Search Results (200)

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article

Urban Air Mobility (UAM) is envisioned to revolutionize urban transportation by improving traffic efficiency and mitigating surface-level congestion. One of the fundamental challenges in implementing UAM systems lies in the optimal siting of vertiports, which requires a delicate balance among infrastructure construction costs, passenger access costs to their assigned vertiports, and the operational connectivity of the resulting vertiport network. This study develops an integrated mathematical model for vertiport location decision, aiming to minimize total system cost while ensuring UAM network connectivity among the selected vertiport locations. To efficiently solve the problem and improve solution quality, a hybrid genetic algorithm is developed by incorporating a Minimum Spanning Tree (MST)-based connectivity enforcement mechanism, a fundamental concept in graph theory that connects all nodes in a given network with minimal total link cost, enhanced by a greedy initialization strategy. The effectiveness of the proposed algorithm is demonstrated through numerical experiments conducted on both synthetic datasets and the real-world transportation network of New York City. The results show that the proposed hybrid methodology not only yields high-quality solutions but also significantly reduces computational time, enabling faster convergence. Overall, this study provides practical insights for UAM infrastructure planning by emphasizing demand-oriented vertiport siting and inter-vertiport connectivity, thereby contributing to both theoretical development and large-scale implementation in complex urban environments. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. Full article

Urban public transport represents a fundamental pillar of a sustainable transport system and a key subsystem within the broader mobility framework in urban environments. This paper focuses on the analysis and optimization of the public transport system in the city of Prievidza and the nearby town of Bojnice in Slovakia, which currently face challenges such as low system attractiveness, operational inefficiency, and weak integration with regional railway transport. This study presents the results of a comprehensive analysis of existing public transport services in Prievidza and Bojnice, including an assessment of passenger flows, line network structure, transfer connections, and operational parameters. Based on the identified deficiencies, a new urban public transport network system is proposed, emphasizing direct links to the railway network. This methodology is developed in the context of an integrated timed-transfer timetable, with defined system time slots at the main transfer hub and a newly designed line network with standardized paths and regular intervals. The proposed system ensures significantly improved connectivity between urban transport and rail services, reduces deadhead kilometres, lowers the number of required vehicles, and leads to a reduction in operational costs by up to 20%. The resulting model serves as a transferable example of efficient service planning in medium-sized cities, with a focus on functional integration, operational efficiency, and sustainable urban development. Full article

The article explores the capacity of zero-emission urban public transport (PT) and proposes a standardised method for calculating it across different PT corridors (bus, tram, metro and urban railway). As the European Union (EU) tightens regulations on emissions, targeting also PT, cities are increasingly shifting to electric and hydrogen-powered vehicles. A significant challenge was the lack of a unified methodology to calculate the capacity of zero-emission vehicles, e.g., battery-powered buses carry fewer passengers than diesel ones due to weight restrictions. The article addresses this gap by creating capacity matrices for various vehicle types based on standardised assumptions. Vehicle capacity is calculated based on seating and standing space, with standing passenger space standardised to 0.2 m²/person (E Level of Service). A detailed rolling stock analysis shows how modern designs and floor layouts impact passenger space. Matrices were developed for each mode of transport, showing the number of transported passengers per hour depending on vehicle type and service frequency. The highest capacity is achieved by metro and urban railway systems (up to 95,000+ passengers/hour/direction), while buses offer the lowest (up to 7800 passengers/hour/direction). The authors recommend standardising calculation methods and integrating matrices into planning tools for urban PT corridors. Full article

The increasing disparity in global food distribution has amplified the urgency of addressing food waste and food insecurity, both of which exacerbate economic, environmental, and social inequalities. Traditional food bank models often struggle with logistical inefficiencies, limited accessibility, and a lack of transparency in food distribution, hindering their effectiveness in mitigating these challenges. This study proposes a novel Food Bank Network Redesign (FBNR) that leverages the Quito Metro system to create a decentralized food bank network, enhancing efficiency and equity in food redistribution by introducing strategically positioned donation lockers at metro stations for convenient drop-offs, with donations transported using spare metro capacity to designated stations for collection by charities, reducing reliance on dedicated transportation. To ensure transparency and operational efficiency, we integrate a blockchain-based traceability system with smart contracts, enabling secure, real-time tracking of donations to enhance stakeholder trust, prevent food loss, and ensure regulatory compliance. We develop a multi-objective optimization framework that balances food waste reduction, transportation cost minimization, and social impact maximization, supported by a mixed-integer linear programming (MIP) model to optimize donation allocation based on urban demand patterns. By combining decentralized logistics, blockchain-enhanced traceability, and advanced optimization techniques, this study offers a scalable and adaptable framework for urban food redistribution, improving food security in Quito while providing a replicable blueprint for cities worldwide seeking to implement circular and climate-resilient food supply chains. Full article

Bus stops are an essential component of public transportation systems, significantly impacting human health, wellbeing, and overall user experience. As primary interaction points for passengers, they are integral to the urban landscape and, as such, their designs influence people’s experiences within the public realm. Despite their importance, the design of bus stops and bus shelters remains an under-researched area. This paper aims to review the existing peer-reviewed research on bus-stop design, identifying areas for future inquiry. Twenty-two peer-reviewed journal articles were selected and included in this study. The most common theme in the published research was the manner in which bus stops could address extreme weather and heat, along with other themes, including accessibility, sustainable energy, air pollution, and noise. Further empirical research is necessary to understand how bus-stop design affects the user experience, emphasizing qualitative methods to explore human experiences, perceptions, motivations, and challenges related to bus-stop usage and public transportation. Full article

In this study, a Long Short-Term Memory (LSTM) model with extra variables such as weather conditions and school days was developed within a multi-scale framework in order to forecast passenger flow in both bus and rail systems, covering both regional and route-level analyses. In addition, the performance of the LSTM model was compared against three separate deep learning models. Among these, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) time series model produced the lowest error values, achieving a high level of accuracy. While no considerable changes were observed in regional rail passenger flow as a result of the inclusion of weather-related variables, a 2.2% drop in the RMSE value was achieved in bus passenger flow at the regional level; however, this improvement remains relatively modest. In contrast, at the route level, RMSE values declined by 2.4% for rail and 3.69% for bus routes. These findings reveal that the inclusion of weather-related variables significantly improves the prediction of bus passenger flow, underlining the benefits of integrating such data into forecasting models. Furthermore, the findings of this study analytically support transportation planners in making more informed, data-driven decisions regarding scheduling and capacity management. Full article

The introduction of Bus Rapid Transit (BRT) systems has the potential to alleviate urban traffic congestion. However, in certain cities in China, the increasing prevalence of privately owned vehicles, combined with the underutilization of bus lanes due to infrequent bus departures, has contributed to heightened congestion in general lanes. The advent of Internet of Things (IoT) technology offers a promising opportunity to develop intelligent public transportation systems, facilitating efficient management through seamless information transmission to end devices. This paper presents an IoT-based shared bus lane (IoT-SBL) that integrates intersection information, real-time traffic queuing conditions, and bus location data to encourage passenger vehicles to utilize the bus lane. This encouragement can be communicated through traditional signaling methods or future Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication technologies. To evaluate the effectiveness of the IoT-SBL strategy, we proposed a stochastic model that incorporates queuing effects and derived a series of performance metrics through model analysis. The experimental findings indicated that the IoT-SBL strategy significantly reduces vehicle queuing, decreases vehicle delays, enhances intersection throughput efficiency, and lowers fuel consumption compared to the traditional bus lane strategy. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article

Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety. Full article

Urban transportation systems, particularly integrated bus–metro networks, play a critical role in sustaining city functions but face significant vulnerability during extreme flood disasters. Taking Zhengzhou, China, as a case study, this study developed a comprehensive assessment model to evaluate the resilience of urban bus–metro hybrid networks under flood scenarios. First, a complex network-based bus–metro hybrid transportation network model was established, incorporating quantifiable flood disaster risk indices considering disaster-inducing factors, hazard-prone environments, and disaster-bearing entities. A cascading failure model was then constructed to simulate the propagation of node failures and passenger load redistribution during flood events. Subsequently, network resilience was evaluated using the topological metric of the relative size of the largest connected component and the functional metric of global efficiency. The analysis examined the influence of the load capacity sensitivity parameters α and β on resilience outcomes. Simulation results indicated that the parameter combination α = 0.8 and β = 2.0 yielded the highest resilience under the tested conditions, offering a balance between redundancy and the targeted protection of high-load nodes. Additionally, recovery strategies prioritizing nodes based on betweenness centrality significantly improved resilience outcomes. This study provides valuable insights and practical guidance for improving urban transportation resilience, assisting policymakers and planners in better mitigating flood disaster impacts. Full article

Urban transportation systems evolve toward greater diversification, scalability, and complexity. To address the escalating issue of urban traffic congestion, leveraging modern information technologies to enhance the integration of multiple transportation modes and maximize overall efficiency has emerged as a promising strategy. This study focuses on the decision making problem of urban multimodal transportation travel paths, integrating the time-varying characteristics of public transportation schedules and networks. We consider passengers’ diverse needs and systematically investigate how to optimize travel paths to minimize travel time while adhering to constraints, such as the number of interchanges and travel costs. To address this NP-hard problem, we propose and implement two optimization algorithms: a variable-length coding genetic algorithm (V-GA) and a full permutation coding genetic algorithm (F-GA). Detailed numerical analysis validates the effectiveness of both algorithms, with the V-GA demonstrating significant advantages over the F-GA in terms of solution efficiency. Our findings provide novel perspectives and methodologies for optimizing urban multimodal transportation travel paths, offering robust theoretical foundations and practical tools for enhancing urban traffic planning and travel service efficiency. Full article

Integrating passenger and parcel transportation via transit (also known as transit co-modality) has been regarded as a potential solution to sustainable transportation, in which well-planned locations for parcel lockers are crucial for transferring parcels from transit to last-mile delivery vehicles. This paper proposes a data-driven optimization framework on parcel locker locations in a transit co-modal system, where last-mile delivery is realized via a ride-pooling service that pools passengers and parcels using the same fleet of vehicles. A p-median model is proposed to solve the problem of optimal parcel locker locations and matching between passengers and parcel lockers. We use the taxi trip data and the candidate parcel locker location data from Shenzhen, China, as inputs to the proposed p-median model. Given the size of the dataset, an optimization framework based on random sampling is then developed to determine the optimal parcel locker locations according to each candidate’s frequency of being selected in the sample. The numerical results are given to show the effectiveness of the proposed optimization framework, explore its properties, and perform sensitivity analyses on the key model parameters. Notably, we identify five types of optimal parcel location based on their ranking changes according to the maximum number of planned parcel locker locations, which suggests that planners should carefully determine the optimal number of candidate locations for parcel locker deployment. Moreover, the results of sensitivity analyses reveal that the average passenger detour distance is positively related to the density of passenger demand and is negatively impacted by the number of selected locations. We also identify the minimum distance between any pair of selected locations as an important factor in location planning, as it may significantly affect the candidates’ rankings. Full article