Inferring transportation modes from GPS trajectories using a convolutional neural network

Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C: Emerging Technologie

Inferring Community Resilience through the Accessibility of Goods Delivery

This study analyzed community resiliency by evaluating access to essential delivery services before and during the COVID-19 pandemic. Data were collected from October 2020 to September 2021 in a stated-preference survey about delivery services in Southwest Virginia. A significantly larger proportion of respondents without vehicle access relied on third-party restaurant app delivery use than those with a vehicle. Compared to more urban areas, respondents who lived in rural locations were three times more unsatisfied with delivery services due to a lack of accessibility to stores and delivery options

Compressed Natural Gas Vehicles: Financially Viable Option?

Natural gas vehicles are being developed because of increasing concerns about energy dependence, air quality and emissions, and, more recently, climate change. The major advantage of natural gas vehicles is their lower fuel cost. Several economic and technical factors such as limited range and availability of relevant infrastructure prevent widespread adoption of natural gas vehicles. A model for the financial analysis of the possibility of compressed natural gas (CNG) vehicles being competitive with gasoline-powered vehicles is offered. The model evaluates the extent to which commuters find adoption of CNG vehicles to be economically viable in the United States. The results indicate that the percentage of commuters who would adopt CNG vehicles is small, even if fueling infrastructure were fully developed and CNG vehicles were widely available for purchase. A larger number of vehicle miles traveled and increased gasoline prices encourage commuters to adopt CNG vehicles, while higher fuel economy and purchase price differentials result in lower adoption rates. In some cases, which vary in accordance with the values of the model’s parameters, commuters purchase a CNG vehicle as their second car and keep a gasoline-powered car as their first

Analysis of the Electric Vehicles Adoption over the United States

Increasing the use of electric vehicles (EVs) has been suggested as a possible method to decrease fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In this study, the relationship between the market share of electric vehicles and the presence of government incentives, and other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The results demonstrated that electricity prices were negatively associated with EV use while urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with theories about diffusion of new technology

Assessment of Sign Retroreflectivity Compliance for the Development of a Management Plan

The Manual on Uniform Traffic Control Devices (MUTCD) specifies minimum retroreflectivity requirements that include an obligation for agencies to develop a strategy for maintaining compliance. States were given a deadline of January 1, 2012 for the implementation of an assessment or management plan, which led to an increased emphasis on sign asset management. However, a new rule was submitted to the federal register to extend and modify the deadlines. With budget considerations it is important that a transportation agency implement an assessment or management plan that is efficient and provides compliance with the standards required by the Manual on Uniform Traffic Control Devices (MUTCD). The development of an efficient plan requires knowledge of the overall condition of an agency's assets as well as unique considerations regarding their performance. Through a review of previous data collection efforts, this paper details the development of a data collection strategy for assessing the performance of traffic signs maintained by the Utah Department of Transportation (UDOT). Agency operations, site selection, and attribute collection were all considered while developing a collection plan for an agency where limited inventory and installation data was available. Retroreflectivity measurements were taken for 1,433 UDOT signs. This sample provided a snapshot of current compliance and assisted in the selection of an asset management plan for maintaining sign retroreflectivity. Results from the study showed that UDOT’s signs were well over 90% compliant to the MUTCD standards and preliminary management strategies were presented to address vandalism and other damage.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Transportation Research Board of the National Academies and can be found at: http://www.trb.org/Main/Blurbs/154702.aspx

Calmodulin-like proteins localized to the conoid regulate motility and cell invasion by Toxoplasma gondii

Toxoplasma gondii contains an expanded number of calmodulin (CaM)-like proteins whose functions are poorly understood. Using a combination of CRISPR/Cas9-mediated gene editing and a plant-like auxin-induced degron (AID) system, we examined the roles of three apically localized CaMs. CaM1 and CaM2 were individually dispensable, but loss of both resulted in a synthetic lethal phenotype. CaM3 was refractory to deletion, suggesting it is essential. Consistent with this prediction auxin-induced degradation of CaM3 blocked growth. Phenotypic analysis revealed that all three CaMs contribute to parasite motility, invasion, and egress from host cells, and that they act downstream of microneme and rhoptry secretion. Super-resolution microscopy localized all three CaMs to the conoid where they overlap with myosin H (MyoH), a motor protein that is required for invasion. Biotinylation using BirA fusions with the CaMs labeled a number of apical proteins including MyoH and its light chain MLC7, suggesting they may interact. Consistent with this hypothesis, disruption of MyoH led to degradation of CaM3, or redistribution of CaM1 and CaM2. Collectively, our findings suggest these CaMs may interact with MyoH to control motility and cell invasion