Andreas Freymann, Emanuel Reichsoellner, Damir Ravlija, Ingo Trautwein, Mirko Sonntag
{"title":"Integrating Topographical Map Information in SUMO to Simulate Realistic Micromobility Trips in Hilly and Steep Terrains","authors":"Andreas Freymann, Emanuel Reichsoellner, Damir Ravlija, Ingo Trautwein, Mirko Sonntag","doi":"10.52825/scp.v5i.1131","DOIUrl":null,"url":null,"abstract":"Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrain lies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a route in the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SUMO Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52825/scp.v5i.1131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrain lies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a route in the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle.
如今,作为传统汽车的替代品,共享微型交通已成为城市的一种趋势,尤其是在短途旅行中。它在减少汽车数量方面也发挥了重要作用,从而减少了空气污染和交通拥堵。然而,共享微型流动性受到地形特征的影响。如果站点之间的地形陡峭,车队运营区域内的海拔高度变化会导致微型交通站点的使用不平衡。这也会影响电动微型交通车辆(如电动自行车和电动滑板车)的能耗。因此,要模拟微型交通牵引电池的充电状态(SOC),就必须在模拟模型中加入海拔数据。本文提出了城市移动性仿真(SUMO)的工作流程,包括具体实施和验证的几个步骤,以准备和定义带有微型移动站的仿真模型,并使用 REST API 集成高程数据。在德国斯图加特丘陵地带的一条路线上,对海拔高度和自行车站点数据的整合进行了验证。通过记录能源消耗的变化和驱动的高度差,对有海拔数据和无海拔数据的微移动出行进行了模拟比较。建议的工作流程为更复杂的使用案例提供了基础,例如分析微型交通的业务领域、改进车辆的性能、提高车辆的安全性和可靠性。