On November 3, we all went through the familiar ritual of changing our clocks back an hour at the end of daylight saving time. Originally introduced to reduce energy consumption during World War I, DST (which is when clocks are ahead an hour—when it gets darker later—during the summer) potentially affects things far beyond how light it is when we get up. TV-watching patterns, crime rates, and our drinking habits could all be altered by this twice-annual change.
A popular question that arises regarding DST is how it affects driver safety. It’s an interesting question, to be sure—but less interesting for me and many other city dwellers, who rely on walking, biking, or other forms of transportation to get home. Instead, as I sit looking out of my offices' windows at the dying evening light, I wonder if, rather than affecting how we go home, DST affects when we go home. Or, more generally, do commute times shift because people want to leave home after sunrise and get back before sunset?
Commuting on Capital Bikeshare
Because DST would alter commute times by only minutes, answering this question requires data with detailed departure times. Though major toll roads and subways often provide aggregated ridership data, there aren’t any datasets that include the precise time of every passenger journey (that I know of anyway).
Fortunately, Capital Bikeshare, Washington DC’s bikeshare program, does provide such data. The program, which allows its customers to rent bikes from one self-serve station and leave it at other, has grown to nearly 300,000 rides per month. Over 100,000 of these trips are likely commutes: They occurred during commuting hours on weekdays, and are taken by customers who have an annual subscription to the bikeshare service.
ill steal ur honey like i share ur bike
To see if commute times are affected by DST, I looked at how commutes are distributed during peak commuting hours in the week before and the week after changes to DST. (There are six such instances in the Captial Bikeshare data: The start of DST in 2011, 2012, and 2013, and the end of DST in 2010, 2011, and 2012). To correct for the variance in the total number of trips taken during these periods, the distributions are shown as the percent of trips taken during the morning or evening commute rather than the absolute number. If the hypothesis is correct, a larger share of commutes should be occurring later during DST.
Over the twelve weeks around these six shifts, it seems that people do—if only slightly—alter their commute toward daylight hours. You can see from the distributions below that commute times are later during DST, when it gets darker later.
People commute a bit later after DST starts…
…and a bit earlier after it ends
As an interesting aside, commutes are much more concentrated in the mornings. Maybe people feel pressure to get to work, maybe most variability in hours worked comes from staying late rather than leaving early, or maybe people are out at one of DC’s 528 happy hours (why they didn’t find ten more and make it 538, I’ll never know). Perhaps a topic for another day.
Breaking down the distributions by the day of the week reveals that almost all of the shifts come from Mondays and Fridays, particularly the Friday afternoon after DST starts and the Monday morning after it ends. This makes some sense: people are able to stay later on Friday in the spring knowing they can still enjoy some sunlight once they leave, and the unfamiliar light on Monday morning might jar us awake in the fall. (Interestingly, this bias on Mondays and Fridays only occurs on the first Monday and Friday. If the analysis is extended to include the three Mondays and Fridays around the switch, the effect disappears, suggesting we fall back into old habits.)
A Few Problems
Before we conclude that DST is all a con to get us to come to work earlier and stay later, it’s worth noting a few potential problems with this conclusion.
First, the shifts highlighted above could be caused by factors other than the time change. People could choose to ride to work at different times because of the changing seasons, because of major political events during the sample period (DST ends around election day in November), because of bad weather on any given day, or because of other factors that aren’t known.
To correct for a few obvious external events that severely limited Bikeshare use, several days were excluded from the analysis. October 29 and 30, 2012, are excluded because Capital Bikeshare was forced to shut down during Hurricane Sandy. March 6, 2013, was excluded because of a snowstorm, and November 4, 2010 and March 10, 2011, were excluded for unknown reasons (there were exceptionally few trips those days). Nevertheless, without being able to control for these and all the other factors mentioned above, we can’t say for certain if DST caused the shift.
Second, because our analysis is limited to only bike commuters, the apparent shift in commute times could actually represent a move to different methods of commuting. Commuters who have to work late may decide to drive to work or take the Metro when DST ends so that they don’t have to bike in the dark.
There’s some evidence that this is true. As the distribution of trips by count rather than as a percentage of riders shows, there a lot more evening riders during DST (the number of morning riders isn’t as affected). This could suggest that during DST, rather than leaving later, workers who are already leaving late choose to bike rather than look for other forms of transportation.
Who doesn’t want to ride a shiny red bicycle in the sun?
Finally, bike commuters—especially those using Capital Bikeshare—aren’t a representative sample of all commuters. Capital Bikeshare’s riders are younger, more white, more male, and more educated than DC’s general population. Moreover, its riders live within biking distance of their work, which excludes the massive commuter population that lives outside of DC.
Given the demographic using Capital Bikeshare, the conclusion above might need to be adjusted. Salaried workers who don’t have to clock in and have the flexibility to come in late may change their commute to times when it’s more pleasant. For workers who don’t have that luxury, we probably still can’t say.
Bike trip data was provided by Capital Bikeshare. I combined the quarterly data into a single table, which was analyzing using a PostgreSQL database. Charts were created in Excel. The SQL queries and charts can all be found in this Github folder. Though the large data table is too big to upload to Github, I’ll be happy to share it.
We're always looking for new datasets and cool problems to write about. If you have questions that you think could be answered with data — on topics ranging from Obamacare to how startups are affected by different types of investors and drug prices—we'd love to hear new ideas. Feel free to reach out at firstname.lastname@example.org.
Mnamo Novemba 3, kila mtu alishiriki katika ibada ya kugeuza saa za nyumbani kwa saa moja. Wakati wa majira ya joto, wakati wa majira ya joto, wakati wa saa ni mbele ya saa moja, wakati inakuwa giza baadaye, wakati wa majira ya joto, inaweza kuathiri mambo mbali zaidi ya jinsi mwanga ni wakati sisi kupata juu. Mfano wa kutazama televisheni, kiwango cha uhalifu, na tabia zetu za kunywa pombe, zote zinaweza kubadilishwa na mabadiliko haya ya mara mbili kwa mwaka. Swali linalojulikana sana kuhusu saa za kiangazi ni jinsi inavyoathiri usalama wa dereva. Ni swali la kuvutia, kwa hakika - lakini lisilo la kuvutia kwangu na kwa wakazi wengine wengi wa jiji, ambao hutegemea kutembea, kuendesha baiskeli, au aina nyingine za usafiri ili kufika nyumbani. "Kama mimi kukaa kuangalia nje ya madirisha ya ofisi yangu katika giza jioni mwanga, mimi ajabu kama, badala ya kuathiri jinsi sisi kwenda nyumbani, DST huathiri wakati sisi kwenda nyumbani. """ Au, kwa ujumla zaidi, je, nyakati za kusafiri hubadilika kwa sababu watu wanataka kuondoka nyumbani baada ya jua kuchomoza na kurudi kabla ya jua kutua? Kwa sababu DST ingebadilisha nyakati za kusafiri kwa dakika tu, kujibu swali hili inahitaji data na nyakati za kuondoka kwa undani. Ingawa barabara kuu za kulipa na reli za chini ya ardhi mara nyingi hutoa data ya jumla ya abiria, hakuna seti yoyote ya data ambayo inajumuisha wakati sahihi wa kila safari ya abiria (ambayo najua kwa vyovyote vile). Kwa bahati nzuri, Capital Bikeshare, programu ya kugawana baiskeli ya Washington DC, hutoa data kama hizo. Programu hiyo, ambayo inaruhusu wateja wake kukodisha baiskeli kutoka kituo kimoja cha kujitumikia na kuiacha kwenye kingine, imekua hadi karibu safari 300,000 kwa mwezi. Zaidi ya safari 100,000 za usafiri ni uwezekano wa kusafiri: Zilitokea wakati wa masaa ya kusafiri siku za wiki, na huchukuliwa na wateja ambao wana usajili wa kila mwaka wa huduma ya bikeshare. Ili kuona kama nyakati za kusafiri zinaathiriwa na DST, nilitazama jinsi usafiri unavyogawanywa wakati wa masaa ya kilele ya kusafiri katika wiki kabla na wiki baada ya mabadiliko ya DST. Katika data ya Capital Bikeshare, kuna matukio sita: mwanzo wa DST katika 2011, 2012, na 2013, na mwisho wa DST katika 2010, 2011 na 2012. Ili kurekebisha kwa ajili ya kutofautiana katika idadi ya jumla ya safari zilizofanywa wakati wa vipindi hivi, usambazaji ni kuonyeshwa kama asilimia ya safari zilizofanywa wakati wa asubuhi au jioni kusafiri badala ya idadi kamili. Ikiwa nadharia ni sahihi, sehemu kubwa ya kusafiri inapaswa kutokea baadaye wakati wa DST. Zaidi ya wiki kumi na mbili karibu na hizi zamu sita, inaonekana kwamba watu kufanya <unk>kama tu kidogo<unk> kubadilisha safari yao kuelekea saa za mchana. Kwa mfano, katika sehemu ya chini ya picha, unaweza kuona kwamba wakati wa kusafiri ni baadaye wakati wa DST, wakati inakuwa giza baadaye. Watu kusafiri kidogo baadaye baada ya DST huanza......na kidogo mapema baada ya kumalizika Kama upande kuvutia, kusafiri ni zaidi ya umakini katika asubuhi. Labda watu wanahisi shinikizo la kwenda kazini, labda mabadiliko mengi katika masaa ya kazi yanatokana na kukaa marehemu badala ya kuondoka mapema, au labda watu wako nje katika moja ya masaa ya furaha ya DC 528 (kwa nini hawakupata kumi zaidi na kuifanya 538, sitakujua). Labda ni habari ya siku nyingine. Kugawanya usambazaji kwa siku ya wiki inaonyesha kwamba karibu mabadiliko yote yanatokana na Jumatatu na Ijumaa, hasa Ijumaa alasiri baada ya DST kuanza na Jumatatu asubuhi baada ya kumaliza. Hii ina maana: watu wanaweza kukaa baadaye Ijumaa katika majira ya kuchipua, wakijua bado wanaweza kufurahia mwanga wa jua mara tu wanapoondoka, na mwanga usiojulikana Jumatatu asubuhi unaweza kutufanya tuamke wakati wa kuanguka. (Inavutia, upendeleo huu Jumatatu na Ijumaa hutokea tu Jumatatu ya kwanza na Ijumaa. Ikiwa uchambuzi huo unaongezwa kujumuisha Jumatatu na Ijumaa tatu karibu na kubadili, athari hiyo inapotea, ikidokeza kwamba tunarudi kwenye tabia za zamani.) Kabla hatujafikia mkataa kwamba DST ni con yote ya kutufanya tuje kufanya kazi mapema na kukaa baadaye, ni muhimu kuzingatia matatizo machache ya uwezekano na hitimisho hili. Kwanza, mabadiliko yaliyotajwa hapo juu yangeweza kusababishwa na mambo mengine mbali na mabadiliko ya wakati. Watu wanaweza kuchagua kuendesha gari kwenda kazini kwa nyakati tofauti kwa sababu ya mabadiliko ya misimu, kwa sababu ya matukio makubwa ya kisiasa wakati wa kipindi cha sampuli (DST inaisha karibu na siku ya uchaguzi mnamo Novemba), kwa sababu ya hali mbaya ya hewa siku yoyote, au kwa sababu ya sababu nyingine ambazo hazijulikani. Ili kurekebisha kwa matukio machache ya wazi ya nje ambayo yalipunguza sana matumizi ya Bikeshare, siku kadhaa ziliondolewa katika uchambuzi. Oktoba 29, 2012 - Mlipuko wa Kimbunga Sandy ulisababisha kampuni ya Capital Bikeshare kufungwa kwa sababu ya dhoruba ya Sandy. Tarehe 6 Machi 2013 ilifutwa kwa sababu ya dhoruba ya theluji, na tarehe 4 Novemba 2010 na 10 Machi 2011 zilifutwa kwa sababu zisizojulikana (kulikuwa na safari chache sana siku hizo). Hata hivyo, bila kuwa na uwezo wa kudhibiti kwa ajili ya haya na sababu nyingine zote zilizotajwa hapo juu, hatuwezi kusema kwa uhakika kama DST kusababisha mabadiliko. Kwa sababu uchambuzi wetu umepunguzwa kwa wasafiri wa baiskeli tu, mabadiliko ya dhahiri katika nyakati za kusafiri kwa kweli yanaweza kuwakilisha hatua ya njia tofauti za kusafiri. Wasafiri ambao wanahitaji kufanya kazi marehemu wanaweza kuamua kuendesha gari kwenda kazini au kuchukua Metro wakati DST inamalizika ili wasilazimike kuendesha baiskeli gizani. Kuna uthibitisho fulani kwamba hilo ni kweli. Kama usambazaji wa safari kwa kuhesabu badala ya kama asilimia ya wapanda farasi inaonyesha, kuna mengi zaidi ya wapanda farasi jioni wakati wa DST (idadi ya wapanda farasi asubuhi si kama kuathiriwa). Hii inaweza kupendekeza kwamba wakati wa DST, badala ya kuondoka baadaye, wafanyakazi ambao tayari wanaondoka marehemu huchagua kuendesha baiskeli badala ya kutafuta aina nyingine za usafiri. Ni nani asiyetaka kuendesha baiskeli nyekundu yenye kung'aa kwenye jua? Hatimaye, baiskeli commuters <unk> hasa wale kutumia Capital Bikeshare <unk> si sampuli mwakilishi wa commuters wote. Capital Bikeshare's wapanda ni vijana, zaidi nyeupe, zaidi ya kiume, na zaidi ya elimu kuliko DC's idadi ya watu kwa ujumla. Zaidi ya hayo, wapanda baiskeli wake wanaishi katika umbali wa baiskeli kutoka kwa kazi yao, ambayo inazuia idadi kubwa ya wasafiri wanaoishi nje ya DC. Kutokana na idadi ya watu kutumia Capital Bikeshare, hitimisho hapo juu inaweza haja ya kurekebishwa. Wafanyakazi wanaolipwa mshahara ambao hawana wakati wa kuingia na wana kubadilika-badilika kuja marehemu wanaweza kubadilisha safari yao ya kwenda kazini kwa nyakati ambazo ni nzuri zaidi. Kwa wafanyakazi ambao hawana anasa hiyo, labda bado hatuwezi kusema. Takwimu za safari za baiskeli zilitolewa na Capital Bikeshare. Kwa mfano, data ya kila robo mwaka inaweza kuunganishwa katika meza moja, ambayo inaweza kuchambuliwa kwa kutumia database ya PostgreSQL. Chati ziliundwa katika Excel. SQL maswali na chati zote zinaweza kupatikana katika hii Github folda. Ingawa meza kubwa ya data ni kubwa sana kupakia kwenye Github, nitakuwa na furaha kuishiriki. Sisi ni daima kuangalia kwa seti mpya ya data na matatizo baridi kuandika kuhusu. Kama una maswali ambayo unafikiri inaweza kujibiwa na data <unk> juu ya mada kuanzia Obamacare kwa jinsi startups ni kuathiriwa na aina tofauti ya wawekezaji na bei ya dawa <unk> tungependa kusikia mawazo mapya. Jifunze zaidi kuhusu jina la kwanza, jina la mwisho, na jina la mwisho la kwanza.