Transport poverty meets the digital divide : accessibility and connectivity in rural communities

Peer reviewedPublisher PD

Using Volunteer Tracking Information for Activity-Based Travel Demand Modeling and Finding Dynamic Interaction-Based Joint-Activity Opportunities

Technology used for real-time locating is being used to identify and track the movements of individuals in real time. With the increased use of mobile technology by individuals, we are now able to explore more potential interactions between people and their living environment using real-time tracking and communication technologies. One of the potentials that has hardly been taken advantage of is to use cell phone tracking information for activity-based transportation study. Using GPS-embedded smart phones, it is convenient to continuously record our trajectories in a day with little information loss. As smart phones get cheaper and hence attract more users, the potential information source for self-tracking data is pervasive. This study provides a cell phone plus web method that collects volunteer cell phone tracking data and uses an algorithm to identify the allocation of activities and traveling in space and time. It also provides a step that incorporates user-participated prompted recall attribute identification (travel modes and activity types) which supplements the data preparation for activity-based travel demand modeling. Besides volunteered geospatial information collection, cell phone users’ real-time locations are often collected by service providers such as Apple, AT&T and many other third-party companies. This location data has been used in turn to boost new location-based services. However, few applications have been seen to address dynamic human interactions and spatio-temporal constraints of activities. This study sets up a framework for a new kind of location-based service that finds joint-activity opportunities for multiple individuals, and demonstrates its feasibility using a spatio-temporal GIS approach

Introducing Two New Weed Control Tools: A Smart Spray Wand and a Wildland Weed Treatment Time Model

Millions are spent managing invasive weeds on public lands each year. Wildland invasive weed treatment bids are based primarily on acreage or hours but can be influenced by variables that increase treatment time and cost. Often neither the agency contracting the treatment nor the contractor has a clear idea of the amount of time that will be involved based on these variables. This makes it difficult to develop an accurate budget or bid for invasive weed control projects. It also limits managers in seeking funding and justifying treatment costs. A model has been developed that can predict herbicide application time due to four variables, weed canopy cover, slope, land cover, and weed visibility. Other variables were explored. The “smart” spray wand (SSW) is a new precision tool used to develop this model. The SSW is a spray wand with an integrated GPS and a flow meter for use with any type of spray system. The wand records the GPS location, herbicide flow,application time, and associated data of each treatment spray point. This information provided necessary data for the treatment time model. Weed control total treatment time (TTot) was hypothesized to include both treatment time (Tt) and rest time (Rt). The development and benefits of a wildland weed treatment time model are discussed. An accurate treatment time model could 1) establish an accurate standard for contractors and land managers, 2) assist in planning and managing limited treatment resources, and 3) justify funding requests and expenditures. The primary influence of the model is due to weed canopy cover (p=2=0.5607), with smaller impacts by other variables. If canopy cover, slope, land cover, and weed visibility can be obtained for a weed control project, the model can be used

The Application of Smart Phone, Weight-Mile Truck Data to Support Freight-Modeling, Performance Measures and Planning

Oregon is one of the few states that currently charge a commercial truck weight-mile tax (WMT). The Oregon Department of Transportation (ODOT) has developed a data-collection system – Truck Road Use Electronics (TRUE) – to simplify WMT collection. The TRUE system includes a smart phone application that collects and records Global Positioning System (GPS) data. The TRUE data has enormous advantages over GPS data used in previous research due to its level of geographic detail and the potential to also integrate trip origin and destination, vehicle class, and commodity-type data. This research evaluates the accuracy of the TRUE data and demonstrates its use for significant ODOT ancillary applications. Specifically, ancillary applications that address ODOT freight modeling, performance measures, and planning needs are explored. The use of the data for highly accurate trip-generation rates and mobility performance measures is demonstrated. In addition, it is shown that the TRUE data has strong potential to be used for safety, accessibility and connectivity, system condition and environmental stewardship performance measures. The potential use of the TRUE data for emissions estimates that take into account truck-type details, truck weight and detailed speed profiles is considered. Results indicate that TRUE data, integrated with ODOT weigh-in-motion (WIM) data, will greatly improve the accuracy of emission estimates at the project and regional level. This research confirms the potential use of the TRUE data for significant ancillary applications and demonstrates the regional value of the TRUE data to enhance existing freight modeling, performance measures and planning

Media Mapping: Using Georeferenced Images and Audio to provide supporting information for the Analysis of Environmental Sensor Datasets

Field based environmental monitoring projects often fail to gather supporting temporal information on the surroundings, yet these external factors may play a significant part in understanding variations in the collected datasets. For example when sampling air quality the values may change as a result of a bus passing the sampling point, yet this temporal local information is difficult to capture at a con-sistently high resolution over extended time periods. Here we develop an applica-tion which runs on a mobile phone able to capture visual and audio data with cor-responding time and location details. We also develop a desktop analysis tool which synchronises the display of this dataset with those captured from environ-mental sensors. The result is a tool able to assist researchers in understanding local changes in environmental datasets as a result of changes in the nearby surrounding environment