Evaluating the Impact of Interventions on Network Capacity

Transcription

1 Evaluating th Impact of Intrvntions on Ntwork Capacity by Sujith Rddy Rapolu Bachlor of Tchnology in Civil Enginring Indian Institut of Tchnology, Roork, India (2008) Submittd to th Dpartmnt of Civil and Environmntal Enginring in partial fulfillmnt of th rquirmnts for th dgr of Mastr of Scinc in Transportation at th ARCHIVES ASSACHUSETS INSTinT OF TECHNOLOGY JUL MASSACHUSETTS INSTITUTE OF TECHNOLOGY Jun 2010 LIBRARI ES C 2010 Massachustts Institut of Tchnology. All rights rsrvd. Author Dpartmnt of Civil and Environmntal Enginring March 25, 2010 Crtifid by Mosh E. Bn-Akiva Edmund K. Turnr Profssor of Civil and Environmntal Enginring Thsis Suprvisor Crtifid by Charisma Farhn Choudhury Assistant Profssor, Dpartmnt of Civil Enginring Bangladsh Univrsity of Enginring and Tchnology Thsis Co-Suprvisor Accptd by - - Danil Vnziano Chairman, Dpartmntal Committ for Graduat Studnts

2 2

3 Evaluating th Impact of Intrvntions on Ntwork Capacity by Sujith Rddy Rapolu Submittd to th Dpartmnt of Civil and Environmntal Enginring On March 25, 2010 in partial fulfillmnt of th rquirmnts for th dgr of Mastr of Scinc in Transportation Abstract Analyzing th capacity impact of diffrnt divrs intrvntions on th ntwork is ssntial in undrstanding th causs of congstion. In this thsis, a framwork to undrstand th ffcts of diffrnt disruption vnts and activitis on th ntwork has bn prsntd. A common unit, indpndnt of ntwork and typ of intrvntion, has bn usd in this rgard. Exprssing th capacity impacts on this common unit (rfrrd to as 'common capacity currncy' in this thsis) will b usful in assssing th rlativ scal or intnsity of th diffrnt typs of intrvntions across ntworks of diffrnt siz and traffic flow lvls. A ntwork from cntral London, U.K. has bn usd to quantify th capacity impact of intrvntions. Th ntwork, locatd nar Victoria station ara of London, is a complx and dns urban ntwork within th congstion charging zon. MITSIMLab, a microscopic traffic simulation laboratory dvlopd for valuating diffrnt traffic managmnt systms has bn usd for th purpos of capacity analysis. To masur th capacity of a ntwork in MITSIMLab, th ntwork is floodd with vhicls by scaling th origin-dstination (OD) matrix. Th ntwork is assumd to rach its capacity whn pr-trip quus start forming that is no furthr vhicls can b loadd in th ntwork. Th total distanc travlld by all th vhicls in on hour whn th ntwork has rachd its capacity ar notd and convrtd to passngr-car-unit (PCU)-km pr hour. Th avrag spds of th vhicls at capacity ar also compard. To undrstand th impact of intrvntions on ntwork capacity, strt-works and illgally parkd vhicls ar simulatd at diffrnt lvls of complxity. Th common capacity. currncis (PCU-km pr hour) ar compard with th bas cas which didn't includ any intrvntions. Th rsults of th capacity analysis prdictd a drop in ntwork capacitis and avrag spds undr diffrnt scnarios corrctly as xpctd. Strt-works rsultd in a gratr drop in ntwork capacity and avrag spd than a nar-sid lan disruption. Furthr, among th scnarios tstd for nar sid lan disruptions, a 1 minut disruption vry 3 minuts causd th gratst rduction in ntwork capacity and avrag spd.

4 Thsis Suprvisor: Mosh E. Bn-Akiva Titl: Edmund K. Turnr Profssor of Civil and Environmntal Enginring Thsis Co-Suprvisor: Charisma Farhn Choudhury Titl: Assistant Profssor, Dpartmnt of Civil Enginring Bangladsh Univrsity of Enginring and Tchnology

5 Acknowldgmnts I would lik to thank my advisor Prof. Mosh Bn-Akiva, for his faith in m. His shr brillianc was highly inspiring. I would also tak this opportunity to xprss my sincr gratitud to my co-advisor Dr. Charisma Choudhury for guiding m at ach and vry stag of my work in th lab. Hr motivation and support wr th most valuabl during my stay at MIT. This rsarch is fundd by Transport for London (TfL) as part of its collaboration with MIT. I would lik to acknowldg Andy Emmonds and Jonathan Turnr of TfL for th support thy hav lnt to this projct, as wll as th staff of Road Ntwork Prformanc and Rsarch and Invntory for Ntwork Capacity and Activity - Amitabh Bos, Alistair Davis, Kathrin Blair, Allison Cowi and Andrw Wallac for thir assistanc and fdback. Thanks to Emmt Ruxton and Vladimir Vorotovic for thir hlp with th VISSIM modls. I would also lik to thank Dr. Gorg Kocur for agring to guid m through th final six units of cours-work during th most crucial phas of my stay at MIT. Thanks to Patty Gliddn, Kris Kipp and Jantt Marchocki for hlping m with th administrativ procsss. I am xtrmly gratful to all my frinds at th ITS lab for bing so hlpful. I would lik to thank Enyang Huang and Dr. Yang Wn, Oracl Corp. for thir hlp in solving th issus with Linux and MITSIMLab sourc cod. Thanks to Li Qu for hr trmndous hlp in making m undrstand th finr aspcts of MITSIMLab. I would also lik to acknowldg th frindship and support of my othr frinds in CEE: Zhng Wi, Anwar Ghauch, Varun Ramanujam, Vikrant Vaz, Sarv Diwan, Cristian Anglo Guvara, Swapnil Rajiwad. Thanks to Tina Xu for making th lab a vibrant and livly plac. I would lik to thank Chaithanya Bandi from th bottom of my hart for his inspirational thoughts and prspctiv on virtually vry topic and for his constant hlp. Thanks also to Nitish Umang for his ncouragmnt during th strssful tims. I would lik to thank all my frinds at MIT for making my stay a thoroughly njoyabl xprinc. I would also lik to xprss my gratitud towards Mahndr Mandala for always bing thr in vry good and bad xprinc. Abov all, I thank my family: my parnts and my sistr, for thir lov and unnding support.

6 6

7 Tabl of Contnts Abstract Acknowldgm nts....5 Tabl of Contnts List of Tabls List of Figurs Introduction Motivation Thsis Contributions Thsis Outlin Litratur Rviw Capacity Analysis using Empirical Mthods Analyzing capacity using simulation approach Summary M thodology Ntwork Capacity Modling Framwork MITSIMLab Calibration Framwork Goodnss-of-fit masurs Masuring Ntwork Capacity S um m ary... 43

8 4. Cas Study - Victoria Ntwork Datast dscription Study ara Datast ovrviw Aggrgat Calibration Aggrgat Validation Bas Capacity Scnario Analysis Long Trm Strt-works Nar-sid lan disruptions Summ ary C onclusion Thsis summ ary Contributions Dirctions for Futur Rsarch Appndix A: Dtaild Rsults of A2 Ntwork A. 1 Long Trm Strt-works A.2 Nar-sid lan disruptions Bibliography

9 List of Tabls Tabl 2.1: Valu of Tim for ach vhicl typ Tabl 2.2: Total cost saving (f pr 1000 vhicls) Tabl 2.3: Total cost saving (f pr 1000 trips) Tabl 3.1: Succssiv itrations for th scaling factor of an OD matrix...42 Tabl 3.2: Succssiv itrations for th scaling factor of an OD matrix...43 Tabl 4.1: Statistics of calibration and validation data...49 Tabl 4.2: Calibration rsults Tabl 4.3: Goodnss of fit statistics for traffic spd comparison...51 Tabl 4.4: Corrctd Goodnss of fit statistics for traffic spd comparison...52 Tabl 4.5: M O Es for spds...53 Tabl 4.6: Corrctd MOEs for spds...53 Tabl 4.7: Strt-works - Ntwork Capacity valus...57 Tabl 4.8: Strt-works - Impact on avrag spd across th whol ntwork...58 Tabl 4.9: Combination of Strt-works - Ntwork capacity valus...60 Tabl 4.10: Combination of Strt-works - Impact on avrag spd across th whol ntwork Tabl 4.11: Nar sid lan disruptions - Ntwork Capacity valus...63 Tabl 4.12: Nar sid lan disruptions - Rduction in ntwork capacity...65 Tabl 4.13: Nar sid lan disruptions - Impact on avrag spd across th whol ntwork Tabl A. 1: Strt-works avrag spd summary (mph) Tabl A.2: Strt-works spd impact summary...77 Tabl A.3: Economic corridor analysis of strt-works tsts (F pr 1,000 vhicls) Tabl A.4: Economic corridor analysis of strt-works tsts (f pr 1,000 trips) Tabl A.5: Avrag Journy Tim (sc) for strt-works 1 and Tabl A.6: Avrag Journy tim (sc) for strt-work Tabl A.7: Avrag Journy tim (sc) for strt-work Tabl A.8: Nar sid road disruption cofficint of variation...83

10 Tabl A.9: Nar sid road disruptions - spd impact (mph) Tabl A. 10: Narsid disruption conomic analysis (f pr 1,000 vhicls) Tabl A. 11: Narsid disruption conomic analysis (f pr 1,000 trips) Tabl A. 12: Narsid road disruption avrag spd pr sction (mph)... 85

11 List of Figurs Figur 3.1: Hypothtical ntwork with two links Figur 3.2: Elmnts of MITSIMLab and thir intractions Figur 3.3: Calibration and validation framwork...34 Figur 3.4: Framwork for masuring ntwork capacity Figur 4.1: N tw ork D scription Figur 4.2: Ntwork as sn in MITSIMLab Figur 4.3: Location of snsors Figur 4.4: Victoria Ntwork - Location of Strt-work Figur 4.5: Victoria Ntwork - Location of Strt-work Figur 4.6: Victoria Ntwork- Location of Strt-work Figur 4.7: Victoria Ntwork - Location of Strt-work Figur 4.8: Strt-works - Ntwork Capacity Figur 4.9: Strt-works - Impact on spd Figur 4.10: Strt-works - Capacity v/s Spd Figur 4.11: Victoria Ntwork - Locations of nar-sid lan disruptions Figur 4.12: Nar sid lan disruptions - Ntwork Capacity...64 Figur 4.13: Nar sid lan disruptions - Ntwork Capacity comparison...65 Figur 4.14: Nar sid lan disruptions - Impact on spd Figur A. 1: Strt-works locations on th ntwork Figur A.2: Journy tim sctions as survyd...76

12 12

13 Chaptr 1 Introduction 1.1 Motivation Traffic congstion is a major problm in all th major citis around th world. According to th 2009 Urban Mobility Rport (Schrank and Lomax 2009), in 2007, congstion causd urban Amricans to travl 4.2 billion hours mor and to purchas an xtra 2.8 billion gallons of ful for a congstion cost of $87.2 billion - an incras of mor than 50% ovr th prvious dcad. Furthr, with th rapid growth of population and car ownrship, thr is trmndous prssur on th xisting roads, thrby worsning th problm of traffic congstion. Thrfor, addrssing this problm has bn a major transportation priority in all th major citis. City planning and urban dsign practics can hav a hug impact on th lvls of futur traffic congstion. Congstion can also b rducd by ithr incrasing th road capacity or by rducing traffic dmand. Road capacity can b incrasd by building nw roads or through traffic managmnt improvmnts. Traffic dmand can b rducd through Th stratgis of this typ includ flxibl work schduls (that allow mploys to travl offpak), transit-orintd rgional dvlopmnt, community-basd car-sharing tc. as wll as rstrictiv masurs such as parking rstrictions, road and congstion pricing tc.. Congstion pricing is a systm of charging usrs of a transportation ntwork in priods of pak dmand to rduc traffic congstion. This has bn applid on urban roads in citis lik London, Stockholm, Singapor tc. In London, a f of E8 is chargd on som vhicls for ach day th vhicl ntrs or travls within crtain parts of London dsignatd as th Congstion Charging Zon (CCZ).

14 According to th sixth annual impacts monitoring rport (Transport for London, 2008) on cntral London congstion charging, 2003 and th yars immdiatly following th introduction of th original schm - saw avrag rductions in congstion of 30 prcnt against th rprsntativ 2002 baslin. Furthr, th lvl of traffic of all vhicl typs ntring th cntral Congstion Charg Zon was now consistntly 16% lowr in 2006 than th pr-charg lvls in 2002 (Transport for London, 2007). Also, th congstion charg brought in an annual oprating nt incom of 89.1 m for TfL during th financial yar 2006/07 (Transport for London, 2007). But, rcnt masurmnts of congstion hav indicatd that conditions ar dtriorating. Data from th congstion charging monitoring programm in cntral London indicat a substantial loss of charging-rlatd dcongstion bnfits ovr th last 18 months within th original charging zon. Th avrag rduction for th 2005 calndar yar was 22 prcnt, lowr than 2003/ and 2007 howvr saw acclrating loss of th original congstion bnfits. Avrag congstion in 2006 was just 8 prcnt blow pr charging lvls. Avrag congstion in 2007 was idntical to rprsntativ pr charging valus. This is in spit of sustaind rduction in th volum of traffic circulating within th original charging zon (Transport for London, 2008). Th convntionally-assumd rlationship btwn traffic volums and dlay appars to hav rvrsd: in rcnt yars, falling traffic has bn associatd with incrasd dlay. This firmly points to a rduction in ffctiv ntwork capacity. Thr can b a numbr of rasons for th loss of ffctiv capacity. Som of thm ar chang in vhicl traffic flt composition, incras in th numbr and lngth of bus and cycl lans, incras in th numbr of advancd stop lins at traffic signals, incras in th numbr of non-rcurrnt congstion causs lik strt-works and incidnts, incras in th numbr of traffic signals and pdstrian crossings and mod shift to buss, cycls and othr mods of public transport. Furthr, ths intrvntions may or may not hav similar impacts across diffrnt ntworks. Howvr, thr is littl in th way of dirct causal vidnc to substantiat this hypothsis and, smingly, no stablishd framwork

15 for xprssing th capacity impact - and hnc th congstion impact - of th many divrs intrvntions in a ntwork as wll as across ntworks. This has motivatd th attmpt to stablish a common capacity currncy and a quantitativ framwork to undrstand ths ffcts and to arbitrat mor rationally btwn thm. Is it 'bttr' to dvot ntwork capacity to contributing to fwr popl killd in road traffic accidnts, or to providing fastr and mor rliabl journys to, for xampl, fright and srvicing trips? Is th combind impact of svral diffrnt intrvntions gratr than th sum of thir individual impact; do thy intract in a compounding way? To answr ths qustions, thr is a nd for a framwork that can account for road capacitis at link, junction and ntwork lvl. 1.2 Thsis Contributions Th main contribution of this thsis is th dvlopmnt of a framwork that can account for road capacitis at a ntwork lvl. This has bn don by stablishing a common currncy so that th impact of diffrnt activitis can b compard across scnarios. In this rsarch, w propos to masur ntwork capacity in trms of vhicl-km pr hour. This is th total distanc travld by all th vhicls that can b accommodatd in th ntwork ovr a priod of on hour. It is important to associat th avrag spd of th vhicls with this masur of ntwork capacity to bttr undrstand th impact of various intrvntions on th ntwork. W propos a simulation framwork using a microscopic traffic simulator, MITSIMLab (Yang and Koutsopoulos, 1996) to masur th capacity of a ntwork with and without intrvntions. Th thortical capacity of th ntwork can b obtaind by flooding th modl i.. by scaling th O-D matrix. Th maximum numbr of vhicls which can b accommodatd in th ntwork (i.. can b loadd in th ntwork bfor pr-trip quus start forming) is dfind as th capacity of a ntwork. To th bst of our knowldg, this

16 is th first tim whr th flooding approach (incrasing th traffic dmand) has bn usd to analyz th capacity at a ntwork lvl. Th framwork dvlopd is applid to a ntwork from London, U.K. to valuat th impact of strt-works and nar sid lan disruptions on th ntwork. Using th calibratd modl, capacity analysis is don for diffrnt locations of strt-works and nar-sid lan disruptions. 1.3 Thsis Outlin Th rmaindr of th thsis is organizd as follows. A rviw of th litratur on ntwork capacity is prsntd in Chaptr 2. Th modling mthodology and framwork to masur ntwork capacity is dtaild in Chaptr 3. Chaptr 4 is a cas-study of a ntwork from th city of London, U.K. whr MITSIMLab, a microscopic traffic simulation laboratory dvlopd for valuating diffrnt traffic managmnt systms, is calibratd and capacity analysis don using th fram-work mntiond in chaptr 3 to valuat th impact of strt-works and nar sid lan disruptions on ntwork capacity. Finally, th thsis summary and dirctions for futur rsarch ar discussd in Chaptr 5.

17 Chaptr 2 Litratur Rviw This chaptr is prsntd in two parts: th first sction rviws th work don to stimat road traffic capacity using mpirical mthods. Th scond sction dtails studis focusing on capacity analysis using simulation tools. 2.1 Capacity Analysis using Empirical Mthods Th Highway Capacity Manual (TRB 2000) provids th traditional basis for a standardizd analysis of road traffic qualitis. It contains concpts, guidlins, and computational procdurs for computing th capacity and quality of srvic of various highway facilitis, including frways, highways, artrial roads, roundabouts, signalizd and unsignalizd intrsctions, rural highways, and th ffcts of mass transit, pdstrians, and bicycls on th prformanc of ths systms. Traffic quality is classifid into six "lvls of srvic" (LOS) which ar dnotd by th lttrs A (fr flow traffic) through F (congstd). Th LOS concpt as it is currntly usd is strictly bound to a short intrval valuation priod (.g., 1 hour). LOS classifications ar basd on on or mor "masur of ffctivnss" (MOE), such as avrag travl vlocity. Th MOEs incorporat th dcisiv aspcts of traffic quality. Usually thr is no objctiv way to dtrmin th thrshold MOE valus usd to dfin a particular LOS. A mor rational mannr of drivation would b dsirabl, spcially to discriminat btwn th highr LOS lik D (sufficint) to E (capacity) to F (ovrsaturation) (Brilon, 2000). Gistfldt (2008) compard th stochastic capacitis with convntional capacity valus. Convntional dsign capacitis givn in guidlins lik th HCM (TRB, 2000) or th Grman HBS (FGSV, 2001) ar basd on th analysis of spd-flow diagrams. Th

18 volum at th apx of th spd-flow rlationship is tratd as th capacity of th facility. In contrast, mthods for stochastic capacity analysis dlivr a capacity distribution function, which rprsnts th probability of a traffic brakdown in dpndnc on th flow rat. For a considrabl numbr of data sampls from frway sctions in Grmany, th brakdown probability that corrsponds to th capacity obtaind from th spd-flow diagram was dtrmind. Compard to th impact of spd diffrncs in fluid traffic, a traffic brakdown ntails significant dlays for th usrs of a frway. Hnc, th rsarchr proposs that th brakdown probability b usd an important masur of ffctivnss, bcaus it rprsnts th rliability of traffic opration. Dfining a maximum accptabl brakdown probability could thrfor b considrd as an altrnativ way to driv dsign capacitis. Hyd and Wright (1986) proposd two xtrm valu mthods to stimat road traffic capacity. Th rsarchrs gav considration to th variations in flow which occur ovr a tim during normal traffic conditions, and to th charactristics of th xtrm valus which occur from tim to tim undr ths conditions. Two distinct typs of statistical thory can b applid to xtrm valus. First, on can apply straightforward probability thory, to prdict th largst flows likly to b obsrvd during a givn priod, assuming an idalizd traffic stram with a known flow counting distribution. Scond, on can attmpt to dduc an uppr limit from obsrvd flow data using asymptotic mthods of th kind which ar frquntly usd in connction with mtorological and flood dfns problms. Both mthods wr applid to a sampl of 9000 flow valus rcordd at a sit in London. Both mthods showd a rasonabl fit to th data, but only th asymptotic mthod rvals a clar uppr limit. Th drawback is that it might b difficult in applying ths mthods undr spcific intrvntion scnarios, particularly in attributing th loss of capacity whnvr an incidnt occurs on th road. Mindrhoud t al. (1997) studid th mpirical capacity stimation for unintrruptd roadway sctions. Hadways, traffic volums, spd, and dnsity ar traffic data typs usd to idntify four groups of capacity stimation mthods. Aspcts such as data rquirmnt, location choic, and obsrvation priod wr invstigatd for ach mthod.

19 Among th mthods studid wr th hadway distribution approachs, th bimodal distribution mthod, th slctd maxima, and th dirct probability mthod. Of th mthods basd on traffic volum counts, th rsarchrs rcommnd th product limit mthod for practical application bcaus of sound undrlying thory. Attmpts to dtrmin th validity of xisting roadway capacity stimation mthods wr disappointing bcaus of th many ambiguitis rlatd to th drivd capacity valus and distributions. Lack of a clar dfinition of th notion of capacity is th main hindranc in undrstanding what xactly rprsnts th stimatd capacity valu or distribution in th various mthods. If this dficincy is corrctd, promising mthods for practical us in traffic nginring ar th product limit mthod, th mpirical distribution mthod, and th wll-known fundamntal diagram mthod, in that ordr. Ovrall, though rsarch has bn don on analyzing capacity using othr indicators lik spd and dnsity, thr has not bn much study in capacity prdiction, particularly on quantifying th capacity impact of intrvntions on th ovrall ntwork. Th nxt sction discusss th work don to analyz capacity using simulation. Rsults from a cas-study in London ar also prsntd. Th work discussd in th nxt sction is closly rlatd to th work don in this thsis. 2.2 Analyzing capacity using simulation approach Thr hav bn svral studis mntiond in th litratur whr capacity was analyzd through th us of simulation tools. Sinha t al. (2007) xamind th modling of incidnts in microscopic simulation modls and th ffcts of calibration paramtrs on th simulatd rductions in capacity du to incidnts. It is ssntial that simulation programs b abl to modl corrctly th rductions in highway capacity du to incidnts and th lan changing bhaviors of drivrs ahad of incidnt locations. Th rsarchrs simulatd a basic frway sgmnt using thr widly usd microscopic simulation modls - CORSIM (FHWA 2006), VISSIM (PTV 2009), and AIMSUN (Transportation Simulation Systms 2009). Calibration paramtrs of th thr modls wr varid to

20 dtrmin if it is possibl to calibrat th modls to achiv targt link capacity valus for both incidnt and no incidnt conditions. Th targt capacity valus usd in th invstigation wr thos prsntd in th HCM It was found that thr is a nd to calibrat modl paramtrs in all th thr modls to produc accptabl rductions in capacitis du to incidnts. Furthr, thr is a nd to introduc incidnt-spcific timvariant calibration paramtrs in AIMSUN and VISSIM. In this study, th capacity of a link in a simulation modl was dfind as th throughput in vhicls pr hour that can pass through th link whn thr is nough traffic dmand to rach this capacity. Th traffic dmand volum in th simulation modl was incrasd until th throughput rachs its maximum valu. This maximum valu was thn considrd as th link capacity. Jha (1998) also varid th dmand in his simulation xprimnts around capacity for th analysis of th impact of frway bottlncks. Also, Jha and Birlair (1998) studid th rduction in throughput du to a bottlnck at a frway mrging sction in a simulation framwork by fixing th main-lin dmand and varying th onramp dmand. Howvr, this study was also limitd to a link lvl and not tstd at a ntwork lvl. Mindrhoud and Bovy (1999) conductd a simulation study to assss th ffct of an (autonomous) intllignt cruis control (ICC) on th traffic-flow charactristics on motorways. Tn diffrnt ICC dsigns ar invstigatd and compard with a rfrnc situation without such support systms. A capacity analysis was prformd for a common bottlnck situation: an on-ramp to a two-lan motorway. On th basis of th simulation rsults, som unxpctd findings mrgd. Support systms that support th drivr at all spds and that do not rstrict th dclration lvl giv ris to capacity gains of about 12 prcnt. Howvr, th first-gnration ICC systms will hardly incras traffic-flow prformanc. A spcial stop-and-go ICC dsign did not improv th traffic-flow quality. It was found that, rgardlss of th ICC typ, a hadway stting of 1.2 s did not chang roadway capacity nar an on-ramp bottlnck significantly. In London, a study was carrid out to assss th impact of typical ntwork changs on traffic capacity. Th work don in this study is closly rlatd to th work don in th

21 thsis. Hnc th rsults of this study ar prsntd in dtail. A VISSIM micro-simulation modl basd on a sction of th A2 road was chosn for this study. Th matrial prsntd in th subsqunt part of this sction is basd on th rport for Transport for London (TfL) titld "Impact of Intrvntions on Road Capacity in London", In this study, th following six diffrnt scnarios wr tstd: A chang in spd limit Mod shift Bus and cyclist intractions Long trm strt-works Narsid lan disruptions (tmporary parking) * Pdstrian facilitis Each tst conductd in this study was rportd moving from a 'macro' to 'micro' scal. Th thr scals considrd wr: th ntwork, th corridor, th sctions. Th 'ntwork' includs th ntir VISSIM ntwork, including sid roads and th sctions of th main corridor byond th dg of th survyd sctions. This scal of analysis nabls th usag of ntwork wid dfault statistics which ar mor rliabl than th aggrgation of rcordd localizd statistics. Th 'corridor' corrsponds to th sctions of th ntwork for which journy tim survys wr carrid out. Th 'sctions' corrspond to th initial sctions of th journy tim survys. Ths smallr lmnts of th ntwork nabl a mor rfind analysis, which is particularly usful for th local intrvntion tsts. Th first thr tsts - chang in spd limit, mod shift and bus and cyclist intraction - rlat to ntwork-wid changs. Th nxt thr tsts - long trm strt-works, narsid road disruptions, pdstrian facilitis - corrspond to mor localizd intrvntions or disruptions. In ordr to valuat th impact of intrvntions at diffrnt lvls of saturation, th trip matrix was adjustd by a uniform factor. Th study idntifid th impact, both in trms of traffic spd and th rsulting chang in conomic cost to road usrs, of th intrvntions listd abov. Th approach takn was to modl th changs in journy tim by mod (car, LGV, HGV, bus, motorcycl and cycl)

22 and to apply a valu of tim in lin with valus providd by TfL in th Businss Cas Dvlopmnt Manual (BCDM). Du to th possibility of diffrncs in vhicl numbrs btwn bas and tst scnarios (.g. du to off-ntwork quuing), th sam lvl of vhicl flow for th bas/tst comparison was assumd in ach cas, so that th ffct of th journy tim chang can b valuatd i.. a fixd-trip matrix was usd. Th 2007 valus of tim wr usd; ths ar shown in Tabl 2.1. Thy hav bn xprssd as valus of tim pr vhicl, so avrag vhicl occupancy has bn takn into account. Tabl 2.1: Valu of Tim for ach vhicl typ Vhicl Typ Valu of Tim (E/hr) Car LGV HGV Bus Motorcycl 8.27 Pdstrian 5.23 Cycl 6.59 Th conomic analysis was carrid out at th corridor and sction scals only, as th ntwork outsid th corridor was not validatd against journy tim survys and ntry links to th ntwork ar not rgardd as ncssarily rprsntativ of ntwork conditions. (In rspct of th lattr, th arrival flow profil might not b in accordanc with strt conditions and th upstram junctions ar simply not rprsntd). A summary of th rsults undr ach intrvntion scnario is prsntd in th rst of this sction. Chang in spd limits Th rsults from th comparison of a chang in spd limit btwn 30mph and 20mph ar:

23 Th avrag spd across th ntwork is lowr than th spd limit, so th impact of any chang is thrfor likly to b small Vhicls spnd 20% of thir tim abov 20mph in th bas cas scnario with a 30mph spd limit Saturatd conditions rsult in an incras in traffic flow instability Th impact on th avrag spd of th 30mph to 20mph spd limit chang is a rduction in avrag spd of btwn 11 and 12% i.. (11.1mph to 10.0mph at 85% saturation) Vhicls hav a smoothr progrssion with lss tim quuing with a lowr spd limit. Mod shift A proportionat transfr of trips from car to bus, to cycl (and to both) was tstd; th charactristics of th bas mod shar is thrfor rlvant to th analysis - in th modl ara bus us accounts for around 25% of prson trips, and th impacts of this tst ar gratr than for a shift to cycling, which has a lowr local mod shar. At currnt dmand lvls, th shift to bus or cycl rsults in a spd incras of btwn 11% and 57%; at highr dmand lvls a lowr bnfit is obsrvd. Som dtaild obsrvations rgarding th mod shift comparison ar as follows: Th two ky implications of mod shift for traffic ntwork capacity (and stability) ar (1) th ffcts it has on traffic volum and thrfor ntwork saturation, and (2) th intraction btwn ths vhicls within and across lans - an issu of ntwork fficincy. Th shift in traffic conditions (saturation) rsulting from changs in mod has th gratst impact. A mod shift which movs th ntwork conditions from saturatd to fully ovr saturat gnrats additional instability in a singl sction of th ntwork and maks it difficult to draw widr conclusions. On th othr hand, a drop in traffic larg nough to crat fr flowing ntwork conditions has a gratr impact than that gnratd by th oprations of an individual transport mod.

24 Th incras in cyclist volums has lss oprational impact on othr usrs whn th rst of th ntwork is busy, although in all cass thr is an impact on bus oprations in bus lans sinc buss xprinc difficultis in ovrtaking cyclists. Stting asid capacity issus, a mod shift that rsults in fwr vhicls in total is likly to b bnficial sinc, at last in modling trms, th gratr th intraction btwn vhicls, th gratr is likly to b th variation in ntwork traffic spd from on run to th nxt. * Incrasing bus volums has a ngativ conomic impact on gnral traffic journy tim at a low lvl of mod shift; at th highr lvl tstd, thr is a positiv conomic impact. Bus & bicycl intractions This tst asssss th impact of incrasing th volum of cyclists in th bus lans. Th rst of th gnral traffic rmains th sam and thrfor this tst is not dirctly comparabl to th othr tsts prformd for this study. Th rsults from th bus and cycl intraction tst in bus lans show that: * In unsaturatd conditions, th incras in cycl volums has a limitd impact on othr mods' spd, but buss ar affctd. * Cyclists 'jump' th quus and thrfor can gnrat major dlays to othr vhicls on narrow and congstd strtchs of road. Long trm strt-works Th rsults from th long-trm 80m strt-work comparison show that impact can b significant dpnding on th intrvntion location. This dpnds on: Th xisting saturation lvl and th futur saturation lvl at that location Whthr th strt-works mrly shifts traffic managmnt faturs (.g. a mrg) from an xisting 'normal' mrg to an upstram 'strt-works' mrg, or is a 'nw' intrvntion.

25 Howvr for th ntwork tstd, th ffct of any individual intrvntion is minimal at a ntwork lvl, providd such an intrvntion dos not mak th individual location ovrsaturatd. Howvr whn a numbr of strt-works tak plac at th sam tim in th sam ara, thy hav a combind ffct markdly gratr than th sum of th individual intrvntions. Narsid lan disruption Th narsid lan disruption shows that: A 20 minut parking stay has a mor ngativ impact than an quivalnt numbr of 1 or 5 minut stays. * Narsid road usrs, buss in particular, ar mor affctd than th rst of th gnral traffic. * Narsid lan disruptions incras journy tim variability by up to 18% on th surrounding road sctions, vn in fr flow conditions. Pdstrian facilitis This tst analyss th impact of th upgrad of traffic signal pdstrian intrgrn from th old standards to TTS6. Th prvious 'pdstrian to gnral traffic' intrgrn was assumd to corrspond to th pdstrian claranc tim at a walking spd of 1.2 mtrs pr scond. Th currnt TTS6 standard provids mor grn tim for pdstrians. Th currntly validatd VISSIM modl complis with TTS6 standards and thrfor th VISSIM modl has bn downgradd to th 1.2 m/s claranc tim. Th rsults of this chang in pdstrian intrgrn tim show that: Th chang affcts a vry limitd numbr of intr-grns. Th chang has a significant impact whr it is implmntd. Th conomic cost of th intr-grns updat dpnds on th balanc btwn th volum of traffic and th volum of pdstrians and is thrfor sit spcific.

26 Scnario Comparison Tabl 2.2 shows a summary of th tsts in trms of conomic cost pr 1000 vhicls. Tabl 2.2: Total cost saving (f pr 1000 vhicls) Total cost saving (E pr 1000 vhicls) Saturation % 79% 85% 90% 92% 95% 103% 30mph to 20mph spd limit Strt-work Strt-work Long Trm Strt-works Narsid lan disruption Pdstrian facility Strt-work Strt-work Sum of Individual Strt-works All strt-works min incidnt min incidnt min incidnt Bfor to aftr TTS6 intr-grns Tabl 2.3 shows a summary of th tsts in trms of conomic cost pr 1000 trips.

27 30mph to Tabl 2.3: Total cost saving (f pr 1000 trips) Total cost saving (f pr 1000 trips) Saturation % 79% 85% 90% 92% 95% 103% Avrag Rank 20mph spd limit Strt-work Strt-work Long Strt-work Trm Strt-work Strt- works works Individual Sum of Narsid Strt-works All strt-works min incidnt lan 5 min incidnt disruption 20 min incidnt Bfor to aftr Pdstrian TTS6 intr facility grns Two scnario sts of rsults sts ar not comparabl to th othrs, but with rfrnc to th un-wightd avrag impact pr 1000 trips across all saturation lvls in Tabl 2.3, th four most important individual intrvntions ar as follows:

28 " Th narsid lan disruption (20 minut incidnt) Th 30 mph to 20 mph spd limit chang Th pdstrian facility Th 80 mtr strt-works Buss ar likly to b mor affctd than othr gnral traffic by th narsid lan disruption and th conomic analysis wighs th impact on buss mor havily than for othr mods by virtu of th avrag loading assumd. Th analysis has indicatd th complxitis of th ntwork intrvntions and th intrprtation of th rsults. A mor dtaild discussion of th rsults from this study is prsntd in Appndix A. Th drawback of this study is that capacity is analyzd by using th avrag spds of vhicls and conomic impacts as standards of masur. Absolut valu of ntwork capacity undr diffrnt intrvntion scnarios, usful for comparing th capacity valus is not at all prsntd. 2.3 Summary In summary, rviw of litratur rvald that diffrnt mpirical and simulation mthods wr usd to masur road capacity. Rsarch has bn don to analyz capacity using th spd-flow diagram and othr mpirical mthods. But, thr has not bn much study in capacity prdiction, particularly on quantifying th capacity impact of intrvntions on th ovrall ntwork. In a fw of th simulation basd capacity analysis mthods prviously usd, capacity has bn dtrmind by incrasing th travl dmand and rcording th maximum throughput. Howvr, such analysis has bn limitd to link lvl and no litratur was found on application of such mthodology in ntwork lvl. Th nxt chaptr dtails th mthodology and framwork to masur ntwork capacity.

29 Chaptr 3 Mthodology This chaptr prsnts a gnral mthodology and framwork to masur ntwork capacity. As mntiond in th prvious chaptr, thr has bn no indpndnt unit dvlopd to masur th capacity of a ntwork as a whol. Sction 3.1 dals with dvloping a common unit indpndnt of ntwork and typ of intrvntion for masuring capacity. Sction 3.2 laborats th modling framwork dtailing th traffic simulator usd in this study, th calibration and validation framwork including th various goodnss-of-fit masurs usd and finally th framwork for masuring ntwork capacity. 3.1 Ntwork Capacity It is ssntial to dvlop a common currncy to masur ntwork capacity so that diffrnt disruption vnts and activitis on th ntwork can b xprssd on a common basis. This would facilitat xploration of traffic impacts in conjunction with a suitabl modlling or simulation framwork, and would provid a basis for assssing th rlativ scal or intnsity of th diffrnt typs of intrvntion. It could b nvisagd that an ordrd procss for dtrmining th capacity of th ntwork, strt-by-strt, junction-by-junction (link-by-link, nod-by-nod) could b constructd. Thr is, howvr, a fundamntal issu that nds to b addrssd. In convntional linkbasd ntwork analysis, capacity is dfind by th maximum numbr of vhicls (or passngr car units) that can pass a point in a fixd tim. Passngr Car Unit (PCU) is a wightd masur for diffrnt vhicl typs. PCU valus for diffrnt typs of vhicls dpnd on th various charactristics of th vhicl lik its hight, lngth and width. A

30 car is givn a PCU valu of 1. Havy vhicls lik buss and trucks hav PCU valus gratr than 1 and two whlrs lik bicycls and motorcycls hav PCU valus lss than 1. Such throughput capacity can dpnd on othr flows in th systm lading to a 'nonsparabl' problm, and th ffctiv capacity of a ntwork can b limitd by that of a bottlnck, whr links ar connctd dynamically by th rout pattrn. Thrfor, summing link capacitis is not sufficint to dfin th ffctiv capacity of a ntwork. For xampl, considr a hypothtical two link ntwork as shown in Figur 3.1 A B C Figur 3.1: Hypothtical ntwork with two links Link AB with four lans is connctd with a two lan link BC. Th dirction of traffic flow is from A to C. Hr, th capacity of link AB is twic th capacity of link BC. But whn th two links ar connctd togthr to form a simpl ntwork, th ovrall ffctiv capacity of this ntwork is limitd by th bottl-nck at B whr th four lans shrink to two lans. Hnc th ffctiv capacity of this ntwork cannot b qual to th sum of th two link capacitis individually. Th numbr of stationary vhicls that can b physically accommodatd in a ntwork (static capacity) is also an insufficint mans of dtrmining ntwork capacity as th valu of dynamic capacity is of mor importanc. Nvrthlss, both static and throughput capacitis contribut to and ultimatly dtrmin th ffctiv capacity of th whol ntwork. Thrfor, th following indpndnt unit of masurmnt for ntwork capacity is proposd in this rsarch. Just as link throughput capacity is dfinabl in units such as

31 PCUs/hour, th logical masur of ntwork capacity is th amount of travl possibl in a givn tim, i.. PCU-km/hour. It is th total distanc travlld by all th vhicls ovr a priod of on hour. Furthr, th avrag spd of th vhicls can b associatd with this valu of ntwork capacity for bttr undrstanding of th impact of various intrvntions on th ntwork. In this study, w propos to masur th capacity of a ntwork through a simulation framwork by taking a small sizd ntwork. A thortical ntwork capacity can b producd by flooding th modl i.. by changing th scaling factor in th OD matrix. This has bn furthr discussd in dtail in th subsqunt sctions of this chaptr. Thus, it appars fasibl to hav a mthod that provids a ralistic mans of masuring th ntwork capacity and also b abl to incorporat into it th impact of intrvntions that hav a dtrimntal impact on ntwork capacity. Th matrial in this sction is basd on th TfL rport titld "Invntory of Ntwork Capacity and Activity: A Mthod for Calculating th Capacity of th CCZ", Modling Framwork MITSIMLab MITSIMLab (Yang and Koutsopoulos, 1996) is a simulation-basd laboratory that was dvlopd for valuating th impacts of altrnativ traffic managmnt systm dsigns at th oprational lvl and assisting in subsqunt rfinmnt. Exampls of systms that can b valuatd with MITSIMLab includ advancd traffic managmnt systms (ATMS) and rout guidanc systms. MITSIMLab is a synthsis of a numbr of diffrnt modls and has th following charactristics: Rprsnts a wid rang of traffic managmnt systm dsigns; Modls th rspons of drivrs to ral-tim traffic information and controls;

32 Incorporats th dynamic intraction btwn th traffic managmnt systm and th drivrs on th ntwork. Th various componnts of MITSIMLab ar organizd in thr moduls: " Microscopic Traffic Simulator (MITSIM) Traffic Managmnt Simulator (TMS) Graphical Usr Intrfac (GUI) Th intractions among th various MITSIMLab moduls ar shown in Figur 3.2. A microscopic simulation approach, in which movmnts of individual vhicls ar rprsntd, is adoptd for modling traffic flow in th traffic flow simulator (MITSIM). This lvl of dtail is ncssary for an valuation at th oprational lvl. Th Traffic Managmnt Simulator (TMS) rprsnts th candidat traffic control and routing logic undr valuation. Th control and routing stratgis gnratd by th traffic managmnt modul dtrmin th status of th traffic control and rout guidanc dvics. Drivrs rspond to th various traffic controls and guidanc whil intracting with ach othr. Graphical Usr Intrfac (GUI) Figur 3.2: Elmnts of MITSIMLab and thir intractions

33 Traffic Flow Simulator (MITSIM): Th rol of MITSIM is to rprsnt th "world". Th traffic and ntwork lmnts ar rprsntd in dtail in ordr to captur th snsitivity of traffic flows to th control and routing stratgis. Th main lmnts of MITSIM ar: " Ntwork Componnts: Th road ntwork along with th traffic controls and survillanc dvics ar rprsntd at th microscopic lvl. Th road ntwork consists of nods, links, sgmnts (links ar dividd into sgmnts with uniform gomtric charactristics), and lans. Travl Dmand and Rout Choic: Th traffic simulator accpts as input timdpndnt origin to dstination trip tabls. Ths OD tabls rprsnt ithr xpctd conditions or ar dfind as part of a scnario for valuation. A probabilistic rout choic modl is usd to captur drivrs' rout choic dcisions. Driving Bhavior: Th origin/dstination flows ar translatd into individual vhicls wishing to ntr th ntwork at a spcific tim. Bhavior paramtrs (such as dsird spd, aggrssivnss, tc.) and vhicl charactristics ar assignd to ach vhicl/drivr combination. MITSIM movs vhicls according to car-following and lan-changing modls. Th car-following modl capturs th rspons of a drivr to conditions ahad as a function of rlativ spd, hadway and othr traffic masurs. Th lan-changing modl distinguishs btwn mandatory and discrtionary lan changs. Mrging, drivrs' rsponss to traffic signals, spd limits, incidnts, and tollbooths ar also capturd. Traffic Managmnt Simulator (TMS): Th traffic managmnt simulator mimics th traffic control systm in th ntwork undr considration. A wid rang of traffic control and rout guidanc systms can b simulatd, such as: Ramp control Frway mainlin control Lan control signs (LCS) Variabl spd limit signs (VSLS) Portal signals at tunnl ntrancs (PS) * Intrsction control

34 Variabl Mssag Signs (VMS) In-vhicl rout guidanc TMS has a gnric structur that can rprsnt diffrnt dsigns of such systms with logic at varying lvls of sophistication (from pr-timd to rsponsiv). Graphical Usr Intrfac (GUI). Th simulation laboratory has an xtnsiv graphical usr intrfac that is usd for both, dbugging purposs and dmonstration of traffic impacts through vhicl animation Calibration Framwork Th procss of calibration of th simulation systm aims to st that obsrvd traffic conditions ar accuratly rplicatd. framwork is summarizd in Figur 3.3. th various paramtrs so Th ovrall calibration Goodnss of Fit Statistics Tst =Originally stimatd paramtrs P =Calibratd paramtrs OD =Origin dstination flows Figur 3.3: Calibration and validation framwork

35 Th calibration procss consists of two stps: initially, th individual modls of th simulation ar stimatd using disaggrgat data. Disaggrgat data includs dtaild drivr bhavior information such as vhicl trajctoris. Th rquird xplanatory variabls including spds and rlations btwn th subjct vhicl and othr vhicls can b gnratd from th trajctory data. Th disaggrgat analysis is prformd within statistical softwar and dos not involv th us of a simulation systm. In th scond stp, th simulation modl as a whol is calibratd using aggrgat data lik flows, spds, occupancis, tim hadways, travl tims, quu lngths tc. Th procss of aggrgat calibration of th simulation systm aims to adjust th various paramtrs so that obsrvd traffic conditions ar accuratly rplicatd. Ths paramtrs consist of th paramtrs of th bhavior modl (initially stimatd paramtrs p0 adjustd to pg) and th travl dmand (xprssd in trms of origin - dstination or OD flows). Also, in spcial cass, du to limitations of th availabl disaggrgat datast it may not b possibl to stimat all th paramtrs of th modl in th first stp. For xampl, if th stimation datast dos not hav toll lan, it will not b possibl to captur th ffcts of th toll lan-spcific variabls during th stimation stp. In such cass, th valus of ths omittd paramtrs can b capturd during th aggrgat calibration. Onc th calibration is complt, th valus of th full st of bhavioral paramtrs ar fixd (fp) and a scond st of data is usd for validation. Application of th simulation to rplicat this datast also rquirs OD flows as input. Howvr, ths may b diffrnt from th ons obtaind in th calibration phas and so th OD stimation componnt of th calibration must b r-don for this datast. Ths nw OD flows and th calibratd paramtr valus ar usd as inputs to th simulation systm. Problm Formulation Aggrgat calibration can b formulatd as an optimization problm, which sks to minimiz a function of th dviation of th simulatd traffic masurmnts from th obsrvd masurmnts and of th dviation of calibratd valus from th a-priori

36 stimats of th OD flows and th stimatd bhavior paramtrs. Th formulation prsntd hr assums that th obsrvations ar drawn during a priod in which stady stat traffic conditions prvail. That is, whil OD flows and modl paramtrs may vary for various obsrvation days, ths diffrncs ar du to random ffcts and do not rprsnt a chang in th undrlying distributions of ths variabls. Furthrmor, driving bhavior paramtrs ar assumd to b stabl ovr th priod of obsrvation. It is important to not that th stady stat assumption concrns th variability btwn obsrvation days, and not within ach obsrvation day. Th formulation is shown blow. Th first and scond trms in th objctiv function ar a masur of dviation btwn obsrvd and simulatd masurmnts and btwn a priori OD flows and th stimatd OD flows rspctivly. Th first constraint shows th dpndnc of simulatd masurmnts on th driving bhavior paramtrs, OD flows and th ntwork conditions. Th scond constraint is a non-ngativity constraint for th OD flows. min (Ms" -M,obs) W-' (Ms'"-Mobs) +(OD-ODO V-' (OD-OD) OD = s.t. MS'" = S (,OD) OD >0 Whr, 8 =driving bhavior paramtrs OD =OD flows ODO =a priori ODflows N =numbr of days for which snsor data is availabl MS"" =simulatd masurmnts M obs =obsrvd masurmnts for day i S =th simulation modl function, which gnrats simulatd traffic masurmnts W= varianc-covarianc matrix of th snsor masurmnts V = varianc-covarianc matrix of th ODflows

37 Th snsor masurmnts in this cas constitut of th traffic flows and spds masurmnts at all snsor stations and all tim intrvals. Th formulation prsntd abov is difficult to solv bcaus of th absnc of analytical formulations that rlat th affct of bhavior paramtrs to th snsor masurmnts and rlativly larg numbr of paramtrs to calibrat. An itrativ solution approach is thrfor adoptd. In ach itration, first th driving bhavior paramtrs ar kpt fixd and th OD flows ar stimatd. Thn th OD flows ar kpt fixd and th driving bhavior paramtrs ar stimatd. Th numbr of bhavior paramtrs in th simulation modl is vry larg. It is not fasibl to calibrat all of thm. A snsitivity analysis is oftn don to idntify th paramtrs that contribut most in improvmnt of th objctiv function. In snsitivity analysis, th impact of an individual factor on th ovrall prdictiv quality of th simulator is masurd whil kping all othr paramtrs at thir original valus. Th dtails of th calibration mthodology ar prsntd by Bn-Akiva t al. (2003) Goodnss-of-fit masurs Modl validation typically includs in it th tasks of aggrgat calibration and aggrgat validation. Th aggrgat calibration procss involvs adjusting th valus of th paramtrs of th bhavioral modls and stimating travl dmand, in th form of OD flows, on th ntwork bing studid in ordr to obtain a bttr fit of th modl output with th actual traffic flow. Th aggrgat validation procss involvs using th calibratd modl on a diffrnt datast to dtrmin th xtnt to which th modl accuratly rplicats traffic bhavior.

38 A numbr of goodnss-of-fit masurs can b usd to valuat th ovrall prformanc of th simulation modl. Popular among thm ar th root man squar rror (RMSE) and root man squar prcnt rror (RMSPE). Th two masurs ar givn by: RMSE - (Ysim -Ybs 2 VNn=1 ' N sun _ bs 2 RMSPE = -1 sr - ysj Nl 1 Ybs Whr, Y"bs and YS'" ar th avrags of obsrvd and simulatd masurmnts at spactim point n, calculatd from all availabl data (i.. svral days of obsrvations and/or multipl simulation rplications). RMSE and RMSPE pnaliz larg rrors at a highr rat rlativ to small rrors. Othr masurs includ - Man Error (ME) and Man Prcnt Error (MPE) ME and MPE indicat systmatic undr-prdiction or ovr-prdiction in th simulatd masurmnts. Ths masurs ar givn by: ME =- (Y'- -Y"obs N ' M= N y sin _yobs MPE=- " "nyobs N _= Y" Whr, Yb' and Y2' ar th avrags of obsrvd and simulatd masurmnts at spactim point n, calculatd from all availabl data (i.. svral days of obsrvations and/or multipl simulation rplications).

39 3.2.4 Masuring ntwork capacity Onc th modl is calibratd and validatd, it can b usd to find th capacity of a ntwork. Th flowchart in Figur 3.4 xplains how th capacity of a ntwork is masurd in MITSIMLab. Symbols usd in Figur 3.4: ODF = Scaling factor of OD matrix UB = Uppr bound on th scaling factor of OD matrix LB = Lowr bound on th scaling factor of OD matrix TOL = Tolranc Th initial valu of ODF will b qual to th scaling factor of OD matrix in th calibratd modl. Gnrally, this valu is on. Th lowr bound can b st to zro. Highr valus can b usd for fastr convrgnc. Th valu of uppr bound should b such that pr-trip quus always form in th ntwork for this valu of th scaling factor. Th tolranc can b st to 0.01 for all practical purposs. Th tolranc can furthr b lowrd dpnding on th run-tim of th simulation.

40 Multipl simulation runs in MITSIMLab Output: Ntwork Capacity, Avrag spd of vhicls Figur 3.4: Framwork for masuring ntwork capacity 40

41 In MITSIMLab, th maximum numbr of vhicls which can b accommodatd in th ntwork bfor 'pr-trip quus' 1 start forming is dnotd as th capacity of th ntwork. Vhicls bfor ntring th simulation ar quud up at ach and vry ntry link. Such quus ar rfrrd to as pr-trip quus. Th modl is run in MITSIMLab with th original OD matrix and th numbr of vhicls in th pr-trip quus obsrvd throughout th simulation. Dpnding on th prsnc or absnc of vhicls in th pr-trip quus, th scaling factor in th OD matrix is ithr rducd or incrasd and th simulation is run onc again. This procss is rpatd till w rach a scaling factor at which point thr ar no vhicls in th pr-trip quus and furthr any slight incras in th scaling factor will rsult in non-zro vhicls in th prtrip quus. This is known as "flooding th ntwork". In MITSIMLab, it is possibl to flood th ntwork with just a particular vhicl typ. In th currnt study, th ntwork was floodd with th sam vhicl mix as prsnt in th actual ntwork i.. any chang in th scaling factor will corrspondingly chang th vhicl mix by th sam factor. Th tolranc for th boundary scaling factors was st to 0.01 in this study. Th tolranc can b furthr rducd for mor accurat rsults, but doing this is much mor tim consuming. For all practical purposs, this accuracy should suffic. It should b notd that in this study th OD dmand was loadd at vry 15 minuts and whnvr th scaling factor in th OD matrix was changd, th changs wr applid for all sts of OD dmands simultanously. Onc th critical scaling factor is found out, th simulation is run multipl tims to account for th stochastic modls usd in MITSIMLab. Th outputs from th simulation includ th maximum numbr of vhicls that can b accommodatd in th ntwork, th avrag spd of th vhicls that hav rachd thir dstination and th distanc travlld by ach vhicl from origin nod to dstination nod in th ntwork. Aftr vry run ths valus ar rcordd and finally th avrag valus of ntwork capacity and spd ar rportd. 1 During th simulation, th numbr of vhicls in pr-trip quus is printd out by MITSIMLab aftr vry minut.

42 Th abov framwork for masuring ntwork capacity is furthr clarifid through th following xampl. Considr a calibratd modl with th scaling factor of OD matrix (ODF) qual to on. Lt th lowr and uppr bounds of th scaling factors b qual to zro (LB) and four (UB) rspctivly and th tolranc b qual to 0.01 (TOL). Assum that pr-trip quus form in th ntwork for this valu of ODF. Hnc a = 0 and b = 1. Now, assum th following st of itrations (Tabl 3.1) tak plac till th scaling factors convrg. Tabl 3.1: Succssiv itrations for th scaling factor of an OD matrix ODF Vhicls in pr-trip quus a b b-a 0.5 No No No Ys No No Thrfor th final ODF is qual to Using this valu of scaling factor for th OD matrix, th simulation is run multipl tims in MITSIMLab and th final outputs obtaind. Now, assum that pr-trip quus do not form in th ntwork with th initial valu of ODF (qual to on). Using th sam valus for th bounds and tolranc, w gt a= 1 and b = 4. Assum th following st of itrations (Tabl 3.2) tak plac till th scaling factors convrg.

43 Tabl 3.2: Succssiv itrations for th scaling factor of an OD matrix ODF Vhicls in pr-trip quus a b b-a 2.50 Ys No No Ys No No Ys No Th final ODF is qual to 2.29 in this cas. Using this valu of scaling factor for th OD matrix, th simulation is run multipl tims in MITSIMLab and th final outputs obtaind. 3.3 Summary A gnral mthodology and framwork to masur ntwork capacity using a microscopic traffic simulator has bn prsntd in this chaptr. Sinc it is not sufficint to sum th link capacitis to find th ffctiv capacity of th ntwork, a common unit indpndnt of ntwork and typ of intrvntion (PCU-km pr hour) has bn usd to masur ntwork capacity. Th avrag spd of th vhicls is also associatd with this indpndnt unit. A gnral calibration framwork and various goodnss-of-fit masurs hav bn discussd. In th microscopic traffic simulator MITSIMLab, ntwork capacity is masurd by flooding th ntwork with vhicls (i.. scaling th OD matrix). Th ntwork is assumd to rach its capacity whn thr ar no vhicls prsnt in th pr-trip quus. Th scaling factor in th OD matrix is changd rpatdly till this condition is

44 achivd. Finally using this scaling factor, th simulation is run multipl tims and th valu of ntwork capacity can b calculatd. Th nxt chaptr dmonstrats th application of this framwork on a sub-ntwork from London.

45 Chaptr 4 Cas Study: Victoria Ntwork Th prvious chaptr dscribd th ovrall modling framwork for capacity analysis. In this chaptr, a ral ntwork with complx traffic flow pattrns has bn usd to assss th impact of typical ntwork changs on ntwork capacity. A ntwork nar th Victoria station ara in Cntral London, U.K. has bn usd for this purpos. MITSIMLab has bn usd for calibration and validation purposs and also for capacity analysis. Th chaptr is organizd as follows: a brif dscription of th study ara and th datasts usd is prsntd in sction 4.1. Th rsults of aggrgat calibration and aggrgat validation ar prsntd in sctions 4.2 and 4.3 rspctivly. Sction 4.4 prsnts th bas capacity of this ntwork followd by th capacity analysis undr various intrvntion scnarios in sction 4.5. Th impact of long trm strt-works and nar-sid lan disruptions (illgally parkd vhicls) on th capacity of th ntwork has bn valuatd in dtail in th sction daling with intrvntion scnarios. 4.1 Datast dscription Study ara Th study datast rprsnts traffic nar th Victoria station ara locatd in Cntral London, U.K. (Figur 4.1). Victoria station is a major cntral London railway trminus, London undrground and coach station in th city of Wstminstr namd aftr th British monarch Qun Victoria. Th ntwork usd in this study consists of all th major urban roads around this station. Th roads in U.K. ar mainly classifid into motorways (Mprfix), 'A' roads and 'B' roads (road numbrs with prfixs A and B rspctivly). In Figur 4.1, grn colord roads ar major 'A' roads, dark yllow or orang colord roads

46 ar minor 'A' roads, light yllow roads ar 'B' roads and othr local strts ar whit in color. Motorways (not prsnt in Figur 4.1) ar blu in color. Figur 4.1: Ntwork Dscription Th computr rprsntation of this ntwork (Figur 4.2) consists of 187 nods connctd by 221 links and 53 signal hads 2. Th actual signal controllrs in th fild ar adaptiv. Although MITSIMLab has th ability to simulat th widst possibl rang of signal controllrs, th signals in th ntwork ar simulatd as pr-timd controllrs i.. th signal stats chang according to a pr-dtrmind squnc, bcaus th signal timing data that was availabl could only rplicat this typ of controllr. Th MITSIMLab modl covrs th AM pak priod from 7:15 to 9:00 on a wk-day. 2 A signal had controls on or mor traffic-strams that ar givn right-of-way simultanously.

47 Figur 4.2: Ntwork as sn in MITSIMLab Datast ovrviw Data is collctd continuously using Automatic Traffic Countrs (ATCs) and Automatic Numbr Plat Rcognition (ANPR) camras placd at diffrnt locations in th ntwork. Th ATCs giv th counts data whil th ANPR camras giv th travl tims of vhicls btwn two points by capturing th licns plat numbrs at ths two points. ANPR and ATC data is availabl at vry 15 minut intrvals. Figur 4.3 shows th location of count and spd snsors in th ntwork.

48 Figur 4.3: Location of snsors It should b notd that som of th snsors in th ntwork ar locatd on both sids of th road, particularly on thos links which srv as ntry/xit into th ntwork. On th whol thr ar 14 snsors ach to rcord counts and spds at diffrnt locations in th ntwork. Th statistics for counts and spds ar prsntd in Tabl 4.1.

49 Tabl 4.1: Statistics of calibration and validation data Snsor Avrag Counts Avrag Spds (km pr hour) For th purpos of calibration, tn wk-days of data has bn usd and fiv wk-days of data has bn usd for validation 4.2 Aggrgat Calibration Th calibration problm has bn formulatd as an optimization problm which sks to minimiz a function of th dviation of th simulatd traffic masurmnts from th obsrvd masurmnts. Th optimization has bn don in MATLAB using Box's

50 complx algorithm. (Box, 1965) A dtaild dscription of th calibration mthodology was prsntd in th prvious sction. Basd on prvious xprinc, th following paramtrs hav bn slctd for calibration: * Car-following paramtrs - Acclration Constant - Dclration Constant * Dsird Spd - Man - Standard Dviation " Critical Gaps - Lad Gap constant - Lad Gap standard dviation - Lag Gap constant - Lag Gap standard dviation " Lan Utility Modl - Currnt Lan constant - Rightmost Lan constant Tabl 4.2 shows th initial and calibratd valu of th paramtrs.

51 t Car following Dsird Spd Tabl 4.2: Calibration rsults Paramtr 3 Initial Valu Calibratd Valu Acclration Constant Dclration Constant Man Standard dviation Lad Gap Constant Lad Gap Standard dviation Critical Gaps Lag Gap Constant Lag Gap Standard dviation Lan Utility Currnt Lan Constant Rightmost Lan Constant i T bl 43I Al h1i-t T n lit of th modl to th calibration data is prstilu n a.. t. oug pon- - point travl tims wr availabl from ANPR data for this location, goodnss-of-fit statistics for travl tims ar not prsntd bcaus many of th points (camras in this cas) ar locatd outsid th ntwork and hnc it is not fasibl to compar th travl tims in many of th links. Tabl 4.3: Goodnss of fit statistics for traffic spd comparison Statistic Bfor Calibration Aftr Calibration Improvmnt RMSPE % RMSE (m/s) % MPE % ME (m/s) % 3 Gnral Paramtrs usd in MITSIMLab. Ths ar dscribd in Ahmd (1999).

52 As sn from th tabl, th calibratd modl has providd an improvd prformanc whn compard with th initial modl. But, th valus of MPE and RMSPE ar vry high. This is du to th fact that som of th spd snsors in th ntwork ar locatd on th ntry links. This rsults in larg spds for som of th snsors bcaus th vhicls ntr th simulation at high spds. But th locations of ANPR camras in th fild rsults in rlativly vry low spds for ths snsors. Hnc, du to th ovr-stimation of simulatd spds, som of th MOE statistics ar vry high. Anothr rason for this diffrnc in simulatd and obsrvd spds can b du to th fact that th signals ar simulatd as pr-timd controllrs (du to th absnc of data rquird for coding adaptiv signals), though th actual signal controllrs in th fild ar adaptiv. To account for this ovr-stimation in simulatd spds, th data from all such snsors whr th spds had bn ovr-stimatd wr rmovd and th goodnss-of-fit statistics rcalculatd. Th nw rsults ar prsntd in Tabl 4.4. It can b sn that th valus of MPE and RMSPE ar far bttr than thos prsntd in th prvious tabl. Tabl 4.4: Corrctd Goodnss of fit statistics for traffic spd comparison Statistic Bfor Calibration Aftr Calibration Improvmnt RMSPE % RMSE (m/s) % MPE % ME (m/s) % 4.3 Aggrgat Validation In this stp, th calibratd MITSIMLab modl is applid on a diffrnt st of data to prdict th traffic for th validation tim-fram. Th fit btwn simulatd and obsrvd traffic in trms of spds is summarizd in Tabl 4.5.

53 Tabl 4.5: MOEs for spds RMSPE 1.45 RMSE (m/s) 5.09 MPE 0.91 ME (m/s) 3.33 Similar corrction to account for ovr-stimation of simulatd spds as mntiond in th aggrgat calibration sction has bn applid to gnrat th corrctd MOEs, as shown in Tabl 4.6. Tabl 4.6: Corrctd MOEs for spds RMSPE 0.23 RMSE (m/s) 4.45 MPE 0.07 ME (m/s) Bas Capacity As mntiond prviously, th capacity of th ntwork is masurd in trms of PCUkm/hour. It should b notd that all th rsults prsntd in this sction ar in trms of vhicl-km pr hour. This is bcaus in th currnt ntwork, most of th vhicls wr cars. Sinc a car has a PCU valu of 1, PCU-km pr hour is th sam as vhicl-km pr hour. If th composition of othr havy vhicls is substantial, thn th outputs should b corrspondingly convrtd to PCU valus. Th calibratd modl is usd to find th bas capacity of th ntwork. Bas capacity is dfind as th capacity of th ntwork without any intrvntions. Th bas capacity of a ntwork can b affctd by a numbr of actions. Ths includ changs to th configuration of th ntwork lik road-works, strt-works, incidnts and vnts.

54 Aftr finding th critical OD matrix, th simulation is run 10 tims to obtain avrag valus of th ntwork capacity. Th procdur is dtaild in sction Following th stps dscribd in sction for th calibratd Victoria Ntwork, th bas ntwork capacity is obtaind and is qual to vhicl-km pr hour. Th avrag spd is km pr hour. Th nxt sction dals with capacity analysis undr diffrnt intrvntion scnarios. 4.5 Scnario Analysis Long trm strt-works and nar-sid lan disruptions ar th two intrvntions which hav bn analyzd. Both ths intrvntions wr simulatd by crating an incidnt in th ntwork at diffrnt locations. It was assumd that strt-works affct th lft-most lan only. Similar tsts can b don by closing th right-most lan as wll. To find out th ntwork capacity undr th various intrvntion scnarios, th sam procdur as mntiond in sction has bn usd. Thr will b a drop in ntwork capacity undr ach of ths scnarios Long Trm Strt-works This tst analyzs th impact of various strt-works on th capacity of th ntwork. Th impact of this chang was masurd for individual strt-works at diffrnt locations in th ntwork and diffrnt combination of strt-works. In MITSIMLab, strt-works hav bn modld in such a way that th lft-most lan (in th dirction of traffic) is compltly blockd for traffic movmnt and th spd limits in th adjacnt lans rducd. Th lngth of strt-works is 80m and th intrvntions wr modld for th whol duration of th simulation. Four diffrnt locations of strt-works hav bn chosn.

55 * Strt work 1 is situatd on Grosvnor Gardns btwn Buckingham Palac Road and Bston Pl. (Figur 4.4) Figur 4.4: Victoria Ntwork - Location of Strt-work 1 Strt work 2 is situatd on Lowr Grosvnor Plac btwn Bston P1 and Victoria Squar (Figur 4.5). Figur 4.5: Victoria Ntwork - Location of Strt-work 2

56 Strt work 3 is situatd on Vauxhall Bridg Road btwn Victoria Strt and Nathous Pl. (Figur 4.6) Figur 4.6: Victoria Ntwork- Location of Strt-work 3 Strt work 4 is situatd on Grosvnor P1 btwn Bston P1 and Hobart Pl. (Figur 4.7) Figur 4.7: Victoria Ntwork - Location of Strt-work 4

57 Capacity Analysis: Individual Strt-works Tabl 4.7 shows th impact of th four strt-works on th ovrall capacity of th ntwork. Tabl 4.7: Strt-works - Ntwork Capacity valus Incidnt Ntwork Capacity (vhicl-km pr hour) % chang from bas Bas Strt-work % Strt-work % Strt-work % Strt-work % Graph showing th variation of ntwork capacity is plottd in Figur Ntwork Capacity V U S Bas Strt-work I Strt-work 2 Strt-work 3 Strt-work 4 Inddnt Figur 4.8: Strt-works - Ntwork Capacity

58 Nxt, th impact of th four strt-works individually on th avrag spd of th vhicls in th ntwork is prsntd in Tabl 4.8 and Figur 4.9. Tabl 4.8: Strt-works - Impact on avrag spd across th whol ntwork Incidnt Spd (km pr hour) % chang from bas Bas Strt-work % Strt-work % Strt-work % Strt-work % inpact on spd Bas Strt-vork 1 Strt-work 2 Strt-work 3 Strt-work _ '1 _ ~ l ~ ~ Icidnt Figur 4.9: Strt-works - Impact on spd Th rsults from Tabls 4.7 and 4.8 show that a strt-work on an avrag rducs th capacity of th ntwork from th bas cas without any intrvntions by about 11% and th avrag spd is rducd by about 3.7% (nglcting strt-work 2). As mntiond bfor, a strt-work is modld such that a lan is compltly blockd and th spds on th adjacnt lans ar slightly rducd. Furthr, th strt-work is simulatd for th whol

59 duration of th simulation. This has a dirct ffct on th numbr of vhicls raching thir dstination bcaus th vhicls using that particular link on which a strt-work is prsnt will xprinc fwr lans and lowr spds and ovr th cours of th simulation it rsults in a lowr numbr of vhicls raching th dstination compard to th bas ntwork. Sinc th dfinition of ntwork capacity incorporats th distanc travlld by th vhicls raching thir dstination, thr is a largr rduction in ntwork capacity. Strt-works only impact th spds of th vhicls on th link containing this intrvntion and probably th upstram link. Hnc th rduction in avrag spd of th vhicls is lowr. Figur 4.10 shows th variation of ntwork capacity with avrag spd Capacity v/s Spd Strt-work Strt-work 3 Strt-work 1 Strt-work Ntwork Capacity (vhicl-kin pr hour) Figur 4.10: Strt-works - Capacity v/s Spd Figur 4.10 shows that thr is an approximat linar rlationship btwn ntwork capacity and avrag spd for individual strt-works.

60 Combination of strt-works To assss th impact of multipl strt-works on ntwork capacity, th following thr scnarios wr chosn: Two strt-works in th ntwork. Thr strt-works in th ntwork. Four strt-works in th ntwork. In th scnario whr two strt-works ar prsnt in th ntwork, strt-works at thos locations wr chosn which causd th highst and scond highst rduction in ntwork capacity individually. Hnc, in this cas locations 1 and 3 wr chosn. Similarly for th scnario whr thr strt-works ar prsnt, locations 1, 3 and 4 wr chosn and in th third scnario, all th four locations wr chosn. Tabls 4.9 and 4.10 summariz th impact of strt-works on th capacity of th ntwork and th avrag spd. Tabl 4.9: Combination of Strt-works - Ntwork capacity valus Ntwork Capacity (vhicl-km pr % chang from bas Incidnt %cag rmbs hour) Bas Strt-work % Strt-work % Strt-work % Strt-work % Strt-work % Strt-work % Strt-work %

61 Tabl 4.10: Combination of Strt-works - Impact on avrag spd across th whol ntwork Incidnt Avrag Spd (km pr hour) % chang from bas Bas Strt-work % Strt-work % Strt-work % Strt-work % Strt-work % Strt-work % Strt-work % Th rsults from Tabls 4.9 and 4.10 show that a combination of strt-works causs a gratr rduction in ntwork capacity than individual strt-works which is intuitiv Nar-sid lan disruptions This tst analyzs th impact of nar-sid lan disruptions on th capacity of th ntwork. Th impact of this chang was masurd: " For a 1 minut nar-sid lan disruption vry 3 minuts. * For a 5 minut nar-sid lan disruption vry 15 minuts. For a 20 minut nar-sid lan disruption vry 45 minuts. Th nar-sid lan disruption has bn modld as an on-strt parking vnt with a singl-car parkd on th narsid lan. To rplicat actual parking violations, fiv diffrnt locations on th ntwork wr idntifid using traffic nforcmnt data. This data contains th xact locations of illgally parkd vhicls in th ntwork. It is important to not that th tst has bn conductd sparatly for ach of th thr tim-

62 priods mntiond abov and that a parkd vhicl was simulatd at all th fiv locations for vry tim-priod. Th locations wr nar-sid lan disruptions hav bn simulatd ar mntiond blow: Disruption 1 is situatd on Brssndn Pl. road btwn Arlington Strt and Victoria Strt. * Disruption 2 is situatd on Victoria Strt btwn Wilton Road and Buckingham Palac Road. * Disruption 3 is situatd on Vauxhall Bridg Road btwn Victoria Strt and Nathous Pl. * Disruption 4 is situatd on Grosvnor Gardns btwn Buckingham Palac Road and Bston Pl. * Disruption 5 is situatd on Lowr Grosvnor Pl. btwn Bston Pl. and Buckingham Palac Road. Figur 4.11 shows th locations of nar-sid lan disruptions in th ntwork.

63 Figur 4.11: Victoria Ntwork - Locations of nar-sid lan disruptions Capacity Analysis: Nar sid lan disruptions Tabl 4.11 and Figur 4.12 show th impact of nar sid lan disruptions on ntwork capacity. Tabl 4.11: Nar sid lan disruptions - Ntwork Capacity valus Incidnt Ntwork Capacity ( vhicl-km pr hour) Bas min vry 45 min min vry 15 min min vry 3 min