2019

Chowdhury Siddiqui, South Carolina Department of Transportation

This paper focuses on the freight component of the federal rulemaking for performance management that assesses freight travel time reliability on the Interstate system. Since this is the first time agencies (DOTs and MPOs) have been tasked with establishing targets for freight reliability (and other measures), the paper proposes a modeling approach to systematically account for the traffic and roadway characteristics and predict possible changes in the Truck Travel Time Reliability (TTTR) ratio due to construction projects. While different trendlines can be utilized to provide a range of TTTR index for future years, the subjective nature of predicting a target value in a future year demands a more systematic approach in capturing the effect of construction projects on the variability of the TTTR ratio. This study uses South Carolina’s Interstate system to develop the two generalized linear models (GLMs) and a Bayesian GLM. Length and type of Traffic Message Channel, type of construction project, area type, average annual daily traffic, and number of lanes were found to be among the significant predictors associated with the change of TTTR ratio due to construction activity. The paper discusses the association of these causal factors and their influence on the freight reliability. Due to its contemporary nature, literature does not provide any research on predicting this particular type of freight reliability, and as such, this work is thought to be relevant and beneficial for transportation engineers, planners and forecasters.

Eren Yuksel, University of South Florida
Robert Bertini, USF Center for Urban Transportation Research
Seckin Ozkul, University of South Florida
Nikhil Menon, USF Center for Urban Transportation Research
Avinash Unnikrishnan, Portland State University
Rajit Garg, Portland State University

State and local agencies invest resources to collect traffic data, including truck counts for freight planning purposes. Traffic data can help identify bottlenecks, provide information to travelers, determine impacts of developments, track economic impacts on travel demand, reveal traffic changes due to incidents and construction, and can be converted to performance measures. These metrics are often required for federal, state and regional reporting purposes, can help prioritize capital and maintenance investments, and aid in pavement management. It is rare for traffic surveillance to systematically offer volumes and speeds by vehicle classification—typically vehicle classification (by type or length) is done at specific location (e.g. weigh stations or permanent automatic traffic recorder sites) rather than in a widespread fashion across a city, region or state. Real time and widespread knowledge of where and when trucks and other heavy vehicles are traversing our highways can be achieved without additional investment in surveillance infrastructure. In this paper, high-resolution, continuously collected, length-based vehicle count and speed data were analyzed and interpreted using data from Interstate 5 in Portland, Oregon. The objective of this analysis was to explore advanced data visualization techniques for the length-based traffic count and speed data. Results showed the merit of applying more disaggregate data for accurate visualization in order to capture sudden changes in average speed, truck volume, and truck percentage. The results of this study can be used by public and private entities in the planning and routing of freight and general traffic in areas with heavy freight traffic/movement.

Mobashwir Khan, Cambridge Systematics, Inc.
Anurag Komanduri, Cambridge Systematics, Inc.
Kalin Pacheco, California Department of Transportation (CALTRANS)
Cemal Ayvalik, No Organization
Kimon Proussaloglou, Cambridge Systematics, Inc.
Jim Brogan, Cambridge Systematics, Inc.
Mark McCourt, Redhill Group, Inc.
Ryan Mak, Redhill Group, Inc.

This paper describes the findings from the California Vehicle Inventory and Use Survey (CA-VIUS) which was administered between June 2016 and January 2018 and obtained data from a total of 11,118 fleets and 14,790 trucks. The surveys were segmented by registration, geography, vehicle type, and vehicle age and the data collection effort exceeded sampling targets across almost all segments. The CA-VIUS is the largest statewide commercial vehicle data collection effort in the United States and will replace the 2002 National VIUS in transportation planning and emissions studies throughout California. Currently, the wealth of information provided by the survey is supporting the development of the California Statewide Freight Forecasting Model (CSFFM) – which is a fine-grained behavioral freight model. This model will allow Caltrans and its partners to make more informed infrastructure and operational investment decisions. The VIUS data will also be useful to researchers and practitioners hoping to understand the impacts and benefits of commercial vehicle movements on air quality, economic activity, safety, and vehicle usage. This paper documents key sampling and survey approaches, but mainly focuses on the key findings observed in the survey. This is a practical paper geared towards practitioners who are seeking to analyze a new VIUS survey and those who wish to implement one of their own.

Makarand Gawade, HDR
Michael Gilbrook, HDR
Chauhan Arjun, HDR
Tanner Martin, HDR
Jennifer King, Florida Department of Transportation
Jerry Scott, Florida Department of Transportation
Chris Edmonston, Florida Department of Transportation

The purpose of this study was to apply advanced geospatial analytics to identify Freight Activity Areas in the state of Florida. This study defined Freight Activity Area (FAA) as a cluster or group of freight facilities that generate, distribute or attract substantial freight movement and has a significant impact on Florida’s transportation system and economy. A FAA cannot be a transportation hub like an airport, seaport, spaceport, intermodal logistics center or freight rail terminal, but can be in areas close to a transportation hub. The methodology included running an Inverse Distance Weighted (IDW) interpolation analysis for freight-related parcel data and freight-related employment data independently and a weighted overlay analysis that combined the output of the two interpolation analyses to produce a single map that averaged the inputs. The final step involved reclassification of the weighted overlay analysis to produce freight clusters for areas with high values (“hot spots”). Six freight clusters had more than 1% of Florida’s freight parcel living area and 1% of Florida’s freight employment within or partially within the cluster boundary. These clusters were identified as FAAs and all six FAAs were in close proximity to transportation hubs or major interstate corridors. This study had actionable next steps and the derived outcomes will assist in enhancing the freight planning efforts as well as identifying potential investment and freight growth opportunities.