🎯 Top Personalized Recommendations
ITL Trisakti
AI Summary
- Deep learning and big data analytics are being used to improve transportation efficiency, safety, and sustainability. [3]
- Transportation management is evolving with the help of artificial intelligence (AI) and Internet of Things (IoT). [2]
- Data privacy and security concerns [1]
Abstract
The theoretical landscape of transportation cost planning is shifting from deterministic linear models to dynamic, data-driven optimization. As supply chains face volatility, static 20th-century cost assumptions prove increasingly inadequate. Despite rapid technological advancements, a unified framework linking economic production theory with the operational realities of autonomous, sustainable logistics remains absent. Existing models fail to address non-linear stepwise costs and real-time stochastic variables introduced by market dynamics. This study reconstructs transportation cost planning theory by synthesizing Grand, Middle-Range, and Applied theories. It aims to integrate stepwise cost functions, AI-driven decision-making, and environmental externalities into a cohesive planning model. A systematic theoretical synthesis was conducted using 28 high-impact papers published primarily between 2018 and 2025, employing multi-layered analysis to reconstruct cost drivers. The study identifies three critical shifts: the transition from linear to stepwise fixed costs, the necessity of AI-driven dynamic pricing for revenue optimization, and the role of Autonomous Electric Vehicles (AEVs) in minimizing long-term marginal costs. A "Dynamic-Sustainable Cost Planning Theory" is proposed, arguing that cost efficiency now depends on algorithmic prediction and autonomous fleet utilization rather than simple distance minimization.
The theoretical landscape of transportation cost planning is shifting from deterministic linear models to dynamic, data-driven optimization. As supply chains face volatility, static 20th-century cost assumptions prove increasingly inadequate. Despite rapid technological advancements, a unified framework linking economic production theory with the operational realities of autonomous, sustainable logistics remains absent. Existing models fail to address non-linear stepwise costs and real-time stochastic variables introduced by market dynamics. This study reconstructs transportation cost planning theory by synthesizing Grand, Middle-Range, and Applied theories. It aims to integrate stepwise cost functions, AI-driven decision-making, and environmental externalities into a cohesive planning model. A systematic theoretical synthesis was conducted using 28 high-impact papers published primarily between 2018 and 2025, employing multi-layered analysis to reconstruct cost drivers. The study identifies three critical shifts: the transition from linear to stepwise fixed costs, the necessity of AI-driven dynamic pricing for revenue optimization, and the role of Autonomous Electric Vehicles (AEVs) in minimizing long-term marginal costs. A "Dynamic-Sustainable Cost Planning Theory" is proposed, arguing that cost efficiency now depends on algorithmic prediction and autonomous fleet utilization rather than simple distance minimization.
Why we think this paper is great for you:
This paper addresses the evolving complexities of transportation costs, aligning with an interest in optimizing travel planning and understanding dynamic pricing strategies. The focus on AI-driven approaches and sustainable autonomy directly relates to current trends in travel industry innovation.
This paper addresses the evolving complexities of transportation costs, aligning with an interest in optimizing travel planning and understanding dynamic pricing strategies. The focus on AI-driven approaches and sustainable autonomy directly relates to current trends in travel industry innovation.
Universidade de Vigo
Abstract
The International Air Transport Association (IATA) states that the revenue from interline tickets must be shared among the different airlines according to a weighted system. We analyze this problem following an axiomatic approach, and our theoretical results support IATA's procedure. Our first result justifies the use of a weighted system, but it does not specify which weights should be applied. Assuming that the weights are fixed, we provide several results that further support the use of IATA's mechanism. Finally, we provide results for the case in which all flights can be considered equivalent and no weighting is required.
The International Air Transport Association (IATA) states that the revenue from interline tickets must be shared among the different airlines according to a weighted system. We analyze this problem following an axiomatic approach, and our theoretical results support IATA's procedure. Our first result justifies the use of a weighted system, but it does not specify which weights should be applied. Assuming that the weights are fixed, we provide several results that further support the use of IATA's mechanism. Finally, we provide results for the case in which all flights can be considered equivalent and no weighting is required.
Why we think this paper is great for you:
The research tackles revenue allocation within the airline industry, a core component of travel planning and understanding pricing models. Analyzing this problem through an axiomatic approach provides valuable insights into the mechanics of travel costs.
The research tackles revenue allocation within the airline industry, a core component of travel planning and understanding pricing models. Analyzing this problem through an axiomatic approach provides valuable insights into the mechanics of travel costs.
ITA Aeronautical Techinl
AI Summary
- It also increases seat density and allows for deeper segmentation within economy class. [3]
- The designed by Jacob-Innovations, consists of alternating raised seats that take advantage of vertical space inside aircraft using steps. [3]
- This concept provides greater comfort to passengers while maintaining the same seat density on board. [3]
- Cozy Suite: A seat design that incorporates a second side headrest in addition to individual armrests, increasing seat density and allowing for deeper segmentation within economy class. [3]
- The concept of the which is a two-story individual cocoon space for passengers, has been introduced by Factorydesign. [2]
- The developed by Thompson Aero Seating, incorporates a second side headrest in addition to individual armrests, offering a solution to the conflict generated by shared use of armrests. [0]
Abstract
This study investigates how the layout and density of seats in aircraft cabins influence the pricing of airline tickets on domestic flights. The analysis is based on microdata from boarding passes linked to face-to-face interviews with passengers, allowing us to relate the price paid to the location on the aircraft seat map, as well as market characteristics and flight operations. Econometric models were estimated using the Post-Double-Selection LASSO (PDS-LASSO) procedure, which selects numerous controls for unobservable factors linked to commercial and operational aspects, thus enabling better identification of the effect of variables such as advance purchase, reason for travel, fuel price, market structure, and load factor, among others. The results suggest that a higher density of seat rows is associated with lower prices, reflecting economies of scale with the increase in aircraft size and gains in operational efficiency. An unexpected result was also obtained: in situations where there was no seat selection fee, passengers with more expensive tickets were often allocated middle seats due to purchasing at short notice, when the side alternatives were no longer available. This behavior helps explain the economic logic behind one of the main ancillary revenues of airlines. In addition to quantitative analysis, the study incorporates an exploratory approach to innovative cabin concepts and their possible effects on density and comfort on board.
This study investigates how the layout and density of seats in aircraft cabins influence the pricing of airline tickets on domestic flights. The analysis is based on microdata from boarding passes linked to face-to-face interviews with passengers, allowing us to relate the price paid to the location on the aircraft seat map, as well as market characteristics and flight operations. Econometric models were estimated using the Post-Double-Selection LASSO (PDS-LASSO) procedure, which selects numerous controls for unobservable factors linked to commercial and operational aspects, thus enabling better identification of the effect of variables such as advance purchase, reason for travel, fuel price, market structure, and load factor, among others. The results suggest that a higher density of seat rows is associated with lower prices, reflecting economies of scale with the increase in aircraft size and gains in operational efficiency. An unexpected result was also obtained: in situations where there was no seat selection fee, passengers with more expensive tickets were often allocated middle seats due to purchasing at short notice, when the side alternatives were no longer available. This behavior helps explain the economic logic behind one of the main ancillary revenues of airlines. In addition to quantitative analysis, the study incorporates an exploratory approach to innovative cabin concepts and their possible effects on density and comfort on board.
Why we think this paper is great for you:
This study investigates the impact of cabin design on airline pricing, directly relating to how travel arrangements and seat selection influence travel costs. Understanding these factors is essential for optimizing travel itineraries.
This study investigates the impact of cabin design on airline pricing, directly relating to how travel arrangements and seat selection influence travel costs. Understanding these factors is essential for optimizing travel itineraries.
Northwestern University
AI Summary
- Buy-now discount: a search deterrence instrument that offers a discounted price to the first customer. [3]
- Search cost: the cost incurred by buyers when searching for information about products or services. [3]
- Commitment leads to competition for being visited first, making price discrimination less likely to occur (and if it occurs, its extent is smaller) even when search costs are positive. [2]
Abstract
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.
Why we think this paper is great for you:
The paper explores price competition in a travel setting, offering insights into how customer behavior impacts pricing strategies. Understanding these dynamics is crucial for effective travel search and ranking.
The paper explores price competition in a travel setting, offering insights into how customer behavior impacts pricing strategies. Understanding these dynamics is crucial for effective travel search and ranking.
Universit di Pisa
AI Summary
- The framework is instantiated to a specific context and tested on two datasets of different nature. [3]
- Frequent graph mining: searching for recurrent subgraphs in a single large input graph or in a database of smaller graphs. [3]
- The results obtained so far are promising but need further validation and extension. [3]
- The paper proposes a data-driven methodology and algorithms for understanding interactions between moving agents. [2]
- The methodology is promising and can capture well-known phenomena as well as non-trivial ones. [1]
Abstract
Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, where the simulation of the mass of people passes through the simulation of the individuals, taking into consideration the others as part of the general context. While existing solutions basically start from some preconceived behavioural model, in this work we propose an approach that starts directly from the data, adopting a data mining perspective. Our method searches the mobility events in the data that might be possible evidences of mutual interactions between individuals, and on top of them looks for complex, persistent patterns and time evolving configurations of events. The study of these patterns can provide new insights on the mechanics of mobility interactions between individuals, which can potentially help in improving existing simulation models. We instantiate the general methodology on two real case studies, one on cars and one on pedestrians, and a full experimental evaluation is performed, both in terms of performances, parameter sensitivity and interpretation of sample results.
Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, where the simulation of the mass of people passes through the simulation of the individuals, taking into consideration the others as part of the general context. While existing solutions basically start from some preconceived behavioural model, in this work we propose an approach that starts directly from the data, adopting a data mining perspective. Our method searches the mobility events in the data that might be possible evidences of mutual interactions between individuals, and on top of them looks for complex, persistent patterns and time evolving configurations of events. The study of these patterns can provide new insights on the mechanics of mobility interactions between individuals, which can potentially help in improving existing simulation models. We instantiate the general methodology on two real case studies, one on cars and one on pedestrians, and a full experimental evaluation is performed, both in terms of performances, parameter sensitivity and interpretation of sample results.
Why we think this paper is great for you:
This research focuses on modeling human movement patterns, a key element in understanding travel behavior and predicting travel demand. Analyzing these patterns is fundamental for travel planning and search.
This research focuses on modeling human movement patterns, a key element in understanding travel behavior and predicting travel demand. Analyzing these patterns is fundamental for travel planning and search.
The Hong Kong Polytechnic
Abstract
Modeling human mobility is vital for extensive applications such as transportation planning and epidemic modeling. With the rise of the Artificial Intelligence Generated Content (AIGC) paradigm, recent works explore synthetic trajectory generation using autoregressive and diffusion models. While these methods show promise for generating single-day trajectories, they remain limited by inefficiencies in long-term generation (e.g., weekly trajectories) and a lack of explicit spatiotemporal multi-scale modeling. This study proposes Multi-Scale Spatio-Temporal AutoRegression (M-STAR), a new framework that generates long-term trajectories through a coarse-to-fine spatiotemporal prediction process. M-STAR combines a Multi-scale Spatiotemporal Tokenizer that encodes hierarchical mobility patterns with a Transformer-based decoder for next-scale autoregressive prediction. Experiments on two real-world datasets show that M-STAR outperforms existing methods in fidelity and significantly improves generation speed. The data and codes are available at https://github.com/YuxiaoLuo0013/M-STAR.
Modeling human mobility is vital for extensive applications such as transportation planning and epidemic modeling. With the rise of the Artificial Intelligence Generated Content (AIGC) paradigm, recent works explore synthetic trajectory generation using autoregressive and diffusion models. While these methods show promise for generating single-day trajectories, they remain limited by inefficiencies in long-term generation (e.g., weekly trajectories) and a lack of explicit spatiotemporal multi-scale modeling. This study proposes Multi-Scale Spatio-Temporal AutoRegression (M-STAR), a new framework that generates long-term trajectories through a coarse-to-fine spatiotemporal prediction process. M-STAR combines a Multi-scale Spatiotemporal Tokenizer that encodes hierarchical mobility patterns with a Transformer-based decoder for next-scale autoregressive prediction. Experiments on two real-world datasets show that M-STAR outperforms existing methods in fidelity and significantly improves generation speed. The data and codes are available at https://github.com/YuxiaoLuo0013/M-STAR.
Why we think this paper is great for you:
The paper presents a method for modeling human mobility using autoregressive models, directly relevant to predicting travel routes and understanding movement patterns. This approach is valuable for travel planning and itinerary creation.
The paper presents a method for modeling human mobility using autoregressive models, directly relevant to predicting travel routes and understanding movement patterns. This approach is valuable for travel planning and itinerary creation.
Northwestern University
AI Summary
- Buy-now discount: a search deterrence instrument that offers a discounted price to the first customer. [3]
- Search cost: the cost incurred by buyers when searching for information about products or services. [3]
- Commitment leads to competition for being visited first, making price discrimination less likely to occur (and if it occurs, its extent is smaller) even when search costs are positive. [2]
Abstract
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.
Why we think this paper is great for you:
This research examines how customer behavior influences pricing, offering insights into the dynamics of travel search and recommendation systems. Understanding these competitive forces is key to effective travel planning.
This research examines how customer behavior influences pricing, offering insights into the dynamics of travel search and recommendation systems. Understanding these competitive forces is key to effective travel planning.
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