How to Improve the Capture of Urban Goods Movement Data?

The surveys specifically focused on the thorough knowledge of urban freight transport appeared about ten years ago. The local problematic of goods transport at local level was partially taken into account by the city planners and by the researchers: until recent years, the integration of goods transport in the total urban flows models was estimated applying a multiplying factor to car traffic. Delivering goods was not considered like a concern.Because of the quick growth of car traffic in the cities, the main stakes changed too: the fight against traffic congestion, the management of the lack of space (shipment consolidation and storage), the attempts to reduce local environmental impacts and global externalities (energy saving, reduction of greenhouse gas emissions), and economic valuation of city centres (under the pressure of a slowed down economic growth).All these changes were taking place in a context in which available rooms for manoeuvre were limited by factors such as congestion, concerns about the quality of urban life and budget restriction. It resulted in a growing unease on the freight transport industry and the city authorities, the latter having little or no data, methods and references in order to elaborate a satisfactory policy framework.surveys on urban freight transport ; urban freight movements ; urban freight data collection ; urban goods data collection ; diversity of measurement units and methods ; state of the art

Equity in Transportation: Data Driven Analysis of Transportation Services and Infrastructures

Achieving equity in transportation is an ongoing challenge, as transportation options still vary tremendously when it comes to marginalized populations. This dissertation addresses this challenge by conducting a comprehensive review of existing transportation equity literature and identifying two critical gaps: the lack of data-driven approaches to studying spatial mismatch between transportation supply and demand, and limited information on women\u27s perceptions and expectations towards emerging transportation services. Chapter two introduces the concept of transportation deserts, specifically transit deserts and walking deserts, and develops data-driven frameworks to identify and investigate neighborhoods with limited transportation service supply but high demand. The frameworks compare mobility demand and supply for active transportation modes and utilize statistical modeling techniques to reveal the inequitable distribution of transportation services. The identification of transportation deserts provides valuable insights for investment and redevelopment, highlighting areas of underinvestment. Chapter three focuses on gender equity and the lack of understanding about transportation user preferences, particularly for women. Through a gender-sensitive analysis of online reviews using text-mining techniques, the chapter presents an empirical analysis of rider satisfaction with scooter services. The study utilizes online data from app store reviews and employs machine learning techniques to uncover factors that influence overall satisfaction across genders. The findings enhance our understanding of gendered differences in micromobility rider sentiment and satisfaction. In conclusion, this dissertation offers a comprehensive examination of transportation equity from multiple perspectives. It identifies critical gaps in existing literature and employs innovative analytical methodologies to address these gaps. The research findings have important policy implications for city planners, transportation managers, urban authorities, and decision-makers striving to create inclusive and vibrant urban spaces that benefit all members of society. By addressing these gaps, policymakers can promote equitable transportation services and ensure access to safe, reliable, and affordable transportation options for all individuals

The use of behavioural sciences in targeted health messages to improve the participation in cervical and breast screening programmes

The aim of this thesis was to examine the effects of behaviourally informed interventions deployed in text message reminders and invitation letters on the participation in cervical and breast cancer screening. Cancer screening saves lives through detecting cancer or precancerous changes early, when medical intervention is more likely to reduce morbidity and mortality. A key factor in the success of any screening programme is public participation. Although some individuals may object to cancer screening, evidence suggests that public support for cancer screening provision in the UK is above 90%. Yet despite this, participation rates across all three cancer screening programmes (breast, cervical and colorectal) remain lower than expected given reported intentions. This thesis explores the role of decision making – both reflective and automatic in the context of cancer screening behaviour and highlights the potential for the application of behavioural economic theory and behavioural science to inform intervention design aimed at increasing cancer screening uptake. Through the application of frameworks informed by behaviour change theory, three randomised controlled trials were designed to test the impact of behavioural interventions on participation rates in regional cervical and breast cancer screening programmes within the London area. The intervention design of each trial focused on the message content within either text message reminders or invitation letters. The first randomised controlled trial (RCT) tested different behaviourally-informed invitation letters in cervical screening and found that a shortened letter that contained a loss framed message has a small but significant positive impact on cervical screening rates. The second RCT tested different text message reminder content against a no-text message control and found that text message reminders can improve participation in cervical cancer screening. However, the content of such text message reminders further affects screening participation behaviour. The final RCT tested the effect different behaviour message content in text message reminders for timed appointments in the breast screening programme. No significant difference in breast screening participation was noticed as a result of the message content within text message reminders. However, due to logistical barriers encountered during the trial, which included a reconfiguration of regional screening services, this study had to be closed early, prior to the sample size being reached and was therefore underpowered. This research highlights the importance of the message content within health communications in cancer screening to improve participation rates. Exploratory subgroup analyses within these trials, indicates that different subgroups of women with common characteristics such as age, level of deprivation or previous exposure to cancer screening affected which message content was most effective and improving cancer screening participation.Open Acces

Conditions of everyday technology use and its Interplay in the lives of older adults with and without dementia

Background: Increased reliance on technology in society incurs a risk that older adults with and without dementia could become excluded from participating in aspects of everyday life in and outside home. This thesis responds to a gap in present understanding about the conditions for Everyday Technology (ET) use (i.e. ticket machines, smartphones) in different international and geographical contexts. By generating new knowledge about the interplay of these conditions on participation, practical information and guidance follow to support both dementia- and age-friendliness as well as general inclusivity in society. Aim: To illuminate the conditions, particularly different country and geographical contexts, of ET use and the interplay of these conditions with participation and inclusion in everyday life both in and outside the home for older adults living with and without mild stage dementia. Methods: Participants with dementia (n =99) and with no known cognitive impairment (n =216) were recruited in the US (sub-study i, n =114), Sweden (sub-study i, n =73, ii, n =69), and England (sub-studies i, iv, n =128, rural sub-study iii, n =10). These four cross-sectional studies used multiple predominantly quantitative methods (i, ii, iv) and a case study approach also involved qualitative data (iii). Structured home-based interviews used the Everyday Technology Use Questionnaire to map respondents’ use of technologies, and the Participation in Activities and Places Outside Home Questionnaire to investigate the amount and pattern of participation outside home. Qualitative data included fieldnotes, observations, annotated maps and more. The findings of the four studies were synthesised using an approach to triangulation. Findings: The triangulation approach yielded three themes: 1) Dementia as a condition of ET use, 2) National, geographical, public and home context as a condition of ET use, 3) Interplay of conditions with participation.1) Dementia was generally not found to be a condition that impacted the challenge of ETs, however groups with dementia typically regarded less ETs to be relevant. There were notable exceptions in both instances. 2) The varying social, infrastructural, and service conditions surrounding national and geographic contexts were seen to shape the constitution and use of ETs outside home. 3) There was a complex interplay between the conditions of ET use and participation outside home. Close and distant human relationships, structural inequalities and transportation options were implicated as stabilising and de-stabilising everyday life. Conclusions: Insights are provided into the interplay between the conditions of ET use and participation in everyday life outside home among older adults with and without dementia. These insights provide opportunities for many different people in societies, communities, neighbourhoods and household to take action. Reducing any friction that people encounter when using ETs in public places and allowing opportunities for manualised participation in occupations outside home could lead to a more inclusive everyday life

The Design and Implementation of Intelligent Labor Contraction Monitoring System based on Wearable Internet of Things

In current clinical practice, pregnant women who have entered 37 weeks cannot correctly judge whether they are in labor based on their subjective feelings. Wrong judgment of labor contraction can lead to adverse pregnancy outcomes and endanger the safety of mothers and babies. It will also increase the healthcare pressure in the hospital and the healthcare efficiency is reduced. Therefore, it is very meaningful to be able to design a system for monitoring labor contraction based on objective data to assist pregnant women who have entered 37 weeks in deciding the suitable time to go to hospital. For the above requirements, this thesis designs and implements an intelligent labor contraction monitoring system based on wearable Internet of Things. The system combines the Internet of Things technology, wearable technology and machine learning technology to collect contraction data through wearable sensing device. It uses the Long Short-Term Memory (LSTM) neural network to classify and identify the collected contraction data and realize real-time processing. It improves the accuracy of model recognition to 93.75%. And the recognition results are fed back to the WeChat applet so that pregnant women can view them in real time. The prototype of the wearable sensing device has been integrated by 3D printing and the proof-of-concept system has been demonstrated. Pregnant women can use this system to detect the contraction status and view the contractions in real time through the WeChat applet results. They can judge whether it is suitable for labor, and this system assists in making decisions about the best time to go to hospital

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations. To address this methodological need, we use advanced machine-learning techniques and spatial analyses to classify trucks by industry based on activity patterns derived from large streams of truck GPS data. The major components are: (1) derivation of truck activity patterns from anonymous GPS traces, (2) development of a classification model to distinguish trucks by industry, and (3) estimation of a spatio-temporal regression model to capture rerouting behavior of trucks. First, we developed a K-means unsupervised clustering algorithm to find unique and representative daily activity patterns from GPS data. For a statewide GPS data sample, we are able to reduce over 300,000 daily patterns to a representative six patterns, thus enabling easier calibration and validation of the travel forecasting models that rely on detailed activity patterns. Next, we developed a Random Forest supervised machine learning model to classify truck daily activity patterns by industry served. The model predicts five distinct industry classes, i.e., farm products, manufacturing, chemicals, mining, and miscellaneous mixed, with 90% accuracy, filling a critical gap in our ability to tie truck movements to industry served. This ultimately allows us to build travel demand forecasting models with behavioral sensitivity. Finally, we developed a spatio-temporal model to capture truck rerouting behaviors due to weather events. The ability to model re-routing behaviors allows transportation agencies to identify operational and planning solutions that mitigate the impacts of weather on truck traffic. For freight industries, the prediction of weather impacts on truck driver’s route choices can inform a more accurate estimation of billable miles