Multiagent Spatial Simulation of Autonomous Taxis for Urban Commute: Travel Economics and Environmental Impacts

Abstract

With the likelihood of autonomous vehicle technologies in public transport and taxi systems prior to privately-owned vehicles increasing, their actual impact on commuting in realworld road networks is insufficiently studied. In this study, an agent-based model is developed to simulate how commuters travel by autonomous taxis (aTaxis) in real-world road networks. The model evaluates the travel costs and environmental implications of substituting conventional personal vehicle travel with aTaxi travel. The proposed model is applied to the City of Ann Arbor, MI to demonstrate the effectiveness of aTaxis. Our results indicate that to meet daily commute demand with wait times less than 3 minutes, the optimized autonomous taxi fleet size is only 20% of the conventional solo-commuting personal car fleet. The commuting cost decreases by 38%, and daily vehicle utilization increases from 14 minutes to 92 minutes. In case of utilizing internal combustion engine aTaxis, energy consumption, GHG emissions, and SO2 emissions are respectively 16%, 25%, and 10% higher than conventional solo commuting, mainly due to unoccupied repositioning between trips. Given the emission intensity of the local electricity grid, the environmental impacts of electric aTaxis do not show significant improvement over conventional vehicles.

A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

“…Studies showed that one SAV could feasibly replace anywhere from 5 to 14 private vehicles. 26,29,31,88,89 The replacement rate of SAEVs depends on battery capacity and charger availability. 33,87 SAEVs have lower replacement rates than SAVs because SAEVs need to be charged, a process that takes longer than conventional refueling.…”

Impact of the Electric Mobility Implementation on the Greenhouse Gases Production in Central European Countries

“…Unlike a lot of previously published research studies only evaluating GHG production by the operation of electric vehicles from a regional perspective [45][46][47], some studies also take into account GHG production from the perspective of electricity generation [48,49]. The submitted manuscript takes into account the share of the primary sources in electricity production and the efficiency of electricity production and distribution as well.…”

Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound

“…While recent research on the energy use impacts of vehicle automation does consider the impact of time cost changes (for example, Wadud et al [5]), it tends to borrow fuel and time cost elasticities that are estimated elsewhere, in isolation from each other, and without the aim of developing CAV-specific predictions. Most studies focus on how changes in mobility -especially changes in the vehicle-level energy efficiency of CAVsaffect energy use, holding travel demand constant (for instance, [21][22][23][24]). The assumption of fixed demand almost certainly leads to overestimation of the environmental benefits of this technology [2].…”