<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><br></div><div>This might be useful:</div><div><div><br></div><div>Enhancing the OSRM Route Engine by Incorporating Real-Time Traffic Data:</div></div><div><a href="https://engineering.purdue.edu/~ychu/ee673/Projects.F12/comp2_rtengine_final.pdf">https://engineering.purdue.edu/~ychu/ee673/Projects.F12/comp2_rtengine_final.pdf</a></div><div><br></div><div><br></div><div>-Emil</div><div><br></div><div><br></div><br><div><div>On Jun 18, 2013, at 18:20 , James Litton <<a href="mailto:litton.james@gmail.com">litton.james@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div dir="ltr"><div>> Hi James,<br></div><div>> </div><div>> the basic Contraction Hierarchies approach is not well suited for</div><div>> dynamic updates unless they remain few before a complete reprocessing is</div>
<div>> done. By now, there exist better algorithms to deal with changing edge</div><div>> costs like "Customizable Route Planning", though they face their own</div><div>> difficulties. You would have to replace the complete algorithmic core of</div>
<div>> OSRM to incorporate these dynamic updates - and introducing dynamic</div><div>> updates that can also cope with addition/deletion of edges is still on</div><div>> another page.</div><div>> </div><div>> Regarding the paper of Veit Batz, there seems to be available a much</div>
<div>> extended journal version as of recently. But using that method you would</div><div>> still have to replace a lot of the OSRM algorithmic core. Also, memory</div><div>> consumption would likely be prohibitive high for the OSM world graph.</div>
<div>> That said time-dependent CH and your dynamic updates are two different</div><div>> problems. The first has average edge weights for each hour (or whatever</div><div>> granularity) of the day but does not allow for changing these weights</div>
<div>> for example when there is a traffic jam due to an accident. Slow traffic</div><div>> due to rush hours can be modelled with time-dependency but not irregular</div><div>> events.</div><div>> </div><div>> By the way, what kind of traffic data do you have? Does this only</div>
<div>> include highways or inner city streets, too? And did you already try to</div><div>> aggregate travel-time functions from your data?</div><div>> </div><div>> best regards</div><div>> </div><div>> Dennis Schieferdecker</div>
<div><br></div><div style="">Thanks, that's helpful. I now have a better understanding of what challenges we would have to overcome with these two approaches.</div><div style=""><br></div><div style="">Our traffic data includes most of the road network that gets significant traffic. So for a major city, it includes the inner city streets, but in the suburbs the coverage is more sparse.</div>
<div style=""><br></div><div style="">We haven't created aggregate travel time functions yet, though we have the historic data to do so. </div><div style=""><br></div><div style="">I will read through the paper you mentioned.</div>
<div style=""><br></div><div style="">Thanks again,</div><div style=""><br></div><div style="">James </div></div>
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