Inferring transportation mode from smartphone sensors:Evaluating the potential of Wi-Fi and Bluetooth

Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications

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

Modellierung der Zugänglichkeit zu öffentlichen Verkehrsmitteln auf der Grundlage von Raumbewegungsdaten

The thesis serves three objectives: 1) exploration of biking distances at individual transit stations from trajectory and smart card data, 2) investigation of transit catchment area to raise the public awareness of the transit accessibility at a general level, and 3) inspection of accessibility constrained by crowdedness at a fine-grained level.Die Dissertation hat drei Ziele: 1) Untersuchung der Fahrraddistanzen an den einzelnen Transitstationen anhand von Trajektorien- und Smartcard-Daten, 2) Untersuchung des Transit-Einzugsgebietes zur Sensibilisierung der Öffentlichkeit für die Zugänglichkeit des Transits auf allgemeiner Ebene und 3) Untersuchung der durch Überfüllung eingeschränkten Zugänglichkeit auf Detailebene

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Exploring equity in public transportation planning using smart card data

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments