improve public transport services. Originality/Value: The study uniquely combines supply and demand analysis using DEA in a mid-sized city like Cuenca, offering actionable insights.