Abstract
The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.
Highlights
Automated Vehicles (AV) will be a huge part of road transportation systems in the future
Thesimulation simulationoutputs outputs and analysis focus on four traffic parameters: the daily vetraveled kilometers, the daily traveled hours,hours, total delay for thefor whole network hicle traveled kilometers, the vehicle daily vehicle traveled total delay the whole netand mean for passenger cars class forclass the entire network
This paper investigates the estimated expected change in traffic operations caused by the spread of AVs at different penetration levels in a network
Summary
Automated Vehicles (AV) will be a huge part of road transportation systems in the future. The shift from conventional vehicles to automated vehicles follows the six automation levels presented in the traffic stream (0, No Automation; 1, Hands on; 2, Hands off; 3, Eyes off; 4, Mind off; 5, Steering wheel optional) [1,2]. It depends on the penetration rate of AVs of the total traffic. AVs will partially reduce or even eliminate human factors from traffic flow, reducing gap for lane changing, headway, reaction time using 360-degree sensors and cameras expected to increase road capacity, leading to lower congestion [3]
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