Road network extraction with OSMNx and SUMOPy

7 pages•Published: June 25, 2018

Ali Enes Dingil, Joerg Schweizer, Federico Rupi and Zaneta Stasiskiene

Abstract

The microsimulation of larger cities would be of a considerable gain for urban planning. However, compared with traditional traffic flow assignment models, transport networks suitable for microsimulations require a large amount of data (connectivity, properties, road/rail signaling, etc.). One persistent problem with microsimulation models has been that the creation of large urban networks is very resource consuming. Therefore, it would be of considerable value if the modeling process of microsimulation networks could be largely automated.
SUMO is one of the most versatile, open source mobility simulators with the capability of modeling virtually all urban transport systems, including roads, rail and even water- ways. Openstreetmap (OSM) is an open and feature-rich editable map of the world that is potentially the best data-source for creating micro-simulation networks. But OSM data is incomplete, imprecise and imperfect as it is edited by volunteer laymen, and it has many descriptive, but ambiguous attributes that leave ample room for interpretation.
The present work proposes an alternative method to import OSM data into SUMO with the goal to generate a functioning SUMO microsimulation network that is as close as possible to reality, while overcoming some of the deficiencies buried in OSM data. For this purpose, a recent software package, called OSMNx is used, which generates a directed graph (as networkx graph object) from OSM data, by resolving many topological issues. Both, OSMnx and networkx are Python packages. Within the SUMOPy framework, the following additional software is developed: (i) a converter from networkx graph to a SUMO network; the conversion is performed by processing additional information from the original OSM data; (ii) a realistic traffic light system generator for major intersections.
It is shown that the software converts even complex junctions correctly into a SUMO network. However, the creation of efficient traffic light systems (a prerequisite for simulating a larger urban area) turns out to be problematic as deficient information from OSM is difficult to replace by heuristic methods.

Keyphrases: activity based demand, microsimulation, networkx, osm, osmnx, sumo

In: Evamarie Wießner, Leonhard Lücken, Robert Hilbrich, Yun-Pang Flötteröd, Jakob Erdmann, Laura Bieker-Walz and Michael Behrisch (editors). SUMO 2018- Simulating Autonomous and Intermodal Transport Systems, vol 2, pages 111-117.

Links:	https://easychair.org/publications/paper/Bwmq
	https://doi.org/10.29007/t7pk

BibTeX entry

@inproceedings{SUMO2018:Road_network_extraction_with,
  author    = {Ali Enes Dingil and Joerg Schweizer and Federico Rupi and Zaneta Stasiskiene},
  title     = {Road network extraction with OSMNx and SUMOPy},
  booktitle = {SUMO 2018- Simulating Autonomous and Intermodal Transport Systems},
  editor    = {Evamarie Wießner and Leonhard Lücken and Robert Hilbrich and Yun-Pang Flötteröd and Jakob Erdmann and Laura Bieker-Walz and Michael Behrisch},
  series    = {EPiC Series in Engineering},
  volume    = {2},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/Bwmq},
  doi       = {10.29007/t7pk},
  pages     = {111-117},
  year      = {2018}}

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