Attribute and technology value mapping for conceptual product design phase

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Mechanical Engineering Science following peer review. The final, definite version of this paper has been published in Journal of Mechanical Engineering Science, Aris Georgiou, George Haritos, Moyra Fowler, and Yasmin Imani, ‘Attribute and technology value mapping for conceptual product design phase’, Vol. 230(11): 1745-1756, May 2016, published by SAGE Publishing, available online at doi: https://doi.org/10.1177/0954406215585595. Copyright © 2016 The Author(s).The main focus of this paper is how the concept design phase of the product development process can be improved by using an objective data-driven approach in selecting a final concept design to progress further. A quantitative new test-bed ‘Product Optimisation Value Engineering’ (PROVEN) is presented to critically assess new and evolving powertrain technologies at the concept design phase. The new test-bed has the ability to define a technology value map to assess multiple technical options as a function of its attributes, whose precise values can be determined at a given cost. A mathematical model that incorporates a highly adaptable, data-driven and multi-attribute value approach to product specification and conceptual design is developed, novel to the concept design process. This creates a substantially optimised product offering to the market, reducing overall development costs while achieving customer satisfactionPeer reviewe

Examining the Bus Ridership Demand:Application of Spatio-Temporal Panel Models

An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process. </p

Regional Assessment Of Exposure To Traffic-Related Air Pollution: Impacts Of Individual Mobility And Transit Investment Scenarios

This paper describes the design and application of an integrated model for the prediction of exposure to traffic related air pollution in an urban area as a result of transport policy scenarios. For this purpose, a travel demand model linked with models for traffic assignment, emissions, and air quality was used to simulate population exposure to ambient Nitrogen Dioxide (NO2) in a base year (2008) and in a horizon year (2031) while incorporating population and demographic projections. The integrated model was used to evaluate the impacts of the planned regional transit and vehicle technology improvements on exposure to NO2. In the 2031 business as usual scenario, an average decrease of 19% in exposure to NO2 is observed across the sample population, compared to the 2008 base case. This decrease is primarily attributed to projected improvements in vehicle technology. In the 2031 transit scenario, we observed an average 10% decrease in exposure compared to the 2031 business as usual. In terms of the spatial variability in air pollution, the transit scenario was observed to achieve large reductions in NO2 concentrations within the downtown area and moderate reductions throughout the suburbs

Modelling The Spatio-Temporal Distribution Of Ambient Nitrogen Dioxide And Investigating The Effects Of Public Transit Policies On Population Exposure

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NOx) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO2) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO2concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO2hot spots. This is associated with a reduction in total NOxemissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO2concentration across the study domain and the mean NO2exposure across the sample population results from the fact that NO2concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains

Regional assessment of exposure to traffic-related air pollution: Impacts of individual mobility and transit investment scenarios

This paper describes the design and application of an integrated model for the prediction of exposure to traffic related air pollution in an urban area as a result of transport policy scenarios. For this purpose, a travel demand model linked with models for traffic assignment, emissions, and air quality was used to simulate population exposure to ambient Nitrogen Dioxide (NO2) in a base year (2008) and in a horizon year (2031) while incorporating population and demographic projections. The integrated model was used to evaluate the impacts of the planned regional transit and vehicle technology improvements on exposure to NO2. In the 2031 business as usual scenario, an average decrease of 19% in exposure to NO2 is observed across the sample population, compared to the 2008 base case. This decrease is primarily attributed to projected improvements in vehicle technology. In the 2031 transit scenario, we observed an average 10% decrease in exposure compared to the 2031 business as usual. In terms of the spatial variability in air pollution, the transit scenario was observed to achieve large reductions in NO2 concentrations within the downtown area and moderate reductions throughout the suburbs

Modelling the spatio-temporal distribution of ambient nitrogen dioxide and investigating the effects of public transit policies on population exposure

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NOx) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO2) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO2 concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO2 hot spots. This is associated with a reduction in total NOx emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO2 concentration across the study domain and the mean NO2 exposure across the sample population results from the fact that NO2 concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains