详情

Feasibility of Estimating Commodity Flows on Highways with Existing and Emerging Technologies

项目名称：	Feasibility of Estimating Commodity Flows on Highways with Existing and Emerging Technologies
摘要：	In the opening session of the 2016 North American Travel Monitoring Exhibition and Conference, Tianjia Tang (Federal Highway Administration (FHWA) Chief of Travel Monitoring and Surveys Division) discussed the need to better translate all of the traffic data being collected into usable information, particularly in the area of performance measures. He specifically emphasized the need to estimate commodity flows of freight movements on the highway network because right now, delay performance measures are typically quantified in terms of the value of time for drivers. There are no systematic ways to quantify the value of goods being transported on specific highways or the cost of delays for those deliveries. In order for an agency to adequately allocate resources to reduce freight delay on the highway network, they need to know the quantity and type of freight/commodity traveling on that corridor. In a connected vehicle world, it is likely that a vehicle�s cargo information could be communicated to the infrastructure for agencies to archive, but there is an existing need to collect this information for planning and monitoring purposes using the technology currently available. Each unique commodity (e.g., livestock, fuel, machinery, etc.) is hauled in a specific type of trailer. Therefore, if the trailer type can be identified, this will narrow the possible commodity types. The goal of this research project is to determine whether the trailer type can be automatically identified using existing technologies, which is a necessary component of estimating the type of commodity being hauled. This goal will be achieved through the following objectives. (1) Develop a reference library of unique trailer types and their axle characteristics (e.g., number, spacing, weight) by analyzing existing data sets that consist of weigh‐in‐motion (WIM) data and vehicle photo. (2) Statistically match individual vehicles to corresponding reference vehicles based on axle characteristics obtained from WIM data. (3) Develop an algorithm for automatically identifying commercial vehicle trailer type from a side‐fire light detection and ranging (LiDAR) sensor.
状态：	Completed
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	Mid-Atlantic Transportation Sustainability Center
执行机构：	Old Dominion University
主要研究人员：	Cetin, Mecit
开始时间：	20170501
预计完成日期：	20190228
实际结束时间：	20190320