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

Demand model estimation from smartphone data: an application to assert new urbanistic development scenarios

© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The pervasive use of mobile devices has brought a valuable new source of data. The work presented here has a twofold objective: firstly, to demonstrate the capability of mobile phone records to feed traditional trip-based demand models and, secondly, to assert the possibilities of using developed models to estimate the effects of new urbanistic development scenarios. Detailed trip data for the metropolitan area of Barcelona are reconstructed from mobile phone records. This information is then employed as input for building a set of demand trip-based models and to apply these daily-based models to the appraisal of new development scenarios in a VISUM model of the city. The model calibration and validation process proves the quality of the models obtained. Our results show the way in which the generated trips are distributed into the study area and modal share is modified in the considered scenarios.This research was funded by TRA2016-76914-C3-1-P Spanish R+D Programs, Secretaria d’Universitats-i-RecercaGeneralitat de Catalunya- 2017-SGR- 1749. The authors are grateful for the support given by the Autoritat del Transport Metropolità (ATM) providing us with their model (Ll. Alegre, F. Calvet, and X. Sanyer). We are also grateful to Dra. L. Pagés (CARNET-UPC), J. Llinàs and A. Ortiz (Barcelona Regional) for their support.Peer ReviewedObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::10 - Reducció de les DesigualtatsPostprint (published version

A bimodal accessibility analysis of Australia using web-based resources

A range of potentially disruptive changes to research strategies have been taking root in the field of transport research. Many of these relate to the emergence of data sources and travel applications reshaping how we conduct accessibility analyses. This paper, based on Meire et al. (in press) and Meire and Derudder (under review), aims to explore the potential of some of these data sources by focusing on a concrete example: we introduce a framework for (road and air) transport data extraction and processing using publicly available web-based resources that can be accessed via web Application Programming Interfaces (APIs), illustrated by a case study evaluating the combined land- and airside accessibility of Australia at the level of statistical units. Given that car and air travel (or a combination thereof) are so dominant in the production of Australia’s accessibility landscape, a systematic bimodal accessibility analysis based on the automated extraction of web-based data shows the practical value of our research framework. With regard to our case study, results show a largely-expected accessibility pattern centred on major agglomerations, supplemented by a number of idiosyncratic and perhaps less-expected geographical patterns. Beyond the lessons learned from our case study, we show some of the major strengths and limitations of web-based data accessed via web-APIs for transport related research topics

A Generalisable Data Fusion Framework to Infer Mode of Transport Using Mobile Phone Data

Cities often lack up-to-date data analytics to evaluate and implement transport planning interventions to achieve sustainability goals, as traditional data sources are expensive, infrequent, and suffer from data latency. Mobile phone data provide an inexpensive source of geospatial information to capture human mobility at unprecedented geographic and temporal granularity. This paper proposes a method to estimate updated mode of transportation usage in a city, with novel usage of mobile phone application traces to infer previously hard to detect modes, such as bikes and ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate socioeconomic and demographic attributes of the local resident population into the model. We tested the method in a case study of Santiago (Chile), and found that changes from 2012 to 2020 in mode of transportation inferred by the method are coherent with expectations from domain knowledge and the literature, such as ride-hailing trips replacing mass transport.Comment: 19 pages, 8 figure

Distribution 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, USA 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-h 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

Incorporating Long-Distance Travel intoTransportation Planning in the United States

In the early years of transportation planning and highway infrastructure development in the United States the focus was on intercity or long-distance travel, a contrast to the metropolitan travel and state-based models that dominate today. Daily home and work-based travel, which have been the focus of data collection and models since the 1950s, are well-modeled by regional agencies and a limited number of state travel demand models even include some long-distance travel. Nonetheless, long-distance travel demand and factors affecting behavior are not thoroughly considered in transportation planning or behavior research. Only one recent activity-based model of national travel demand has been created and its scope was limited by a severe lack of data. The conceptualization of models to consider intercity long-distance travel has changed little since its inception in the 1970s and 1980s. In order to comprehensively consider transportation system sustainability, there is a critical need for improved nation-wide annual overnight activity data and models of overnight travel (a re-focus and important distinct re-framing of long-distance trips that this white paper suggests). Truly addressing the economic, environmental, and social equity issues required to create a sustainable global transportation system will entail completely updating our existing planning framework to meaningfully include long-distance travel. It is clear that long-distance passenger miles must be accounted for when addressing greenhouse gas (GHG) emissions and other negative environmental externalities. Less well-known are the questions of social justice that loom large when one considers the details of long-distance travel. Travel in our society is becoming increasingly associated with quality of life. Those without intercity access may miss opportunity and social capital. However, without representative long-distance travel data it is impossible to compare the relative participation by different groups and to consider latent demand. It is difficult to measure who comprises the global mobile elite and who lacks sufficient intercity mobility for reasonable social network obligations and personal services. This white paper suggests utilizing a common framework for long-distance data collection and tabulation that re-defines long-distance travel into daily or overnight. The author advocates using overnight as the defining characteristic for data collection, which complements existing daily travel surveys already capturing long day-trips. Within frameworks moving forward it is important to clearly characterize all trip purposes, including mixed purposes and purposeless travel, which comprise an appreciable portion of long-distance travel. Spatial data that distinguish between simple out-and-back trips and spatially complex trips are necessary and mobile devices have now made this measurement of long-distance tours feasible. In order to truly model all travel in the current system, we must move away from the idea that most travel is routine, within region, and home-based. Many people, especially the most frequent travelers, have long-distance routines including multiple home bases. Additionally, our models should not assume that travelers staying at a second home, hotel, or friend’s home travel like residents. Efforts to measure and model non-home-based travel or travel at destination are essential to accurately modeling behavior. Daily surveys such as the 2017 National Household Transportation Survey are increasingly doing this. A nation-wide annual activity model of overnight travel must fully incorporate both surface and air travel to allow full consideration of alternative future system scenarios