Naturalistic Study of Truck Following Behavior

Abstract

The Federal Highway Administration provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. REPORT DOCUMENTATION PAGEForm Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Volpe conducted the Naturalistic study of truck following behavior to gain a better understanding of how trucks follow other vehicles in the real world, with the ultimate goal of supporting the Federal Highway Administration in the development of automated truck platooning applications. Using two naturalistic heavy truck data sets collected from previous related research, Volpe quantified heavy truck following behavior in the following categories: how closely trucks follow other vehicles on freeways, how following behavior varies by road condition, weather and visibility, following distances at which cut-in events occur, and the safety impact of different headways. Key results from this 2015 study showed that trucks follow passenger cars at shorter distances and headways than they follow heavy trucks at distances under 60 mph, but follow other heavy vehicles at shorter distances and headways at speeds greater than 60 mph; truck drivers generally follow other vehicles at much shorter headways (around 2 seconds) than are recommended in CDL driver handbooks; vehicles rarely cut-in between two trucks who are following at a distance of 40 m or less; and crash risk increases considerably when trucks follow other vehicles at headways of less than 1 s.

Calibration of Gipps’ car‐following model for trucks and the impacts on fuel consumption estimation

“…In the literature on car-following calibration, many GoF functions are used [14][15][16][17][18][19]. This paper will focus on Theil's inequality coefficient, described in (2):…”

“…The study of car-following models calibration has already a long history, but very few studies focus specifically on heavy duty vehicles (HDVs) behaviour [1,2], although truck's reactions to traffic are different from those of cars as shown in [3][4][5]. For the truck's trajectory, one can observe that spacing would be larger, acceleration capabilities are smaller, speed profiles are more complex due to more complex engine chains.…”

“…Inertia also modifies the driving behaviour of HDVs due to the important weights of vehicles. The main studies taking into account truck following behaviour focus on the differences in following reactions [2,3,5,6] depending of the follower and the leader type (passenger car or HDV). Other existing studies which consider truck following behaviour describe the influence of heterogeneous traffic flow on traffic instability [7][8][9].…”

Optimal Eco-Driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

“…Ref. [33] studies the truck following behavior and indicates: first, trucks follow other vehicles at an average headway of about 2 s according to the experimental data, and second, a headway around 1.0 s is recommended for an autonomous truck platoon to minimize crash risk. In our study, the minimum headway 𝜏 from the preceding vehicle was set to 1.2 s for the autonomous following trucks to ensure traffic safety.…”

Driver Response Time in Cut-Off Scenarios from the Second Strategic Highway Research Program Naturalistic Database

“…The following variables were identified as being significant influences on whether a driver merged (not response times): speed of the vehicles, gap between the vehicles, speed of a third lead vehicle ahead, and length of an on-ramp if applicable (15). Also, merging in front of another vehicle was least likely when two vehicles were 1 s apart and most likely when two vehicles were 3 s or more apart (16).…”