Search Results (204)

Existing timetable optimisation models for urban rail transit predominantly adopt operator-oriented objectives with assumed passenger-related weights. This paper proposes a passenger satisfaction-oriented timetable optimisation framework in which satisfaction weights are empirically derived from confirmatory factor analysis and Cramér’s V analysis of survey data collected from 439 passengers on Nanning Rail Transit Line 1. Seven scheduling-related service attributes are formally expressed as functions of timetable decision variables, establishing a direct linkage between passenger perception and scheduling decisions. A multi-objective model minimises a weighted combination of passenger dissatisfaction, operational cost, and stranded passenger ratio, solved by a Passenger Satisfaction-oriented Adaptive Dispatch Heuristic (PS-ADH) integrating simulated annealing with a passenger flow simulation module. Case study results demonstrate simultaneous improvements of 3.78% in composite objective value, 3.25% in passenger dissatisfaction, and 3.25% in operational cost, with a 27.4% reduction in stranded passengers. The optimised strategy is selected consistently across all ten random initialisations (CV = 0.13%). Sensitivity analysis reveals a structural break at cost weight $\beta =0.4$, beyond which the optimal strategy shifts qualitatively toward cost minimisation at the expense of service quality. The framework provides a transferable methodology for integrating passenger perception data into rail transit scheduling for emerging urban rail systems. Full article

Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities. Full article

Railway transport is increasingly recognized as a key pillar of sustainable mobility, offering a low-carbon and energy-efficient alternative to road and air transport and playing a critical role in achieving climate objectives, regional connectivity, and sustainable tourism development. Despite extensive research on service quality, sustainability, and tourism, their interrelationship within the railway sector remains insufficiently explored. This study aims to systematically analyze the intersection of quality management systems (QMS), sustainability, and tourism in passenger railway transport and to identify structural gaps that hinder their integration. A systematic literature review was conducted following the PRISMA methodology, resulting in a final sample of 37 studies. The findings reveal a significant research gap, particularly the absence of integrated and empirically supported QMS frameworks linking passenger satisfaction with sustainability and tourism objectives. Quality-management-oriented constructs appear in 48.6% of the analyzed studies, sustainability in 32.4%, and tourism in 24.3%, while none demonstrate full integration of all three dimensions. The study contributes by providing a conceptual basis for future research on the integration of operational quality management, environmental performance, and passenger-oriented service quality in railway systems. Full article

Retaining bus users is a practical condition for sustainable urban mobility, but transport agencies need defensible passenger-centered measures to judge whether service experience is credible enough to support continued use. This study tests an exploratory measurement approach linking perceived bus service quality, satisfaction, perceived value, and loyalty, using urban bus-user data collected in Pakistan as the empirical setting rather than as a national-representative case. A mixed-mode survey produced 230 responses; 227 valid cases remained after pre-specified screening of ineligible and out-of-frame records. The analysis used item diagnostics, exploratory factor analysis, HC3-robust regression, bootstrapped indirect-association testing, subgroup description, and qualitative coding of open-ended responses. The retained sample was young, student-oriented, and strongly transit-dependent, so the results mainly describe regular bus users with limited modal flexibility. Service-quality items were factorable (KMO = 0.725; Bartlett’s chi-square = 851.56, df = 435, p < 0.001), but parallel analysis supported one dominant general service-quality signal rather than a stable five-dimensional SERVQUAL structure. General service quality (GSQ) is therefore treated as an empirical indicator of perceived service credibility, not as a validated new latent construct. GSQ was positively associated with satisfaction (beta = 0.382, p < 0.001), perceived value (beta = 0.389, p < 0.001), and loyalty after satisfaction and perceived value were included (beta = 0.233, p < 0.001). Bootstrap results were consistent with indirect association through satisfaction (estimate = 0.032, 95% CI [0.011, 0.057]) and perceived value (estimate = 0.035, 95% CI [0.010, 0.062]), while a residual direct association remained. Frequent riders rated reliability, responsiveness, perceived value, and loyalty more critically, but interaction testing did not confirm moderation by bus-use frequency. Open-ended comments repeatedly pointed to punctuality and passenger information, with safety also appearing as a salient threshold concern. The findings support passenger-centered sustainability monitoring by identifying service credibility, reliability, responsiveness, safety, and perceived value as practical indicators for retaining transit-dependent bus users, while ruling out any claim that the imported SERVQUAL structure was fully validated in this sample without confirmatory revalidation. Full article

High-density commercial interchange hubs are important components of transit-oriented urban regeneration. However, high passenger flow does not necessarily generate sustained underground-space vitality. This study examines the Licun high-density commercial interchange hub in Qingdao, China. It explores how amenity supply influences sustainable underground-space vitality through user behavioral responses. Based on 426 valid questionnaire responses, an amenity evaluation system was developed across five dimensions. These dimensions include natural-environment, facility-and-service, consumption-experience, socio-cultural, and transport-connection amenities. The entropy weight method was used to identify the perceptual differentiation of amenity indicators. PLS-SEM was then applied to examine the pathway of “amenity supply–user behavioral response–underground-space vitality.” The results show that natural-environment amenities present the strongest perceptual differentiation, while facility-and-service amenities play a fundamental role in supporting user behavioral responses. Consumption-experience amenities promote visit choice, stay duration, and satisfaction, but may weaken interaction and participation, indicating a potential tension between commercial vitality and public interaction. Transport-connection amenities mainly affect visit choice rather than sustained use. Among user behaviors, stay duration, interaction and participation, and satisfaction feedback are positively associated with underground-space vitality, whereas simple visit choice is not. These findings suggest that sustainable vitality in a high-density commercial interchange hub should not be understood as passenger volume alone. It should be understood as the transformation of transit flow into voluntary stay, interaction, satisfaction, and repeated use. This study contextualizes amenity theory in a high-density commercial interchange hub. It also offers planning implications for underground-space regeneration. Full article

Sustainable transport management (STM) has become an increasingly important issue in recent years, as cities have faced growing traffic congestion, air pollution, and other transport-related challenges. Travel time (TT) represents one of the critical determinants of Quality of Service (QoS) and user satisfaction in public passenger transport (PPT). TT extends beyond in-vehicle duration and encompasses a sequence of temporal components, including access, waiting, transfer, and egress times. TT reflects the complexity of an integrated passenger transport system (IPTS), where users experience transport services as a door-to-door journey rather than isolated trips. This article analyses the TT within IPTSs through the lens of European quality standards EN 13816 and EN 15140 for PPT. Standard EN 13816 provides a normative framework for defining TT as a key QoS criterion reflecting user expectations and a user-oriented perspective, while standard EN 15140 operationalises this framework by specifying methodological requirements for the measurement and evaluation of the delivered TT quality at system-level performance objectives. This research highlights a structural gap between the conceptualisation of TT as a door-to-door journey, a user-oriented phenomenon, and its measurement through fragmented, mode-specific performance metrics. It limits the ability of transport authorities and operators to accurately evaluate the QoS and to design efficient urban mobility (UM) systems. Full article

Transit delays can adversely affect passengers, operational efficiency, and daily lives. It is important to develop effective methods to identify and analyze train stations vulnerable to delays. This paper proposes a three-stage analytical framework for analyzing train station delays. In the first stage, the 3-sigma rule defines normal passenger volume ranges and establishes a time window affected by delays. Next, a multivariate time series clustering method identifies stations with stable demand and high volume, considering passenger volume differences both among and within stations. In the final stage, the effects of delays on these key stations are assessed by examining starting, duration, and ending times, and passenger volume variation, providing a comprehensive analysis of delay impact. The proposed framework is illustrated using two real-world incidents: the 2021 delay incident at Longyang Road Station of Shanghai Metro and the 2019 delay incident on the Taoyuan–Luohu section of Shenzhen Metro. Case studies revealed that affected stations are not limited to the specific line or direction of the delay, but also include opposite-direction and transfer stations. Station impacts exhibit phased onset and recovery patterns. Additionally, both increases and decreases in passenger volumes due to the delay present considerable implications. While both incidents exhibit common propagation and recovery patterns, the Shanghai incident displays wider passenger impacts and longer recovery periods, whereas the Shenzhen incident exhibits narrower impacts and faster recovery. Our results will aid transit managers in better managing delays, thereby improving passenger satisfaction and operational efficiency. This paper also offers an integrated station-level analytical framework and initial cross-case empirical evidence, while broader validation remains needed. Full article

This study examines passengers’ perceptions of environmental sustainability practices at Al-Ahsa International Airport and investigates whether these practices are reflected in passenger satisfaction, within the broader policy context of Saudi Arabia’s Vision 2030. It contributes to the emerging literature on perceived environmental sustainability in airport service environments, particularly in regional and developing aviation contexts. The analysis draws on a structured questionnaire administered to 302 passengers, supported by relevant secondary data, and combines descriptive statistics, a SWOT analysis and an ordinal logistic regression model to explore three practical dimensions of environmental performance, namely energy and climate initiatives, waste management practices, and environmentally supportive infrastructure. The results indicate that passengers are generally satisfied with the airport’s environmental performance, with waste management and sustainability-oriented infrastructure showing a statistically significant and positive association with passengers’ satisfaction. Energy and climate practices also exhibit a statistically significant positive effect; however, their impact is comparatively weaker than that of waste management and infrastructure. The findings therefore point to the need to expand clean and renewable energy investments while also making such efforts more visible through targeted awareness activities for passengers and staff, alongside continued improvements in infrastructure that support environmentally responsible behaviour, as part of the airport’s transition towards a greener and more tourism-supportive facility. Full article

In the environment in which competition in the aviation industry is intensifying and services are becoming increasingly homogeneous, a deep understanding of the key factors influencing passenger loyalty is beneficial for airlines to adopt more differentiated policies, thereby enabling long-term development. Exploring these dynamics within the framework of social and economic sustainability in the aviation sector, this study conducted a questionnaire survey of 375 Air China passengers. The collected data were analyzed using SEM (structural equation modeling) to investigate the relationships among service experience, corporate social responsibility (CSR), brand love, customer satisfaction, and reuse intention. The empirical results show that service experience has a significant and positive impact on both brand love and customer satisfaction. Also, CSR is key to boosting passengers’ emotional attachment and overall satisfaction. The findings also show that passengers with higher brand love and satisfaction are more likely to reuse the airline’s services. The results highlight how integrating CSR and service experience into an emotional–cognitive path serves as a strategic roadmap for securing a sustainable competitive advantage in a homogeneous market. Based on these insights, the research proposes practical managerial implications to help Air China develop sustainably and stay competitive in the long run. Full article

Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as fuzzy variables and a fuzzy chance-constrained programming model is developed, aimed at minimizing total recovery costs. The model is transformed into a deterministic equivalent using trapezoidal fuzzy numbers. An improved Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is designed to efficiently solve the problem, balancing solution quality and computational efficiency through insert, exchange, and cancel. The local search process is enhanced by incorporating the acceptance criteria of the simulated annealing algorithm. The proposed method is validated using real-world airline data. Results show that, compared to the traditional GRASP algorithm, the improved GRASP algorithm can obtain better solutions in a shorter time; the solutions in deterministic scenarios tends to be more conservative, leading to resource waste; the proposed method can achieve airline recovery at the minimum recovery cost. Sensitivity analysis reveals that selecting an appropriate confidence level significantly influences recovery costs. This paper provides a robust framework for enhancing operational resilience and passenger satisfaction under uncertain conditions, offering practical insights for real-world application. Full article

Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article

With the rapid growth of the civil aviation industry, airport gate resources—especially those equipped with jet bridges (more convenient than shuttles)—have become increasingly scarce, posing new challenges to the sustainable management of airport operations. In a real-world application of the airport transport optimization study field, the airport gate assignment problem (AGAP) has emerged as a critical scheduling task in airport operations with the rapid growth of passenger demand. In this study, a mixed-integer linear programming model is developed for AGAP, aiming to minimize baggage transfer vehicle usage, maximize airline satisfaction, reduce passenger boarding time, and enhance the overall sustainability of airport operations. To efficiently address the computational complexity of this integrated modeling framework, a customized multi-objective particle swarm optimization (MOPSO) algorithm is proposed, augmented by a tabu search (TS) strategy. The TS algorithm provides high-quality initial solutions for MOPSO and performs local intensification on elite particles, thereby enhancing both convergence speed and solution quality. Extensive numerical experiments demonstrate that the proposed hybrid approach significantly outperforms the standalone MOPSO algorithm, achieving a 26.37% improvement over the original gate assignment scheme and a further 1.25% improvement compared to the standalone MOPSO, confirming the effectiveness and practicality of the proposed method. Full article

Subway public spaces have been identified as a vital medium for showcasing urban culture. The design quality of these spaces has been shown to have a profound influence on passengers’ spatial perception and cultural experience. However, amid rapid urbanization, subway stations commonly face issues such as homogeneous spatial interfaces and unclear cultural themes, resulting in diminished station identifiability. This study integrates post-use evaluation with Importance–Performance Analysis (IPA) to establish an assessment and optimization pathway aimed at systematically identifying and prioritizing key design elements for enhancing cultural identifiability. Taking Tianjin Gulou Station as a case study, user feedback collected through questionnaires identified 12 indicators influencing identifiability satisfaction. The reliability and validity of the questionnaire were confirmed through validity analysis and paired-sample t-tests, while IPA was employed to clarify improvement priorities. The results indicate that the overall perceived importance of cultural identifiability at Gulou Station significantly exceeds satisfaction levels. Landmark installations, art walls, and vertical transportation fall within the “high importance-low satisfaction” quadrant, which is identified as a primary area of focus for enhancement. Basic interface elements such as flooring and ceilings require enhancement, while transfer entrances and station name walls constitute advantageous designs warranting preservation. Based on the findings of the present study, three targeted design strategies are proposed: enhancing spatial perception, constructing cultural continuity, and integrating multidimensional experiences. These approaches seek to address the “spatial-cultural” perception gap, providing actionable pathways for the distinctive renewal of subway spaces. Full article

Demand-responsive transit (DRT) requires real-time vehicle assignment under dynamically arriving requests, where each decision may alter multi-stop routes and affect both onboard and newly arriving passengers. However, DRT simulations often face three key limitations: rapidly increasing computational complexity as fleet size and demand grow, insufficient integration of traffic congestion into routing decisions, and limited consideration of passenger-oriented service quality in final vehicle assignment. To address these issues, this study proposes an integrated DRT simulation incorporating three core algorithms: Fréchet Distance-based Candidate Vehicle Selection (FD-CVS), Congestion-Aware Path Planning (CA-PP), and Satisfaction-Aware Vehicle Assignment (SA-VA). FD-CVS reduces computational burden by filtering candidate vehicles based on route similarity. CA-PP extends conventional path planning by incorporating congestion-adjusted travel costs derived from public transportation data. SA-VA determines the final vehicle assignment by jointly evaluating passenger waiting time, in-vehicle travel time, and capacity constraints. The algorithms are implemented within a discrete-event simulation environment using real-world data. Experimental results demonstrate that FD-CVS significantly reduces execution time under high-demand conditions, while SA-VA improves passenger waiting time and acceptance rates. Overall, the proposed three-algorithm framework enables more realistic and computationally efficient DRT system evaluation. Full article

Climate-induced natural hazards are increasingly disrupting metro operations in megacities, necessitating robust and generalizable frameworks for system-wide resilience. While current studies often treat infrastructure degradation, operational adjustment, and passenger flow redistribution as separate problems, this study proposes a resilience-oriented decision framework that couples these universal processes together to address diverse disruptive events. Taking extreme rainfall as a critical representative scenario, a multi-objective recovery optimization model is developed to jointly optimize repair resource cost and average section saturation. Resilience is quantified through the demand satisfaction ratio over the disruption–recovery process, ensuring the framework’s applicability across different hazard types. A case study of the Shanghai metro system under a real extreme rainfall event demonstrates the model’s efficacy in capturing complex system dynamics. Results show a clear Pareto trade-off between repair resource cost and average section saturation, while increasing service capacity on adjacent lines improves the Pareto frontier. Prioritizing repairs on lines with the fewest damaged sections effectively reduces network saturation by restoring corridor throughput. The resilience curve proves that higher repair resources not only shorten recovery time but also raise the minimum demand satisfaction ratio. These findings provide a scalable methodology for designing resilient metro recovery strategies under various climate-related disruptions globally. Full article