Length-Based Vehicle Classification
I. RESEARCH PROBLEM STATEMENT
Vehicle classification data are of considerable use to agencies involved in almost any aspect of transportation planning and engineering. Some examples include the following:
· pavement design and pavement management;
· scheduling resurfacing, reconditioning, and reconstructing of highways based on projected pavement life remaining;
· predicting commodity flows and freight movements;
· providing design input relative to current and predicted capacity of highways;
· developing weight enforcement strategies;
· accident record analysis;
· environmental impact analysis, including air quality studies; and
· analysis of alternative highway regulatory and investment policies.
In short, vehicle classification data are extremely important as transportation agencies and State legislatures grapple with the need to determine and allocate the costs associated with maintaining the highway system and in selecting the improvements that will be programmed. So the States are in the process of increasing their collection of vehicle classification data.
There are several typologies for classifying vehicles. FHWA has defined a 13-class typology that covers most data needs. This includes both the number of axles and the number of trailers in each vehicle. However, this requires sensors to be installed in or on the roadway. Particularly in urbanized areas, this is a significant expense and disruption (closing one or more lanes, cutting the pavement), which in high volume roads may be prohibitive.
An alternative is the use of off-road, non-intrusive sensors that can detect vehicle length or profile. These are sensors such as video, microwave, acoustic, etc., which are becoming more common. Vehicle lengths are then correlated with various vehicle classes such as car, single-unit truck, and combination truck. Classification based solely on vehicle length is an alternative to axle-based classification but there has been no systematic study of how well it works -- or how it should work. So the quality of length-based classification data is unknown.
The proposed research would fill this gap and develop a scheme of “length bins” that would divide vehicles into classes based on their overall length or magnetic length. State and local traffic data collectors will use these length bins to improve the quality of their vehicle classification data. However, one scheme will not be applicable to the entire Nation. The research would also show how to modify and calibrate the scheme to a particular State or area.
The TRB Committee on Highway Traffic Monitoring (ABJ35) has also looked at this issue and agreed with the need for this research.
II. LITERATURE SEARCH SUMMARY
The absence of research on vehicle length classification was confirmed by a search of TRIS online and the Research In Progress database. Research has concentrated on collecting vehicle length data from single loops or video sensors, not on the subsequent classification of vehicle lengths into vehicle class bins.
III. RESEARCH OBJECTIVE
The final product is a scheme of classifying vehicles given their overall length. The classes used shall be: passenger car and light truck, (large) bus, single unit truck, single trailer truck, and multi-trailer truck. The needed tasks are as follows: (1) collect information concerning vehicle length characteristics and length bins from practitioners and vendors; (2) collect ground truth data on vehicle length and classification from a representative sample of traffic monitoring sites around the country; (3) develop and validate a recommended scheme of estimating vehicle classifications using vehicle length alone; and (4) develop guidance on modifying and calibrating the scheme for local conditions.
IV. ESTIMATE OF PROBLEM FUNDING AND RESEARCH PERIOD
Recommended Funding: $250,000
Research Period: 18 months
V. URGENCY, PAYOFF POTENTIAL, AND IMPLEMENTATION
At the same time that the demand for vehicle classification data is increasing, it is becoming harder to collect vehicle classification data based on in-road sensors as required by the 13-class typology. An alternative for many purposes is the use of vehicle length to classify vehicles into fewer classes. With the growing use of above ground traffic detection systems as well as the many loop detectors that can output vehicle length, there is increased interest in classifying based on vehicle length.
But there has been no systematic study of vehicle length classification so practitioners in the States must use ad hoc schemes instead. This research would fill that need and develop the first systematic scheme for classification based on vehicle length. What happened with “scheme F” for axle-based vehicle classification would likely happen here: the States and vendors will adopt it as the default classification scheme for traffic detectors that output vehicle length.