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The Fall 2015 issue contains the usual wide variety of contemporary transportation topics that is the distinguishing characteristic of JTRF. Topics in this issue include the following:

In “Comparative Evaluation of Technologies and Data Sources to Capture Travel Time at Section-Level on Urban Streets,” Pulugurtha and co-authors capture travel times on urban streets in Charlotte, North Carolina, using three different technologies (Bluetooth, INRIX and GPS). The authors found that the ability of the technologies to measure accurate travel tim,e increases with increases in traffic volume. Also accurate measurement of travel times varies by time of day. The authors concluded that Bluetooth is less accurate and undependable compared with GPS and INRIX.

Kamrul Islam and co-authors develop a model based on the Markov Chain technique to evaluate the performance of a public transport route in “A Simplified Method for Performance Evaluation of Public Transit Under Reneging Behavior of Passengers.” The model addresses a special situation where a passenger left behind by a bus leaves the system without any further waiting. The authors offer insights to the problem faced by transit system designers with regard to fleet size and the size of vehicles. The authors used the following metrics to evaluate performance: number of passengers served by the system, number of passengers that were unable to use the service because of space unavailability, and number of unused spaces throughout the transit operation. Authors conclude that their analysis provides insights for optimum selection of fleet size and vehicle size.

In “Traffic Impact Analysis (TIA) and Forecasting Future Traffic Needs: Lessons From Selected North Carolina Case Studies,” Pulugurtha and co-authors conduct an evaluation of TIA case studies, review current practice, and recommend procedures that could be used to better forecast and plan future traffic needs. The authors found that considering regional traffic growth rate, peak hour factor, heavy vehicle percentage, and other off-site developments would yield better forecasts.

Michael W. Babcock and Bebonchu Atems study the relationship of intrarailroad competition and rail rates for wheat in the nine largest wheat producing states in “Intrarailroad and Intermodal Competition Impacts on Railroad Wheat Rates.” The overall objective is to investigate railroad pricing behavior for wheat shipments. The rate model was estimated with OLS in double-log specification utilizing the 2012 Confidential Waybill sample and other data. The authors found that the distance from origin to destination and the total shipment weight had the expected negative relationships with railroad wheat rates and were statistically significant. The distance from origin to the nearest barge loading location had the expected positive relationship to railroad wheat rates and was also significant. The weight of each covered hopper car and the Herfindahl-Hirschman Index were both non-significant. However, the authors used other data to determine that the intrarailroad competition for wheat shipments within states appears to be present in most of the nine states.

In “Northern Plains Grain Farm Truck Marketing Patterns,” Kimberly Vachal conducted a survey of 6,000 farm operators in the Northern Plains region to gather information about on-farm storage and truck markets. The objective was to provide information about farm truck grain marketing patterns since there is no other source for these data. The author found that 79% of the wheat and soybeans was delivered to elevators, whereas the share of corn delivered to elevators was 54%. The author noted that farmers could use the results for their investment assessments and that local and regional planners and policy makers can use the information in calibrating travel demand and freight flow models for investment and asset management choices.

Savannah Gleim and James Nolan examine both transportation allocation and infrastructure capacity problems associated with moving grain from western Canada to export position. The analysis is conducted with GIS software using grain industry data. After developing and estimating base model results, the authors simulate the impact of larger trains with capacities of 50, 100, and 150 cars. This scenario resulted in significantly fewer total hours traveled and total distance traveled. The authors simulate the impact of greater grain volumes moving through the system (i.e., grain demand and supplies are doubled). The authors found railroad network capacity should not constrain any major expansion of grain movement in the system for the foreseeable future.