Latent Demand and Induced Travel

Abstract Background Emergency department (ED) attendances are contributing to rising costs of the National Health Service (NHS) in England. Critically assessing the impact of new services to reduce emergency department use can be difficult as new services may create additional access points, unlocking latent demand. The study evaluated an Acute Visiting Scheme (AVS) in a primary care context. We asked if AVS reduces overall ED demand and whether or not it changed utilisation patterns for frequent attenders. Method The study used a pre post single cohort design. The impact of AVS on all-cause ED attendances was hypothesised as a substitution effect, where AVS duty doctor visits would replace emergency department visits. Primary outcome was frequency of ED attendances. End points were reduction of frequency of service use and increase of intervals between attendances by frequent attenders. Results ED attendances for AVS users rose by 47.6%. If AVS use was included, there was a more than fourfold increase of total service utilisation, amounting to 438.3%. It shows that AVS unlocked significant latent demand. However, there was some reduction in the frequency of ED attendances for some patients and an increase in time intervals between ED attendances for others. Conclusion The study demonstrates that careful analysis of patient utilisation can detect a differential impact of AVS on the use of ED. As the new service created additional access points for patients and hence introduces an element of choice, the new service is likely to unlock latent demand. This study illustrates that AVS may be most useful if targeted at specific patient groups who are most likely to benefit from the new service.

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Transportation mobility and health among older adults: Examining missed trips and latent demand

Journal of Transport & Health ◽

10.1016/j.jth.2021.101069 ◽

2021 ◽

Vol 21 ◽

pp. 101069

Author(s):

Noelle L. Fields ◽

Courtney Cronley ◽

Stephen P. Mattingly ◽

Erin M. Roark ◽

Sarah R. Leat ◽

...

Keyword(s):

Older Adults ◽

Latent Demand

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Packaging Innovations to Reduce Food Loss and Waste: Are Italian Manufacturers Willing to Invest?

Sustainability ◽

10.3390/su13041963 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1963

Author(s):

Antonella Cammarelle ◽

Mariarosaria Lombardi ◽

Rosaria Viscecchia

Keyword(s):

Technological Opportunity ◽

Case Study Methodology ◽

Food Loss ◽

Latent Demand ◽

2030 Agenda ◽

Multiple Case ◽

Study Methodology ◽

Per Capita ◽

Global Food

The target 12.3 of the 2030 Agenda by the United Nations (UN) calls for halving per capita global food loss and waste. In this regard, the Food & Drink industry (F&D) could play a crucial role in reducing food waste and improving food safety by adopting healthy and eco-innovation packaging. Thus, this paper aims to investigate the F&D manufacturers’ willingness to invest in packaging innovations, such as active, intelligent, and compostable ones to achieve the UN target. In order to reach the stated objective, a multiple case study methodology was developed and administered to a sample of Italian micro and small-medium entrepreneurs located in the Apulia region. Results show that many firms were aware of their need for packaging innovation and of the available technological opportunity. However, only the F&D manufacturers who showed a Real demand, according to a taxonomy approach which also considers the Potential and Latent demand for the innovation, were effectively prompt to invest. Finally, most of the interviewed manufacturers were willing to invest in at least one packaging innovation, choosing mainly between the active packaging and the compostable one.

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Conceptual Framework for Understanding Latent Demand: Accounting for Unrealized Activities and Travel

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2668-08 ◽

2017 ◽

Vol 2668 (1) ◽

pp. 78-83 ◽

Cited By ~ 6

Author(s):

Kelly J. Clifton ◽

Filipe Moura

Keyword(s):

Conceptual Framework ◽

Social Exclusion ◽

Unmet Needs ◽

Travel Demand ◽

Social Economic ◽

Technological Changes ◽

Induced Demand ◽

Latent Demand ◽

Theoretical Perspectives ◽

Transport Disadvantage

Historically, latent demand—the activities and travel that are desired but unrealized because of constraints—has been examined from the standpoint of understanding the effects of proposed capacity or service improvements on travel demand. Drawing on work from a variety of theoretical perspectives, this paper presents a broader conceptual view of latent demand that provides a useful framework for researching and understanding these unmet needs. This view is important from an equity standpoint, as it provides insights into questions of transport disadvantage, social exclusion, and poverty. The framework presented here is theoretical in nature and untested empirically. This paper aims to promote discussion and ultimately a more developed theory that can inform transportation planning and forecasting. A better definition and quantification of latent (or induced) demand can aid transportation planners in better predicting the effects of future transportation investments and other social, economic, and technological changes.

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Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming

Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare ◽

10.1115/dscc2018-9177 ◽

2018 ◽

Cited By ~ 5

Author(s):

R. Austin Dollar ◽

Ardalan Vahidi

Keyword(s):

Autonomous Vehicle ◽

Mixed Integer ◽

Quadratic Program ◽

Vehicle To Vehicle ◽

Trip Time ◽

Induced Travel ◽

Vehicle Acceleration ◽

Vehicle Technology ◽

Rule Based Approach ◽

Integer Quadratic Program

Autonomous vehicle technology provides the means to optimize motion planning beyond human capacity. In particular, the problem of navigating multi-lane traffic optimally for trip time, energy efficiency, and collision avoidance presents challenges beyond those of single-lane roadways. For example, the host vehicle must simultaneously track multiple obstacles, the drivable region is non-convex, and automated vehicles must obey social expectations. Furthermore, reactive decision-making may result in becoming stuck in an undesirable traffic position. This paper presents a fundamental approach to these problems using model predictive control with a mixed integer quadratic program at its core. Lateral and longitudinal movements are coordinated to avoid collisions, track a velocity and lane, and minimize acceleration. Vehicle-to-vehicle connectivity provides a preview of surrounding vehicles’ motion. Simulation results show a 79% reduction in congestion-induced travel time and an 80% decrease in congestion-induced fuel consumption compared to a rule-based approach.

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