I vote for Steve Smith's approach as the best thinking so far. The foundational key is, it seems to me, the ability to tow a second or third bike. With that, the options market could be do-able. -tj
On Mon, Aug 19, 2013 at 4:25 PM, Steve Smith <[email protected]> wrote: > I'm having trouble with the Discuss list, having chimed in several times > but not seeing my "chime" show up. Let's see how FRIAM handles this. > > Unfortunately *everyone* (t)here seems to be ignoring the most obvious > premise... there simply are not enough resources (bicycles and/or parking > locations for them) to meet the transient demand *during* commute hours > *at* specific sources (some subway terminals). The city is trying to make > up for that by re-using bicycles during these peak moments, moving them in > trucks from sink back to source. Trips/riders roughly conserve over a > day, but during the commute, there is a huge disparity. > > Ignoring implementation details (like a good FRIAMer), why not set up an > "options market" for the bicycles... ultimately the price of bicycles at > high-demand (source) nodes would be higher than ones at low demand (sink) > nodes. Entrepreneurs could deliberately seek out reverse commute routes > and make money riding bikes in a "counter-rotation" cycle. Thrifty > commuters might adjust their schedules (nominally early and late) to > minimize the negative spread or even adjust their pickup/drop points... > getting off a subway stop early or late for a "cheaper" pickup and > under/overshooting their destination for a better dropoff price... > > The Network Queuing theory and/or agent models could help entrepreneurs > *find* cycles in the graph... essentially riding upstream for a few hours > every morning (with some short walks between nearby stations) to optimize > the spread. > > Given the article's claim that bikes can tow other bikes, there is an > immediate multiplier on whatever spread there is. Move 3 bikes at once at > a $3 spread and make $9 for a leg... Add PediCabs to the mix and one can > now circulate (carrying passengers downstream and bikes upstream)... > > Could this system be implemented ON TOP of the current system (e.g. > without permission of the city authorities)? Would it be managed by > mobile apps or something simpler? I don't see how they currently manage > membership/utilization... do you insert a card into a rack to retrieve a > bike and then again to return it? Perfect opportunity for a debit/credit > transaction. > > There is a need for some game-theoretic analysis to avoid creating a > situation where entrepreneurs would "game" the system. Show up early to > get a "discount" then "shark" that bike to someone at a higher pickup > rate? Maybe this is a feature... suddenly these sharks become alternative > "parking"... allowing 100 bikes to be available conventionally and maybe > 100 more in the hands of sharks ready to hand off a bike? > > With mobile apps, it seems like the options market could extend to > "doorstep delivery". Put out an "ask" for a bicycle to be at your > doorstep at a certain time for a certain price. Entrepreneurs would pick up > a bike, drop it off, etc. as needed if the price was right. Maybe the > spread would include enough value that some people would ride a bike home > from their subway terminus in the evening knowing they can get a higher > "bid" in the morning at the same location? > > Imagine roving bicycle "trains" acting a lot like cabs... a line of > pedicabs with bicycles in tow queued up at a busy location offering to drop > a bike with you (for a price) then when empty, offer a ride to some > location near a "sink" where more bikes can be picked up for a low "bid" > price? > > I don't know what ever happened to the Apps that purported to let you > "sell" your parking space to someone else as you were leaving and then > "buy" one at the other end from someone else. I don't know if they were > tendered in real $$ or in some kind of credit within the system. The bike > situation is less symmetric (people either need to get to work/home and > can't be moving bikes around for others, though some shift-workers/reverse > commuters might be able to supplement their income this way?). > > > > > one would have to determine the main >>> "sources and sinks" -- the places where riders show up needing >>> transport and where they most often reach their destination, >>> relinquishing the bikes. >>> >> For designing a sharing program, I would immediately think of an agent >> based model layered built on a geospatial database or a graph. The >> database/graph would encode the features of interest in the region, and >> the >> constraints and distances of connectivity between the features. The >> relevant sites could be collected from travel brochures or guides, or >> expected business travel (e.g. restaurant owner runs across town to buy >> small items from cash-n-carry). The distances would come from the >> database >> (a map). >> >> I imagine what would happen is that the users of the bicycles would have >> some itinerary, drawn from some set of known-popular or expected >> attractions and starting locations (e.g. hotels, time shares, etc). I >> expect a person touring all day would have to intend to start early, while >> other folks just zipping across town would have fewer time constraints. >> So >> there could be more or less variance in when they started their trip. >> >> Anyway, define your itineraries and sample from it randomly. Bicycles >> make >> their trips and are deposited and reclaimed. Iterate that process >> hundreds >> or thousands of times and see where the bikes tend to pile up vs. where >> they tend to get exhausted. Prioritize load balancing between those two. >> To close the loop, introduce agents that are the bike movers (pick-up >> trucks) which automatically load balance.. Continue to iterate to make >> sure a steady state is achieved and that exhaustion events are rare. >> >> Or, if the sharing program is already underway, do site-by-site human >> surveillance (or put a GPS/RFID on each bike with the needed >> telemetry/sensors) and directly enter the data into a geospatial database. >> Prioritize the load balancing between the high and low demand regions on >> whatever frequency is needed. Do bikes run out in a day, a week or what? >> A geospatial database makes it easy to calculate those averages over >> different periods and plot 'em. >> >> I haven't looked into the APIs for Google Maps, but one way might be to >> drive that with a robot agent, and scrape/collect the trips from "Get >> directions" pages. That is, don't be concerned with an explicit >> representation of the map in the model, but rather just think of it >> transactionally. "I was at Site A now at Site B", with the elapsed time a >> linear function of the distance of the "Get Directions" guidance of Google >> Maps. >> >> Marcus >> >> ------------------------------**------------------------------**-------- >> myhosting.com - Premium Microsoft® Windows® and Linux web and application >> hosting - >> http://link.myhosting.com/**myhosting<http://link.myhosting.com/myhosting> >> >> >> >> ==============================**============================== >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> to unsubscribe >> http://redfish.com/mailman/**listinfo/friam_redfish.com<http://redfish.com/mailman/listinfo/friam_redfish.com> >> >> > > ==============================**============================== > FRIAM Applied Complexity Group listserv > Meets Fridays 9a-11:30 at cafe at St. John's College > to unsubscribe > http://redfish.com/mailman/**listinfo/friam_redfish.com<http://redfish.com/mailman/listinfo/friam_redfish.com> > -- ========================================== J. T. Johnson Institute for Analytic Journalism -- Santa Fe, NM USA<http://www.analyticjournalism.com/> 505.577.6482(c) 505.473.9646(h) Twitter: jtjohnson http://www.jtjohnson.com [email protected] ==========================================
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