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This study examines the impacts of cooperative adaptive cruise control (CACC) and cooperative lane changing (CLC) on the delay and the riding comfort in autonomous car–autonomous truck (AC–AT) mixed traffic at a freeway merging area. For this task, AC–AT mixed traffic on a 5.25 km section of freeway was analyzed using the Aimsun Next microscopic traffic simulation. The effects of different CACC parameters and CLC on the average speed and acceleration distributions as the measures of delay and riding comfort, respectively, were evaluated. It was found that (1) lower sensitivity to the lead vehicle reduced the merging time, (2) shorter time gaps between autonomous vehicles and between platoons decreased the delay, and (3) longer time gaps reduced the delay at higher percentage of ATs. These results demonstrate that the delay of AC–AT mixed traffic at a freeway merging area can be reduced and riding comfort can be increased by adjusting CACC parameters. [ABSTRACT FROM AUTHOR]

This study develops a differential variable speed limit (DVSL) which assigns different speed limits for car and truck, and varies speed limits based on traffic conditions. The proposed DVSL algorithm changes speed limits in real time based on truck percentage and occupancy immediately upstream of the ramp and the average speed of the control road sections upstream of the ramp. DVSL algorithm also considers spatial coordination of speeds, which gradually changes the speed limits in successive road sections upstreamof the ramp when the severe congestion occurs. The study tested the impacts of DVSL and three other speed limit strategies on delay and safety for a section of the Gardiner Expressway in Toronto, Canada using the VISSIM traffic simulation model. The other strategies are 1) uniform speed limit (USL), 2) differential speed limit for car and truck (DSL), and 3) USL & DSL (U&D) e i.e., USL at low truck percentage and DSL at high truck percentage. It was found that DVSL showed the lowest delays for both car and truck among the four strategies. This is mainly because DVSL increased the spacing between vehicles in the right lane upstream of the on-ramp and facilitated vehicles' merging into the mainline freeway. It was also found that DVSL showed the lowest likelihood of rear-end crash between the lead and following vehicles among the four strategies. This study demonstrates that the proposed DVSL algorithm can better control car and truck speeds to reduce delay and improve safety of car-truck mixed traffic flow on freeways. [ABSTRACT FROM AUTHOR]

This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver’s reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U.S.A. It was found that the CPI was generally higher for the following heavy vehicle than the following car due to heavy vehicle’s lower braking capability. This study also validates the CPI using the simulated traffic data which replicate the observed traffic conditions a few minutes before the crash time upstream and downstream of the crash locations. The observed data were obtained from crash records and loop detectors on a section of the Gardiner Expressway in Toronto, Canada. The result shows that the values of CPI were consistently higher during the traffic conditions immediately before the crash time (crash case) than the normal traffic conditions (non-crash case). This demonstrates that the CPI can be used to capture rear-end collision risk during car-following maneuver on freeways. The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors. [ABSTRACT FROM AUTHOR]