Sensing vehicle dynamics for determining driver phone use

This paper utilizes smartphone sensing of vehicle dynamics to de-termine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in cen-tripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure ap-proach is flexible with different turn sizes and driving speeds. Ex-tensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving envi-ronments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90 % with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95%) with a lower false positive rate

Transportation for an Aging Population: Promoting Mobility and Equity for Low-Income Seniors

This study explores the travel patterns, needs, and mobility problems faced by diverse low-income, inner-city older adults in Los Angeles in order to identify solutions to their mobility challenges. The study draws information from: (1) a systematic literature review of the travel patterns of older adults; (2) a review of municipal policies and services geared toward older adult mobility in six cities; (3) a quantitative analysis of the mobility patterns of older adults in California using the California Household Travel Survey; and (4) empirical work with 81 older adults residing in and around Los Angeles’ inner-city Westlake neighborhood, who participated in focus groups, interviews, and walkabouts around their neighborhood

Robust Algorithms for Estimating Vehicle Movement from Motion Sensors Within Smartphones

Building sustainable traffic control solutions for urban streets (e.g., eco-friendly signal control) and highways requires effective and reliable sensing capabilities for monitoring traffic flow conditions so that both the temporal and spatial extents of congestion are observed. This would enable optimal control strategies to be implemented for maximizing efficiency and for minimizing the environmental impacts of traffic. Various types of traffic detection systems, such as inductive loops, radar, and cameras have been used for these purposes. However, these systems are limited, both in scope and in time. Using GPS as an alternative method is not always viable because of problems such as urban canyons, battery depletion, and precision errors. In this research, a novel approach has been taken, in which smartphone low energy sensors (such as the accelerometer) are exploited. The ubiquitous use of smartphones in everyday life, coupled with the fact that they can collect, store, compute, and transmit data, makes them a feasible and inexpensive alternative to the mainstream methods. Machine learning techniques have been used to develop models that are able to classify vehicle movement and to detect the stop and start points during a trip. Classifiers such as logistic regression, discriminant analysis, classification trees, support vector machines, neural networks, and Hidden Markov models have been tested. Hidden Markov models substantially outperformed all the other methods. The feature quality plays a key role in the success of a model. It was found that, the features which exploited the variance of the data were the most effective. In order to assist in quantifying the performance of the machine learning models, a performance metric called Change Point Detection Performance Metric (CPDPM) was developed. CPDPM proved to be very useful in model evaluation in which the goal was to find the change points in time series data with high accuracy and precision. The integration of accelerometer data, even in the motion direction, yielded an estimated speed with a steady slope, because of factors such as phone sensor bias, vibration, gravity, and other white noise. A calibration method was developed that makes use of the predicted stop and start points and the slope of integrated accelerometer data, which achieves great accuracy in estimating speed. The developed models can serve as the basis for many applications. One such field is fuel consumption and CO2 emission estimation, in which speed is the main input. Transportation mode detection can be improved by integrating speed information. By integrating Vehicle (Phone) to Infrastructure systems (V2I), the model outputs, such as the stop and start instances, average speed along a corridor, and queue length at an intersection, can provide useful information for traffic engineers, planners, and decision makers

Predictive Power of Criminal Background on Losses

Product and data science teams for the auto insurance industry have been trying to increase pricing segmentation with validated rating variables to decrease rate subsidization. The criminal background data availability provided a new behavior variable to test against insurance-based credit scores as a potential predictive variable in the generalized linear rating model. Criminal background was analyzed using a Poisson Log Linear model and other key insurance rating variables for predicting loss costs. The study supported the inclusion of the criminal background data in combination with insurance-based credit score as the variable’s addition could improve the overall fit of the predictive model. The study also acknowledged there was a statistically significant association between criminal background and insurance-based credit score, but the overall size of the effect was small and weak. The overall contribution of value criminal background variable needs to be considered with a full rating dataset to determine if other, less powerful variables could be removed from the generalized linear to reduce the overall model complexity

Gender and the Sharing Economy

While the sharing economy has been celebrated as a flexible alternative to traditional employment for those with family responsibilities, especially women, it presents challenges for gender equality. Many of the services that are “shared” take place in the context of intimacy, which can have substantial consequences for transacting, particularly by enhancing the importance of identity of both the worker and the customer. Expanding on previous research on intimate work — a critical area that exists largely in limbo between the law of the market and the law of the family — this Article, written for the Cooper-Walsh Colloquium, explores the significance of intimacy in the sharing economy and the implications for its regulation of the sharing economy and for sex equality. It argues that the intimacy of many sharing economy transactions heightens the salience of sex to these transactions, in tension with sex discrimination law’s goal of reducing the salience of sex in the labor market. But even if existing sex discrimination law extends to these transactions, the intimacy of the transactions again limits the law’s ability to promote gender equality in the same transformative way that it has in the traditional economy. The sharing economy thus raises serious concerns for proponents of sex equality