Application of the VISEVA demand generation software to Berlin using publicly available behavioral data

Transcription

1 Justen, Beuck, Nagel 1 Application of the VISEVA demand geneation softwae to Belin using publicly available behavioal data Submission date: 15-Nov-06 Wods: 5973 Figues and tables: 6 ( = 1500 Wods) Total: appox Andeas Justen DLR - Geman Aeospace Cente Institute of Tanspot Reseach Ruthefodstaße Belin Phone: Fax: andeas.justen@dl.de Ulike Beuck TU Belin Institute fo Land and Sea Tanspot Systems Tanspot Systems Planning and Tanspot Telematics (VSP) Salzufe 17-19, SG12 Gemany Phone: Fax: beuck@vsp.tu-belin.de Kai Nagel TU Belin Institute fo Land and Sea Tanspot Systems Tanspot Systems Planning and Tanspot Telematics (VSP) Gemany Phone: Fax: nagel@vsp.tu-belin.de

2 Justen, Beuck, Nagel 2 ABSTRACT In this pape the EVA algoithm developed by Lohse is applied in ode to geneate Belin s aveage wokday taffic based on a minimum of data input. Behavioal paametes ae deived fom the Geman tavel suvey Mobilität in Deutschland (MiD). The EVA appoach allows geneating tip pupose and time dependent OD matices fom geneal input data used in tanspot modeling. This model output can be used fo standad OD-matix-based static o dynamic assignment, but povides us with pimay activity location choice and scheduling infomation necessay to geneate initial conditions fo agent-based tanspot simulation packages like MATSIM. The pape descibes the basic concept of the EVA model and specifications of the Belin scenaio. Since the ange of possible input data fo demand geneation is limited, ou aim was to use the established demand geneation model VISEVA with a minimum of input data, which has to be commonly available and easy to puchase (making tansfe of tanspot models to othe study aeas easie). The model output is displayed and compaed with output esulting fom Belin s official demand geneation model. Besides that, the simulation esults ae compaed to eal-wold data fom taffic counts. It can be shown that even though we educe data equiements to a minimum, the esults have a stuctue adequate fo Belin and could seve as input fo initial condition geneation fo MATSIM.

3 Justen, Beuck, Nagel 3 INTRODUCTION Most tanspot models used in pactice apply the fou-step pocess. The fist thee steps tip geneation, destination choice, mode choice concen modeling the demand, finally descibed in tems of oigin-destination (OD) matices. In these thee steps, vaious chaacteistics of the tavele, the land-use, and the netwok ae bought togethe. In the fouth step, the demand is assigned to the netwok. Thee is widespead ageement that the fou-step pocess, in its conventional fom, is unable to captue impotant aspects of tanspot planning. This concens, in paticula, all tempoal aspects, such as peak speading, congestion toll modeling, o impotant envionmental aspects (e.g. tailpipe emissions, depending on engine tempeatue). The fist step to impove this situation is to un sepaate demand geneation and netwok assignments fo the moning and the aftenoon peak. This is, howeve, inceasingly poblematic with the incease of non-homebased tips. A eaction to this situation is activity-based demand geneation (ABDG; see, e.g., (1, 2)), whee tavel is seen as demand that is deived fom the demand to pefom diffeent activities at diffeent locations. Howeve, despite much pogess, ABDG is at this point not vey much standadized: thee ae many diffeent models and implementations aound (3, 4, 5, 6, 7, 8). This is due to a wide vaiety of diffeent appoaches, fo example concening the methods (e.g. Random Utility Modeling vs. ule-based systems) o the level of detail/esolution (e.g. based on half tous, full tous, o complete day plans). Expeience and diligent investigation will hopefully demonstate the espective advantages and disadvantages of each method. In the meantime, it makes sense to conside altenative methods, which emove some of the disadvantages of the fou-step pocess, while not going the full distance towads ABDG. One such model is the EVA modeling appoach of Lohse et al. (9). It extends the methodology of the taditional fou-step pocess, which is essentially a method to geneate OD matices fo home-based tips, to a methodology to geneate OD matices that connect abitay tip puposes. Fo example, thee will be the typical OD matices fo home wok and wok home, but thee will also be matices fo, say, wok shop o wok leisue. OD matices may, in addition, be segmented by demogaphic goups. Moe details ae povided late in the pape. EVA has been developed ove many decades, including a sound mathematical foundation based on pobability theoy. A futhe advantage is that it is now publicly available as VISEVA as pat of the PTV tanspotation planning package (10), thus poviding a standadized access to the package allowing scientifically sound compaisons of esults. In this situation, it would be convenient if it an fom standadized and easily available data. In Gemany, such a data set is the Mobilität in Deutschland (MiD) data set (11, 12). It is essentially a mico-data sample of the Geman population, with special emphasis on tanspot-elated questions. Unfotunately, most geo-coding was emoved fom the data set befoe it was made available to us. Nevetheless, it is a good stating point, in paticula since it is available in standadized fom fo all of Gemany. The main question to be answeed in the pesent pape is, in consequence, in how fa this data set, possibly augmented by othe publicly available souces, is able to povide useful input fo the VISEVA demand geneation package. An additional use of such a VISEVA un would be to use it as input to ou multi-agent taffic simulation package, MATSIM (13, 14, 15). This is paticulaly appopiate since Gemany does not povide data-diven commuting matices that ae esolved beyond the city level, and in consequence the coupling between esidences and wok locations has to be model geneated. In this situation, having a standad package such as VISEVA based on standad input data such as MiD appeas like a good fist step to make pogess. VISEVA BASIC CONCEPT The applied EVA algoithm developed by Lohse (9) handles tip geneation, tip distibution and mode choice simultaneously. This algoithm implemented in the commecial softwae package VISEVA (16) distibuted by PTV AG is a disaggegate desciption of the demand. The demand is disaggegated into activity-pupose pais at oigin and destination zones. Tip geneation, distibution and mode choice ae based on the activity-pupose pai classification. Each activity-pupose pai associates with a cetain tip pupose, e.g. the home-wok pai contains tips fom home to wok, and can be associated with all o a subgoup (behavioally homogenous goups) of taveles. Only employed pesons leave home to wok, this means that the activity-pupose pai home-wok is associated with employees. Othe pais like home-education contain tips of, fo instance, high school and univesity

4 Justen, Beuck, Nagel 4 students. The concept of activity-pupose pais allows obtaining matices by tip pupose. Summing up these matices gives total demand in a defined peiod. Basically, one can define any classification of activity-pupose pais that seves the specific poblem best. Some standad classifications have aleady poven thei usefulness. We chose one with 6 activity-puposes esulting in 13 activity-pupose pais (table 1). The pais can be gouped into types accoding to the location of the home activity at oigin o destination (type 1 and 2). Wok can also be the home activity, when the pai lacks the oiginal home activity. Activity pupose pais containing neithe home no wok at oigin o destination ae of type 3. Tip poduction is calculated with tip ates pe activity-pupose pai at oigin accoding to its type. At destinations, tip attactions ae calculated as a popotional to the capacity of activity oppotunities. These capacities can be used as had o soft constaints. Geneally, fo pimay activities capacities ae modeled as had constaints. Soft constaints allow exceeding the given capacity to a cetain degee. Thus, only an uppe limit can be set at fist; the final numbe of attactions (numbe of tips of a cetain activity-pupose pai attacted by a zone) cannot be defined without joint tip distibution and mode choice. That is, spatial competition can be modeled (e.g. diffeent shopping locations). To distinguish had and soft constaints is an advantage of the EVA appoach compaed to simple destination choice models, which only enfoce the constaint at the oigin. As just mentioned, the calculation stats with calculating tip poduction fo each activity-pupose pai at the home location (accoding to the activity-pupose pai s type) in each zone. H e = TPp BPep u p V = H e (1) with: TP p BP ep u p p poduction ate of peson goup p numbe of pesons of goup p in zone e shae of intazonal tips fo goup p in zone e with: H e V e tips at home location (accoding to the activity-pupose pai s type) total numbe of tips of the activity pupose pai in the study aea Tip attactions can be deived (nomalized accoding to the sum of tip poductions ove all zones) when had constaints ae given. Z ER j = j' with: Z j ER SZ j Z max ER SZ j SZ j ' V taffic attacted to zone j attaction ate of attacto volume of attacto in zone j Fo soft constaints only an uppe limit value is calculated as aleady mentioned. F j j = j' ER ER SZ SZ j j ' V with: F j additional load facto of zone j consideing attacto The EVA model applies its activity-pupose pai appoach pe subgoup of taveles to the joint destination and mode choice as well. The maginals of the geneated matices ae known (in case of soft constaints as maximum numbe of tips) and the shae of tips with mode k between zones i and j ae calculated as a function of the genealized costs of tavel using diffeent model foms. This conditional pobability is: (2) (3)

5 Justen, Beuck, Nagel 5 ( W ( A E M ) BW ijk = P i j k (4) with andomly chosen pobabilities that A i zone i is oigin E i zone j is destination M k mode k is used W tip fom i to j using k is accepted with egad to the genealized costs Although an abitay function can be used tansfom tanspot costs w into pobabilities, we used the EVA function (16), which obtains with its thee paametes E, F, G a flexible shape of the elasticity ε ove the ange of the genealized costs. G w BW = f ( w) = 1 + F E G ε df / f dw / w G w F + w ( w) = = E G G The paametes of the EVA function can futhe be diffeentiated accoding to the subgoups of taveles. Taffic flows (v ijk ) ae calculated consideing simultaneously the genealized costs, the pobabilities of the events P(A i ), P(E j ) and P(M k ) and the constaints with espect to (maximum) taffic volumes at oigin and destination zones. The fomulation is stuctually a Bayesian model: v ijk = P i j k ( Ai E j M k ) W ) P ( A E M ) ( W ) i j k V = P i j k ( Ai ) P( E j ) P( M k ) P( W ( Ai E j M k ) P( A ) P( E ) P( M ) P( W ( A E M ) V i j k i j k (5) (6) Futhe explanations and solution algoithms ae descibed moe in detail in (9), (17) o (18). It has to be mentioned that thee is a need to iteate between the tavel demand calculations, following the EVA appoach, and the assignment, geneating the tavel cost values, to obtain a mutually consistent solution. The softwae tool VISEVA (16) povides tools to implement an iteation scheme in conjunction with the assignment softwae VISUM (10). ESTIMATION OF DEMAND FOR AVERAGE WORKDAY TRAFFIC BERLIN SCENARIO Following the above descibed steps in VISEVA, aveage wokday taffic is deived fom geneal input data on land-use and population. Two diffeent models wee built. In the fist model (model I), demand was deived fom land-use data and the population without any futhe diffeentiation. The second model (model II) distinguishes subgoups of the population (homogeneous behavioal goups). The second model is moe flexible, but we wee also inteested how well demand deived fom an undiffeentiated population will pefom. Both models have the same definition of activity-pupose pais. Based on 6 activities, 13 activity-pupose pais can be distinguished. Home (H) Wok (W) Kindegaten (K) Education (E) Shopping (S) Othes (O) Home (H) - HW HK HE HS HO Wok (W) WH - WO Kindegaten (K) KH Education (E) EH Shopping OW OO (S) SH Othe (O) OH

6 Justen, Beuck, Nagel 6 TABLE 1: Activity-pupose pais definition In total fo all fou modeled modes (motoized pivate tavel, tansit, walking, biking) 52 matices of 889 x 889 zones ae calculated fo the fist model with no population diffeentiation. The second model calculates fo each subgoup 52 matices of 889 x 889 zones. The following subgoups wee used: Employees with ca Employees without ca Non-employed people with ca Non-employed people without ca Univesity student Pupils/ high school students Childen < 6 yeas old Appentice Even though it would have been possible to use diffeent paametes of the EVA function and of the modal split values accoding to the specific subgoup of taveles and activity-pupose pai, this functionality was not used fo the pesent study. In both models, EVA paametes ae chosen based on expeiences povided by Lohse (16, 17), modal split values wee used accoding to the activity-pupose pai only. Assigning depatue time intevals to demand is possible by using estimations of houly demand accoding to the tip puposes. This kind of infomation can be found in suveys as well. Lohse (17) gives an example fom pevious pojects. In ode to compae the demand geneated in the poject descibed hee to the data fom the model used by Belin s planning depatment, 24-h matices containing all puposes ae calculated (planning depatment uses only 24-h OD matices). Tavel costs in tems of tavel times ae mainly deived fom a VISUM model of the oad infastuctue of Futhe infomation on available input data and necessay data pocessing is descibed in the next section. INPUT DATA As aleady mentioned, a minimum ange of input data should be used. Input data is gatheed fo a peiod of time aound the yea Data fo demand geneation has to contain infomation on land-use, composition of population and tavel behavio, and the netwok to obtain tavel costs between zones. Data equiements depend among othe things on the defined activity-pupose pai classification. Since only a oad netwok was available, tavel costs of the othe modes (tansit, walking, biking) wee estimated based on linea distance measued and aveage mode speed estimated and schedules of the tansit, available on the intenet (19). Input data wee available on diffeent spatially aggegated levels. The lagest entities ae disticts. Up to the yea 2000, the study aea of Belin consisted of 23 disticts, 195 statistical aeas, 881 taffic analysis zones (TAZ), and blocks. The 881 TAZ wee aleady included in ou oad netwok of 1998, which the planning depatment povided us with. Outside Belin 8 zones wee defined in ode to model commuting tavel. The population is available on block level, but as the only attibute age is given. The land-use of Belin is based on taffic analysis zone level and consists of capacities fo home, wok, and education. Shopping capacities had to be defined manually (see below). Unfotunately, thee is no commuting matix available fo Belin. Infomation on commuting is gatheed by the authoities between municipalities only and Belin as a whole is only one municipality. Fo this scenaio thee is no commute matix available, which would be desiable fo a calibation pocedue of the VISEVA models. Accoding to the population subgoups used in poduction and attaction calculations, behavioal data has to be deived fom suveys. The city s planning depatment based its model on an extensive data ecod, but we did not have access to it. Thee is howeve the Geman tavel suvey Mobilität in Deutschland (MiD) (11, 12), which is easy to access fo scientific puposes. Fome suveys of 1976, 1982 and 1989 wee conducted in westen Gemany only; MiD 2002 is the fist tavel suvey embacing unified Gemany. The following sections descibe how behavioal paametes ae deived based on MiD and aspects of landuse data acquisition and pocessing. Besides giving an oveview of elevant land-use data and thei souces, the desciption focuses on pocessing infomation about shop floo aeas in Belin in ode to model activity-pupose pais othe than wok o education elated.

7 Justen, Beuck, Nagel 7 Behavioal data The main souce fo the geneation of behavioal paametes was the nation-wide suvey MiD conducted in 2002 by the Fedeal Ministy of Tanspot, Building and Uban Affais (BMVBS). Objective of this suvey is the geneation of eliable and epesentative infomation about socio-demogaphic aspects of pesons and households in combination with thei daily tavel behavio. This data ecod distinguishes between the Geman Fedeal States. As Belin is not only a municipality but also a Fedeal State, data evaluation could be done fo Belin diectly, which should assue that specific behavioal aspects of Belin ae ecognized. Additionally, MiD povides aggegated data based on cetain spatial categoies. Spatial categoies ae classifications by habitants density, deliveed by the Fedeal Office fo Building and Regional Planning. The oveall national sample captued 25,000 households, 62,000 pesons and 190,000 epoted tips. The Belin sample of the suvey holds 7,616 tips epoted by 2,163 Pesons (taken only the tip data set as efeence the pesons data set is based on 2,849 inteviewed pesons). The following paametes wee extacted fom MiD to be used in ou two VISEVA models: specific tip poduction ate of the population of Belin diffeentiated by tip puposes (model I) specific tip poduction ates of the population of Belin diffeentiated by homogeneous behavioal goups and tip puposes (model II) modal split values diffeentiated accoding to the needs of model I and II As modal split values could be extacted apidly by using standad data pocessing softwae, futhe comments do exclusively concentate on the geneation of the specific tip poduction ates. Extensive coding of the oiginal MiD tip data set was necessay in ode to calculate these essential paametes calculating demand accoding to the VISEVA appoach. Tip poduction ate The specific tip poduction ate is defined as the aveage numbe of tips made by peson pe day and pupose and is pimaily detemined by socio-demogaphic chaacteistics and availability of a pivate ca. In ode to calculate tip poduction ates to be used in model II, size of subgoups had to be extacted fom MiD as well. Aveage specific tip poduction ate used fo fist evaluations of the Belin tip data set is calculated as follows: Numbe of tips pe peson (of subgoup p) ove all activity pupose pais TP p = Numbe of pesons in goup p Specific tip poduction ates by activity-pupose pai to be used in ou VISEVA models ae calculated as: Numbe of tips pe peson ( of goup p) in the specific activity pupose pai x TP px = Numbe of pesons in goup p Peson goups In model II, tip poduction ates had to be calculated fo each subgoup defined. Thus, subgoup size had to be detemined (in each activity-pupose pai) as well. In MiD, subgoups ae aleady defined, and two diffeent classifications ae available. MiD distinguishes 9 o 12 behavioally homogeneous goups. Fo this study, the oiginal classification consisting of 12 subgoups was educed to 9 subgoups, which is not identical with the oiginal MiD classification into 9 goups. On top of that, the suvey itself povides the necessay vaiables to geneate moe sophisticated goups. But in context of this study, a classification into 9 subgoups seems sufficient. Table 2 summaizes the size of subgoups and thei popotion in total of the tip data set. The goup of othes was not evaluated any futhe. (7) (8)

8 Justen, Beuck, Nagel 8 Behavioal homogeneous goups Numbe of people Shae in % Employed peson with ca Employed peson without ca Non-employed peson with ca Non-employed peson without ca Students with & without ca Appentice with & without ca Childen unde the age of Pupils N (excluding Othes ) 1,890 Othes 273 Total 2, TABLE 2: Goup sizes Numbe of tips pe peson and activity-pupose pai Joining the infomation of goup sizes and tips ealized pe day (by peson and pupose) the specific poduction ate can be calculated. Substantial coding modifications of the oiginal data set of epoted tips wee necessay, because the activity-pupose pai definition was not pat of MiD. Activity pais had to be computed based on the epoted tip chains of household membes. Additionally, we had to aggegate cetain tip puposes to match ou activity-pupose pai classification. Table 3 summaizes tip poduction ates by activity-pupose pais fo two diffeent data sets. MiD 2002: tips in agglomeations Shae in % tip poduction ate tip poduction ate Shae in % MiD 2002: tips in Belin HW 2, HW WH 2, WH HE HE EH EH HK HK KH KH HS 3, HS SH 4, SH HO 8, ,631 HO OH 7, ,431 OH WO WO OW OW OO 4, OO Sum 38, ,164 Sum N 11,060 2,102 N TABLE 3: Compaison of tip poduction ates deived fom two diffeent MiD samples (befoe coection egading immobile pesons) On the ight, poduction ates deived fom the Belin sample can be seen. Both the aveage tip poduction ate with 3.41 daily tips pe peson and the specific tip ate calculated accoding to the definition of activitypupose pais have plausible values. But the small sample size (N denotes numbe of pesons in the data) is

9 Justen, Beuck, Nagel 9 poblematic. Theefoe, we also deived the tip poduction ates fom the lage sample of all egions classified as spatial categoy agglomeations with an outstanding cente (left half of the table). Belin is classified as one such outstanding cente. When compaing Belin s tip poduction ates to the lage sample size on the left, one sees that even though the Belin sample is small, plausible tip poduction ates wee calculated fo ou models. Finally, the esulting tip poduction ates had to be modified accoding to the infomation on how many inteviewees pefomed no out of home activity. This modification was necessay, because the poduction ates wee deived fom the tip data of mobile pesons only. The esulting ates wee used in both VISEVA models. Futhe model elevant paametes The MiD data base offes moe possibilities fo the geneation of model elevant paametes. In paticula, elevant infomation fo model calibation can be obtained. Fo example, distibutions of tavel distances and times can be calculated fom the MiD data set. This kind of data can be used both fo VISUM uns and fo multi-agent simulations. When using MiD fo calibation/validation puposes, one has to take cae whethe vey shot distance tips can be found in the model. Synthetic population and land-use data As basic spatial unit in ou models, the taffic analysis zones have to be descibed by thei land-use and population data. Theefoe, infomation fom both aeas has to be aggegated o disaggegated to this level depending on thei oiginal spatial esolution. It was impotant to us to use only publicly accessible data as input, in ode to demonstate possibilities and estictions in data acquisition and to guaantee a self-detemined pocess with data in modified applications of the tanspot model. The extensive pocessing of shop floo aea as an impotant land-use data used by the model is exemplified in the following section. This data will be also impotant in multi-agent applications, since shopping oppotunities can diffe consideably but detailed and disaggegated data on it is had to get. Land-use data pocessing: shop floo aeas Oiginal infomation about distibution of shop floo aeas was available on the spatial level of the 12 new disticts (aggegation of the fome 23 disticts) (20). Additional figues existed fo selected shopping aeas with a high popotion of shop floo aea in m² (21). Combining both data sets made it possible to distibute data spatially on block level by a sequential appoach, as follows. Figue 1 illustates the pocedue fo the inne-city distict Mitte. In a fist step, the shop floo aea (SFA) of specific, individually known stoes ("high concentation" in figue 1) was manually assigned to the specific blocks. In a second step, the emaining SFA fo each selected shopping aea was manually assigned to suounding blocks ("medium concentation" in figue 1). In a thid step, emaining amounts of SFA on the distict level wee distibuted popotional to population density pe block. Finally, the SFA was e-aggegated fom the block level to the TAZ level. These steps esult in a hieachically oganized assignment of infomation about shop floo aeas. Thus, effects of commecial concentation can be integated into a tanspotation model. The hieachical assignment of shop floo aea povides a moe ealistic mapping of tanspotation attactions thoughout the city. In the futue, also diffeent shopping activities with diffeent fequencies can be modeled based on the data set ceated.

10 Justen, Beuck, Nagel 10 FIGURE 1: Shop floo aeas in Belin s distict Mitte RESULTS Since the mid 1980s, Belin s planning depatment has employed an activity based demand geneation model, oiginally developed by Kutte (22, 23), and based on 72 diffeent peson goups with common tanspot behavios. The detailed infomation on population composition and behavioal paametes has thei souce in geo-coded suvey data of extensive amount (unfotunately not available to us). This model is used to geneate 24-h OD matices using the same zonal system as we did, at least fo Belin; its suoundings ae epesented with a highe spatial esolution by the planning depatment. Since no futhe detailed output of this official planning model was available, we calculated a 24-h OD matix of pivate motoized taffic by summing up the pupose specific matices in ode to compae ou model esults to the ones of the official model. Fist, we compaed the values of selected matix cells of the official planning model and the model I (no subgoup diffeentiation). Both matices contain individual motoized taffic of the study aea. A compaison of the tip poductions is shown in figue 2. As both matices epesent calculation esults fo Belin associated taffic, but model the suoundings with the diffeent level of detail, we compae selected 370 TAZ of an inne-city pat of Belin. As it can be seen, the values of the VISEVA model tend to be highe than the compaable tip poductions geneated with the official planning model. Nonetheless, the stuctue of Belin s demand could be epoduced with the simple model I, although the input data was less disaggegated and MiD contained only a small sample size fo Belin.

11 Justen, Beuck, Nagel 11 FIGURE 2: Compaison of tip poductions geneated by VISEVA model I and by the official planning model of Belin s planning depatment fo 1998 (370 inne-city TAZ included) Although the efeence model was caefully chosen, compaing model output of one model to output of anothe is not enough. Theefoe, fo both matices of individual motoized taffic (geneated by the VISEVA model I and the official 24-h matix of the planning depatment), we employed the official assignment model and pocedue in ode to compae the esulting link volumes to eal-wold counts. The planning depatment povided us with count data, but only fou counting stations had counts fo 24 hous. Thee of these fou 24-h counts could be assigned to the simulation netwok. Theefoe, only these thee stations can be used fo this compaison. At evey station volumes of diffeent vehicle classes of both diections wee measued. Both VISEVA models geneate individual motoized taffic only. The official assignment model and pocedue include matices of commecial and long-distance taffic passing though Belin (though taffic) as well. These additional oad use segments affect the oute choice of individual motoized taffic. By applying an assignment pocedue assigning all thee oad use segments simultaneously, as in the official assignment model, we make sue to captue this effect. In ode to use the matices fo commecial and long-distance taffic used in the official model, we had to adapt the spatial esolution of the official and the VISEVA model. This concens the taffic analysis zones of Belin s suoundings. In the official model, this egion is descibed by 139 TAZ in ou VISEVA model we made use of only 8 TAZ in the immediate vicinity of Belin. Since ou study focuses on the city of Belin, this modeling appoach seems suitable. Also the compaison of link volumes is conducted fo the Belin aea only. In a pe-test we made sue that the esults of the spatially adapted official model ae simila to the esults of the official model with its oiginal spatial esolution. Table 4 shows esults of this compaison of the official model (spatially adapted) and the VISEVA model I. Pesented ae simulated 24-h volumes of individual motoized taffic and counts of vehicles classified as cas (which could also be used by commecial and though taffic). At each counting station the measued volumes fo both diections ae compaed to the simulated volumes in the VISEVA model I and the official model. Additionally, elative eos ae calculated fo each pai of volumes (cas measued and simulated). Since the available taffic count data does not povide us with the explicit counts fo individual motoized taffic, we expected that both, the official planning model and ou VISEVA model, will give lowe taffic volumes

12 Justen, Beuck, Nagel 12 of passenge cas as counted in the eal-wold. Table 4 poves this assumption ight; no simulated volume exceeds the measuements, which can be diectly elated to the fact that vehicles detected as passenge cas ae also used by commecial and though taffic. TABLE 4: Compaison with eal-wold taffic counts The elative eos ae elatively high and it can be questioned whethe these eos can be completely explained by the missing oad uses using passenge cas as well. To answe this question, additional infomation on location was included in this compaison (last column). All of the measuements wee taken fom oad segments not pat of the inne-city aea (370 selected TAZ), and two of the thee stations ae feede oads. Those ae cicumstances, which explain the high eo values of both models athe well. On aveage, elative eos of the volumes geneated by the VISEVA demand ae lowe. CONCLUSIONS AND FUTURE WORK The esults pesented in the pevious section look pomising. With a minimum amount of publicly available input data we deived Belin s demand, which allows compaison to esults deived fom the official planning model. The demand was deived by applying the EVA modeling appoach. As it was stated in the intoduction, this modeling appoach can help to ovecome some of the shotcomings the taditional fou-step modeling appoach has. With input data easily to get in almost any study egion one can get faily good esults. At the same time it offes possibilities diffeentiating the demand model. Additionally one can assign depatue time intevals to the pupose diffeentiated matices. VISEVA poduces houly demand based on houly shaes on daily taffic, specific to activity-pupose pais. Thus, time and pupose dependent OD matices poduced with VISEVA shall also be sufficient as input fo geneation of initial agents plans. Such initial plans could be used as input to MATSIM iteative optimization pocess. In such a multi-agent simulation famewok individualized infomation is pocessed and maintained at evey level. Such an appoach captues the tempoal effects of taffic and would allow to model eactions to toll diffeentiated by demogaphics. When elaxation is eached in such a simulation, MiD can povide futhe infomation fo validation. Simulated tip length and time distibution can be compaed to the coesponding data epoted in MiD. Of couse thee ae seveal possibilities to impove the two VISEVA models pesented. Obviously, diffeentiated behavioal paametes the paametes of the EVA function and the modal split, which could not only

13 Justen, Beuck, Nagel 13 be diffeentiated by activity-pupose pais but also by subgoups model, have to be included in VISEVA model II. Anothe aspect concening both VISEVA models is to diffeentiate taffic elated to shopping and othe not futhe specified puposes, but a efining like this could be also intoduced while tansfoming the time and pupose dependent OD matices into agents plans. A stong agument suppoting the latte is that shop floo aeas wee made available on block level that is the basis of all spatial entities in Belin. Geneally, when tansfoming VISEVA output into agents plans the demand to date should be epesented. Especially the netwok changed consideably fom 1998 up to today. But also land-use data change a lot (also this data should be athe easy to obtain). Both envionment changes can be patly explained by Belin s special situation afte the eunification of Gemany. New VISEVA uns with updated data could solve this. Finally we want to point out that Belin s suoundings namely the fedeal state of Bandenbug wee only modeled vey oughly (only commuting taffic was modeled). As long as we ae inteested in the inne-city aea this is not poblematic. If the focus changes, the suoundings have to be modeled moe pecisely. Concluding, it can be stated that it was possible to model the stuctue of Belin s demand fo individual motoized taffic. When the pupose diffeentiated matices ae also time dependent, which can be done with VISEVA, we can poduce initial plans fo ou multi-agent simulation of individual motoized taffic in Belin.

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