Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

Schweizer, Joerg; Poliziani, Cristian; Rupi, Federico; Morgano, Davide; Magi, Mattia

doi:10.3390/ijgi10030165

Cited by 32 publications

(33 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another useful tool to make things easier is sumopy, a free open-source python library (https://github.com/schwoz/sumopy) gathering all the steps required to build a sumo simulation in a very comprehensive Graphic User Interface (Schweizer, 2013). Sumopy was used to build the realworld scenario of the city of Bologna (Schweizer et al, 2021).…”

Section: Netconvert: Converting the Osm Map Into The Xml Format Input...mentioning

confidence: 99%

Building a real-world traffic micro-simulation scenario from scratch with SUMO

Clemente¹

2022

SUMO Conf Proc

View full text Add to dashboard Cite

Simulation of Urban Mobility (SUMO) is a powerful traffic simulation program which can work at different scales, from sub-microscopic to macroscopic. Depending on the available input dataset, it is possible to build lots of different configurations, changing routing and car-follow algorithms, and many parameters. Building a basic SUMO scenario is a multi-step activity involving the followings: preparing the transportation network, traffic definition, setting a routing algorithm, and running the simulation. The aim of the present work is to show a detailed real case study explaining how to build a complete scenario and run simulations, starting from the preparation of the network from Open Street Map. The last part of the present paper is about how to use the SUMO output files with MongoDB in order to keep track of significant information resulting from each simulation.

show abstract

Section: Netconvert: Converting the Osm Map Into The Xml Format Input...mentioning

confidence: 99%

Building a real-world traffic micro-simulation scenario from scratch with SUMO

Clemente¹

2022

SUMO Conf Proc

View full text Add to dashboard Cite

show abstract

“…The used validated microsimulation model for the medium size city of Bologna, Italy, with approximately 500,000 inhabitants is explained in detail in [9]. The entire demand has been created through the tool SUMOPy [8] while the simulation itself is performed by the SUMO simulator [1].…”

Section: The Microsimulation Scenariomentioning

confidence: 99%

“…Micro-simulating realistic, large scale urban areas is challenging, when considering all modes of transport, including pedestrians and public transport [9]. Nevertheless, the performance assessment of connected, autonomous vehicles (CAVs) necessitates a microscopic simulation approach, especially when it comes to platooning.…”

Section: Introductionmentioning

confidence: 99%

Simulating platooned connected autonomous vehicle in a large scale urban scenario

Schweizer¹,

Poliziani²,

Rupi³

2022

SUMO Conf Proc

Self Cite

View full text Add to dashboard Cite

This article is concerned with the performance evaluation of connected, autonomous vehicles (CAVs) in a realistic large-scale microsimulation scenario. In particular the question is: how much could a high diffusion of CAVs possibly change (1)~the average travel speeds (2)~the trip times of all traffic participants, including pedestrians, and (3)~the energy/fuel consumption? For this purpose, admittedly favourable assumptions are made: a 100\% diffusion of platooning-capable CAVs as substitution for private cars as well as a high maximum speed of platooned vehiclesin order to enable platoon formation. The morning rush hour scenario of the metropolitan area of Bologna, Italy has been selected for assessment. This scenario, which has been created and validated in previous works, represents an activity based demand model with travel plans for individual citizens, including all relevant transport modes. The microsimulation is performed by means of the SUMO simulator. The entire demand has been generated with the SUMOPy tool. For the platooning of CAVs, SUMO's SIMPLA module has been used, which controlls the vehicles via the interactive TRACI API. Results show an increased speed and reduced travel time for CAV vehicles, with respect to human driven cars, in particular in the periphery and less in the center with a dense road network. However, the reason for improved speeds and travel times is predominantly the higher maximum speed allowed for vehicles trying to catch up and join a platoon. Furthermore these higher speed would also be resposible for an increase in fuel consumption of approximately 5\%. In conclusion, CAVs alone are unlikely to reduce congestion in an urban area. To make the platooning concept work, additional technology and infrastructure is required in order to merge platoons effectively at freeways and at traffic lights. The latter could be simulated with GLOSA.

show abstract

“…Thus, this expanded spatial scope allows capturing the complete traffic demand in the denser part of the urban area of Barcelona. Differently than in previously published microsimulation scenarios, where the network size and complexity in extended urban regions are frequently cropped or topologically simplified [50,51], in this research, the extended metropolitan region (RMB) is initially simulated with full detail to provide the complete spatial distribution of trips within the core area. This core area (see Figure 2b) is not based on administrative divisions but on the functional and typological characterization defined by the main ring roads of the city (i.e., Ronda Dalt and Ronda Litoral, hence its name, Rondes).…”

Section: Geographical Scopementioning

confidence: 99%

Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

Sánchez-Vaquerizo

2021

IJGI

View full text Add to dashboard Cite

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

show abstract

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

Cited by 32 publications

References 54 publications

Building a real-world traffic micro-simulation scenario from scratch with SUMO

Building a real-world traffic micro-simulation scenario from scratch with SUMO

Simulating platooned connected autonomous vehicle in a large scale urban scenario

Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

Contact Info

Product

Resources

About