Visual Analysis of Place Connectedness by Public Transport

The concept of place connectedness (traditionally termed `accessibility') refers to the ability of people to reach various services and to participate in activities. Connectedness by public transport is especially important for underprivileged and elderly people while the active use of public transport by the general population contributes in reducing traffic congestions and air pollution in cities. Place connectedness analyses are performed for a variety of purposes. In communication with transportation experts, we performed the conceptual modeling of the domain of problems related to place connectedness, defined the system of analysis tasks, and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the task typology and present the visual analytics techniques using several example scenarios of place connectedness analyses

Travel chains in urban public transportation: Identifying user needs, travel strategies, and travel information system improvements

The implementation of a functional public transportation network has many benefits for a city, among other things, a way of sustainable mobility. Today, urban areas face the challenge of keeping up with technological trends and encouraging mobility activities using public transportation. For this reason, it is important to understand public transportation user behavior and, consequently, the motives and challenges related to urban travel. Research in the field of urban transportation mainly focuses on systematic and network-related issues to improve the travel experience. However, examining urban travel from a user’s perspective is equally essential to improving a city’s transportation network. With the help of twenty participants, an extensive travel study in the urban area of Zurich took place. The research design consists of a three-step mixed method approach. Data on travel behavior, mobility preferences, and information needs are obtained. The data is explored using an advanced travel chain structure, revealing results in the context of individual travel phases. The results show that urban travel relies heavily on the information apps provide, especially when planning. This need is mainly bound to spatial and temporal properties, for which app elements such as maps, dynamic timetables, and real-time information are most valued. Furthermore, travel using public transportation is approached by evaluating suggested routes according to the journey’s duration, efficiency, and complexity. However, decisions are often based on familiarity with the general area or interchange points. Uncertainties during urban travel are mitigated by walking when suitable, avoiding complex interchanges, and monitoring all phases with the help of an app. User results also indicate no serious issues regarding the City of Zurich as a public transportation provider. Nonetheless, measures could include integrating crowdsourced and context-aware data to meet the demands of adaptive and accurate travel information needs. The broader implications of the thesis outcome support cities and transportation service providers in understanding travel behavior. Consequently, this insight enables them to address specific needs and thus encourage sustainable mobility

Mapping public transport stops in smartphone apps: Dynamic information visualization in the context of hiking

Public transport (PT) offers an efficient and environmentally friendly way of transportation. However, especially for leisure activities only a small fraction of the total distances traveled are accomplished with PT. One way to promote public transport usage is to improve the provision of respective information. Smartphones have revolutionized information access, allowing users to access context-specific data anytime and anywhere. Currently, diverse PT apps are available, often focusing on temporal information, particularly in urban areas. Few cater to outdoor leisure activities, and those that do often provide static transit-related data. Information needs to be tailored to individual user needs to simplify PT usage for leisure activities. Therefore, improving the visualization of PT stops in maps and their respective information is addressed in this thesis. For specification, the scenario of a hiker who wants to find a fitting PT stop to get home was chosen. A mixed method approach was applied to analyze the specific context; find possible visualization options by comparing apps; and evaluate the effectiveness of suggested improvements with a qualitative user study. Results show that current apps do not sufficiently provide dynamic visualization of information for outdoor activities. Information related to the spatio-temporal proximity of PT stops and their connections was found to be particularly effective for the decision-making process. The results suggest that even more additional information could enhance the experience by enabling users to decide according to their personal preferences

Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for all trips made within the city since November 1, 2018. The data comprises the trip ends (pick-up and drop-off locations), trip timestamps, trip length and duration, fare including tipping amounts, and whether the trip was authorized to be shared (pooled) with another passenger or not. Therefore, the main goal of this dissertation is to develop a comprehensive data-driven framework to understand and model the system using this data from Chicago, in a reproducible and transferable fashion. Using data fusion approach, sociodemographic, economic, parking supply, transit availability and accessibility, built environment and crime data are collected from open sources to develop this framework. The framework is predicated on three pillars of analytics: (1) explorative and descriptive analytics, (2) diagnostic analytics, and (3) predictive analytics. The dissertation research framework also provides a guide on the key spatial and behavioral explanatory variables shaping the utility of the mode, driving the demand, and governing the interdependencies between the demand’s willingness to share and surge price. Thus, the key findings can be readily challenged, verified, and utilized in different geographies. In the explorative and descriptive analytics, the ride-sourcing system’s spatial and temporal dimensions of the system are analyzed to achieve two objectives: (1) explore, reveal, and assess the significance of spatial effects, i.e., spatial dependence and heterogeneity, in the system behavior, and (2) develop a behavioral market segmentation and trend mining of the willingness to share. This is linked to the diagnostic analytics layer, as the revealed spatial effects motivates the adoption of spatial econometric models to analytically identify the ride-sourcing system determinants. Multiple linear regression (MLR) is used as a benchmark model against spatial error model (SEM), spatially lagged X (SLX) model, and geographically weighted regression (GWR) model. Two innovative modeling constructs are introduced deal with the ride-sourcing system’s spatial effects and multicollinearity: (1) Calibrated Spatially Lagged X Ridge Model (CSLXR) and Calibrated Geographically Weighted Ridge Regression (CGWRR) in the diagnostic analytics layer. The identified determinants in the diagnostic analytics layer are then fed into the predictive analytics one to develop an interpretable machine learning (ML) modeling framework. The system’s annual average weekday origin-destination (AAWD OD) flow is modeled using the following state-of-the-art ML models: (1) Multilayer Perceptron (MLP) Regression, (2) Support Vector Machines Regression (SVR), and (3) Tree-based ensemble learning methods, i.e., Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost). The innovative modeling construct of CGWRR developed in the diagnostic analytics is then validated in a predictive context and is found to outperform the state-of-the-art ML models in terms of testing score of 0.914, in comparison to 0.906 for XGBoost, 0.84 for RFR, 0.89 for SVR, and 0.86 for MLP. The CGWRR exhibits outperformance as well in terms of the root mean squared error (RMSE) and mean average error (MAE). The findings of this dissertation partially bridge the gap between the practice and the research on ride-sourcing transportation systems understanding and integration. The empirical findings made in the descriptive and explorative analytics can be further utilized by regional agencies to fill practice and policymaking gaps on regulating ride-sourcing services using corridor or cordon toll, optimally allocating standing areas to minimize deadheading, especially during off-peak periods, and promoting the ride-share willingness in disadvantage communities. The CGWRR provides a reliable modeling and simulation tool to researchers and practitioners to integrate the ride-sourcing system in multimodal transportation modeling frameworks, simulation testbed for testing long-range impacts of policies on ride-sourcing, like improved transit supply, congestions pricing, or increased parking rates, and to plan ahead for similar futuristic transportation modes, like the shared autonomous vehicles

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Revealing Differences in Willingness to Pay due to the Dimensionality of Stated Choice Designs: An Initial Assessment

Stated choice (SC) methods are now a widely accepted data paradigm in the study of behavioural response of agents (be they individuals, households, or other organizations). Their popularity since the pioneering contributions of Louviere and Woodworth (1983) and Louviere and Hensher (1983) has spawned an industry of applications in fields as diverse as transportation, environmental science, health economics and policy, marketing, political science and econometrics. With rare exception, empirical studies have used a single SC design, in which the numbers of attributes, alternatives, choice sets, attribute levels and ranges have been fixed across the entire design. As a consequence the opportunity to investigate the influence of design dimensionality on behavioural response has been denied. Accumulated wisdom has promoted a large number of positions on what design features are specifically challenging for respondents (eg the number of choice sets to evaluate); and although a number of studies have assessed the influence of subsets of design dimensions (eg varying the range of attribute levels), there exists no single study (that we are aware of) that has systematically varied all of the main dimensions of SC experiments. This paper reports the findings of a study that uses a Design of Designs (DoD) SC experiment in which the ‘attributes’ of the design are the design dimensions themselves including the attributes of each alternative in a choice set. The design dimensions that are varied are the number of choice sets presented, the number of alternatives in each choice set, the number of attributes per alternative, the number of levels of each attribute and the range of attribute levels. This paper details the designs and how they are used in the search for design impacts on willingness to pay (ie attribute valuation), using a sample of respondents in Sydney choosing amongst trip attribute bundles for their commuting trip