Abstract
Traffic congestion has become increasingly prevalent in many urban areas, and researchers are continuously looking into new ways to resolve this pertinent issue. Autonomous vehicles are one of the technologies expected to revolutionize transportation systems. To this very day, there are limited studies focused on the impact of autonomous vehicles in heterogeneous traffic flow in terms of different driving modes (manual and self-driving). Autonomous vehicles in the near future will be running parallel with manual vehicles, and drivers will have different characteristics and attributes. Previous studies that have focused on the impact of autonomous vehicles in these conditions are scarce. This paper proposes a new cellular automata model to address this issue, where different autonomous vehicles (cars and buses) and manual vehicles (cars and buses) are compared in terms of fundamental traffic parameters. Manual cars are further divided into subcategories on the basis of age groups and gender. Each category has its own distinct attributes, which make it different from the others. This is done in order to obtain a simulation as close as possible to a real-world scenario. Furthermore, different lane-changing behavior patterns have been modeled for autonomous and manual vehicles. Subsequently, different scenarios with different compositions are simulated to investigate the impact of autonomous vehicles on traffic flow in heterogeneous conditions. The results suggest that autonomous vehicles can raise the flow rate of any network considerably despite the running heterogeneous traffic flow.
Highlights
Traffic congestion is one of the major problems in urban areas, causing significant delays during a daily commute
Multiple scenarios with different compositions have been simulated for the purposes of the current Sreussteaainracbhili.tyT2h0e20c, o12m, xpFaOriRsoPnEEsRmRaEdVeIEiWn each scenario are based upon traffic fundamentals diagram11s,oif.2e1., speed, density and flow rate
We have investigated the heterogeneity in traffic by comparing different modes of autonomous vehicles and manual vehicles
Summary
Traffic congestion is one of the major problems in urban areas, causing significant delays during a daily commute. As the research in this field continues, automobiles, as well as technology companies, aim to simulate a completely driverless scenario, where vehicles can drive and navigate themselves on existing roads and interact with the surrounding environment. If successfully implemented, such technologies have the potential to transform the urban transportation network dramatically, as they can be programmed to follow traffic rules, increase the capacity of existing roads by optimizing the use of lanes, react quickly, reduce fuel consumption and emissions. Traffic crashes remain the primary cause of death of Americans between the ages of 15 and 24 [2]
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