@proceedings {41, title = {Developing and Optimizing a Transportation Mode Inference Model Utilizing Data from GPS Embedded Smartphones}, year = {2015}, month = {01/2015}, pages = {16}, publisher = {Transportation Research Board}, address = {Washington, D.C.}, abstract = {

Advances in wireless communications and technologies provide the opportunity to collect detailed information on travel trajectory using smart-phones equipped with GPS and accelerometers. These types of smart-phones are ubiquitous and, as such, present an opportunity to conveniently collect spatial and temporal data at regular time intervals. This can be useful to utilize as a method to document travel behavior \– origin, destination, departure time, route choice, trip purpose, and mode choice. One of the challenges that has been addressed in the literature is how to identify the transportation mode of travel. This paper presents a data-driven classification model to infer transportation mode choice from data collected with GPS equipped smart phones. Rather than making a priori assumptions, the authors instead employ an optimization method to objectively produce the following classifier components and methods: a ranked feature vector based on the power of differentiation between different modes; the classification technique between the range of candidate classifiers; the number of ranked attributes to include in the feature vector; data formatting; and optimal model parameters. The model is trained and tested using known transportation mode segments \– limits of travel by a given mode. The calibrated model is evaluated by testing its ability to classify travel mode correctly for GPS data at a different level of disaggregation than the one used in the model training step. The model provides an accuracy of approximately 86\% at the disaggregated level (e.g. Walk, Bike, Transit, and Private Automobile) and approximately 94\% at aggregated level (e.g. Non-Motorized and Motorized.)

}, keywords = {Data and Information Technology, Highways, I72: Traffic and Transport Planning, Pedestrians and Bicyclists, Public Transportation}, url = {https://trid.trb.org/view.aspx?id=1338992}, author = {Nour, Akram and Casello, Jeffrey M. and Hellinga, Bruce} } @proceedings {42, title = {Tracking the Transit Rider Experience: Using Smartphones to Measure Comfort and Well-Being Throughout the Trip}, year = {2015}, month = {01/2015}, pages = {15}, publisher = {Transportation Research Board}, address = {Washington, D.C.}, abstract = {

An experience sampling method using a smartphone application enables real-time observation of a rider\’s trip on transit to identify the triggers causing negative perceptions of personal comfort and wellbeing. Transportation models currently lack the sensitivity for these subtle influences on mode choice. This transit-oriented experience survey demonstrates the ability of this data collection technique to provide a rich qualitative and quantitative dataset that clearly traces the changes in transit rider perception through the entire trip chain, while unobtrusively tracking the participant via global positioning system (GPS). The study involved 60 students who were given a smartphone with the survey app installed. These participants took trips by public transit bus, either on a predefined route or for their day-to-day activities. The smartphone survey successfully captured heightened perceptions of anxiety and discomfort when the participants experienced undesirable situations like boarding a crowded bus, or waiting at a bus stop located on a busy street with few amenities. Attributes of the rider experience, such as their emotional state and wellbeing, were rated by the participants during each state of before, during and after their trip on transit. GPS tracking along with voice and text capture also enabled a contextual understanding of the situation the participants were experiencing. Negative experiences can also influence perception of wait time and travel time during a transit trip. By better understanding rider experience and perceptions, improvements can be made to transit service amenities to provide a more comfortable experience, build rider loyalty, and make transit a more desirable mode choice.

}, keywords = {I72: Traffic and Transport Planning, Passenger Transportation, Planning and Forecasting, Public Transportation}, url = {https://trid.trb.org/view.aspx?id=1339490}, author = {Dunlop, Ian and Casello, Jeffrey M. and Doherty, Shannon} } @proceedings {48, title = {An Analysis of Stated Preference and GPS Data for Bicycle Travel Forecasting}, year = {2011}, month = {01/2011}, pages = {18}, publisher = {Transportation Research Board}, address = {Washington, D.C.}, abstract = {

In this paper, we present preliminary results from an ongoing study of cyclists and cycling in the Region of Waterloo, Ontario Canada. The paper describes two data collection efforts. The first is an on-line survey that provides information on cyclists\’ demographics as well as their household composition. The survey also gathers data on respondents\’ motivation for and obstacles to cycling. The second activity collects data on actual cycling trips using GPS units. We describe these units and the steps taken to validate the data. We use the GPS data to produce trip generation and attraction rates for cycling as a function of land use. We also generate a histogram of observed cycling trip lengths that can be used to calibrate a gravity-type model of trip distribution. We then explore the methods by which the survey and GPS data may be combined to develop multi-class and multi-trip purpose generalized cost functions. These formulations may be applied to prioritizing infrastructure investments, as well as for mode and path choice models. We conclude with a discussion of ongoing research work.

}, keywords = {Data and Information Technology, I72: Traffic and Transport Planning, Pedestrians and Bicyclists, Planning and Forecasting}, url = {https://trid.trb.org/view.aspx?id=1093054}, author = {Casello, Jeffrey M. and Nour, Akram and Rewa, Kyrylo and Hill, John} } @proceedings {50, title = {A Framework for Suburban Parking Analysis}, year = {2009}, month = {01/2009}, pages = {16}, publisher = {Transportation Research Board}, address = {Washington, DC}, abstract = {

The provision and pricing of parking are recognized as important tools for achieving transportation demand management (TDM) objectives. The literature on the topic is concentrated on parking in central business districts. This paper presents an analysis of suburban parking supply. We quantify the total number of spaces provided, the footprint required to accommodate the spaces and the employment to which the parking is providing access. We separate the totals by different land use categories and normalize the quantity of parking supplied by total employment to facilitate a comparison across different categories. We also normalize the supply of parking to control for the influence of retail employment on parking needs. We determine that employment is a somewhat weak indicator of parking supply, while weighted employment (considering retail effects) is a much stronger indicator. On average, very high and very low land use densities have the lowest parking requirements per employee while moderate land use densities have the least diversity of land use, yet the highest supply of employee parking. We use our results to provide quantitative targets for policy makers in managing the supply of suburban parking.

}, keywords = {Highways, I72: Traffic and Transport Planning, I73: Traffic Control, Operations and Traffic Management}, url = {https://trid.trb.org/view.aspx?id=881468}, author = {Casello, Jeffrey M. and LaPointe, James and Lambert, Brooke} }