The Impact of Automated Vehicles on Traffic Flow and Road Capacity on Urban Road Networks

Abstract

Automated vehicles (AVs) are believed to have great potential to improve the traffic capacity and efficiency of the current transport systems. Despite positive findings of the impact of AVs on traffic flow and potential road capacity increase for highways, few studies have been performed regarding the impact of AVs on urban roads. Moreover, studies considering traffic volume increase with a mixture of AVs and human-driven vehicles (HDVs) have rarely been conducted. Therefore, this study investigated the impact of gradual increments of AV penetration and traffic volume on urban roads. The study adopted a microsimulation approach using VISSIM with a Wiedmann 74 model for car-following behavior. Parameters for AVs were set at the SAE level 4 of automation. A real road network was chosen for the simulation having 13 intersections in a total distance of 4.5 km. The road network had various numbers of lanes from a single lane to five lanes in one direction. The network consists of a main arterial road and a parallel road serving nearby commercial and residential blocks. In total, 36 scenarios were investigated by a combination of AV penetrations and an increase in traffic volumes. The study found that, as AV penetration increased, traffic flow also improved, with a reduction of the average delay time of up to 31%. Also, as expected, links with three or four lanes had a more significant impact on the delay. In terms of road capacity increase, when the penetration of AVs was saturated at 100%, the road network could accommodate 40% more traffic.

How PTV Vissim Has Been Calibrated for the Simulation of Automated Vehicles in Literature?

“…In 2020, He et al reviewed related works in the literature and calibrated the values to 0 and 500, respectively, for minimum and maximum values in simulating CAVs in a freeway weaving segment [25]. In 2021, Park et al also adjusted the minimum value to 0 and the maximum to 500 in simulating AAVs in an urban road environment with different traffic conditions and geometric configurations, including single lane, multilane, and intersections [30]. Furthermore, Giuffrè et al [55] and Severino et al [56] calibrated the minimum and maximum values to 20 and 200, respectively, for the simulation of CAVs in urban road settings with roundabouts.…”

“…Likewise, Rossen adopted respective minimum and maximum values of 150 and 200 for AAVs and 5000 and 5000 for CAVs from related works in the literature to assess the impacts of these vehicles on freeway traffic consisting of merging, diverging, weaving, and straight segments [27]. Furthermore, Park et al set the minimum value to 0 and the maximum to 500 for AAVs in urban road networks [30].…”

“…Asadi et al assumed a value ranging from 0.70 for caution CAVs to 0.20 for aggressive CAVs in urban road environments [52]. In 2021, Park et al [30] set the value of MinHW to 0.20 for AAVs in urban road environment, while Lee et al [44] considered the same value for AAVs in merging freeway segments. Recently in 2022, Postigo et al [48] considered a value in the range of 1.0 to 0.5 for AAVs in motorways.…”

“…Whereas, some studies assessed the impacts of AVs on a large road network. Even though the majority of works in the literature looks for fully AVs, some studies ( [29,30]) considered level 4 AVs.…”

Macroscopic Experiments on Coexistence of Autonomous Vehicle Behavior on Various Heterogeneous Traffic Conditions

“…With the presence of massive autonomous mobility on demand, it has been found that journeys previously done by private car can be replaced by shared AVs; the modal share of walking, public transportation, and bicycle is also expanding [17,18]. Overall, AVs are thought to have a lot of potential for improving the capacity, traffic flow stability, and efficiency of today's transportation networks [19,20].…”