Meredith Cebelak

Location-Based Social Networking Data: Exploration into Use of Doubly Constrained Gravity Model for Origin–Destination Estimation

Trip distribution is an invaluable portion of the transportation planning process; this distribution leads to the creation of origin–destination (O-D) matrices. Location-based social networking (LBSN) has increased in popularity and sophistication and has emerged as a new travel demand data source. Users of LBSN provide location-sensitive data interactively with mobile devices, including smartphones and tablets. These data can provide O-D estimates with significantly higher temporal resolution at a much lower cost in comparison with traditional methods. An LBSN O-D estimation model based on the doubly constrained gravity model was proposed to improve a previously proposed model based on the singly constrained gravity model. The proposed methodology was calibrated and comparatively evaluated against the O-D matrix generated by the method based on the singly constrained gravity model as well as a reference matrix from the local metropolitan planning organization. The results of this method illustrate significant improvement in reducing the O-D estimation errors caused by the sampling bias from the method based on the singly constrained gravity model.

Determining Strategic Locations for Environmental Sensor Stations with Weather-Related Crash Data

Adverse weather leads to more than 1.5 million vehicular crashes, resulting in 800,000 injuries and 7,000 fatalities nationally. The appropriate deployment of roadway weather information systems (RWIS) has been considered an effective strategy to address safety concerns. However, the current practice of selecting the locations of RWIS stations depends primarily on the knowledge and experiences of field operators. Limited research has been conducted on methodologies that can identify RWIS locations systematically on the basis of widely available safety and geographic information system (GIS) data. This paper proposes a spatial optimization method to identify strategic locations for deploying RWIS stations within a large regional transportation network. Weather-related crash data were converted to a safety concern index and then used to examine routes that provided good spatial coverage of the region for optimal locations for RWIS stations through a maximization algorithm. The proposed method is evaluated with crash and GIS data from a tri-county region in Texas with the coverage for each RWIS station assumed to be 10 mi. The resulting locations illustrate the promising potential for the proposed RWIS location optimization algorithm. Additional sensitivity analysis is also conducted with an evaluation of the resulting changes from yearly crash data variations.

Transportation Planning Through Peer-to-Peer Modeling

With many of today’s metropolitan areas experiencing changes in population and land development faster than the traditional transportation planning efforts can be undertaken, new methods for transportation planning are constantly being explored. This paper presents a novel approach to the transportation planning problem through the use of location-based social networking data and the many-to-many modeling structure of peer-to-peer modeling. With smartphone and tablet use increasing in the United States, many popular social networking sites have begun to include geospatial location in their platforms, and location-based social networking data sources have become attractive data sets for the transportation community because of their ability to be representative of populations and to provide detailed spatial–temporal data. The novel origin–destination estimation method through peer-to-peer modeling is presented, and a case study example of Austin, Texas, provides initial findings through a comparison against a doubly constrained gravity model and an existing origin–destination matrix for the study area. This first look at peer-to-peer modeling for origin–destination estimation revealed the method’s strengths with respect to intrazonal trip estimations and production and attraction estimations and was found to be more computationally efficient than the doubly constrained gravity model.