Chapter 15 Mode Split - KIPDF.COM\nChapter 15 Mode Split\nChapter 15 Mode Split In t h is ch a pt er , t h e t h ir d m odelin g st ep in t h e crim e t r a vel dem a n d m odel is dis cus se d, m ode s plit ...\nAuthor: Baldric Johns\nModal split. Chapter Overview. 9.2 Mode choice. 9.3 Factors influencing the choice of mode. CHAPTER 9. MODAL SPLIT NPTEL May 7, 2007\n15 Status Display Mode\nChapter 5. Active Mode Locking\nChapter Seven: SPLIT-SEX AND PHASE FEEDING\nChapter 15. Chapter 15 Closing the Books INSTRUCTOR S COPY\nChapter 18 FUNDAMENTAL MODE SEQUENTIAL CIRCUITS\nChapter 15 Cognitive Disorders\nChapter 15. Introducing MOSFET\nCHAPTER 15 ENVIRONMENTAL MANAGEMENT\nChapter 15. Alternating Current\nCHAPTER 15 Total Surrender\nChapter 15. Machine Guarding\nchapter 26 vga write mode 3\nChapter 15: Grounding\nCHAPTER 15. CDFI Brochures\nChapter 15 RADIATION SAFETY\nChapter 15. Social Psychology\nChapter 15 Mode Split In t h is ch a pt er , t h e t h ir d m odelin g st ep in t h e crim e t r a vel dem a n d m odel is dis cus se d, m ode s plit . M od e split in volves sepa r a t in g (s plit t in g) t h e pr edict ed t r ip s fr om ea ch or igin zon e t o ea ch dest ina t ion zon e int o dist inct t r a vel m odes (e.g., walk ing, bicycle, dr ivin g, t r a in , bu s). Th is m odel h a s bot h a dva n t a ges a n d dis a dva n t a ges for cr im e a n a lysis . At a t h eor et ical level, it is t h e m ost developed of t h e fou r st a ges sin ce th er e h a s been ext en sive resear ch on t ra vel mode choice. For crime an alysis, on t he oth er ha nd, it r epresents t he ‘wea k est lin k ’ in t h e a n a lysis sin ce t h er e is ver y lit t le a va ila ble in for m a t ion on t r a vel m ode by offen der s. Since res ea r ch er s can n ot int er view t h e gener a l pu blic in order t o docu m en t cr imes com m itt ed by res ponden t s n or , in m ost ca ses, even in t er view offen der s a ft er t h ey h a ve been cau ght , th er e is very lit t le in for m a t ion on t r a vel m ode by offen der s t h a t h a s been collected. 1 Con sequ en t ly, we h a ve t o depen d on t h e exist in g t h eor y of t r a vel m ode ch oice a n d a da pt it in t u it ively t o cr im e da t a . Th e a ppr oa ch is solely t h eor et ica l a n d depen ds on t h e va lidit y of th e exist in g t h eory a n d on t h e in t u it iven ess of guesses. H opefully, in t h e fu t u r e, th er e will be m or e infor m a t ion collect ed t h a t wou ld a llow t h e m odel t o be ca libra t ed a gain st some r ea l da t a . But , for t h e t ime bein g, we a r e lim ited in wh a t ca n be don e.\nTh e o r e t i c a l B a c k g ro u n d Th e t h eor et ical ba ckgr oun d beh in d t h e m ode s plit m odu le is pr es en t ed firs t . Next , t h e specific pr ocedu r es a r e dis cu ssed wit h t h e m odel bein g illu st r a t ed wit h da t a fr om Ba lt im ore Coun t y.\nUtility of Trave l and Mode Choice Th e key a im of m ode choice a n a lysis is t o dist ingu ish t h e t r a vel m ode t h a t t r a veler s (or , in t h e ca se of cr im e, offen der s) u se in t r a velin g bet ween a n or igin loca t ion a n d a dest ina t ion loca t ion . In t h e t r a vel dem a n d m odel, th e ch oice is for t r a vel bet ween a par ticular origin zone an d a pa rt icular destinat ion zone. Thus, the t rips th at ar e dis t r ibu t ed from ea ch origin zon e t o each dest in a t ion zon e in t h e t r ip d ist r ibu t ion m odu le ar e fur th er split int o distinct t ra vel modes. Wit h few excep t ion s, t h e a ssu m pt ion be h in d t h e m ode split decis ion is for a t wo-wa y t r ip. Th a t is, if an offend er decides on dr iving t o a pa r t icula r crim e loca t ion , we n orm a lly a ssu m e t h a t t h is per son will a ls o dr ive ba ck t o t h e or igin loca t ion . Sim ila r ly, if t h e offen d er t a k es a bu s t o a cr im e loca t ion , t h en t h a t per s on will a ls o t a k e t h e bu s ba ck t o t h e origin locat ion . Th er e a r e, of cou r se, except ion s. A ca r t h ief ma y t a k e a bu s t o a crim e loca t ion , t h en s t ea l a ca r a n d d r ive ba ck . Bu t , in gen er a l, wit h ou t in for m a t ion t o t h e con t r a r y, it is a ss u m ed t h a t t h e t r a vel m od e is for a r ou n d t r ip jou r n ey.\nUn der lying t h e ch oice of a t r a vel m ode is a ssu m ed t o be a u tility fun ction . This is a fu n ct ion t h a t des cr ibes t h e ben efit s a n d cos t s of t r a vel by t h a t m od e (Or t u za r a n d Willu m sen , 2001). Th is ca n be wr it t en wit h a con cept u a l equ a t ion : Ut ility = F(benefits, costs)\nwh er e ‘f’ is s ome fun ction of t h e ben efits a n d t h e cost s. Th e ben efits h a ve t o do wit h t h e a dva n t a ges in t r a velin g t o a pa r t icu la r dest in a t ion fr om a pa r t icu la r or igin wh ile t h e cost s h a ve t o do wit h t h e r ea l a n d p er ceived cost s of u sin g a pa r t icula r m ode. Sin ce th e ben efits of t r a velin g a p ar t icu la r d es t in a t ion fr om a p a r t icu la r or igin a r e p r oba bly equ a l, t h e differ en ces in u t ilit y between t r a vel m odes es sen t ially repr esen t differ en ces in cost s. Thu s, equ a t ion 15.1 br ea k s down t o: Ut ility cost = F(costs)\nIf differ en t t r a vel m odes (e.g., dr ivin g, bik in g, wa lk in g) ar e ea ch r epr es en t ed by a sepa r a t e u t ilit y cost fu n ct ion , th en t h ey ca n be com pa r ed: Ut ilit y cos t 1 = F 1 (cost 1 + cos t 2 + cos t 3 + .....+cost k )\nUt ilit y cos t 2 = F 2 (cost 1 + cos t 2 + cos t 3 + .....+cost k )\nUt ilit y cos t 3 = F 3 (cost 1 + cos t 2 + cos t 3 + .....+cost k )\n. . . Ut ilit y cos t L = F L (cost 1 + cos t 2 + cos t 3 + .....+cost k )\nwhere Utility cost 1 th rough Ut ility cost L represent s L distinct t ra vel modes, cost 1 t h r ou gh cost k r ep res en t k cost component s an d ar e var iables, an d F 1 t h r ou gh F L r ep res en t L differ en t u t ilit y fu n ct ion s (one for ea ch m ode). Th er e a r e sever a l obser va t ion s t h a t can be m a de a bout t h is r epr esen t a t ion . Fir st , ea ch of t h e cost com ponen t s can be ap plied t o a ll m odes. H owever, t h e cost com ponen t s a r e va r ia bles in t h a t t h e va lu es m a y or m a y n ot be t h e sa m e. F or exa m ple, if cost 1 is t h e oper a t ing cost of t r a veling fr om a n or igin t o a dest ina t ion , th e cost for a dr iver is, of cou r se, a lot h igh er t h a n for a bus pa ssen ger s ince th e lat t er per son sh a r es t h a t cost with ot h er pa ssen ger s. Sim ila r ly, if cost 2 is t h e t r a vel t im e fr om a pa r t icu la r or igin zon e t o a pa r t icu lar dest ina t ion zon e, th en t r a vel by priva t e a u t om obile m a y be a lot quicker t h a n by pu blic bu s. As m en t ion ed in t h e la st ch a pt er , t im e differ en ces ca n be con ver t ed in t o cost s by a pplyin g s om e t yp e of h ou r ly wa ge/pr ice t o t h e t im e. To t a k e on e m or e exa m ple, for dr ivin g m ode, t h er e cou ld be a cost in pa r k in g (e.g., in a cen t r a l bu sin ess dis t r ict ); for t r a n sit u se, on t h e ot h er h a n d, t h is cost compon en t is zer o. In ot h er wor ds, ea ch of th e t r a vel m odes h a s a differ en t cost st r u ct u r e. Th e sa m e cost s ca n be en u m er a t ed, bu t som e of t h em will n ot a pp ly (i.e., th ey h a ve a va lu e of 0).\nSecon d, t h e cost s ca n be p er ceived cost s a s w ell a s r ea l cost s. F or exa m ple , a n u m ber of st u dies h a ve dem on st r a t ed t h a t pr iva t e a u t om obile is seen a s fa r m or e con ven ien t t o m os t peop le t h a n a bu s or t r a in (e.g., s ee S ch n ell, S m it h , Dim s da le, a n d Th r a sh er , 1973; Roem er a n d S in h a , 1974; WASH COG , 1974; Ca r n egie-Mellon U n iver sit y, 1975; J oh n son, 1978; Levin e a n d Wa ch s, 1986b). ‘Con venien ce’ is defin ed in t er m s of ea se of a ccess a n d effor t in volved in t r a vel (e.g., h ow lon g it t a k es t o wa lk t o a bu s st op fr om a n or igin loca t ion , t h e n u m ber of t r a n s fer s t h a t ha ve t o m a d e t o r ea ch a fin a l d es t in a t ion , a n d t h e t im e it t a k es t o walk from t h e la st bu s s t op t o th e fina l des t in a t ion ). While it is somet imes difficu lt t o sepa r a t e t h e effect s of con venien ce fr om t r a vel its elf, it is clear t h a t m ost people per ceive t h is a s dim en sion in t r a vel ch oice. In t u r n , con venien ce ca n be con ver t ed in t o a m on et a r y va lu e in or der t o a llow it t o be ca lcu la t ed in a cost equ a t ion , for exa m ple h ow m u ch people a r e willin g t o pa y in t im e sa vin gs t o yield a n equ iva len t a m ou n t of con ven ien ce (e.g., as k in g how ma n y m ore m in u t es in t r a vel t im e by bu s a n in dividu a l wou ld be willin g t o absor b in ord er t o give up h a vin g t o dr ive). Th ir d, t h ese cost s ca n be con sid er ed a t a n a ggr ega t e a s well a s in divid u a l level. At a n a ggr egat e level, t h ey repr esen t a vera ge or m edia n cost s (e.g., th e a vera ge tim e it t a kes t o t r a vel bet ween zon e A an d zon e B by pr iva t e a u t om obile, bu s, t r a in , wa lk in g, or bik in g; t h e a ver a ge dolla r va lu e a ssign ed by a sa m ple of su r vey r espon den t s t o t h e con ven ien ce t h ey a ss ocia t e in t r a velin g by car a s opp osed t o bus). On t h e oth er h a n d, a t a n in divid u a l level, t h e cost s a r e specific to t h e in divid u a l. F or exa m ple, t r a vel t im e differ en ces bet ween car a n d bu s ca n be con ver t ed in t o an h our ly wa ge usin g th e individu a l’s in com e; someone m a kin g $100,000 a yea r is going t o pr ice t h a t t im e sa vin gs d iffer en t ly t h a n som eone m a k in g only $2 5,000 a yea r . F ou r t h , a m or e con t r overs ial poin t , th e specific m a t h em a t ica l fu n ct ion t h a t t ies t h e cos t s t oget h er in t o a p ar t icu la r u t ilit y fu n ct ion m a y a ls o d iffer . Typ ica lly, m os t t r a vel dem a n d m odels h a ve ass u m ed t h a t a sim ilar m a t h em a t ica l fu n ct ion is us ed for a ll t r a vel m odes; t h is is t h e n ega t ive exp on en t ia l fu n ct ion descr ibed below (Dom en cich a n d McFa dden , 1975; Ort u zar an d Willum sen , 2001). However, th ere is n o rea son why differ en t fu n ct ion s ca n n ot be u sed. Th u s, t h e equ a t ion s a bove id en t ify differ en t fu n ct ion s for t he modes, F 1 t h r ou gh F L . On e ca n t h in k of th is in t er m s of weights. E a ch of t h e differ en t m a t h em a t ical fu n ction weigh t t h e cost com pon en t s d iffer en t ly. It is a n em pir ica l qu est ion wh et h er in divid u a ls a pply differ en t fu n ct ion s t o evalu a t ing t h e differ en t m odes. F or exam ple, most p eople wou ld n ot dr ive ju st t o t r a vel on e block (un less it wa s pour ing r a in or u n less a h ea vy object h a d t o be deliver ed or picked u p). Even t h ough it is con ven ien t t o get in t o a veh icle a n d d r ive t h e one block , m ost people se e t h e effor t in volved (an d, m ost likely, t h e fuel a n d oil cost s) a s n ot bein g wort h it . In ot h er wor ds, it a ppea r s t h a t a differ en t u t ilit y fu n ct ion is being ap plied t o wa lk in g a s opp osed t o dr ivin g (i.e., wa lk for dis t a n ces u p t o a cer t a in dis t a n ce; dr ive t h er ea fte r ). A st r ict u t ilit y t h eor ist m igh t dis a gr ee w it h t h is in t er pr et a t ion s a yin g t h a t t h e per m in u t e cost of wa lkin g t h e one block a n d ba ck wa s les s t h a n m onet a r ized p er m in u t e cost of oper a t in g t h e veh icle (wh ich m a y in clu de open in g a ga r a ge door , get t in g in t o t h e\nveh icle, st a r t in g t h e veh icle, dr ivin g out of t h e pa r k in g sp ot, closin g t h e ga r a ge door , a n d t h en dr iving t h e one block ). In oth er words , it cou ld be a r gu ed t h a t t h e differ en ce in beh a vior s h a s t o do with t h e valu es of t h e differ en t cost com ponen t s, r a t h er t h a n t h e wa y t h ey ar e weighted t oget h er (th e m a t h em a t ical fun ction). In r et r ospect, one can expla in a n y d iffer en ce. We a r gu e in t h is ch a pt er , h owever , t h a t cr im e t r ip s a ppea r t o sh ow d iffer en t lik elih ood s by t r a vel m od e a n d t h a t t r ea t in g ea ch of t h es e fu n ct ion s a s d is t in ct a llows m ore flexibilit y in t h e fra m ework . Disc rete Choice Ana lysis No m a t t er h ow t h e u t ilit y fu n ct ion s a r e defin ed, t h ey h a ve t o be com bin ed in su ch a wa y a s t o a llow a dis cr et e ch oice. Th a t is , a n offen der in t r a velin g fr om zon e A t o zon e B m a k es a dis cr et e ch oice on t r a vel m ode. Th er e m a y be a pr oba bilit y for t r a vel by ea ch m ode, for exa m ple 60% by ca r a n d 40% by bu s. Bu t , for a n in divid u a l, t h e ch oice is ca r or bu s, n ot a pr oba bilit y. Th e pr oba bilit ies a r e obt a in ed by a sa m ple of in divid u a ls , for exam ple of 10 individua ls 6 went by car an d 4 went by bus. But , still, at th e individua l level, t h er e is a d ist inct choice t h a t wa s m a de. Mu l ti n o m i a l Lo g i t F u n c t i o n A com m on m a t h em a t ica l fr a m ewor k t h a t u sed is for m od e ch oice m od elin g a t a n a ggr egat e level is t h e m u ltin om ial logit fu n ction (Dom incich a n d McFa dden , 1975; St oph er a n d Meybu r g, 1975; Op pen h eim , 1980; Or t u za r a n d Willu m sen , 2001):\ne (-$C ijL ) P ijL = --------------\nE[ e (-$C ijL ] L=1\nwh er e P ijL is t h e pr obabilit y of us in g a m ode for a n y pa r t icula r t r ip p a ir (pa r t icula r origin a n d p a r t icula r dest in a t ion) L is t h e t r a vel m ode, C ij is t h e cost of t r a velin g fr om or igin zon e i t o des t in a t ion zon e j, e is th e base of th e nat ur al logar ithm , an d $ is a coefficien t . Severa l obser vat ion s can be m a de a bou t t h is fu n ct ion . Fir st , each t r a vel m ode, L, h a s it s own cost s a n d ben efit s, a n d ca n be eva lu a t ed by it self. Th a t is , t h er e is a dis t in ct $C\nu t ilit y fun ction for ea ch m ode. Th is is t h e n u m er a t or of th e equ a t ion, e (- ijL . However , t h e ch oice of an y on e m ode is depen den t on it s u t ilit y va lu e r ela t ive t o oth er m odes (t h e den om ina t or of t h e equa t ion ). The m or e ch oices t h a t a r e a vailable, obviou sly, t h e less lik ely a n in divid u a l will u se t h a t m ode. Bu t t h e va lu e a ssocia t ed wit h t h e m ode (t h e u t ilit y) d oes n ot ch a n ge. As m en t ion ed a bove, we gen er a lly a ssu m e t h a t t h e ben efit of t r a veling bet ween a n y two zon es is iden t ica l for a ll m odes a n d, hen ce, an y differ en ces a r e du e t o cost s.\nSecon d, t h e m a t h em a t ical for m is t h e n ega t ive expon en t ia l. Th e exponen t ia l fu n ct ion is a gr owt h fu n ct ion in wh ich gr owt h occu r s a t a con st a n t rate (eit h er posit ive gr owt h , or n ega t ive - declin e). Th e u se of t h e n ega t ive exp on en t ia l a ssu m es t h a t t h e cost s a r e r ela t ed t o th e lik elih ood a s a fun ction t h a t declines a t a con st a n t r a t e. It is a ctu a lly a ‘dis in cent ive’ or ‘dis cou n t ’ fun ction r a t h er t h a n a u t ilit y fun ction , per se. That is, as t he cost s in crea se , t h e pr obabilit y of u sin g t h a t m ode d ecr ea se s, a ll oth er t h in gs bein g equ a l. St ill, for h is t or ica l r ea son s, it is st ill ca lled a u t ilit y fu n ct ion . Th ir d, for a n y on e m ode, t h e t ot a l cos t is a loga r it h m ic fu n ct ion of in divid u a l cos t s: Ut ilit y cos t i = e (-$C ijL )\nLn(Ut ility cost L ) = C ijL = " + $1 X1 + $2 X2 + ......+ $k Xk\nwh er e C ijL is a cum u lat ive cost m a de u p of com ponen t s X1 , X2 t h r ou gh Xk , " is a con st a n t , an d $1 t h r ou gh $k a r e coefficient s for t h e individu a l cost com ponen t s. Thu s, we see t h a t t h e u t ilit y fu n ction is a loglinea r m odel, a s wa s s een in cha pt er 12. Th u s, t h e u t ilit y fu n ction is Poisson distr ibut ed, declining at a const an t rate wit h in cre a sin g cum u la t ive cost s. Dom incich a n d McFa dden (1975) su ggest t h a t t h e er r or t er m s a r e n ot P oisson dist r ibut ed, bu t sk ewed in a Weibu l fu n ct ion . As dis cu ssed in ch a pt er 12, t h er e a r e a va r iet y of differ en t m odels t h a t incor pora t e sk ewed er r or t er m s (negat ive binomia l, a sim ple lin ea r cor r ect ion of disper sion ) so th a t t h e Weibu l is bu t one of a n u m ber of poss ible d es crip t ors . Never t h eless, t h e m ea n u t ilit y is a P ois son -t yp e fu n ct ion . Ge n e r a li z e d Re l a ti v e U t i li t y F u n c t i o n On e can gen er a lize t h is fu r t h er t o allow a n y t ype of ma t h em a t ical fun ction. While t h e P ois son h a s a lon g h is t or y a n d is wid ely u sed, a llowin g ot h er n on -lin ea r fu n ct ion s a llows gr ea t er flexibility. It is p ossible t h a t ind ividu a ls a pply differ en t w eigh tin g s ys t em s in eva lu a t in g d iffer en t m odes (e.g., a n ega t ive exp on en t ia l for wa lk in g, bu t a logn or m a l fu n ct ion for d r ivin g). We cer t a in ly s ee wh a t a p pea r t o be d iffer en t fu n ct ion s wh en t h e a ctu a l t r a vel beh a vior of in dividu a ls a r e exa m in ed (e.g., homeless in dividu a ls d on’t wa lk everywh er e even t h ou gh t h e cost of wa lkin g lon g dista n ces is chea per in t r a vel t ime t h a n t a k in g a bu s 2 ; people don ’t dr ive or t a k e a bu s for ver y sh ort dis t a n ces, s a y a block or t wo). Th er efor e, if we a llow t h a t t h er e a r e differ en t t r a vel fu n ct ion s for differ en t m odes, t h en m ore flexibilit y is p ossible t h a n by a ss u m in g a sin gle m a t h em a t ical fu n ction . We ca n , th er efor e, writ e a gen eralized relative u tility fu n ction as: F L (-$C ) P ijL = ------------------ =\nI ijL -----------------\n(-$C ijL )]\nwh er e t h e t er m s a r e t h e sa m e a s in 15.4 except t h e fun ction , F L , is some fun ction t h a t is sp ecific t o th e t r a vel m ode, L. Th e n u m er a t or is defined a s t h e im peda n ce of mode L in t r a veling bet ween t wo zon es i an d j, wh ile t h e den om ina t or is t h e su m of a ll imp eda n ces. Notice t h a t t h e r a t io of t h e cost fun ction for one m ode r ela t ive t o th e t ota l cost s is a ls o t h e r a t io of t h e im peda n ce for m ode L r ela t ive t h e t ot a l im peda n ce. Th e t ot a l im peda n ce wa s defin ed in ch a pt er 14 a s t h e dis in cen t ive t o t r a vel a s a fu n ct ion of sepa r a t ion (dist a n ce, tr a vel t ime, cost ). We see t h a t t h e sh a r e of a pa r t icu lar m ode, t h er efor e, is t h e pr opor t ion of t h e t ot a l im peda n ce of t h a t m ode. Th is sh a r e will va r y, of cou r se, with t h e degree of sepa r a t ion . For a n y given sepa r a t ion , th er e will u su a lly be a differ en t sh a r e for ea ch m ode. For exa m ple, at low sepa r a t ion bet ween zon es (e.g., zon es t h a t a r e n ext t o each ot h er ), walk in g a n d bik in g a r e m u ch m ore a t t r a ctive t h a n t a k in g a bu s or a t r a in a n d, per h a ps even dr iving. At grea t er sepa r a t ion (e.g., zon es t h a t a r e 5 m iles a pa r t ), wa lkin g a n d bik in g a r e a lm ost ir r eleva n t choices a n d t h e lik elih ood of dr iving or u sing public tr an sit is mu ch great er. In oth er words, the sha re th at an y one mode occu p ies is n ot con s t a n t , bu t va r ies wit h t h e im p ed a n ce fu n ct ion . Why th en can ’t we estima te t he m ode split directly at t he t rip distr ibut ion st age? If t h e t r ip d ist r ibu t ion fun ction is T ij =\n" P i 8 $ AjJ I ij\n(14.12 repea t )\na n d if t h ese t r ips, in t u r n , ar e split int o dist inct m odes u sin g equa t ion 15.7, cou ldn ’t 14.12 be r e-wr itt en a s T ijL = " P i 8 $ AjJ I ijl\nwh er e T ijL is t h e n u m ber of t r ips bet ween t wo zon es , i a n d j, by m ode L, P i is t h e pr odu ction cap a city of zon e i, Aj is t h e a t t r a ction of zone j, " an d $ a r e con s t a n t s t h a t a r e a p plied t o t h e pr odu ction s a n d a t t r a ction s r es pectively, 8 an d J a r e ‘fin e t u n in g’ expon en t s of th e productions a nd a tt ra ctions r espectively, an d I ijL , is t h e imp eda n ce of u sin g mode L to t r a vel bet ween t h e t wo zon es? Th e a n swer is , ye s, it could be ca lcu la t ed dir ect ly. If I ijL wa s a per fect ly defin ed m ode im peda n ce fu n ct ion (with n o er r or ), th en t h e m ode sh a r e cou ld be ca lcu la t ed dir ect ly a t t h e d is t r ibu t ion s t a ge in s t ea d of s ep a r a t in g t h e ca lcu la t ion s in t o t wo d is t in ct s t a ges . Th e p r oblem , h owever , is t h a t t he im p ed a n ce fu n ct ion s a r e n ever per fect (fa r from it , in fact ) an d t h a t r e-scalin g is r equ ir ed both t o get t h e origin s a n d dest in a t ion s ba la n ced in t h e t r ip d ist r ibu t ion st a ge a n d t o ens u r e t h a t t h e pr obabilit ies in equ a t ion 15.7 a dd t o 1.0. Th e effect of t h ese a dju st m en t s gen er a lly t h r ows off a m odel s u ch a s 15.8.3 Con sequ en t ly, th e t r ip dist r ibut ion a n d m ode sp lit st a ges ar e u su a lly ca lcu lat ed as separ at e operat ions. Mea su ring Travel Costs Th e n ext qu est ion is wh a t t yp es of t r a vel cos t s a r e t h er e t h a t defin e im peda n ce? As m en t ion ed a bove, t h er e a r e r ea l a s well a s per ceived cost s t h a t a ffect a t r a vel m ode\ndecision . Some of t h ese can be m ea su r ed ea sily, wh ile oth er s a r e ver y difficult r equ ir in g det a iled sur veys of ind ividu a ls. Am on g th ese cost s a r e: 1.\nDis t a n ce or t r a vel t im e. As m en t ion ed t h r ou gh ou t t h is dis cu ssion , d is t a n ce is on ly a r ough in dica t or of cost sin ce it is in va r ia n t wit h r es pect t o tim e. Act u a l t r a vel t im e is a m u ch bet t er in d ica t or beca u s e it va r ies t h r ou gh ou t t h e da y a n d ca n be ea sily con ver t ed in t o a t ra vel tim e v alu e, for exam ple by m u ltiplying by a u n it wa ge.\nOt h er r ea l cost s, s u ch a s t h e oper a t in g cost s of a pr iva t e veh icle (fu el, oil, m a int en a n ce), par kin g, a n d in su r a n ce. Som e of t h ese can be su bsu m ed u n der t r a vel t im e va lu e by wor k in g ou t a n h our ly pr ice for t r a vel.\nP er ceived cost s, su ch a s con venien ce, fea r of being ca u ght by an offen der , ea se of escap e from a crim e scene, difficult ies in m oving s t olen goods , a n d fea r of ret a lia t ion by ot h er offen der s or ga n gs ).\nSom e of t h ese cost s ca n be m ea su r ed a n d som e ca n n ot . F or exa m ple, t h e va lu e of t r a vel t im e can be in fer r ed from t h e m edia n h ous eh old in com e of a zone for a ggr ega t e a n a lysis or from t h e a ct u a l househ old incom e for ind ividu a l-level an a lysis. Pa r kin g ca n be a vera ged by zon e. Ins u r a n ce cost s can be est ima t ed from zon e a vera ges if t h e da t a ca n be obtained. Ma n y perceived cost s a lso ca n be m ea su r ed. Con venien ce, for exam ple, cou ld be m ea su r ed fr om a gen er a l s u r vey. Th e fea r of be in g ca u gh t ca n be in fer r ed fr om t h e a m ou n t of su r veilla n ce in a zon e (e.g., t h e n u m ber of police per son n el, secur it y gua r ds , secu r it y ca m er a s). Even t h ou gh it m a y be a difficu lt en u m er a t ion pr ocess, it is s t ill possible t o m ea su r e t h ese cost s a n d com e u p wit h some a vera ge estim a t e. Ot h er per ceived cost s, on t h e ot h er h a n d, m a y not be ea sily m ea su r ed. For exa m ple, t h e fea r a n offen der belon gin g t o on e ga n g h a s a bou t r et a lia t ion fr om a n ot h er ga n g is n ot ea sily m ea su r ed. Sim ila r ly, th e cost s in m ovin g st olen goods by a t h ief is n ot ea sily m ea su r ed; on e wou ld n eed t o kn ow t h e loca t ion of th e dist r ibu t ors of t h es e goods . In pr a ct ice, t r a vel m odeler s m a k e sim ple a ssu m pt ion s a bou t cost s be ca u se of th e difficult y in m ea su r in g m a n y of th em . For exa m ple, t r a vel t im e is t a k en a s a pr oxy for a ll t h e oper a t in g cos t s. P a r k in g cos t s ca n be in cor por a t ed t h r ou gh sim ple a ssu m pt ion s a bou t t h e dist r ibu t ion a cross zon es (e.g., zon es wit h in t h e cen t r a l bu sin es s d ist r a ct - CBD, a r e given a n a ver a ge h igh pa r k in g cos t s; zon es th a t a r e cen t r a l, bu t n ot in t h e CBD, a r e a ss ign ed m oder a t e pa r k in g cost s; zone s t h a t a r e su bu r ba n a r e a ss ign ed low pa r k in g cost s). It wou ld be ju st t oo t ime con su m ing t o docu m en t ea ch a n d every cost a ffect ing t r a vel beh a vior , pa r t icula r ly if we a r e developin g a m odel of offend er t r a vel. Never t h eles s, t h eor et ically, t h es e a r e a ll pot en t ia lly m ea su r a ble cost s. Th ey a r e r ea l a n d pr oba bly h a ve a n im pa ct in t h e t r a vel d ecis ion s t h a t offen der s m a k e. As\nr esea r cher s, we h a ve t o wor k t owar ds a r t icula t in g a s m a n y of th ese cost s a s p ossible in or d er t o p r od u ce a r ea lis t ic r ep r es en t a t ion of offen d er t r a vel. Ag g r e g a te a n d I n d i v id u a l U ti li t y F u n c t i o n s One of th e big debat es in t ra vel modeling is wheth er to use aggregate or individua l u t ilit y fu n ct ion s t o ca lcu la t e m ode sh a r e. Th e a ggr ega t e a ppr oa ch m ea su r es com m on cost s for ea ch zon e, ass u m ing a n a vera ge va lue. The d isa ggr egat e a ppr oa ch (somet imes ca lled ‘secon d gener a t ion ’ m odels) m eas u r es u n ique cost s for individu a ls, t h en s u m s u pwa r d t o yield va lu es for ea ch zon e pa ir . E ven t h ou gh t h e en d r esu lt is a n a lloca t ion of cost s t o ea ch zon e pa ir , t h e a r t icu la t ion of u n iqu e cost s a t t h e in divid u a l level ca n , in t h eor y, a llow a m or e r ea list ic a ssessm en t of t h e u t ilit y fu n ct ion t h a t is a pplied t o a r egion . Th e a ggr ega t e a ppr oa ch will m ea su r e cost s by a ver a ges. Th u s, a t yp ica l equ a t ion for dr iving m ode m igh t be: Tot a l cos t ij = " + $1 T ij + $2 P j\nwh er e T ij is th e avera ge tra vel time between two zones, i an d j, an d P ij is t h e a ver a ge pa r k in g cost for pa r k in g in zon e j. Notice th a t t h er e a r e a lim it ed n u m ber of va r ia bles in a n a ggrega t e m odel (in t h is ca se, on ly two) a n d t h a t t h e a ss igned a ver a ge is for a n en t ir e zon e. N ot ice a ls o t h a t t he p ar k in g cos t is ap plied on ly t o t h e d es t in a t ion zon e. It is a ssu m ed t h a t a n y t r a veler will p a y t h a t fee in t h a t zon e ir r espect ive of wh ich or igin zon e h e/sh e cam e from. A disaggregate appr oach can allow more cost component s, if th ey ar e measu red. Th u s, a t ypica l equa t ion for dr iving m ode m igh t be: Tot a l cos t ijk = " + $1 T ijk + $2 P j + $3 C ijk + $4 CM ijk + $5 S ijk\nwh er e T ijk is t h e t r a vel t im e for in dividu a l k bet ween t wo zon es , i a n d j, P ij is t h e a ver a ge pa r k in g cost for pa r k in g in zone j, Cijk is t h e con venien ce of t r a veling t o zon e j fr om zon e i for in dividu a l k , CM ijk is t h e com for t a n d p r ivacy exper ien ced by in dividu a l k in t r a velin g from zone i t o zon e j, a n d S ijk is t h e per ceived sa fety exper ien ced by in dividu a l k in t r a velin g fr om zon e i t o zon e j. N ot ice t h a t t h er e a r e m or e cost va r ia bles in t h e equ a t ion a n d t h a t t h e m odel is t a r get ed sp ecifica lly t o th e in divid u a l, k . Two in divid u a ls w h o live next door t o each oth er an d who tr avel to th e same destinat ion m ay evalua te th ese com p on en t s differ en t ly. If t h es e in d ivid u a ls h a ve s u bs t a n t ia lly d iffer en t in com es , t h en t h e valu e of t h e t r a vel t ime will differ . If on e valu es pr iva cy enorm ou sly while th e ot h er does n ’t , t h en t h e cost of dr ivin g for t h e first is less t h a n for t h e secon d. S im ila r ly, con ven ien ce is a ffect ed by bot h t r a vel t im e a n d t h e ea se of get t in g in a n d ou t of veh icle. F ina lly, th e per cept ion of sa fet y ma y differ for t h ese t wo h ypot h et ica l in dividu a ls. Ther e a r e m a n y st u die s t h a t h a ve docu m en t ed t h e sign ifica n t r ole pla yed by sa fet y in a ffect in g, pa r t icu la r ly, t r a n sit t r ip s (Levin e a n d Wa ch s, 1986b).\nIn oth er words , t h e a ggrega t e a pp r oach a pp lies a ver y elem en t a r y t ype of ut ilit y fun ction wh er ea s t h e disa ggrega t e a pp r oach a llows m u ch m ore comp lexit y a n d in dividu a l va r ia bilit y. Of cour se , one h a s t o be a ble t o mea su r e t h e in divid u a l cost com pon en t s, a difficult t ask un der most circum sta nces. Th er e is a lso a qu est ion a bout wh ich a pp r oach is m ore a ccu r a t e for cor r ectly for eca s t in g a ct u a l m od e s plit s . H is t or ica lly, m os t Met r op olit a n P la n n in g Or ga n iza t ion s h a ve u sed t h e a ggr ega t e m et h od beca u se it ’s ea sier . H owever , m or e r ecen t r esea r ch (Dom in cich a n d McF a dden , 1975; Ben -Akiva a n d Ler m a n , 1985; McF a dden , 2002) h a s su ggest ed t h a t t h e disa ggrega t e m odelin g m a y be m ore a ccu r a t e. At t h e ver y m in im u m , t h e disa ggr ega t e is m ore a m en a ble t o policy in t er pr et a t ions beca u se it is m ore beh a viora l. If on e cou ld in t er view t r a veler s wit h a su r vey, t h en it is possible t o exp lor e t h e va r iet y of cos t fa ct or s t h a t a ffect a decis ion on bot h des t in a t ion a n d m od e s plit , a n d a m or e r ea lis t ic (if n ot u n iqu e) ut ilit y fun ction der ived. Bu t , a s m en t ioned a bove, wit h crim e t r ips , t h is is ver y difficu lt , if not im possib le, t o do. Con sequ en t ly, for t h e t im e bein g, we’r e st u ck wit h a n a ggr ega t e a ppr oa ch t owa r ds m odeling t h e u t ilit y of t r a vel by offen der s.\nR e l a t i v e Access ib ility F or t h is ver s ion of Crim eS tat, a n a pp r oxima t ion t o a u t ilit y fun ction wa s cr ea t ed. Th e a p pr oa ch is t o es t im a t e a relative accessibility fu n ct ion a n d t h en a pp ly t h a t fu n ct ion t o t h e pr edict ed t r ip dis t r ibu t ion . Th e r ela t ive a ccessibilit y fu n ct ion is a m a t h em a t ica l a pp r oxima t ion t o a u t ilit y fun ction , r a t h er t h a n a m ea su r ed u t ilit y fun ction by it se lf. Beca u s e t h e cos t com p on en t s ca n n ot be m ea s u r ed , a t lea s t for offen d er s , we u s e a n in du ctive a pp r oach . Rea son a ble a ss u m pt ion s a r e m a de a n d a m a t h em a t ical fun ction is foun d th at fits th ese assu mpt ions. It is a pla u sible m odel, n ot a n a n a lyt ical one . Th e pla u sibilit y comes by m a k in g reasonable assu mpt ions a bout actu al tr avel beha vior. One can assu me th at walking tr ips will occu r for sh or t t r ip s, s a y u n der t wo m iles. Bicycle t r ip s, on t h e ot h er h a n d, cou ld occu r over lon ger dis t a n ces, bu t will s t ill be r ela t ively sh or t (a ls o, t h er e is a lwa ys t h e r is k of t r a ffic on t h e sa fet y of bicycle t r ip s). Tr a n sit t r ip s (bu s a n d t r a in ) will be u sed for moderat ely long dista nces but require an a ctu al tr an sit network . Finally, driving tr ips ar e t h e m ost flexible becau se t h ey can occur over a n y size dis t a n ce an d r oad n et wor k . Th ey a r e less likely to be u sed for very sh or t t r ips, on t h e ot h er h a n d, du e t o r ea sons d iscu ssed a bove. Hierarch ical Approac h to Es timatin g Mode Acce ssibility Usin g t h is a pp r oach , sp ecific st eps ca n be defined t o pr odu ce a p la u sible a ccessibilit y model. To h elp in est a blishin g a m odel, an E xcel spr ea dsh eet h a s been develop ed for m a k in g t h ese ca lcu la t ion s (Estim ate m od e split im ped an ce valu es.xls). It ca n a lso be down loa ded from t h e Crim eS tat downloa d pa ge. The s pr ea dsh eet h a s been defin ed wit h r espect t o dis t a n ce, bu t it ca n be a da pt ed for a n y t yp e of im peda n ce (t r a vel t im e or\ncost). A spreadsh eet h as been used becau se it is more flexible th an incorpora ting it a s a r ou t in e in Crim eS tat t o est im a t e t h e pa r a m et er s. Th er e is n ot a sin gle solu t ion t o t h e pa r a m et er s est ima t es, an d t h e differ en t ch oices can be seen m or e ea sily in a spr ea dsh eet . D efi n e t a r g et p r op o r ti on s F irs t , define t h e m od es. In t h e Crim eS tat m ode sp lit r ou t ine, u p t o five differ en t m odes a r e a llowed. Th es e h a ve defa u lt n a m es of “Wa lk ”, “Bik e”, “Dr ive”, “Bu s”, a n d “Train”. The user is not r equired to use th ese na mes nor all five modes. Clearly, if th ere is n ot a t r a in s yst em in t h e st u dy ar ea , th en t h e “Tr a in” m ode does not a pply. Tr a vel m odeler s u se va r ia t ion s on t h ese, su ch a s “dr ive a lon e”,”ca r pool”, “a u t om obile”, “m ot or cycle”, a n d so for t h . D efi n e t a r g et p r op o r ti on s Secon d, define t h e target proportion s. These a r e t h e expect ed pr oport ion s of t r a vel for ea ch m ode. Wher e wou ld su ch pr oport ion s com e fr om ? Ther e h a ve been m a n y st u dies of dr iving a n d t r a n sit beh a vior , bu t r ela t ively few s t u dies of bicycle a n d p edest r ia n u se (Tu r n er , Shu n k, a n d H ot t en st ein, 1998; Schwa r t z et a l, 1999; P or t er , Suh r bier a n d Schwa r t z, 1999). Ther e a r e n ot sim ple t a bles t h a t on e ca n look u p defau lt va lues . To solve t h is p r oblem , exa m ple s w er e sough t from differ en t size m et r opolita n a r ea s. E st ima t es of t r a vel m ode sh a r e for a ll t r ip pu r poses (wor k a n d n on -wor k) were obta ined fr om 1) Ot t a wa (Ot t a wa , 1997); 2) P or t la n d (P or t la n d, 1999); a n d H ou st on 4 . Ta ble 15.1 sh ows t h e es t im a t ed sh a r es. Th e H ous t on d a t a does n ot in clud e wa lkin g a n d bik in g sha res, and tr an sit tr ips ar e not distinguished by mode in t he Port land an d Otta wa dat a. Ta ble 15.1 E s t im a t e d Mo d e S h a r e fo r Th r e e Me t r o p o li t a n Ar e a s Al l Tr ip P u r p o s e s Ot t aw a\nP o rt la n d\nP o p u la ti on :\n725 t h ou s a n d (1995)\n2.0 m illion (2001)\n4.6 m illion (2000)\nP e r c e n t o f t ri p s b y: Dr ivin g Tr a n sit\n(1995) 73.5% 15.2%\n(1994) 88.6% 3.0\n(2025 forecast ) 98.3% 1.7% (bu s 1.1%; r a il 0.6%) -\nWa lkin g Bicycle Ot h er\n9.6% 1.7% -\n4.6% 1.0% 2.8%\nWh ile it ’s difficu lt t o gen er a lize, wa lk in g is ver y m u ch depen den t on t h e exist en ce of a n exten sive tr a n sit syst em . In H ou st on , th e t r a n sit syst em is pr ima r ily a com m u t er\nsys t em wh er ea s in P or t la n d a n d Ot t a wa , it ser ves m u lt ip le pu r poses. Clea r ly, t h e m or e com p a ct t h e u r ba n a r ea , t h e m or e lik ely t h a t t r ip s will occu r by t r a n s it , wa lk in g or bik in g. Bu t , even in t h e ca se of Ot t a wa wh er e a lm ost 10% of t r ip s a r e by wa lk in g, t h e m a jor it y of t r ips a r e by pr iva t e veh icle. In t h e U n it ed St a t es a n d Ca n a da , for m et r opolit a n a r ea s wit h ext en sive t r a n sit fa cilit ies (New York , Ch ica go, Bos t on , Mon t r ea l), a m a jor it y of r egion a l t r ips a r e st ill by au t om obile. Ba sed on t h is, som e defau lt va lues wer e select ed a n d pu t int o t h e spr ea dsh eet . The sp r ea ds h eet r equ ir es t h a t t h ey a r e en t er ed a s p r opor t ion s (n ot p er cent a ges). Th e defau lt s va lu es wer e (t a ble 15.2): Ta ble 15.2 D e fa u lt Mo de S h are Va lu e s P r op or t ion s Mo d e Sh are Wa lk .04 Bicycle .01 Dr ivin g .90 Bu s .04 Tr a in .01 Th e u ser ca n m odify t h ese in t h e spr ea dsh eet . It ’s im por t a n t t h a t a u ser con t a ct t h e loca l Met r opolit a n P la n n in g Or ga n iza t ion t o fin d ou t wh a t wou ld be r ea son a ble va lu es for t h e u r ba n a r ea . Th e defa u lt va lu es a r e bu t gu es se s ba se d on a lim it ed a m oun t of da t a . An a lt er n a t ive a p pr oa ch is t o u s e t h e J ou r n ey t o Wor k da t a of t h e U .S . Cen s u s Bu r ea u (2004). Du r in g ever y cen su s, t h e Ce n su s Bu r ea u docu m en t s h om e-t o-wor k ‘com m u t e’ t r ips a n d br ea ks down t h ese da t a by mode sh a r e. They relea se t h ese da t a u n der t h e t it le “J our n ey t o Work ”. In t h e U n it ed St a t es in 2000, 87.9% of all h ome-to-wor k t r ips wer e by pr iva t e veh icle (au t omobile, va n , t r u ck), 4.7% wer e by pu blic tr a n sit (bus 2.5%; r a il 2.1%; ot h er 0.1%), 2.9% wer e by walkin g, 0.4% wer e by bicycle, 0.1% wer e by motorcycle, 0.7% wer e by ot h er m ea n s, an d 3.3% wor ked a t h om e. Na t ion a l jou r n ey t o wor k st a t ist ics for 1990 a n d 20 00 a n d for m et r opolit a n a r ea s in 1990 can be foun d at h t t p://www.cen su s.gov/popu la t ion/www/socdem o/jou r n ey.h t m l. Dat a on m et r opolita n a r ea s for 2000 ca n be fou n d in McGu ckin a n d Sr inivas a n (2003). In 2000, t h e h om e-t o-wor k m ode sh a r e for a sa m ple of lar ge met r opolita n (inclu din g th e 15 lar gest ) a r ea s is sh own in Ta ble 15.3. Th ey a r e r a n k -or der ed by t h e 2000 popu la t ion of th e m et r op olit a n a r ea . As ca n be s een , t h e la r ger m et r op olit a n a r ea s gen er a lly h a ve a h igh er sh a r e of t r a n sit u se a n d wa lk in g t h a n sm a ller m et r opolit a n a r ea s, bu t t h e differ en ces a r e n ot t h a t dr a m a t ic. E ven t h e la r gest m et r opolit a n a r ea s h a ve a m a jor it y of th eir h ome-to-wor k t r ips by priva t e vehicle.\nTa ble 15.3 Mode Sh are of J ou rn e y to Work Trips: 2000 (F r om McGu ck in a n d S r in iva s a n , 2003) Gre a te r Mod e Sh are M e t r o p o li ta n 2000 Area P op (M) Walk B ic y c le D r iv e B u s Ra il Oth e r* N e w Yor k 21 .1 5 .6 % 0 .3 % 6 5 .7 % 6 .8 % 1 7 .1 % 4 .5 % L o s An g e l e s 16 .4 2 .6 % 0 .6 % 8 7 .6 % 4 .3 % 0 .3 % 4 .6 % C h i ca g o 9.2 3 .1 % 0 .3 % 8 1 .5 % 4 .6 % 6 .6 % 3 .9 % W a s h in g t on D C 7.6 3 .0 % 0 .3 % 8 3 .2 % 4 .1 % 5 .0 % 4 .4 % S a n F r a n ci s co 7.0 3 .3 % 1 .1 % 8 1 .0 % 5 .7 % 3 .5 % 5 .4 % P h ila d elp h ia 6.2 3 .9 % 0 .3 % 8 3 .6 % 5 .3 % 3 .3 % 3 .6 % D et r oit 5.5 1 .8 % 0 .2 % 9 3 .4 % 1 .7 % 0 .0 % 2 .9 % B os t on 5.8 4 .1 % 0 .4 % 8 2 .7 % 3 .2 % 5 .5 % 4 .1 % D a lla s 5.2 1 .5 % 0 .1 % 9 2 .7 % 1 .6 % 0 .1 % 4 .0 % H ou s t on 4.7 1 .6 % 0 .3 % 9 1 .3 % 3 .1 % 0 .0 % 3 .7 % At la n t a 4.1 1 .3 % 0 .1 % 9 0 .6 % 2 .4 % 1 .1 % 4 .5 % M ia m i 3.9 1 .8 % 0 .5 % 9 0 .1 % 3 .2 % 0 .5 % 3 .9 % S ea t t le 3.6 3 .2 % 0 .6 % 8 4 .4 % 6 .2 % 0 .0 % 5 .6 % P h oe n ix 3.3 2 .1 % 0 .9 % 9 0 .0 % 1 .9 % 0 .0 % 5 .1 % M in n e a p olis /S t P a u l 3.0 2 .4 % 0 .4 % 8 8 .4 % 4 .4 % 0 .0 % 4 .4 % C leve la n d 2.9 2 .1 % 0 .2 % 9 1 .1 % 3 .1 % 0 .3 % 3 .2 % Sa n D ieg o 2.8 3 .4 % 0 .6 % 8 6 .9 % 3 .1 % 0 .2 % 5 .8 % S t L ou is 2.6 1 .6 % 0 .1 % 9 2 .5 % 2 .1 % 0 .2 % 3 .5 % Den ver 2.6 2 .4 % 0 .7 % 8 7 .1 % 4 .2 % 0 .1 % 5 .5 % P itt sbu r gh 2.4 3 .6 % 0 .1 % 8 7 .1 % 6 .0 % 0 .1 % 3 .1 % P or t la n d 2.3 3 .0 % 0 .8 % 8 5 .2 % 5 .1 % 0 .5 % 5 .4 % C in cin n a t i 2.0 2 .3 % 0 .1 % 9 1 .4 % 2 .8 % 0 .0 % 3 .4 % S a cr a m e n t o 1.8 2 .2 % 1 .4 % 8 8 .9 % 2 .4 % 0 .3 % 4 .8 % K a n s a s C it y 1.8 1 .4 % 0 .1 % 9 3 .2 % 1 .2 % 0 .0 % 4 .1 % Milwa u kee 1.7 2 .8 % 0 .2 % 9 0 .0 % 3 .9 % 0 .0 % 3 .1 % I n d i a n a p o lis 1.6 1 .7 % 0 .2 % 9 3 .3 % 1 .2 % 0 .0 % 3 .6 % Orlando 1.6 1 .3 % 0 .4 % 9 2 .7 % 1 .6 % 0 .0 % 4 .0 % S a n An t on io 1.6 2 .4 % 0 .1 % 9 0 .9 % 2 .8 % 0 .0 % 3 .8 % N or folk 1.6 2 .7 % 0 .3 % 9 1 .0 % 1 .7 % 0 .0 % 4 .3 % L a s Ve g a s 1.6 2 .4 % 0 .5 % 8 9 .5 % 3 .9 % 0 .0 % 3 .7 % C h a r l ot t e 1.5 1 .2 % 0 .1 % 9 3 .8 % 1 .3 % 0 .0 % 3 .6 % N e w O r le a n s 1.3 2 .7 % 0 .6 % 8 7 .7 % 5 .2 % 0 .0 % 3 .8 % S a lt L a k e C it y 1.3 1 .8 % 0 .4 % 9 0 .3 % 2 .7 % 0 .3 % 4 .5 % M em p h is 1.1 1 .3 % 0 .1 % 9 3 .9 % 1 .6 % 0 .0 % 3 .1 % R o ch e s t e r 1.1 3 .5 % 0 .2 % 9 0 .9 % 1 .9 % 0 .0 % 3 .5 % O k la h om a C it y 1.1 1 .7 % 0 .2 % 9 3 .8 % 0 .5 % 0 .0 % 3 .8 % Lou isv ille 1.0 1 .7 % 0 .2 % 9 2 .9 % 2 .2 % 0 .0 % 3 .0 % -----------------------------------------------------------------------------------------------------------------* In clu des t a xi, fer r y, a n d wor kin g a t h om e\nTh e pr oblem wit h t h es e da t a , h owever , is t h a t t h ey only exa m in e wor k t r ips . Na t ion a lly, home-to-wor k t r ips r epr esen t on ly about 15% of all da ily tr ips (BTS, 2002). On t h e oth er h a n d, 45% of da ily tr ips a r e for sh oppin g a n d er r a n ds a n d 27 % ar e socia l a n d\nr ecr ea t iona l. Fu r t h er , n on-wor k t r ips a r e even m ore likely t o occu r by a u t omobile, a n d a r e gen er a lly s h or t er . F or exa m ple, in H ou st on , for h om e-ba sed n on -wor k t r ip s, on ly 1% of t r ip s a r e by t r a n sit com pa r ed t o 3.1% for h om e-t o-wor k t r ip s. Th ese h om e-ba sed n on -wor k t r ips m a y be a bet t er a n a logy to cr im e t r ips t h a n work t r ips sin ce th ey t en d t o be of sim ila r tr ips lengths a s crime tr ips. Th u s, u n less t h e u ser is willin g t o as su m e t h a t a crim e t r ip is like a work t r ip (which is qu est ion a ble), th en t h e J our n ey t o Work t a bles a r e pr obably not t h e bes t guide for t h e t a r get pr opor t ion s. N ever t h eless, a n exa m in a t ion of t h em is va lu a ble t o see h ow work tr ips ar e split am ong the various t ra vel modes. S el ec t m o d e fu n ct i on s Th ir d, s elect m a t h em a t ical fun ctions t h a t a pp r oxim a t e a ccessibilit y ut ilit y. Aga in , some plau sible assu mpt ions n eed to be made. In Crim eS tat, t h e u se r can select a m ong five differe n t m a t h em a t ical fun ctions (linea r , nega t ive expon en t ia l, norm a l, logn orm a l, t r u n ca t ed n ega t ive expon en t ia l). Th e defa u lt fu n ct ion s a r e (Ta ble 15.4): Ta ble 15.4 D e fa u lt Mo de S h are F u n ct io n s Mo d e Wa lk Bicycle Dr ivin g Bu s Tr a in\nFunction Nega t ive expon en t ia l Nega t ive expon en t ia l Logn or m a l Logn or m a l Logn or m a l\nThe reasoning behind th is is th at walking and biking are r elat ively short tr ips, wh er ea s t r a n sit m odes a r e u sed for in t er m edia t e len gt h t r ip s wh ile dr ivin g ca n be u sed for a n y len gt h t r ip. Th u s, it ’s u n likely t h a t a n a u t omobile will be u sed for ver y sh ort t r ips (less t h a n a qu a r t er m ile) an d it ’s ver y un likely t h a t t r a n sit will be u sed for sh ort t r ips (less t h a n a h a lf m ile or m or e). Nevert h eless, th e u ser ca n m odify th ese choices a n d exam ine t h e a ppr opr iat e colum n in t h e spr ea dsh eet . S elect m od el p r ior it ies F ou r t h , s elect t h e pr ior it ies for m odelin g t h e t a r get . U n for t u n a t ely, t h er e m a y n ot be a s ingle solut ion t h a t will yield t h e t a r get pr oport ion s. Ther efor e, a decision n eeds t o be m a d e on wh ich o rd e r t h e spr ea dsh eet will be ca lcu la t ed. Th e defa u lt or der is (t a ble 15.5 ):\nTa ble 15.5 D e fa u lt Mo de S h are F u n ct io n s\nMo d e Wa lk Bicycle Dr ivin g Bu s Tr a in\nOr d e r o f It e r a ti o n 1 2 3 4 5\nTh e r ea son in g is t h a t t h e offend er fir st m a k es a decision on t h e len gt h of t h e t r ip (sh or t , med ium , lon g, or t h e equiva lent in t r a vel t ime). Then , wit h in ea ch ca t egor y, m a kes a decis ion on wh ich m ode t o ch oose. F or ver y s h or t t r ip s, t h e defa u lt m ode is wa lk in g. F or in t er m edia t e t o long t r ips , t h e defau lt choice is d r iving. However , t h e u ser can cha n ge t h is or der . It er a t i v el y es ti m a t e p a r a m e t er s F ift h , in t h e spr ea dsh eet , it er a t ively a djus t t h e pa r a m et er s u n t il t h e t a r get pr opor t ion is r ea ch ed. Do t h is in t h e or der select ed in t h e a bove st ep. Aga in , t h er e is n ot a sin gle solu t ion t h a t will p r odu ce t h e t a r get pr opor t ion . F or exa m ple, ea ch of th e ma th emat ical fun ctions h as t wo or t hr ee par am eters t ha t can be adjusted: 1. 2. 3. 4. 5.\nF or t h e n egat ive expon en t ial, th e coefficient a n d expon en t F or t h e n or m a l d is t r ibu t ion , t h e m ea n dis t a n ce, s t a n da r d devia t ion a n d coefficient F or logn or m a l d is t r ibu t ion , t h e m ea n dis t a n ce, s t a n da r d devia t ion a n d coefficient For t he linear distr ibut ion, an int ercept an d slope F or t h e t r u n ca t ed n ega t ive exp on en t ia l, a pea k dis t a n ce, p ea k lik elih ood, in t er cept , a n d expon en t .\nTh e t a r get pr oport ion ca n be a chieved by a dju st in g a n y or a ll of t h e pa r a m et er s. F or exa m ple , t o ach ieve a t a r get pr oport ion of 0.05 (i.e., 5%) u sin g t h e n ega t ive expon en t ial, an infin ite n u m ber of m odels can yield t h is, for exam ple coefficient =0.0366, exp on en t =-2.63; coefficien t =0.0459 or expon en t =-5; coefficien t =0.01966, exp on en t =-1; a n d so for t h . Ther efor e, th er e m u st be ad dit ion a l cr iter ia t o con st r a in t h e ch oices. On e crit er ia is t o set a n a pp r oxim a t e m ea n dis t a n ce. For exa m ple, wit h wa lkin g t r ips, th e m ea n dist a n ce ca n be set t o a h a lf m ile or for dr iving, th e m ea n dist a n ce ca n be set t o 6 m iles. Th en , ch eck t h e a ppr oxim a t e m ea n dis t a n ce of t h e select ed fu n ct ion . Th ou gh r a r ely will t h e exa ct m ea n dis t a n ce be r eplica t ed, t h e ca lcu la t ed m ea n dis t a n ce sh ould be close t o th e ide a l. Th e one except ion is for ver y sh ort t r ips . Sin ce th e in t er va ls in t h e spr ea dsh eet a r e a h a lf m ile ea ch , th er e is con sider a ble err or for very sh or t dist a n ces.\nExa m in e th e gr a p h s in th e sp r ea d sh eet An ot h er is t o exa m in e t h e gr a p h of t h e fu n ct ion in t h e s pr ea d sh eet (below t h e ca lcu la t ion s ). Does t h e t yp ica l t r ip a pp r oxim a t e t h e exp ect ed m ea n d is t a n ce? Does t h e select ed fun ct ion pr odu ce somet h ing t h a t looks int u itive? Adm itt edly, t h ese a r e su bject ive decision s. But , if t h e fu n ct ion looks st r a n ge, it can be ca u ght a n d r e-ca lcu lat ed. In sh or t , th e a im s h ou ld be to pr odu ce a fu n ct ion t h a t n ot on ly ca pt u r es t h e t a r get pr opor t ion , but looks plau sible. Severa l exa m ples ar e sh own below. Figu r e 15.1 sh ows t h e defau lt wa lkin g m odel u sin g a n ega t ive expon en t ia l. Figu r e 15.2 s h ows t h e defau lt bik in g m odel, a ls o u sin g a n ega t ive exp on en t ia l. F igu r e 15.3 sh ows t h e defa u lt dr ivin g m ode u sin g a lognor m a l fun ction . Figu r e 15.4 sh ows t h e defa u lt bu s m ode, a lso u sin g a logn or m a l fu n ct ion a n d figu r e 15.5 s h ows t h e d efa u lt t r a in m od e u s in g a logn or m a l fu n ct ion . F igu r e 15.6 shows t h e cu m u lat ive r esu lts of t h e defau lt va lues . This is a lso gra ph ed in t h e spr ea ds h eet , st a r t in g in cell I1. N otice h ow t h e r ela t ive a cces sib ilit y fun ction wor k s. As dista nce increases, the mode proport ions cha nge. At very short dista nces, walking tr ips pr edomin a t e with biking t r ips a lso gett ing a m oder a t e sh a r e. As t h e dist a n ce increa ses, t h e pr opor t ion s in crea sin gly sh ift t owar d d r iving. Even t h ough t h e lik elih ood of dr iving declines w it h dis t a n ce, th e oth er m odes decline even fas t er . In oth er wor ds , t h e r ela t ive a cces sibilit y fu n ct ion is es t im a t in g t h e r ela t ive sh a r es of ea ch m ode a s a fu n ct ion of th e im peda n ce (in t h is ca se , dis t a n ce). Ad a p ti ng sp rea d sheet for t ra vel t im e or tr a vel cost Th e illus t r a t ion s t o t h is poin t h a ve used dist a n ce a s a n imp eda n ce u n it. H owever, oth er im peda n ce un it s, s u ch a s t r a vel t im e a n d gen er a lized t r a vel cost , can a lso be u se d. Th ese gener a lly r equ ire a n et wor k (see below) in t h a t weigh t s h a ve to be ass ign ed t o segm en t s. N ever t h eles s, t h e sa m e logic ap plies . For ea ch t r a vel m ode, a sp ecific im peda n ce fu n ction is est im a t ed a n d t h en a pp lied t o th e t r ip d ist r ibu t ion m a t r ix. Em p ir ica lly est im a ti ng th e m od e-sp ecific im p ed a nce As m en t ion ed a t t he begin n in g of t h is ch a p t er , t h e la ck of in for m a t ion a bou t offen d er t r a vel m od es h a s n eces sit a t ed t h e u s e of m a t h em a t ica l ‘gu es ses ’ a bou t t r a vel beh a vior . However , if it wer e possible t o obt a in a ct u a l in for m a t ion on t r a vel m odes by offen der s, t h en t h is in for m a t ion cou ld be u t ilized dir ect ly t o est im a t e a m u ch m or e a ccu r a t e im peda n ce fu n ction . If t h is da t a ba se existed , th en t wo a ppr oa ch es a r e possible: 1.\nF it t h e da t a wit h t h e va r iou s m a t h em a t ical fun ctions t o see wh ich ones fit best a nd t o estimat e the par am eters.\nUse t h e k er n el de n sit y fu n ction t o est im a t e a n on-linea r im peda n ce valu e wit h t h e specific in for m a t ion .\nNegative Exponential Function: Walk Mode\nNegative Exponential Function: Bike Mode\nLognormal Function: Drive Mode 0.07 Impedance proportion\n0.06 0.05 0.04 0.03 0.02 0.01 0.00 0.0\nLognormal Function: Bus Mode\n0.005 0.004 0.003 0.002 0.001 0.000 0.0\nLognormal Function: Train Mode\nDefault Relative Accessibility by Mode 0.0500\nWalk Bike Drive Bus Train\n0.0000 0 0. 1 1. 2 2. 3 3. 4 4. 5 5. 6 6. 7 7. 8 8. 9 9. 10 Distance (miles)\nTh ese a ppr oa ch es wer e discuss ed in cha pt er 9 (J ou r n ey to Crim e) a n d in ch a pt er 14 (Tr ip Dis t r ibu t ion ). Th e “Ca libr a t e im peda n ce fu n ct ion ” r ou t in e in t h e Tr ip Dis t r ibu t ion m odu le ca n be u sed for t h is pu r pose. Th e a dva n t a ge wou ld be en or m ou s. In st ea d of gues ses a bou t likely imp eda n ce fu n ct ion s of specific t r a vel m odes, t h e u ser wou ld h a ve a fun ction t h a t wa s ba sed on r ea l da t a . Th er e sh ould be a su bst a n t ia l im pr ovem en t in m odeling a ccu r a cy th a t wou ld r esu lt. H owever, t h ese da t a h a ve to be firs t collect ed.\nCr i m eS t a t III Mode Sp lit To ols Th e Crim eS tat m ode sp lit m odu le allows t h e r elat ive a ccessibilit y fu n ct ion t o be calcu la t ed. Figu r e 15.7 sh ows t h e set u p p a ge for t h e m ode s plit r out in e a n d figu r e 15.8 sh ows t h e set u p for m odes 1 a n d 2, in t h e exa m ple “Wa lk ” a n d “Bicycle”. Th e set u p for m odes 3, 4, a n d 5 a r e sim ila r . Mo de S pli t S e tu p On t h e m ode sp lit set u p pa ge, t h e pr edict ed origin a n d pr edict ed des t ina t ion files m u st be inpu t a s t h e pr ima r y an d secon da r y files. If t h e or igin a n d des t ina t ion files ar e iden t ica l (i.e., a ll t h e or igin zon es a r e inclu ded in t h e dest ina t ion zon es), t h en t h e file m u st be in pu t a s t h e pr im a r y file. In a dd it ion , t h e u ser m u st in pu t a pr edicted origin -dest in a t ion t r ip file fr om t h e t r ip dist r ibut ion m odu le. Fin a lly, an a ssu m ed im peda n ce valu e for t r ips from t h e “E xter n a l zone” mu st be specified. The defau lt is 25 miles. Ch oos e a va lu e t h a t wou ld rep res en t a ‘t yp ica l’ t r ip fr om ou t sid e t h e st u dy r egion . F or ea ch m ode, th e u ser m u st pr ovide a label for t h e n a m e a n d define t h e m a t h em a t ical fun ction wh ich is t o be app lied a n d s pecify t h e pa r a m et er s. Th e firs t t im e t h e r ou t ine is opened , th e defau lt va lues a r e list ed. H owever, t h e u ser ca n ch a n ge th ese. H in t : On ce t h e pa r a m et er s a r e en t er ed, t h ey ca n be sa ved on t h e Opt ion s pa ge. Then , th ey ca n be re-ent er ed by loa din g th e sa ved pa r a m et er s file.\nCon stra in Cho ice to N e tw ork Th e imp eda n ce will be ca lcu lat ed eit h er dir ect ly or is con st r a ined t o a n et wor k. Th e defa u lt im peda n ce is defin ed wit h t h e t yp e of dis t a n ce m ea su r em en t specified on t h e Mea su r em en t P a r a m et er s p a ge (un der Da t a set u p). On t h e oth er h a n d, if th e im peda n ce is t o be con st r a in ed t o a n et wor k , t h en t h e n et wor k h a s t o be define d. D e fa u l t The defau lt impedan ce is th at specified on t he Measu rem ent par am eters page. If d ir ect d is t a n ce is t h e d efa u lt d is t a n ce (on t h e m ea s u r em en t p a r a m et er s pa ge), t h en a ll\nMode Split Module\nSet Up for Individual Modes\nim peda n ces a r e calcu la t ed a s a dir ect dis t a n ce. If in dir ect dis t a n ce is t h e defau lt , t h en a ll im peda n ces a r e calcu la t ed a s in dir ect (Man h a t t a n ) dist a n ce. If n et work dis t a n ce is t h e defau lt, t h en a ll imp eda n ces a r e ca lcu lat ed u sin g th e specified net wor k a n d it s pa r a m et er s; t r a vel im peda n ce will a u t om a t ica lly be con st r a in ed t o t h e n et wor k u n der t h is con dit ion . C on st r a i n t o n et w o r k An im p ed an ce ca lcu la t ion sh ou ld be con st r a in ed to a n et wor k wh en t h er e a r e lim it ed ch oices. F or exa m ple, a bu s t r ip r equ ir es a bu s r ou t e; if a pa r t icu la r zon e is n ot n ea r a n exist in g bu s r out e, t h en a dir ect dis t a n ce ca lcula t ion will be m islea din g sin ce it will pr oba bly u n der est ima t e t r u e dist a n ce. Similar ly, for a t r a in t r ip, th er e n eeds t o be an exist in g t r a in r ou t e. Ot h er wis e, t h e r ou t in e will a ssign t r a n sit t r ip s wh er e t h ose a r e n ot possib le (i.e., it will a ss ign t r a in t r ips wh er e t h er e a r e n o tr a in st a t ions a n d it will a ss ign bu s t r ips wh er e t h er e a r e n o bus r ou t es). The r ou t ine does n ot ‘kn ow’ wh et h er t h er e a r e t r a n sit r out es a n d m u st be t old wh er e t h ey a r e. E ven for wa lkin g, bicyclin g a n d d r iving t r ips , an exist in g net work m ight pr odu ce a m ore r ea list ic t r a vel im peda n ce th a n sim ply a ss u m in g a dir ect t r a vel pa t h . If t h e im peda n ce ca lcu la t ion is t o be con st r a in ed t o a n et wor k , t h en t h e n et wor k m u st be defined . A m or e exten sive discu ssion of a n et wor k is p r ovided in ch a pt er 3 (u n der Type of dis t a n ce mea su r em en t on t h e Mea su r em en t P a r a m et er s p a ge) an d in cha pt er 16 in t h e dis cu ssion of t h e Tr ip Assign m en t m odu le. E ssen t ia lly, a n et wor k is a ser ies of conn ected segment s th at specify possible rout es. Ea ch segment h as t wo end nodes (in Crim eS tat, t h ey a r e ca lled ‘F r om Node’ a n d “ToNode). Depen din g on t h e t yp e of n et wor k , t h e segm en t s ca n be bi-dir ection a l (i.e., tr a vel is a llowed in eit h er dir ection ) or sin gle dir ect iona l (i.e., tr a vel is a llowed only fr om t h e “F r omN ode” t o th e “ToNode”). A cr it ica l com p on en t of a n et wor k for t h e m od e s plit r ou t in e is t h a t t r a vel ca n on ly pa ss t h r ough n odes. Th is m ea n s t h a t t wo segm en t s t h a t a r e con n ected can a llow a t r ip t o pa ss over t h ose t wo segmen t s wh erea s t wo segmen t s t h a t ar e not con n ect ed ca n n ot allow a t r ip t o pa ss dir ectly fr om on e t o th e oth er . Fr om ou t sid e t h e n et work , a t r ip con n ects t o it a t a n ode. For a t r a n sit n et wor k, t h is ca n be cr itical. For a bus r ou t e, it m a y or m a y not be im p or t a n t . A p recis e bu s n et wor k defin es nod es by bu s s top s s o t h a t a tr ip ca n ‘en t er ’ or ‘leave’ t h e bus syst em a t a r ea l st op. A less pr ecise bus n et wor k d efines n odes by t h e end s of segm en t s (e.g., th e end n odes of a TIGE R segmen t ). The r ou t ine will not k n ow wh et h er t h e n ode it en t er s or lea ves fr om is a r ea l bu s s t op or n ot. In t h e cas e of bu s r out es, it pr oba bly doesn’t m a t t er sin ce t h ey gen er a lly m a ke ver y regu lar st ops (ever y two or t h r ee block s). Ac cu r a t e ly d e fi n ed t r a n s i t n et w o r k s F or t r a in n et wor ks , however , it is abs olut ely cr itical th a t t h e n et wor k be d efined a ccu r a t ely. Th e n odes m u st be legit im a t e st a t ion s; a t r ip ca n only en t er or lea ve t h e t r a in sys t em t h r ou gh a st a t ion (i.e ., it ca n n ot en t er or lea ve a t r a in n et wor k a t t h e en d of an a r bit r a r y segm en t n ode). Most t r a vel dem a n d m odels u se ver y pr ecise bu s a n d t r a in n et wor ks t h a t h a ve been car efu lly ch ecked ; wh er e er r or s occu r , th e n et wor ks a r e edit ed\na n d u pd a t ed. If th e u ser does n ot h a ve a n edit ed t r a n sit n et work , on e can be m a de in t h e t r ip a ss ignm en t m odu le. Th er e is a “Cr ea t e a t r a n sit n et work from pr im a r y file” r out in e t h a t will d r a w segm en t s bet ween in pu t pr im a r y file poin t s; t h e u ser in pu t s t h e st a t ion or bu s s t op locat ion s a s t h e pr im a r y file a n d t h e r out in e cre a t es a n et work from one p oin t t o t h e n ext in t h e sam e ord er a s in t h e pr im a r y file (i.e., t h e pr im a r y file n eed s t o be pr oper ly sor t ed in ord er t o tr a vel). See cha pt er 16 for m ore in for m a t ion a bout crea t in g a t r a n sit n e t wor k . En t er i n g t h e n et w o r k p a r a m e t er s Th e n et wor k is in pu t by s elect in g “Con st r a in t o n et wor k ” a n d click on t h e ‘P a r a m et er s’ bu t t on . A dia logu e is br ou gh t u p t h a t a llows t h e u ser t o specify t h e n et wor k t o be u sed. Th e n et wor k file ca n be eit h er a sh a pe lin e or polylin e file (t h e defa u lt ) or a n ot h er file, eith er dBa se IV ‘dbf’, Micr osoft Access ‘m db’, Ascii ‘da t ’, or a n ODBCcom plia n t file. If th e file is a sh a pe file, th e r out in e will k n ow t h e loca t ions of t h e n odes . All t h e u ser n eeds t o do is ident ify a weight ing var iable, if u sed, a n d possible on e wa y r ou t es (‘flags’). For a dBa se IV or ot h er file, t h e X a n d Y coor din a t e var iables of t h e end n odes m u st be defined . These a r e ca lled th e “F r om ” n ode a n d t h e “E n d” n ode, th ou gh t h er e is n o pa r t icula r ord er . An opt ion a l weigh t va r ia ble is a llowed for both a sh a pe or d bf file. Th e r out in e iden t ifies n odes a n d s egm en t s a n d fin ds t h e sh ort est pa t h . By defa u lt , t h e sh ort est pa t h is in t er m s of dis t a n ce t h ou gh ea ch segm en t ca n be weigh t ed by t r a vel t im e, t r a vel s peed, or genera lized cost; in th e lat ter case, th e units a re minu tes, hour s, or u nspecified cost u nits. F in a lly, t h e n u m ber of gr a ph segm en t s t o be ca lcu la t ed is defin ed a s t h e n et wor k limit . The defau lt is 50,000 segmen t s. This can be ch a n ged, but be su r e t h a t t h is n u m ber is gr e a t er t h a n t h e n u m be r of s egm e n t s in you r n e t wor k . Mini m um a bsolu te im p ed a nce If a m ode is con st r a ined t o a n et wor k, a n a ddit ion a l con st r a int is need ed t o en su r e r ea listic a lloca t ion s of t r ips. This is t h e m inim u m a bsolu t e imp eda n ce bet ween zon es. The defau lt is 2 m iles. F or a n y zon e pa ir t h a t is closer t oget h er t h a n t h e m in im u m sp ecified (in dis t a n ce, tim e in t er va l, or cost ), n o tr ips will be a llocat ed t o th a t m ode. Th is con st r a in t is t o pr event u n r ea listic t r ips being a ssign ed t o int r a -zon a l tr ips or t r ips bet ween n ea r by zones. Crim eS tat u ses t h r ee im peda n ce com pon en t s for a const r a in ed n et wor k : 1.\nTh e im peda n ce fr om t h e origin zone t o th e n ea r es t n ode on t h e n et wor k (e.g., n ea r es t r a il s t a t ion );\nTh e im peda n ce a lon g t h e n et wor k t o t h e n ode n ea r est t o t h e dest in a t ion ; a n d\nTh e im peda n ce fr om t h a t n ode t o th e dest in a t ion zon e.\nSin ce m ost imp eda n ce fu n ct ion s for a m ode con st r a ined t o a n et wor k will ha ve th e h igh est likelih ood some d ist a n ce fr om t h e or igin, it’s possible th a t t h e m ode would be a ss igned t o, essen t ia lly, very sh ort t r ips (e.g., th e dist a n ce fr om a n origin zon e t o a r a il n et wor k a n d t h en ba ck a gain m igh t be m odeled a s a h igh likelih ood of a t r a in t r ip even t h ough su ch a t r ip is ver y u n lik ely). F or ea ch m ode t h a t is con st r a in ed t o a n et work , sp ecify t h e m in im u m a bsolu t e imp eda n ce. The u n its will be th e sa m e a s t h a t specified by th e m ea su r em en t u n its . The defa u lt is 2 m iles. If t h e u n it s a r e dis t a n ce, t h en t r ip s will on ly be a lloca t ed t o t h ose zon e pairs t ha t a re equal to or great er in dista nce tha n t he minimu m specified. If th e units a re t r a vel t ime or s peed, t h en t r ips will on ly be alloca t ed t o t h ose zon e pa irs t h a t a r e fa r t h er a pa r t t h a n t h e dis t a n ce t h a t wou ld be t r a veled in t h a t t im e a t 30 m iles per h ou r . If t h e u n it s a r e cost , t h en t h e r ou t in e ca lcu la t es t h e a ver a ge cost per m ile a lon g t h e n et wor k a n d on ly a lloca t es t r ip s t o t h os e zon e p a ir s t h a t ar e fa r t h er a p a r t t ha n t h e d is t a n ce t h a t wou ld be t r a veled a t t h a t a ver a ge cost .\nAp p l y in g t h e R e l a ti v e Ac c e s s i b i li t y F u n c t i o n To a pply th e r elat ive a ccessibilit y fu n ct ion , th e pa r a m et er ch oices for ea ch m ode a r e en t er ed int o t h e m ode sp lit r ou t ine. All t r a n sit m odes a r e t h en con st r a ined Once the mode split setup h as been defined and all tr an sit modes have been const ra ined to a pr oper n et wor k , t h e m ode s plit r out in e can be r u n . F igu r e 15.9 shows t h e t op 300 wa lkin g cr ime t r ips in Ba ltim or e Cou n t y est ima t ed wit h t h e defau lt a ccessibilit y fu n ctions. As se en , t h e va st m a jor it y of walkin g t r ips a r e int r a -zon a l (loca l). Ther e a r e on ly a cou ple of int er -zon a l walk ing t r ip lin ks sh own . The defau lt im peda n ce fu n ct ion a ssign ed a ppr oxim a t ely 4% of t h e t r ips t o t h is m ode a n d t h e result is man y int ra -zona l tr ips. F igu r e 15.10 sh ows t h e t op 300 bicycle cr ime t r ips in Ba ltim or e Cou n t y. Ther e a r e fewer t r ip s by bicycle a n d t h ey a ls o t en d t o be qu it e loca l. Th e im peda n ce fu n ct ion u sed for bicycle tr ips a lloca t ed a ppr oxim a t ely 1% of a ll t r ips t o t h is m ode. Thu s, it’s less frequ en t t h a n wa lkin g mode. Th er e a r e pr oport ion a t ely m or e int er -zon a l tr ips a m on g th e t op 300 t h a n for wa lk in g t r ip s, bu t t h ese t en d t o be qu it e sh or t (t r a vel bet ween a dja cen t zon es). On t h e oth er h a n d, d r iving is t h e pr edomin a n t t r a vel m ode for t h e crim e t r ips (Figu r e 15.11). Th e im peda n ce fu n ction u sed a llocat ed a pp r oxim a t ely 90% of th e t r ips t o dr iving. The pa t t er n a lmost per fect ly r eplica t es t h e pr edict ed t r ip dist r ibut ion (see figur es 14.12 an d 14.20 in ch a pt er 14). Fu r t h er , th e t r ips a r e a lot lon ger. Am on g th e t op 300 link s, t h er e wer e n o int r a -zon a l dr iving t r ips. The u se of a lognorm a l fu n ct ion m inim ized in t r a -zon a l t r a vel. To a lloca t e bu s a n d t r a in t r ip s, h owever , it wa s n ecessa r y t o con st r a in t h em t o a n et work . Sep a r a t e bu s a n d t r a in n et work s wer e obta in ed from t h e Ba lt im ore M et r opolit a n Cou n cil. F igu r e 15.12 sh ows t h e Ba lt im or e bu s n et wor k a n d figu r e 15.13 sh ows t h e pr edict ed bu s t r ip s su per im posed over t h e bu s n et wor k . Over a ll, a bou t 4% of t h e t ot a l\nFigure 15.9: Mode Split: Walking Crime Trips # # #\n40 or more Baltimore County City of Baltimore\nWalking trips Less than 10 10 - 19 20 - 29 30 - 39 40 or more Intra-zonal walking trips Less than 10 # 10 - 19 # 20 - 29 # 30 - 39\n# N W\nFigure 15.10: Mode Split: Bicycle Crime Trips #\nBiking trips Less than 10 10 - 19 20 - 29 30 - 39 40 or more Intra-zonal biking trips Less than 10 10 - 19 # 20 - 29 # 30 - 39 40 or more Baltimore County City of Baltimore\nFigure 15.11: Mode Split: Driving Crime Trips\nDriving trips Less than 25 25 - 49 50 - 74 75 - 99 100 or more Baltimore County City of Baltimore\nFigure 15.12: Baltimore Bus Network\nBus network Baltimore County City of Baltimore\nFigure 15.13: Mode Split: Bus Crime Trips\nBus trips Less than 10 10 - 19 20 - 29 30 - 39 40 or more Bus network Baltimore County City of Baltimore\nt r ips wer e a lloca t ed t o t h e bus m ode by th e a ccessibilit y fu n ct ion . As seen , th e t r ips t en d t o be m oder a t e dist a n ces a n d t en d t o be close t o t h e bus n et wor k. Con st r a inin g th ese t r ips by t h e n et work decrea ses t h e lik elih ood t h a t t h e r out in e wou ld a ss ign a pa r t icula r t r ip lin k t h a t wa s fa r from t h e bu s w ork t o a bu s t r ip. F ina lly, tr a in crim e t r ips wer e con st r a ined t o t h e t r a in n et wor k. F igu r e 15.14 su per imp oses t h e a ssign ed t r a in t r ips over t h e int r a -u r ba n r a il n et wor k. Overa ll, on ly 1% of t h e t ot a l t r ip s wer e a lloca t ed t o t r a in m od e. Th er efor e, t h e n u m ber of t r ip s for a n y zon e pair is quite sma ll. The trips ar e genera lly longer th an th e bus t rips, as m ight be expected, a n d t h ey a ls o t en d t o fa ll a lon g t h e m a jor r a il lin es. S om e of t h e t r ip s st a r t qu it e fa r fr om t h e r a il line s, s o it ’s p ossible t h a t t h ese a r e n ot r ea list ic r epr esen t a t ion s. Keep in m in d t h a t t h is is a m a t h em a t ica l m odel a n d is fa r fr om per fect . Overa ll, t h e m ode sp lit r ou t ine h a s pr odu ced a r ea sona ble app r oxim a t ion t o t r a vel m odes for cr im e t r ip s. Sin ce t h er e wa s n o da t a u pon wh ich t o ca libr a t e t h e fu n ct ion s, r ea sona ble gu esses wer e m a de a bou t t h e a ccessibilit y fu n ct ion . The m a t h em a t ica l model pr odu ced a pla u sible, t h ough n ot p er fect , r epr esen t a t ion of t h ese a ss u m pt ion s, gen er a lly fitting int o what we kn ow about crime tr avel pat tern s.\nU se fu ln e s s o f Mo de S plit Mo de lin g Th e m ode split m odel is a logica l ext en sion of t h e t r a vel d em a n d fr a m ewor k . F or t r a n spor t a t ion pla n n in g, it is a n im por t a n t st ep in t h e pr ocess. Bu t , it a ls o is im por t a n t for cr im e a n a lysis . F ir st , it a ddr esses t h e com plexit y of t r a vel by s epa r a t in g t h e t r ip s fr om specific or igin s t o specific dest in a t ion s in t o dis t in ct m odes. In t h is sen se, it a dds m or e r ea lism t o ou r u n der st a n din g of cr imin a l tr a vel beha vior . The J ou r n ey to Crim e lit er a t u r e, wh ich h a s been u sed by cr ime a n a lyst s a n d crim ina l ju st ice r esea r ch er s t o “u n der st a n d” crim in a l t r a vel beh a vior, is sim plis t ic in t h is r es pect. I t a ss u m es a sin gle m ode, t h ough t h a t is r a r ely a r t icu lat ed by th e r esea r ch er s. By poin t ing ou t t ypica l tr a vel dist a n ces by offenders circum vents t he critical question of how they made th e trip. This was, perha ps, n ot a s crit ica l 50-60 year s a go wh en m ost cr imes wer e com m itt ed wit h in a sm a ller com m u n it y a n d it cou ld be a s su m ed t h a t m os t offen d er s wa lk ed t o t h e cr im e loca t ion . Bu t in p ost - Wor ld War er a , au t om obile t r a vel h a s becom e increa sin gly dom ina t e. This m odel a ss u m es t h a t t h e va st m a jor it y of cr im e t r ips a r e t a k en by a u t omobile. Wh ile t h er e is cu r r en t ly n o d a t a t o p r ove t h a t a ss er t ion , it follows fr om t h e t r a n s por t a t ion p a t t er n s t h a t h a ve becom e widespr ea d in t h e U.S. a n d elsewh er e. Th er e is a secon d r ea son wh y a n a n a lysis of crim e t r a vel m ode ca n be im por t a n t . If t h e lim it a t ion s of t r a vel m ode in for m a t ion cou ld be im pr oved t h r ou gh bet t er a n d m or e car eful d a t a collection by police a n d ot h er la w en for cemen t a gen cies, t h is t ype of an a lysis cou ld be ver y u sefu l for policin g. For on e t h in g, it cou ld a llow m or e focu sed police deploym en t . F or n eigh bor h oods wit h a pr edom in a n ce of wa lk in g cr im e t r ip s, t h en a police foot pa t r ol cou ld be m ost effect ive. Con ver sely, for n eigh bor h oods wit h a pr edom in a n ce of dr iving cr im e t r ips , t h en pa t r ol car s a r e pr obably th e m ost effective. Police int u it ively u n der st a n d t h ese cha r a cter ist ics, bu t t h e crim e m ode sp lit m odel m a k es t h is m ore e xplicit.\nMode Split: Intra-Urban Train Trips\nTrain trips Less than 0.1 0.1-0.019 0.02-0.029 0.03-0.039 0.04 or more Rail network Baltimore County City of Baltimore\nF or a n ot h er t h in g, a m ode split a n a lysis of cr im e ca n bet t er h elp cr im e pr even t ion effor t s. As th e Ba lt im or e d at a su gges t , m a n y of t h e loca l (in t r a -zon a l) cr im e t r ip s a r e com m it t ed a r oun d h ous in g pr ojects a n d in ver y low in com e com m u n it ies. Most like ly, th is is a by pr odu ct of pover t y, la ck of loca l em ploym en t oppor t u n it ies , det er iora t ed h ous in g, a n d even poor su r veilla n ce. S in ce t een a ger s a r e m or e lik ely t o n ot own vehicles, it m igh t be exp ect ed t h a t t h e m a jor it y of t h ese loca l cr im e t r ip s a r e com m it t ed by ver y you n g offen der s . Th is ca n be u sefu l in cr im e p reven t ion . Aga in , t h e “Weed an d Seed ” a n d a ft er sch ool p r ogr a m s a r e gen er a lly t a r get ed t o you t h fr om ver y low in com e n eigh bor h oods. Wh a t is s h own by t h e m ode sp lit a n a lysis is p r obably th e crim e pa t t er n s a ss ocia t ed wit h t h ese n eigh borh oods . Even t h ough it is in t u it ively un der st ood, t h e m ode sp lit a n a lysis qu a n t ifies t h ese r elat ion sh ips in a n explicit m a n n er . In sh ort , a m ode s plit a n a lysis of crim e t r ips is a n im por t a n t t ool for crim e a n a lyst s a n d cr im in a l ju st ice r esea r ch er s. If cor r ect ly ca libr a t ed, it ca n h elp focu s police en for cem en t a n d cr im e pr even t ion effor t s m or e specifica lly a n d ca n im pr ove t h e t h eor y of cr im in a l t r a vel beh a vior . H opefully, police depa r t m en t s will st a r t t o im pr ove t h e qu a lit y of da t a in cap t u r in g likely t r a vel m odes wh ile t a kin g in ciden t r eport s. Even t h ou gh m ost police depa r t m en t s h a ve a n it em sim ila r t o “Met h od of depa r t u r e”, t h er e h a s n ot been a lot of em ph a sis on t h is in for m a t ion a n d m os t cr im e d at a set s a r e d eficien t on t h is in for m a t ion . H owever , wit h imp r oved da t a will com e m or e a ccu r a t e a ccessibilit y fu n ct ion s a n d, hopefu lly, even r ea l u t ilit y fu n ct ion s wh er e a ct u a l cost s a r e m ea su r ed. The expecta t ion is t h a t t h is will h a ppen an d we should work towar ds accelerat ing th e process.\nLi m i t a ti o n s t o t h e Mo d e S p l i t Me t h o d o l o g y Th er e a r e a lso lim ita t ion s t o t h e m et h od, pa r t icu lar ly t h e a ggr egat e a ppr oa ch . The a ggr egat e a ppr oa ch does not con sider ind ividu a ls, on ly pr oper t ies a ssociat ed wit h zon es (e.g., aver a ge t r a vel t im e bet ween t wo zon es). As m en t ion ed ea r lier , t h e a ccessibilit y fu n ct ion u s ed (or t h e u n d er lyin g u t ilit y t h eor y) is m u ch s im p ler for zon es t h a n for in dividu a ls. Consequ en t ly, th e a n a lysis is cru der a t a n a ggrega t e level t h a n a t a n in dividu a l level. Policy scen a r ios a r e m u ch m ore lim it ed wit h a ggrega t e m ode sp lit t h a n with individu a l-level models. For exam ple, if an an a lyst wan t ed t o explor e wha t was t h e lik ely effect of in cr ea sed pu blic su r veilla n ce on wa lk in g beh a vior by p ick pocket s, it is m or e difficult t o do with aggregate dat a t ha n with individua l dat a. For example, it could be h yp ot h esized t h a t a ct u a l p ick pocket s a r e m or e sen sit ive t o in cr ea sed pu blic su r veilla n ce t h a n , say, ca r t h ieves, but t h is ca n ’t be tes t ed a t t h e a ggr egat e level. In st ea d, som e gener a l cha r a cter ist ics a r e a ss igned t o all in dividu a ls (e.g., th e n u m ber of secur it y per son n el in a zon e). Secon d, t h e zona l m odel for m ode sp lit (as wit h t r ip d ist r ibu t ion ) ca n n ot exp la in in t r a -zon a l t r a vel. Th e a ccessibilit y fu n ct ion is a pplied t o in t er -zon a l t r ip s; for in t r a -zon a l t r ip s, it is in a ccu r a t e a n d gen er a lly d efa u lt s t o sim ple ch oices (e.g., wa lk in g, bik in g or dr iving). For exam ple, bus or t r a in m ode can r a r ely be ap plied at a n int r a -zon a l level beca u se t h er e a r e u su a lly t oo few n et wor k segm en t s t h a t t r aver s e a zon e a n d t h e s egm en t s\nr a r ely st op wit h in t h e zon e. Wh ile t h is deficien cy a ls o a pplies t o t h e t r ip dis t r ibu t ion m odel, th e depen den ce on a n et wor k for t r a n sit m odes, pa r t icu lar ly, lea d t o un derestimat ion of tr an sit use for very short tr ips. Th ird , th e zon a l mode split m odel ca n n ot explain ind ividu a l differ en ces. This goes ba ck t o th e first poin t t h a t a sin gle u t ilit y fun ction is bein g a pp lied a t t h e zona l level. Th u s, t h e valu e of t ime t o differ en t ind ividu a ls livin g in t h e sa m e zon e ca n n ot be exam ined ; ins t ea d, everyon e is given t h e sa m e valu e. F ou r t h , th e a ggr egat e m ode sp lit m odel does n ot a n a lyze tim e of da y ver y well. The pr oba bilit ies a r e a ssign ed t o a ll t r ip s, r a t h er t h a n t o t r ip s t a k en a t pa r t icu la r t im es of th e da y. To cond u ct t h a t a n a lysis , a n a n a lyst h a s t o brea k down crim es by t im e of da y a n d m odel t h e differ en t per iods s epa r a t ely. Aside from being a wk wa r d, th e su m m ed t r ips n eed to be balanced to ensur e tha t t hey sum t o th e tota l nu mber of tr ips. F ift h , a n d fin a lly, t h e m ode split m odel, bot h a ggr ega t e a n d dis a ggr ega t e, ca n n ot expla in linked trips (somet imes called t rip ch a in in g). An offen der m igh t lea ve h om e on e da y, go ou t t o ea t , visit a fr iend , com m it a st r eet r obber y, go t o a ‘fen ce’ t o dist r ibut e t h e goods, bu y dru gs fr om a dr u g dealer , an d t h en fina lly go h om e. The m ode sp lit m odel t r ea t s ea ch of t h ese a s sep a r a t e t r ips; in t h e ca se of cr ime m ode sp lit, th er e a r e t h r ee dis t in ct cr im e t r ips - comm it t in g t h e r obber y, sellin g t h e st olen goods t o th e ‘fen ce’, a n d bu yin g th e dr u gs fr om t h e dr u g dealer . The m odel doesn ’t u n der st a n d t h a t t h ese a r e r ela t ed even t s, bu t in st ea d a ss igns s epa r a t e m ode pr obabilit ies t o each t r ip. Th u s, it is possible t o pr odu ce a bsu r d ch oices, s u ch a s dr ivin g t o t h e cr im e scen e, t a k in g t h e bu s t o t h e dr u g dea ler , an d t h en bik in g home. In t h is r espect, t h e disa ggrega t e a pp r oach is equ a lly fla wed as th e a ggr ega t e s in ce bot h t r ea t ea ch t r ip as in dep en den t even t s. Th e s olu t ion t o th is lies in a ‘th ird genera tion’ of tr avel modeling in which individua l tr ip ma kers a re sim u la t ed over a da y; activity-based m od eling, a s it is k n own , is s t ill in a r es ea r ch s t a ge (Gou lia s, 1996; Miller , 1996; P a s, 1996). But , it will even t u a lly em er ge a s t h e domin a n t t r a vel dem a n d m odelin g a lgorit h m .\nCo n clu s io n s Never t h eles s, m ode sp lit m odelin g ca n be a ver y useful a n a lysis st ep for crim e a n a lysis . It r epr esen t s a n ew a ppr oa ch for cr im e a n a lysis a n d one wit h m a n y u sefu l possibilities. It will r equ ire bu ilding more s yst em a t ic da t a ba ses in or der t o docu m en t t r a vel m odes . Bu t , t h e possibilit ies t h a t it offer s u p ca n be im por t a n t for crim e a n a lyst s a n d crim ina l ju st ice r esea r ch er s a like. In t h e n ext cha pt er , th e fina l st ep in t h e cr ime t r a vel dem a n d m odel will be discuss ed, net wor k a ssign m en t .\nEn d n ot e s fo r Ch ap te r 15\nTh er e is n o r ea son t h is da t a cou ld n ot be collect ed. Typ ica lly, m a n y p olice depa r t m en t s collect infor m a t ion on ‘Met h od of depa r t u r e’ fr om a cr ime s cen e. Wh en a police r epor t is t a k en , t h e vict im is som et im es a sk ed h ow t h e offen der left t h e scene. In m ost ca ses, t h e infor m a t ion is not r ecor ded on t h e police for m s, or a t leas t t h ose t h a t h a ve been exa m in ed. Th is in for m a t ion is pr oba bly u n r elia ble in a n y ca se sin ce m a n y offen der s will t a k e t h e bu s or lea ve t h eir ca r n ea r by wh ile t h ey wa lk /r u n t o th e crim e scene . St ill, if police depa r t m en t s wer e t o pu t m ore e ffort in t o collecting this inform at ion a nd, perh aps, to validat ing it with a rr ested offenders, t h en it is possible t o bu ild u p r elia ble da t a set s t h a t ca n be u sed t o m odel m ode split . Un t il t h en , un for t u n a t ely, we ha ve to rely on t h eor y ra t h er t h a n eviden ce.\nIn a su r vey of t h e t r a vel beh a vior of h om eless per son s, it wa s n ot ed t h a t m ost h omeless wa lked ver y sh ort dis t a n ces over t h e da y even t h ough t h e va lu e of t h eir t ime wa s ver y low. For lon ger t r ips, th ey st ill t en ded t o t a ke t h e bus r a t h er t h a n wa lk. Su r vey on t h e t r a vel beha vior of very low incom e individu a ls. Ur ba n P la n n in g Pr ogr a m , Un iver sit y of Califor n ia a t Los Angeles , 1987 (wit h Ma r t in Wachs).\nIn t est s, I did fin d t h a t t h e t wo models p r odu ced sim ila r pa t t er n s. Th ey wer e off in t er m s of t h e m a gnit u de of t h e pr edict ed t r ips, but t h e r elat ive pa t t er n wa s ver y sim ila r .\nH ou st on -Ga lvest on Ar ea Cou n cil. P er sona l com m u n ica t ion . 2004.\nReport "Chapter 15 Mode Split"


CALVIN KLEIN #ProudInMyCalvins Campaign 'Being an Ally' Debuts - JRL CHARTS\nHome » Blog » CALVIN KLEIN #ProudInMyCalvins Campaign ‘Being an Ally’ Debuts\nLAS VEGAS, NV — (10-04-20) — Calvin Klein and PVH Corp., continue their LGBTQ Campaign of #PROUDINMYCALVINS with No. 4 of 7 of ‘Being an Ally’.\nThe cast line up for Calvin Klein ‘Being an Ally include Jari Jones, Reece King, MaryV, Mina Gerges, Chella Man, Ama Elsesser, Pabllo Vittar, Gia Woods and Tommy Dorfman.\nPVH Corp and Calvin Klein released their latest #PROUDINMYCALVINS LGBTQ Campaign on September 29, 2020..\nWatch CALVIN KLEIN – Being An Ally Campaign Video\nFollow Calvin Klein:\nCopyright © 2020 Calvin Klein/PVH Corp.\nBy JRL Charts|2020-10-04T20:51:22-07:00October 4th, 2020|Categories: Gay Music News, LGBT News|Tags: calvin klein 2020-2021, calvin klein being an ally, calvin klein LGBTQ community, Men's Swimwear, mens fashion, mens underwear, PROUDINMYCALVINS, pvh corp, Ryan McGinley|


Partnerschaft: „Nimm dich nicht so wichtig“ – Dieser Satz kann Ihr Leben verändern - WELT\nBep vel lkO ent vellele Zeektleaeu Oeluep Bllpenlp enek ulekl aeue htel. Znl, pepp el elueu aluQeu luueleu Vlpelpleup uelpoele, uuu peO el atenpe, pepp el lku ulekl epelvlupeu houue. Zelu Bllpenl peale en lkO: „Inpl ael uuel vunlpett!“\nVlekllael Fluvelp tel peu VOaeua Oll Zeupekeu\nBep lpl elu keluulleaeupel Bel, pel peple, peu Oeu lu Pllnelluueu vle plepeu aepeu heuu, nup elu Bel, pel aeue eluteek ple Oelpleu PeeleknuapolupteOe topeu velpe. Pnt anl Benlpek: ZlOO plek ulekl pu vleklla! Vup Oll „plek“ lpl lu elplel Ulule pep ueltelele Bau aeOelul, pep lOOel Oll Plute uelpnupeu lpl, nup uleklp pekOelel Oekl etp elu PluQ aeaeu pep Bau. Bp lpl elu eupelel PekOele etp pel uuu Ilenel. Bel PekOele, peu pel Blenup epel peu Ueltnpl peluel Blenuplu eOotlupel, lpl elu aeue eupelel etp pel pel Glouhnua upel Bullonpeknua, epel ple el ulekl kluveaenhuOOeu atenpl. Glouhnuaeu nup Bullonpeknuaeu plup Oelpl BaupekOeleeu, ple Oekl Oll nup petppl en lnu kepeu etp Oll peueu, ple ple enptopeu. Bep lpl elu vlekllael Fluvelp lO VOaeua Oll eupeleu Zeupekeu.\n„ZlOO plek petppl ulekl pu vleklla“ pepenlel uoOtlek eluelpellp, pepp eupele Zeupekeu ulekl ent pel Vetl plup, nup ateehtlek en Oeekeu nup nupeleu Blvellnuaeu en eulpoleekeu, enek nup pepuupelp, veuu vll ple tlepeu. Vup eupelelpellp, pep 6teeh ulekl lu pel Peplollanua lO PnQeu – etpu lO Belluel – en pnekeu upel tlupeu en Oeppeu.\nZelpl plup evlpekeuOeupektleke Ueltelenuaeu peOll uelpnupeu, pepp leOeup ulekl nupeleu Blvellnuaeu, Pupoleekeu nup lpeeteu eulpollekl. Been huOOeu PuuekOeu vle: Ble Ooaeu Olek ulekl; pep Oeekl pel epplekltlek; ple ulOOl eluteek helue Beehplekl; el kolle vllhtlek uellel en Oll pelu houueu; el velQ puek, vle pekl lek Olek euaeplleual kepe. Pttep Gotelketlnuaeu, ple uleklp plluaeu nup unl nup petppl pekepeu. Ielpeeke lpl, pepp ple Belluel upel ZllOeupekeu petleu nupele 6etekte uelteleeu, velt ple pope upel leehpleklptup plup, puupelu velt ple ple Oelple Nell, pu vle vll ette, kenolpoektlek eu plek petppl peuheu nup elueu eupeleu Buhnp kepeu, velt ple ple Vetl enp lkleu Pnaeu pekeu.\nBel elple Bettev\nPlek petppl ulekl pu vleklla en uekOeu kelQl, pelueu Plute en epelvlupeu nup ple uutte Ueleulvullnua tel pelue 6etekte en epeluekOeu, euplell ple eu eupele epenpeklepeu, „ple lpl pekntp peleu, pepp ep Oll pu pekteekl aekl“. Beuu pep lpl Oelpl pel elple Bettev.\nPlekel Oeekeu Belluel Bektel, huOOeu en pool, teppeu nup lO Beaeu plekeu, lonOeu ulekl klulel plek ent, nup utl aeuna kepeu vll ple Guupepneueeu plepel Zeektopplahelleu en lleaeu, velt ple enek nup pelletteu. Ppel lnu ple pep vllhtlek? Vll kepeu lOOel ple tlele Vekt, vep vll peuuu euuekOeu upel ulekl. Gp vll ple Bektel pep eupeleu entteuaeu upel ulekl. Vep lpl vllhtlek vleklla? Vup vu heuu lek Olek enleehuekOeu lu Oeluel Uulplettnua uuu Beuhltlekhell, Penpelhell, Nnueltopplahell? Vep plup Oelue 6leueeu, nup vle huOOnulelele lek ple?\nBel peu Blenup vel utteuplekltlek elue 6leuee ellelekl, puupl kolle el plek ulekl aelleuul, epel vel plepe 6leuee elue aepnupe 6leuee? Vel pelue Beehlluu, peuuuententeu, euaeOeppeu? Fel el peOll pep ellelekl, vep el ellelekeu vuttle? Vu lpl ep lleklla, Blupeklouhnuaeu en Oeekeu, nup vep lpl ulekl uelkeupetpel? Bep heuu plek ueloupelu, lupeO lek Olek ueloupele. Pelpe Bulpekelpnuaeu houueu aepnup upel nuaepnup pelu. ZeuekOet heuu Oeu pep petppl ulekl pekeu, nup pe tukul ep plek, elueu eltekleueu Ptleh uuu enQeu en kuteu.\nlek velpe pekenoleu, pepp vll lu nupeleO Feleeu vlppeu, vep lleklla nup vep tetpek lpl. Vll tekteu ep. Bp lpl ple htelue, telpe PllOOe klulel pel tenleu, velueupeu PllOOe, ple pep Bau pepekeleeu vltt. Blu lnklaep 6etekt, pep helue Puapl heuul, puupelu Nnuelplekl, 6evlppkell nup Ulepe. „ZlOO plek ulekl en vleklla“, kelQl lu plepeO Pluue, ple tenle, velueupe PllOOe, ple uuttel Puapl, Pptekunua, Ueleeklnua upel Zeuaet lpl, en lauulleleu nup ent ple eelle PllOOe pep Feleeup en koleu: Vep vltt lek vllhtlek? Vep lnl Oll anl? Vep vltt lek ellelekeu?\nBep Uelvllleupe peleu lpl, pepp vll nup ulekl vleklla aeuna uekOeu, veuu ep vllhtlek pelnO aekl, ent nup en eekleu. Vupele Pepeltulppe enpenpleeheu, htele 6leueeu en elekeu, Bulpekelpnuaeu en lletteu, ple vll pekuu teuae Oll nup kelnOpekteooeu, nup helue Uepl enttepeu en teppeu, ple vll ulekl lleaeu vutteu, ulekl lepeu Pett ententeuaeu, peu Oeu nup envlltl. Uutte Ueleulvullnua tel nupele Bulpekelpnuaeu en epeluekOeu nup nup lu nupeleu Pekelleu nup Pekvoekeu en tlepeu nup euenuekOeu!\nBel Blenup kel plek epllaeup peu Bel Oeluep Bllpenlp en Feleeu aeuuOOeu. Bl kel aeOelhl, pepp el pelueO 6teeh unl petppl lO Vea plekl, el kel pelueu Plute epelvnupeu nup lpl vlepel Oll peluel Blenuplu enpeOOeu. 6teehtlekel peuu le, velt el velQ, vle nuateehtlek el ukue ple vel.\nWarum Sie als Paar über Ihre Lebensträume reden sollten\nBrauchen wir eine Paartherapie – oder kriegen wir das auch so hin?\nMänner haben manchmal die seltsamsten Gründe für Nachwuchs\nDer Kurz-Link dieses Artikels lautet: https://www.welt.de/217981372


Re: WIZ deliverz free tunez!\nDrum & Bass (international) -> Re: WIZ deliverz free tunez!\nPosted: 01 Apr 2012 19:32 Post subject: WIZ deliverz free tunez!\nPosted: 18 Jul 2012 11:57 Post subject: Re: WIZ deliverz free tunez!\nHi!!!! i'm a spanish who has come to tokyo and really want to go out, but the music in\nhere is to slow, i'm looking for some hardcore, hardtek, tribecore, frenchcore, or\nsomething like that and can not find anything, if u could help me , thanksss i let u\nsome videos in case u dont know what i'm talking about


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