Search Results (116)

Departure punctuality (KPI01) and ATFM slot adherence (KPI03) have been emphasized by the International Civil Aviation Organization as key performance indicators (KPIs) in the Global Air Navigation Plan. To address the inherent conflict between these two objectives in departure scheduling, a multi-objective optimization model is proposed that aims to simultaneously enhance departure punctuality, ATFM slot adherence, and taxiing efficiency. A simulated annealing algorithm based on a resource transmission mechanism was developed to solve the model effectively. Based on full-scale operational data from Nanjing Lukou International Airport in June 2023, the empirical results confirm the model’s effectiveness in two primary dimensions: (1) Significant improvement in taxiing efficiency: The average unimpeded taxi-out time was reduced by 6.4% (from 17.2 to 16.1 min). The number of flights with taxi-out times exceeding 30 min decreased by 58%. For representative taxi routes (e.g., stand 118 to runway 6), the excess taxi-out time was reduced by 82.3% (from 5.61 to 1.10 min). (2) Enhanced operational punctuality: Departure punctuality improved by 10.7% (from 67.9% to 78.7%), while ATFM slot adherence increased by 31.2% (from 64.6% to 95.8%). This study presents an innovative departure scheduling approach and offers a practical framework for improving collaborative operational efficiency among airports, air traffic management units, and airlines. Full article

With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network. 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

Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing. Full article

To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual–physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual–physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxiing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual–physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin–physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations. Full article

Domestic workers represent an essential yet invisible workforce within urban economies, especially in developing countries. Predominantly women in low-income, single-headed households, they often work informally and rely on buses or minibus taxis for suburb-to-suburb travel. Despite their contributions, their transport needs are overlooked in traditional planning, which prioritises CBD-centric routes over the suburb-to-suburb journeys that define their invisible commute. The purpose of this study is to examine the transport mobility patterns of live-out domestic workers in urban areas, focusing on Centurion, one of the affluent neighbourhoods in the Metropolitan City of Tshwane, South Africa. To assess the transport challenges faced by domestic workers during their commutes, a Likert scale was utilised. The data were analysed using descriptive statistics facilitated by the SPSS software package to identify key trends and patterns in the responses. The key challenges of domestic workers are high transport costs, lack of access to affordable transport modes like rail and long commute times. Minibus taxi is the most commonly used mode accommodating both standard and non-standard working hours. The study also found that most of the domestic workers working in Centurion are migrant workers. To reduce the need to travel to work, mixed-income developments, and inclusionary housing are some of the concepts that can be adopted in affluent suburbs like Centurion. These two concepts not only address the need to travel to work but also spatial inequality and promotion of social integration whereby affordable housing are created within higher income areas. Full article

With the development of transportation networks, countless trajectory data are accumulated, and understanding human mobility from traffic data could be helpful for smart cities, urban computing, and urban planning. Extracting valuable insights from traffic data, such as taxi trajectories, can significantly improve residents’ daily lives. There are many studies on spatiotemporal data mining. As we know, arrival prediction or regional function detection encompasses important tasks for traffic management and urban planning. However, trajectory data are often mutilated because of personal privacy and hardware limitations, i.e., we usually can only obtain partial trajectory information. In this paper, we develop an embedding method to predict the next arrival using the origin–destination (O-D) pair trajectory information and point of interest (POI) data. Moreover, the embedding information contains region latent features; thus, we also detect the regional function in this paper. Finally, we conduct a comprehensive experimental study on a real-world trajectory dataset. The experimental results demonstrate the benefit of predicting arrivals, and the embedding vectors can detect the regional function in a city. Full article

The advancement of autonomous driving technology, particularly Tesla’s launch of its new Robotaxi, marks a transformation in transportation. Understanding the theoretical mechanisms that drive consumers’ intention to use autonomous taxis is essential. This study develops a structural equation model (SEM), extending the applicability of the TAM and TPB model, and incorporates external factors like attitudes, subjective norms, traffic efficiency, and perceived cost–benefit into the model to analyze their impact on consumers’ perceived characteristics (perceived usefulness and perceived ease of use). A survey of 427 valid responses revealed that attitudes, subjective norms, and perceived cost–benefit all have significant positive impacts on perceived usefulness and ease of use, which, in turn, are the primary drivers of consumers’ intention to use. Additionally, perceived risk significantly weakens the positive effects of perceived usefulness and ease of use on the intention to use, underscoring its critical moderating role in the technology acceptance process. This paper suggests strategies to enhance consumer acceptance, including strengthening user perception through marketing and public experience activities, optimizing technology to improve user experience, reinforcing safety and privacy measures to reduce perceived risk, and highlighting the insurance mechanism, convenience, and economic benefits of autonomous taxis in marketing. Full article

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations. Full article

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety. Full article

Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce the Spatio-Temporal Embedding Fusion Transformer (STEFT). This deep learning model leverages attention mechanisms and feature fusion to effectively model dynamic dependencies in traffic data. STEFT includes an Embedding Fusion Network that integrates spatial, temporal, and flow embeddings, preserving original flow information. The Flow Block uses an enhanced Transformer encoder to capture periodic dependencies within neighboring regions, while the Prediction Block forecasts inflow and outflow dynamics using a fully connected network. Experiments on NYC (New York City) Taxi and NYC Bike datasets show STEFT’s superior performance over baseline methods in RMSE and MAPE metrics, highlighting the effectiveness of the concatenation-based feature fusion approach. Ablation studies confirm the contribution of each component, underscoring STEFT’s potential for real-world traffic prediction and other spatial–temporal challenges. Full article

The problem of increasing passenger traffic remains acute for municipal public transport. The value of this indicator is determined by the interest of citizens in this way of making their trips and determines the feasibility of the carrier’s operation. The authors conducted a study of the problems of public transport services in large- and medium-sized cities, which found that the population’s interest in public urban passenger transport has generally been significantly lost. More than 40% of the city population refuses to travel on public transport, half of the population has questions about the reliability of tariff formation, and the same number of people are not satisfied with the regular route network and schedule. City residents increasingly prefer personal vehicles or taxis for their trips, which negatively affects the revenue side of carriers, as well as the level of social comfort and the quality of life of citizens. Efforts to reduce the operating costs of the carrier are aimed at correcting the current situation with urban transport so that tariffs for transportation are more acceptable for passengers. The formation of tariffs for passenger transportation for transport companies is an urgent and complex task. It is necessary to formulate the tariff in such a way as to cover your own transportation costs in the near future and, at the same time, not exceed the psychological threshold for passengers so as not to cause their negative reaction. In addition, since the transportation of passengers by urban public transport is regulated by the authorities, it is also necessary to provide an economic justification for transportation tariffs. This is difficult in the absence of substantiated indicators of consumption rates of material resources in the transport process. To solve this problem, it is necessary to carefully analyze the current costs of operating the bus fleet, as well as forecast costs for future periods. At different periods, researchers have proposed various approaches for planning the cost of operating a bus fleet. The approach we propose is to use standardization of the consumption of material resources, considering the individual operating conditions of the bus fleet and the influence of various factors. Full article

Green transportation is the core embodiment of ecological civilization and the concept of green development within the field of transportation, and it is an important strategic choice for sustainable urban development. National central cities represent the highest level in China’s urban system planning. This paper aims to evaluate the level of green transportation development in national central cities. It established a set of 29 specific evaluation indicators from five dimensions: basic indicators, green transportation infrastructure, traffic environmental protection, traffic travel, and traffic safety. It constructed an evaluation index system for the development level of green transportation. The entropy weight TOPSIS method was utilized to evaluate the development levels of green transportation in nine national central cities from 2020 to 2022. An obstacle degree model was constructed to identify key obstacle factors at both the criterion and indicator layers of the green transportation development level evaluation index system for national central cities. Suggestions were proposed from five aspects: establishing a comprehensive policy framework, promoting regional collaborative development, accelerating infrastructure construction, improving transportation service quality, and fostering the green upgrading of industries. The results showed that the comprehensive ranking of green transportation development levels among the national central cities from high to low for the years 2020–2022 was as follows: Shanghai, Chongqing, Chengdu, Beijing, Guangzhou, Tianjin, Wuhan, Xi’an, Zhengzhou. In terms of the regional spatial layout, the green transportation development levels of the nine national central cities generally exhibited a “high on the periphery, low in the center” distribution characteristic. The comprehensive ranking of the obstacle degree in the criterion layer was as follows: basic indicators, traffic travel, green transportation infrastructure, traffic environmental protection, traffic safety. After screening the criteria level where the obstacle degree calculation results are above 15%, traffic safety is eliminated. The nine cities, which were located in different regions, generally maintained consistent internal obstacle factors and their order. The top five indicators with the highest frequency of obstacle degrees at the indicator layer were as follows: total passenger transport volume, number of taxis, new energy vehicle production, expenditure for transportation, and total freight transport volume. The specific key obstacle factors at the indicator level were different in the nine cities. Full article

(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue. Full article

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis. Full article