Developing Travel Behaviour Models Using Mobile Phone Data

Improving the performance and efficiency of transport systems requires sound decision-making supported by data and models. However, conducting travel surveys to facilitate travel behaviour model estimation is an expensive venture. Hence, such surveys are typically infrequent in nature, and cover limited sample sizes. Furthermore, the quality of such data is often affected by reporting errors and changes in the respondents’ behaviour due to awareness of being observed. On the other hand, large and diverse quantities of time-stamped location data are nowadays passively generated as a by-product of technological growth. These passive data sources include Global Positioning System (GPS) traces, mobile phone network records, smart card data and social media data, to name but a few. Among these, mobile phone network records (i.e. call detail records (CDRs) and Global Systems for Mobile Communication (GSM) data) offer the biggest promise due to the increasing mobile phone penetration rates in both the developed and the developing worlds. Previous studies using mobile phone data have primarily focused on extracting travel patterns and trends rather than establishing mathematical relationships between the observed behaviour and the causal factors to predict the travel behaviour in alternative policy scenarios. This research aims to extend the application of mobile phone data to travel behaviour modelling and policy analysis by augmenting the data with information derived from other sources. This comes along with significant challenges stemming from the anonymous and noisy nature of the data. Consequently, novel data fusion and modelling frameworks have been developed and tested for different modelling scenarios to demonstrate the potential of this emerging low-cost data source. In the context of trip generation, a hybrid modelling framework has been developed to account for the anonymous nature of CDR data. This involves fusing the CDR and demographic data of a sub-sample of the users to estimate a demographic prediction sub-model based on phone usage variables extracted from the data. The demographic group membership probabilities from this model are then used as class weights in a latent class model for trip generation based on trip rates extracted from the GSM data of the same users. Once estimated, the hybrid model can be applied to probabilistically infer the socio-demographics, and subsequently, the trip generation of a large proportion of the population where only large-scale anonymous CDR data is available as an input. The estimation and validation results using data from Switzerland show that the hybrid model competes well against a typical trip generation model estimated using data with known socio-demographics of the users. The hybrid framework can be applied to other travel behaviour modelling contexts using CDR data (in mode or route choice for instance). The potential of CDR data to capture rational route choice behaviour for long-distance inter-regional O-D pairs (joined by highly overlapping routes) is demonstrated through data fusion with information on the attributes of the alternatives extracted from multiple external sources. The effect of location discontinuities in CDR data (due to its event-driven nature), and how this impacts the ability to observe the users’ trajectories in a highly overlapping network is discussed prompting the development of a route identification algorithm that distinguishes between unique and broad sub-group route choices. The broad choice framework, which was developed in the context of vehicle type choice is then adapted to leverage this limitation where unique route choices cannot be observed for some users, and only the broad sub-groups of the possible overlapping routes are identifiable. The estimation and validation results using data from Senegal show that CDR data can capture rational route choice behaviour, as well as reasonable value of travel time estimates. Still relying on data fusion, a novel method based on the mixed logit framework is developed to enable the analysis of departure time choice behaviour using passively collected data (GSM and GPS data) where the challenge is to deal with the lack of information on the desired times of travel. The proposed method relies on data fusion with travel time information extracted from Google Maps in the context of Switzerland. It is unique in the sense that it allows the modeller to understand the sensitivity attached to schedule delay, thus enabling its valuation, despite the passive nature of the data. The model results are in line with the expected travel behaviour, and the schedule delay valuation estimates are reasonable for the study area. Finally, a joint trip generation modelling framework fusing CDR, household travel survey, and census data is developed. The framework adjusts the scaling factors of a traditional trip generation model (based on household travel survey data only) to optimise model performance at both the disaggregate and aggregate levels. The framework is calibrated using data from Bangladesh and the adjusted models are found to have better spatial and temporal transferability. Thus, besides demonstrating the potential of mobile phone data, the thesis makes significant methodological and applied contributions. The use of different datasets provides rich insights that can inform policy measures related to the adoption of big data for transport studies. The research findings are particularly timely for transport agencies and practitioners working in contexts with severe data limitations (especially in developing countries), as well as academics generally interested in exploring the potential of emerging big data sources, both in transport and beyond

Using mobile audiometry (Wulira App) to assess noise induced hearing loss among industrial workers in Kampala, Uganda: A cross-sectional study.

BackgroundOccupational noise is a common cause of hearing loss in low-income countries. Unfortunately, screening for hearing loss is rarely done due to technical and logistical challenges associated with pure tone audiometry. Wulira app is a valid and potentially cost-effective alternative to pure tone audiometry in screening for occupational hearing loss. We aimed to determine the prevalence of occupational hearing loss among workers in a metal industry company in Kampala district.MethodologyWe recruited 354 participants conveniently from a steel and iron manufacturing industry in Kampala. All eligible participants answered a pretested and validated questionnaire and were assessed for noise induced hearing loss in a quiet office room approximately 500 meters from the heavy machinery area using the Wulira app. Descriptive statistics such as proportions were used to describe the study population while inferential statistics were used to determine associations.ResultsOf the 354 participants sampled, 333 (94.1%) were male, and the median age was 27, IQR (25-30). Regarding the risk factors of hearing loss, fourteen (3.9%) had history of smoking and more than half (65.5%) had worked in the industry for more than 2 years. The overall prevalence of hearing loss among industrial workers was 11.3% (40/354). 16.2% and 9% had mild hearing loss in the right and left ear respectively. Bilateral audiometric notch was present where fourteen (4%) of the participants had notch in their right ear while seven (2%) had notch in their left ear. Residing outside Kampala district was associated with hearing loss (OR, 95% CI, 0.213 (0.063-0.725), p = 0.013).ConclusionOne in 10 workers in a metal manufacturing industry in Kampala had occupational hearing loss. Industrial workers residing outside Kampala were likely to develop hearing loss. Periodic screening should be done for early detection and intervention to prevent progression of hearing loss in this population

Usage of and satisfaction with Integrated Community Case Management care in western Uganda: a cross-sectional survey

Background: In some areas of Uganda, village health workers (VHW) deliver Integrated Community Case Management (iCCM) care, providing initial assessment of children under 5 years of age as well as protocol-based treatment of malaria, pneumonia, and diarrhoea for eligible patients. Little is known about community perspectives on or satisfaction with iCCM care. This study examines usage of and satisfaction with iCCM care as well as potential associations between these outcomes and time required to travel to the household's preferred health facility. Methods: A cross-sectional household survey was administered in a rural subcounty in western Uganda during December 2016, using a stratified random sampling approach in villages where iCCM care was available. Households were eligible if the household contained one or more children under 5 years of age. Results: A total of 271 households across 8 villages were included in the final sample. Of these, 39% reported that it took over an hour to reach their preferred health facility, and 73% reported walking to the health facility; 92% stated they had seen a VHW for iCCM care in the past, and 55% had seen a VHW in the month prior to the survey. Of respondents whose households had sought iCCM care, 60% rated their overall experience as "very good" or "excellent," 97% stated they would seek iCCM care in the future, and 92% stated they were "confident" or "very confident" in the VHW's overall abilities. Longer travel time to the household's preferred health facility did not appear to be associated with higher propensity to seek iCCM care or higher overall satisfaction with iCCM care. Conclusions: In this setting, community usage of and satisfaction with iCCM care for malaria, pneumonia, and diarrhoea appears high overall. Ease of access to facility-based care did not appear to impact the choice to access iCCM care or satisfaction with iCCM care

FGD guide tool.

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Consolidated criteria for reporting qualitative research check list.

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Management of children with danger signs in integrated community case management care in rural southwestern Uganda (2014-2018)

Background: In integrated community case management (iCCM) care, community health workers (CHWs) provide home-based management of fever, diarrhea and fast breathing for children aged <5 y. The iCCM protocol recommends that children with danger signs for severe illness are referred by CHWs to health facilities within their catchment area. This study examines the management of danger signs by CHWs implementing iCCM in a rural context. Methods: A retrospective observational study that examined clinical records for all patients with danger signs evaluated by CHWs from March 2014 to December 2018 was conducted. Results: In total, 229 children aged <5 y had been recorded as having a danger sign during 2014-2018. Of these children, 56% were males with a mean age of 25 (SD 16.9) mo, among whom 78% were referred by the CHWs as per the iCCM protocol. The age category of 12 to 35 mo had the highest numbers of prereferred and referred cases (54% and 46%, respectively). Conclusions: CHWs play a key role in early symptomatic detection, prereferral treatment and early referral of children aged <5 y. Danger signs among children aged <5 y, if left untreated, can result in death. A high proportion of the children with danger signs were referred as per the iCCM protocol. Continuous CHW training is emphasized to reduce the number of referral cases that are missed. More studies need to focus on children aged 12-35 mo and why they are the most referred category. Policymakers should occasionally revise iCCM guidelines to detail the types of danger signs and how CHWs can address these

Socio-demographic characteristics of the participants.

Socio-demographic characteristics of the participants.</p

Modelling departure time choice using mobile phone data

The rapid growth in passive mobility tracking technologies has led to departure time choice studies based on GPS data in recent years (e.g. Peer et al., 2013). GPS data however typically has limited sample sizes and is affected by technical issues like signal losses and battery depletion leading to gaps in the data. On the other hand, the rapid growth in mobile phone penetration rates has led to the emergence of alternative passive mobility datasets such as Global System for Mobile communication (GSM) data. GSM data covers much wider proportions of the population and can be used to infer departure time information. This motivates this research where we investigate the potential use of GSM data for modelling departure time choice. We describe practical approaches to extract relevant information from GSM data and propose a modelling framework that accounts for the fact that the desired departure times are unobserved. We assume that the preferred departure times vary randomly across the users and apply the mixed logit framework to jointly estimate the distribution parameters of the preferred departure times and the sensitivities to schedule delay. Comparison of the model results and time valuation metrics derived from the GSM data with similar metrics derived from the GPS data of a subset of the users reveals that the fewer time gaps in the GSM data lead to reliable model outputs. The proposed framework can be used for mobile phone and other passive data sources with unobserved preferred departure times