Abstract
Mobility as a Service (MaaS) is the integrated and on-demand offering of new mode-sharing transport schemes, such as ride-share, car-share or car-pooling. MaaS schemes may solve some of the most pressing mobility problems in large conurbations like London. However, MaaS schemes pose significant implementation challenges for operators and city authorities alike. With the existing transport and traffic planning tools, even basic questions do not have easy answers: e.g. how many vehicles are needed; how should they be deployed; what infrastructure changes are needed, and what will happen with congestion? This paper reports on the novel integration, through co-simulation of two independent agent-based simulators: MATSim and IMSim. MATSim generates realistic transport demand for a city: allocating travellers to the best mobility option according to their preferences; while IMSim provides a highly realistic operational execution of autonomous and manually driven transport fleets. We show how the simulation tools complement each other to deliver a superior Autonomous Mobility on Demand (AMoD) modelling capability. By combining the two, we can evaluate the impact of diverse AMoD scenarios from different standpoints: from a traveller’s perspective (e.g. satisfaction, service level, etc.); from an operator’s perspectives (e.g. cost, revenue, etc.); and from a city’s perspective (e.g. congestion, significant shifts between transport modes, etc.). The coupled simulation methods have underpinned the extensive MERGE Greenwich project investigation into the challenges of offering ride-share services in autonomous vehicles in the Royal Borough of Greenwich (London, UK) for travellers, service-operators, the city, and vehicle manufacturers.
Highlights
In the second set of experiments we studied the impact of using electric vehicles (EV), replacing the Internal Combustion Engine (ICE) minibus tier with an Electric Vehicles (EV) minibus fleet with the following vehicle specifications:
In this paper we have presented a novel simulation tool that enables the simulation of sophisticated ride-share Autonomous Mobility on Demand (AMoD) services
Coupling simulations has been critical in developing an understanding of the challenges faced by key stakeholders offering and adopting autonomous vehicles (AV) ride-share services in Greenwich, be they travellers, service-operators, the city, or vehicle manufacturers
Summary
Most of the modelling approaches that we discuss below allocate a pre-defined set of trips to a pre-configured AMoD fleet. Fagnant and Kockelman (2015) present a simulation model for a ride-share AMoD system in Austin, Texas They use a MATSim model to estimate network conditions (such as link travel times), together with a simple ride-sharing allocation strategy based on homogenous demand and service levels. To minimise the need for coupled iterations between MATSim and IMSim, an initial estimate for AMoD service demand can be calculated solely by MATSim. The demand estimation is imprecise because MATSim does not know the occupancy of each vehicle or the waiting and detour time for each trip. As part of the MERGE Greenwich project (Addison Lee 2018; MERGE Greenwich 2017) different configurations of the IMSim ride-share model were tested to evaluate the potential viability of alternative future AMoD service-blueprints, from the operator-, city- and traveller- perspectives. The model allowed the delivery a (defined) service levels for each tier: 1. Minibus: 80% of trips served; 500% detour ratio tolerated; 25 min max waiting time
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