Estimating the Effect of Connected and Autonomous vehicles (CAVs) on Capacity and Level of Service at Freeway Merge Segments

The aim of the study was to obtain Capacity adjustment factors and Break points which can be utilized for Highway Capacity Manual (HCM6) methodology in obtaining Level of Service for freeways when Connected and Autonomous Vehicles (CAVs) are present inside the traffic stream. Accordingly, various two-lane heterogeneous flow scenarios were modelled which included variations in free-flow speed and percent of heavy vehicles wherein the possible impact of the CAVs on the current traffic system was analyzed. Each scenario was first calibrated inside VISSIM to replicate the results from HCM6 and later CAVs were introduced in various proportions inside the traffic stream of conventional vehicles to access performance improvements using VISSIM. It was concluded that CAVs do improve system capacity and resulted in longer free-flow phase, which is a direct effect of the increased road capacity. Up to 25% CAV-penetration rate, the road capacity increased gradually and beyond 25%, the growth rate was largely decided by the improved capability of the CAVs compared to conventional vehicles. An improved capability corresponded to a higher capacity growth rate and a higher capacity. CAVs with higher penetration rates also resulted in longer free-flow phases but only a few of the scenarios saw a minor improvement in density, which was due to the assumptions and driving behavior parameters utilized to model driving behavior for different vehicle classes.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2019

Author dc:creator

• Patel, Akshay Dinesh

Advisor dc:contributor.advisor

• Kondyli, Alexandra

• Civil engineering
• Transportation
• Breaking points
• Capacity adjustment factors
• Connected and Autonomous Vehicles
• Density
• Freeway merge segment
• Highway Capacity Manual

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:16842

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/30477