Search results

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

Integrated care (IC) is mainly studied from the perspectives of organisations or employees. However, less research is focussed on how patients themselves experience person-centred (PC) IC in practice. This context-specific, small-scale study examines what PC-IC means to older patients who went through joint replacement surgery (JRS).

The data consists of ten in-depth interviews of older patients, focussing on their experiences of care during their patient journey related to joint knee or hip replacement surgery. The data were analysed with thematic analysis.

Three central dimensions of PC-IC for older patients were identified: information sharing, continuity of care and compassionate encountering. Human validation and compassionate encountering were experienced as important aspects of PC-IC. Compassionate encountering was concretised through professionals’ very small everyday practices, which made the patient feel comfortable and respected. Instead, probably due to the medical and quite straight-forward nature of the joint replacement care process, patients seem to be pleased to trust the expertise of professionals and did not necessarily expect an active role or participation in the decision-making.

This Finnish case study focusses on the patients’ authentic perceptions of what is central to person-centred IC in the specific context of JRS.