A problem-structuring model for analyzing transportation–environment relationships

This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose

MATHEMATICAL MODELING CONSIDERING AIR POLLUTION OF TRANSPORTATION: AN URBAN ENVIRONMENTAL PLANNING, CASE STUDY IN PETALING JAYA, MALAYSIA

This paper provides the findings on a project undertaken to develop a geo-spatial mathematical model relating landuse, road type and air quality. The model shows how spatial elements and issues were quantified to accurately represent the usual and unusual urban environment in the development of residential land-use. The mathematical relationship was based on the optimum distance between residential area and urban transportation network. This mathematical analysis would provide a better planning for urban transportation. The spatial data (urban land-use and urban network development) were generated using satellite images, aerial photos and land use maps. Geospatial analyses were performed to find the effect and impact of urban air quality with respect to urban transportation networks. The output of the study would assist the task to reduce negative transport environmental impacts particularly in the field of air pollution. It would also be useful in identifying the potential residential area with respect to urban transportation network towards achieving sustainable development.Transportation, Model, Air pollution, urban environment, land use.

Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut during a week in July, 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-hour maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

The Effects of Traffic Related Air Pollution and Proposed Legal Remedies

This paper intends to examine the environmental issues that accompany air pollution generated from traffic related incidents, and the implications that this mass-generated pollution has on air quality, as well as the quality of life of humans exposed chronically to airborne carcinogens. Traffic related air pollution is an environmental problem that is heightened by urban design and population demographics such as urban sprawl, spatial distribution, population density, and infrastructure design. Furthermore, this paper will scrutinize Canadian legislation that regulates traffic related air pollutants, and develop an argument for how to apply legislation going forward. As Cartier, Benmarinha, and Brousselle (2015) identify, the mechanisms of air quality intervention are overlooked, which is necessary for producing effective legislation. The objective of this paper is to gain insight on a quantifiable problem, and to prescribe solutions through legislation, in order to regulate an issue which presents heath complications on a generational level

Environmental Costs Account: a base for measuring sustainability in transport plans.

Each city need to develop sustainable transport plans according to its fu-ture developments. This means identifying the best policy package of transport measures that could produce more sustainable future scenarios: lowest environmental impact, but also better social standards and at mini-mum cost. To that end, it is necessary to measure the environmental and social costs of each alternative transport mode. This paper proposes a me-thodology to calculate those costs in different city contexts: city centre and metropolitan suburbs. It provides a measure of the following environmen-tal costs: pollution, noise, green house gasses and land taken. Then the so-cial costs as congestion and accident costs. These two cost categories are calculated for each mean of transport: metro, bus, private car and taxi. The methodology has been applied to Madrid Region through modeling its mobility demand in 2004. The outputs are costs per passenger-km in each mode and Area: city centre and metropolitan ring. Therefore it is possible to assign monetary costs to environmental and social costs of each trans-port option; for example, car environmental costs are four times higher than buses on average, but it differs a lot from city centre to outskirt areas. Finally, some guidelines can be extracted to develop a more sustainable transport policy for Madrid Region