Autonomous mobility on demand in SimMobility: Case study of the central business district in Singapore (original) (raw)
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Multi-agent simulation for planning and designing new shared mobility services
Research in Transportation Economics, 2019
Limiting private cars' use while promoting sustainable modes of transport is one of the main challenges of urban transport planning. In this context, characterized by scarce resources and increasing demand for mobility, Demand Responsive Shared Transport (DRST) services can bridge the gap between shared low-quality public transport and unsustainable individual private transport. Taking advantage of Information and Communication Technologies (ICT), they can supply transport solutions ranging from flexible transit to ride sharing services, providing real-time "on demand" mobility through fleets of vehicles shared by different passengers. The optimal design of a DRST service requires a trade-off among efficiency (from the operators' point of view), service quality (from the users' point of view) and sustainability (from the community's point of view). In this paper, an agentbased model (ABM) fed with GIS data is used to explore different system configurations of a specific type of DRST service, i.e. flexible transit, and to estimate the transport demand and supply variables that make the service feasible and convenient. The model reproduces a mixed fixed/flexible route transit service with different fleet size and vehicle capacity in the city of Ragusa (Italy) with the aim to: (i) make a first test of the ABM model with GIS-based demand and road network models; (ii) explore different vehicle dispatching strategies; (iii) find appropriate indicators to monitor the service quality and efficiency. Simulation results show the impact of fleet composition and route choice strategy on the system performance. In particular, they show an optimal range of operating vehicles that minimizes a total unit cost indicator, accounting both for passenger travel time and vehicle operation cost. By reproducing the microinteraction between demand and supply agents (i.e. passengers and vehicles), it is possible to monitor the macroscopic behaviour of the system, and derive useful suggestions for the correct planning, management and optimization of DRST services.
Intelligent Transport Systems for Everyone’s Mobility, 2019
Shared Autonomous Mobility on-Demand (AMoD) systems are prescribed by many as a solution to tackle congestion. In these systems, customers are serviced on demand by a fleet of shared Autonomous Vehicles (AV). The main aim of this novel mobility system is meeting travel aspirations of people while reducing the number of passenger cars on roads. Our study explores the relationship between fleet size and induced Vehicle-Kilometres Travelled (VKT) in AMoD systems in the context of a case study in Melbourne, Australia. To achieve this, an agent based simulation model was developed to investigate this relationship through scenario analysis. Our results show that fleets of on-demand shared AVs have the potential to reduce the number of vehicles by 79% on our roads. These systems, however, lead to 61% more VKT within the transport network. This finding indicates that the vast majority of literature is overoptimistic about the potential of AMoD systems for mitigating congestion. This paper also reports on an investigation into the effects of travel demand pattern on the performance of these systems, and shows that the impact of this phenomenon on their efficiency is not trivial. Further, our simulation results reveal a quadratic relationship between AMoD fleet size and induced VKT in the system, which holds for all travel demand patterns.
Transportation Research Part A: Policy and Practice, 2020
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Sustainable Cities and Society, 2015
Although recent studies of Shared Autonomous Vehicles (SAVs) have explored the economic costs and environmental impacts of this technology, little is known about how SAVs can change urban forms, especially by reducing the demand for parking. This study estimates the potential impact of SAV system on urban parking demand under different system operation scenarios with the help of an agent-based simulation model. The simulation results indicate that we may be able to eliminate up to 90% of parking demand for clients who adopt the system, at a low market penetration rate of 2%. The results also suggest that different SAV operation strategies and client's preferences may lead to different spatial distribution of urban parking demand.
FleetPy: A Modular Open-Source Simulation Tool for Mobility On-Demand Services
Cornell University - arXiv, 2022
The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the opensource agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.
Transportation Research Part A: Policy and Practice
Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Taxi services account for a significant part of the daily trips in most of cities around the world. Due to the fact that most of the taxi markets are regulated by a central authority there is the need for developing tools for understanding the behavior of these markets to policy regulations and supporting decision makers. This paper presents an agent based model for simulating taxi services in urban areas. Taxi models presented in the literature can be grouped into aggregated, equilibrium and simulation models, with the latter having been studied to a lesser extent. Agent simulation is a powerful tool for analyzing such complex problems, where stochastic nature of the variables and spatial distribution hinders the application of aggregated models. The agent-based model is presented together with the behavioral rules of the agents and applied to the city of Barcelona, obtaining the optimum number of vehicles and performance indicators of the provision of taxi services under the dispatching operation mode.
2015
Taxi services account for a significant part of the daily trips in most of cities around the world. Due to the fact that most of the taxi markets are regulated by a central authority there is the need for developing tools for understanding the behavior of these markets to policy regulations and supporting decision makers. This paper presents an agent based model for simulating taxi services in urban areas. Taxi models presented in the literature can be grouped into aggregated, equilibrium and simulation models, with the latter having been studied to a lesser extent. Agent simulation is a powerful tool for analyzing such complex problems, where stochastic nature of the variables and spatial distribution hinders the application of aggregated models. The agent-based model is presented together with the behavioral rules of the agents and applied to the city of Barcelona, obtaining the optimum number of vehicles and performance indicators of the provision of taxi services under the dispa...
Lecture Notes in Computer Science, 2017
Over the last decade, numerous carsharing systems have been deployed around the world. Yet, despite this success, net profit margins of carsharing services are still insufficient due to a complicated demand modelling and high expenses for fleet redistribution. To address these problems, different carsharing paradigms (e.g., one-way versus free floating), operational models and pricing schemes have been proposed. In order to assess the effectiveness of these models and strategies, realistic simulation tools are needed that account for the main parameters that affect system performance. To this end, we have developed a generic software framework that caters for several flavours of carsharing services, such as hybrid systems where both one-way and free floating modes coexist. In addition, the proposed framework accounts for electric vehicles, power sharing capabilities, smart charging policies, booking services, fleet redistribution and membership management. Our tool is based on MATSim, an open-source platform for multi-agent traffic simulation. To validate our simulation model we will use a case study based on data from the 2006 Lyon conurbation household travel survey, which provides information about more than three million trips.
A smoother ride [mobility as a service]
Engineering & Technology, 2016
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.