scholarly journals Operational Considerations regarding On-Demand Air Mobility: A Literature Review and Research Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Xiaoqian Sun ◽  
Sebastian Wandelt ◽  
Michael Husemann ◽  
Eike Stumpf
Keyword(s):  
Aircraft Design ◽  
Problem Definition ◽  
Operational Planning ◽  
Planning Problem ◽  
Full Potential ◽  
Demand Model ◽  
Transportation Demand ◽  
On Demand ◽  
Formal Problem ◽  
Performance Dimensions

The idea and development of on-demand air mobility (ODAM) services are revolutionizing our urban/regional transportation sector by exploring the third dimension: vertical airspace. The fundamental concept of on-demand air taxi operations is not new, but advances in aircraft design and battery/engine technology plus massive problems with congestion and increased travel demands around the world have recently led to a large number of studies which aim to explore the potential benefits of ODAM. Unfortunately, given the lack of an established, formal problem definition, missing reference nomenclature for ODAM research, and a multitude of publication venues, the research development is not focused and, thus, does not tap the full potential of the workforce engaged in this topic. This study synthesizes the recently published literature on operational aspects of ODAM. Our contribution consists of two major parts. The first part dissects previous studies and performs cross-comparison of report results. We cover five main categories: demand estimation methodology, infrastructure/port design/location problem, operational planning problem, operational constraints’ identification, and competitiveness with other transportation modes. The second part complements the report of aggregated findings by proposing a list of challenges as a future agenda for ODAM research. Most importantly, we see a need for a formal problem definition of ODAM operational planning processes, standard open datasets for comparing multiple performance dimensions, and a universal, multimodal transportation demand model.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

Exploratory Evaluation of a Concept Combining Incentivized On-Demand Ridesharing with Congestion Pricing

2021 ◽  
pp. 036119812110116
Author(s):  
Patrick DeCorla-Souza
Keyword(s):  
Demand Management ◽  
Congestion Pricing ◽  
Economic Benefits ◽  
Transportation Demand Management ◽  
Transportation Demand ◽  
Transportation Funding ◽  
On Demand ◽  
Federal Highway ◽  
Planning Tools ◽  
Management Concept

This paper presents an innovative transportation demand management concept involving congestion pricing synergistically combined with incentivized on-demand ridesharing. An exploratory evaluation of the concept was undertaken using sketch-planning tools developed by the Federal Highway Administration. The analysis suggests that the concept could be financially viable, achieve significant economic benefits, and potentially generate surplus revenues that could be sufficient to address transportation funding gaps.

Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability

2021 ◽  
Author(s):  
Daniel Steeneck ◽  
Fredrik Eng-Larsson ◽  
Francisco Jauffred
Keyword(s):  
Inventory Management ◽  
Product Category ◽  
Problem Definition ◽  
Lost Sales ◽  
Negative Consequences ◽  
Demand Model ◽  
Data Set ◽  
Probabilistic Choice ◽  
Substitutable Products ◽  
Choice Sets

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.

Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

2020 ◽  
Author(s):  
Lai Wei ◽  
Roman Kapuscinski ◽  
Stefanus Jasin
Keyword(s):  
Economies Of Scale ◽  
Optimal Solution ◽  
Problem Definition ◽  
Shipment Consolidation ◽  
On Demand ◽  
Numerical Tests ◽  
Optimal Policies ◽  
Shipping Costs ◽  
Multiple Orders ◽  
Managerial Implications

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

scholarly journals Using ANT to Uncover the Full Potential of an Intelligent Operational Planning and Support Tool (IOPST) for Acute Healthcare Contexts

2013 ◽  
Vol 5 (2) ◽  
pp. 29-49 ◽  
Author(s):  
Imran Muhammad ◽  
Fatemeh Hoda Moghimi ◽  
Nyree J. Taylor ◽  
Bernice Redley ◽  
Lemai Nguyen ◽  
...  
Keyword(s):  
Network Theory ◽  
Large Scale ◽  
Actor Network Theory ◽  
Operational Planning ◽  
Full Potential ◽  
Proof Of Concept ◽  
Support Tool ◽  
The Many ◽  
Acute Healthcare ◽  
Large Scale Clinical Trial

Based on initial pre-clinical data and results from focus group studies, proof of concept for an intelligent operational planning and support tool (IOPST) for nursing in acute healthcare contexts has been demonstrated. However, moving from a simulated context to a large scale clinical trial brings potential challenges associated with the many complexities and multiple people-technology interactions. To enable an in depth and rich analysis of such a context, it is the contention of this paper that incorporating an Actor-Network Theory (ANT) lens to facilitate analysis will be a prudent option as discussed below.

Freight Planning Typology

1998 ◽  
Vol 1613 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Reginald Souleyrette ◽  
T. H. Maze ◽  
Tim Strauss ◽  
David Preissig ◽  
Ayman G. Smadi
Keyword(s):  
Full Range ◽  
Freight Transportation ◽  
System Capacity ◽  
Demand Model ◽  
Transportation Demand ◽  
Traffic Demand ◽  
Demand Modeling ◽  
Freight Traffic ◽  
Freight Transportation Demand

A layered architecture for freight transportation demand modeling entails the construction of a statewide freight transportation demand model by separately simulating traffic for one commodity at a time. Layers can then be added together to construct a comprehensive model that includes the most significant freight flows. Most state or regional economies are dominated by a few economic sectors, and models can be constructed for those sectors that generate the most freight traffic and/or are the most important to the regional economy. Freight traffic demand modeling in intercity applications is more likely to focus on economic development, local infrastructure improvements, maintenance, and similar policy and planning concerns than on system capacity issues. Thus, it is more important to understand changes in traffic growth by economic sector than as the composite of all freight traffic. This method is less data intensive and more easily understood by transportation professionals than previous approaches. The layered approach is therefore more likely to achieve the desired objectives than would general models, which attempt to forecast heterogeneous freight transportation demands simultaneously. This approach is demonstrated through a case study using the meat products and farm machinery industries in Iowa. Other commodities will be added in the future to complete a model of Iowa’s statewide freight transportation demand. A framework is presented for organizing and identifying planning goals, key issues, and predominant commodities for intercity freight transportation. Although examples are provided, specific recommendations addressing the full range of issues, data collection activities, tools, and urban applications are suggested for further study. A case study demonstrates the approach used for one issue, one mode, and two commodities, which could be repeated elsewhere for similar applications.

Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

2020 ◽  
Author(s):  
Zhong-Zhong Jiang ◽  
Guangwen Kong ◽  
Yinghao Zhang
Keyword(s):  
System Performance ◽  
Financial Incentives ◽  
Laboratory Experiments ◽  
Policy Design ◽  
Working Hours ◽  
Problem Definition ◽  
Analytical Models ◽  
Behavioral Biases ◽  
On Demand ◽  
Regret Aversion

Problem definition: We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. Although these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers’ relocation decisions and ultimately the system performance. Academic/practical relevance: Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers’ relocation decisions from a behavioral perspective. A deeper understanding of workers’ behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. Methodology: We use a combination of behavioral modeling and controlled laboratory experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled laboratory experiments. Results: We find that regret aversion plays an important role in workers’ relocation decisions. Regret-averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system. Managerial implications: Our research emphasizes the importance and necessity of incorporating workers’ behavioral biases such as regret aversion into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers’ decision may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.

Customer Preference and Station Network in the London Bike-Share System

2020 ◽  
Author(s):  
Pu He ◽  
Fanyin Zheng ◽  
Elena Belavina ◽  
Karan Girotra
Keyword(s):  
Network Effects ◽  
Demand Model ◽  
Station Network ◽  
Customer Preference ◽  
On Demand ◽  
Structural Demand ◽  
City Of London ◽  
The City ◽  
Station Density ◽  
Bike Share

We study customer preference for the bike-share system in the city of London. We estimate a structural demand model on the station network to learn the preference parameters and use the estimated model to provide insights on the design and expansion of the bike-share system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike-share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage and that the currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings in which the available set of products or services depends on demand. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

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