Implementing Activity-Based Modeling Approach for Analyzing Rail Passengers Travel Behavior

Transcription

1 Implementng Actvty-Based Modelng Approach for Analyzng Ral Passengers Travel Behavor Jn K Eom Ph.D. and Dae-Seop Moon Ph.D. Korea Ralroad Research Insttute, Uwang, Korea Abstract Most travel demand models focus on travel patterns of ndvduals choosng ether autos or transt operated n ntra cty regons, not the travel patterns of ntercty ral passengers whch are generally dfferent. Ths paper presents a comprehensve analyss of ntercty ral passengers actvty characterstcs and travel patterns based on the 001 Seoul-Busan ral passengers Travel Survey. Results show that the varables representng personal characterstcs such as age and ncome seem to affect on destnaton choce behavor. However, the ncome was not seen to be a crtcal effect on destnaton choce. The varables representng travel characterstcs such as travel tme, transfer status, and date for travel seem to be affectng on destnaton choce for both work-related and recreaton actvty. However, the destnaton choce would be affected by the sze of cty and economc relatonshp between Seoul and all four destnaton ctes. The nsghts ganed from ths study wll serve as the bass of an actvty-based ral travel demand model. Introducton Forecastng travel demand s a crtcal ssue n transportaton system plannng snce the estmated travel demand hghly affects on a decson of nvestng new transportaton system such as hghway, transt, and hgh-speed ralway. The conventonal four-step approach - a sequental procedure of travel demand estmaton consst of trp generaton, trp dstrbuton, mode choce, and traffc assgnment - has been wdely used n travel demand estmaton. Ths approach only consders the ral passenger demand n the step of mode choce after the total travel demand not classfed by travel modes s decded from the prevous two steps. However, the standard trp generaton and dstrbuton model n the conventonal four-step approach do not provde relable estmates because they do not consder travel patterns at a person or ndvdual household [1,]. Ths s manly because these approaches do aggregate all the data such as soco-economc and ndvdual travel nformaton at a geographcal boundary n terms of Traffc Analyss Zone (TAZ) n whch the travel pattern s assumed to be smlar [3]. New modelng approaches such as actvty-based and tour-based models, consderng travel behavor of ndvdual household or person, seem to be more approprate for forecastng travel demand. However, only a few practcal applcatons have been made snce these approaches usually requre a lot of data resources and computng tme to solve ther complcated model structure [4, 5]. Moreover, the applcaton of these new approaches for analyzng ral passenger travel behavor or estmatng the ral passenger travel demand has not ever made whle the new approaches are manly focusng on the studes of feasblty test of hghway constructon and senstvty analyss regardng the varous polces of transportaton demand management (TDM). Thus, applyng these new approaches to ral mode s a knd of examnaton of whether these approaches would be approprate or applcable to the ral passenger travel demand model and t wll be nformatve to the decson makers who take nto account the ral travel demand when makng a decson n prorty of ralway nvestment. The prmary objectve of ths research s to nvestgate ral passengers travel behavor based on the actvty-based modelng approaches. Ths behavoral study on the ral passenger wll be the bass of developng an actvty-based model for estmatng the ral passenger demand. Yet, not all of requred data for mplementng the actvty-based modelng approaches to ral passengers s avalable whle the motorst s travel behavor s fully nvestgated. Thus, ths study wll nvestgate ral passengers travel behavor wthn a narrow scope of actvty-based approach

2 due to not enough data avalable, and provde an nsght of feasblty of mplementng an actvty-based approach to the ral passenger travel demand estmaton. Approach The research approach apples statstcal analyss and tests to Kyeongbu (Seoul-Busan) ral passenger travel survey data ncludng demographc and travel characterstcs. The data was surveyed n 001 before ntroducng KTX (Korea Tran Express).The analyss focuses on: 1) actvty schedule for ral passengers, ) departure and arrval tme analyss by actvtes (school, work-related, recreaton, other), and 3) destnaton choce (Busan, Daegu, Daejeon, and Cheonan) by actvty type. By dong that, nterestng analyses focus on nvestgatng the mechansm of ral passengers actvtes to ther travel patterns ncludng destnaton and mode choce. Data The KRRI (Korea Ralway Research Insttute) conducted SP (Stated Preference) survey of Kyeongbu ntercty ral passengers n 001[6]. The goal of the survey was to nvestgate the passengers preference of ntercty transportaton modes. The ral passengers randomly sampled were gven to varous scenaros of ntercty modes consst of KTX, express bus, ar plane, exstng ral, and auto. KTX s assumed to be opened as a new mode wth varous fare optons. The passengers were asked to choose ther travel mode based on the comparson of travel cost and tme between KTX and other modes. They were also asked to complete the questonnares regardng ther socoeconomc and travel characterstcs. The demographc nformaton ncludes age, ncome, auto ownershp, occupaton, and locatons of resdence and work. The travel characterstcs nclude orgn/destnaton, purpose of travel, departure and arrval tme, travel cost ncludng transfers. In ths study, the ral passengers travel characterstcs wth respect to ther demographc and actvty characterstcs are focused. Table 1 shows the summary of samples surveyed at Kyeongbu lne connectng Seoul to a few major ctes such as Cheonan, Daejeon, and Daegu. The total of 504 passengers was sampled and the samples were found to be evenly obtaned by orgn and destnaton. Orgn Destnaton Dstance Samples % Busan >300km Seoul Daegu >300km Daejeon >100km Cheonan <100km Daejeon Busan >00km Daegu Busan >100km Total Table 1: Summary of Kyeongbu Ral Passengers Surveyed The descrptve statstcs of passengers demographc and travel characterstcs were shown n Table. Of the 504 passengers, 300 people (59.5%) were male and 04 people (40.5%) were female. 99 passengers (59.9%) were employed; 86 (17.1%) passengers were student; nterestngly 56 (11.1%) passengers were housewfe. About 54% of passengers had no drver s lcense and the average number of vehcles avalable was 1.15 (per household). More than half of the passengers (63.7%) dd not made transfer. The major access mode was found to be bus (33.3%), subway (9.%) and tax (6.6%) respectvely. The major purpose of trps was personnel (49.6%) and work-related (9.6%).

3 Varable Descrpton Codng Statstcs Age Average age Contnuous 31.8 Gender If the person s male, then 0 Otherwse, 1 0: Male 1: Female 300 (59.5%) 04 (40.5%) Employment Employment status 1: Student : Employed 3: unemployed 4: Other 86 (17.1%) 99 (59.3%) 74(14.7%) 45(8.9%) Auto ownershp autos to drve 0: yes 1: no 35 (46.6%) 69 (53.4%) Income Average ncome (1000won) Contnuous 70.5 (per HH) Vehcle Average number of vehcles avalable per Household 1: Auto : Van 3: Truck 4: Tax 1.15(per HH) 0.07(per HH) 0.05(per HH) 0.007(per HH) Transfer If transfer happens, then 0 Otherwse, 1 0: yes 1: no 183 (36.3%) 31 (63.7%) Access mode Type of modes for access 1: Bus : Tax 3: Auto 4: Subway 5: Walk 6: Bcycle 168 (33.3%) 134 (6.6%) 46 (9.1%) 147 (9.%) 9 (1.8%) 0(0.0%) Trp purpose Purpose of trp 1: School : Commute 3: Work-related 4: Shoppng 5: Personnel 6: Other Table : Descrptve Statstcs of Passenger Characterstcs 16 (3.%) 39 (7.7%) 149 (9.6%) (0.4%) 50 (49.6%) 48(9.5%) Actvty Pattern In order to mplement actvty-based approach, ths study focuses on the ral passengers actvty type and schedule, whch need to be defned based on the survey questonnare. Ths s because the survey was not desgned for actvty-based approach. The trp purpose, therefore, s redefned nto actvty types. In general, fve man actvty types such as home, work/school, shoppng, recreaton, and other are defned and these are commonly used n the actvty-based approaches n travel demand modelng [5]. Ths classfcaton could be changed f necessary dependng on the scope of actvty-based model development or wth varous model applcatons. Unlke general actvty classfcaton, n ths study, the home actvty s not ncluded snce the survey only sampled the passengers at ralways statons. The actvty classfcaton defned here s School (1) denotes all school-related actvtes; Work-related () corresponds to all workrelated and commute actvtes; Recreaton (3) focuses on prvate and lesure actvtes; and Other (4) ncludes all actvtes not n the four other actvtes [7].

4 Table 3 shows the purpose of trps by passengers orgns and destnatons. The trps for dong recreaton actvty lke meetng famly, havng vacaton, etc are seen to be hghest (61.1%) n Seoul-Busan and work-related trps (41.3%) are secondly happened n Daegu-Busan. Accordng to the survey result, t s found that the shorter the travel dstance between orgn and destnaton, the more the work-related and commute trps were generated. Hence, t s expected that f KTX s ntroduced, more commute and work-related trps would be expected. Total Seoul- Busan Seoul- Daegu Seoul- Daejeon Daejeon- Busan Daegu- Busan Seoul- Cheonan Number of Sample (1058) (90) (84) (90) (80) (80) (80) Prvate Work-related Commute School Shoppng Other Table 3: Dstrbuton of Trp Purposes by Orgns and Destnatons (%) Another crtcal component of actvty-based approach s actvty schedule whch s defned as a sequence of actvtes or a schedule of actvtes n space and tme. Actvty schedules are llustrated by varous elements that have been wdely modeled n research to create synthetc actvty schedules by traveler group. Followng s the lst of the elements formng an actvty schedule: Actvty frequency, Actvty duraton and tme allocaton, Spatal (space) allocaton, Departure tme decson, and Stop and trp chan characterstcs. The complexty of the actvty-based approach depends on how detaled these elements are modeled and how dsaggregated the model s. As mentoned before, ths complexty has been a challenge for plannng agences to apply ths approach n state-of-practce applcatons. Unfortunately, the survey dd not nclude all the elements requred to develop actvty schedule except for departure tme decson. However, ths s nformatve to upcomng actvty-based model development for ral passengers f researcher does actvty survey for travelers who made external trps. Ths study analyzes how the ntercty passengers actvty schedule forms and affects on ther travel. Fgure 1 shows the ral passengers actvty schedule. For example, person 1 had a work actvty so that he/she made a busness trp to somewhere. The departure tme was around 8am and the travel tme was about 6 hours. After arrved at destnaton, he/she had a work actvty for about 3 hours known as actvty duraton. Then, he/she made return trp to back home. Person had a recreaton actvty so he/she traveled for 7hours to arrve at hs/her destnaton. As seen n the fgure, n order to form the complete actvty schedule, the tme of day actvty and travel survey are requred to fgure out the stop and trp chan characterstcs.

5 Other Travel tme Actvty duraton Recreaton Shoppng Work 1 Person 1 Person Home Tme of day Fgure 1: Example of Ral Passengers Actvty Schedule Fgure shows the pattern of passengers departure and arrval tme. Many passengers choose ther departure tme before 8am (17.1%) and arrve at ther destnaton around 1pm (11.%). They also take ther departure tme around 4pm (.0%) and arrval tme around 10pm (14.%). Ths explans that the passengers would lke to fnsh ther journey wthn 4 hours and would lke to have more tme to do ther prvate and work-related actvtes at ther destnaton. The departure tme vares dependng on actvty duraton, length of travel, and speed of travel mode. If people make a short trp, the departure tme may vary durng a day because they have an opportunty to nvolve more actvtes. On the other hand, f they make a lengthy trp, they have less opportunty to engage n other actvtes durng a day. (%) Departure Arrval < Tme of Day (%) Fgure : Dstrbuton of Departure and Arrval Tme Fgure 3 shows the dstrbuton of departure tme by actvty types. Due to lack of data, only three actvtes such as school, work-related, and recreaton (ncludng all prvate actvtes) are seen n the fgure. The departure tme for recreaton looks smlar to the one for work-related actvty. Ths would be much dependng on ral schedule whch s fxed most of day. However, the departure tme for work-related actvty was found to be begnnng earler around 7-8am compared to recreaton actvty. Ths means that f a person lves n Seoul and he/she has a work-related trp to Busan and needs to back hs/her work place or home, he/she may choose

6 departure tme earler due to the lengthy travel tme (e.g. about 5 hours). The hghest proporton of departure tme for recreaton was seen to be 11am (43%) and more than 15% of passengers choose departure tme between 9am and 5pm and they also made departure n the evenng around 7pm whch was not seen n the passengers dong work-related actvty. Ths explans that passengers dong recreaton actvtes may have more optons n a decson of departure tme. Frequency School Work-related Prvate Tme of Day Fgure 3: Dstrbuton of Departure Tme by Actvty Types MNL (Multnomal Logt) Model In order to mplement actvty-based approach, ths study focuses on the development of choce models ncludng departure arrval tme, destnaton, and access mode choce by actvty type, whch wll be the bass of actvty-based travel demand model. However, only destnaton choce model by actvty type (e.g., work-related and recreaton) s developed here snce not enough data are avalable to develop departure and access mode choce model by actvty type. The dscrete choce model selected here to analyze the destnaton (cty) choce of passengers s the multnomal logt (MNL) model. Choosng a destnaton of actvty s a form of dscrete choce. A random utlty based MNL model for the destnaton choce of the passengers s specfed. The utlty functon s defned as the lnear form as follows. U d = α + β P + β T + β A + ε (1) d dp dt da d where U d : Utlty of destnaton cty d for passenger, α d : Estmable alternatve specfc constants, β d : Estmable coeffcents, ε : Type I extreme value (Gumbel) dstrbuted random error terms, d P,T, A : Vectors of personal, and trp nformaton for passenger, respectvely. If utlty maxmzng behavor s assumed, ths utlty leads to the MNL model:

7 U d e P d = () U d e d D where, P d : Probablty of passenger choosng destnaton cty d U d : Utlty of destnaton cty d for passenger D : Choce set of all avalable alternatve destnaton cty for passenger All varables are specfc to the partcular passenger. The MNL model wll show how the dfferent passenger specfc varables affect destnaton choce. The unavalable alternatve specfc nformaton s captured by the alternatve specfc constant. MNL Model Estmaton In ths study, four ctes are defned as alternatves (destnaton) for both Work-related and Recreaton actvtes n the destnaton choce model. The alternatves are assumed to be the avalable destnaton optons that a decson maker (passenger) s supposed to consder durng the choce process. The choce set for both Work-related and Recreaton actvtes conssts of the four ctes: Busan, Daegu, Daejeon, and Cheonan. Table 4 shows the explanatory varables that represent passengers personal and travel nformaton. Personal Informaton Varable Descrpton Codng Age Income Age n year Average monthly ncome (1000won) Contnuous Contnuous Gender Male and female 0: Male 1: Female Auto autos to drve 0: Yes 1: No Employment Employment status Indcator, 1 f student Indcator, 1 f employed Indcator, 1 f unemployed Indcator, 1 f other Travel Transfer Transfer to other modes 0: Yes 1: No Ttme* Travel tme (mnutes) Contnuous Amode Access mode Indcator, 1 f bus Indcator, 1 f tax Indcator, 1 f auto Indcator, 1 f subway Indcator, 1 f other Date Date for travel 0: Weekday 1: Weekend Table 4: Explanatory Varables Consdered n the Analyss

8 Personal nformaton ncludes age, ncome, gender, auto ownershp, and employment status (student, employed, unemployed, and other). Travel nformaton conssts of transfer status, travel tme, and access mode (bus, tax, auto, subway, and other). These varables are tested and used to develop the MNL destnaton choce model by actvty type. MNL Model Result Table 5 shows the summary of the model tested globally for the effect of each varable on the outcome varable, controllng for the other varables n the model. All the ch-square statstcs are Wald-statstcs, not lkelhood rato statstcs. Each ch-square s a test of the null hypothess that the explanatory varable has no effect on the outcome varable. Actvty Varable χ Work-related Pr> χ χ Recreaton Pr> χ Age * Income * Gender Auto Employment Transfer ** * Ttme <0.0001** <0.0001** Amode Date.3 <0.0001** * N DF Log-lkelhood rato p-value (df:1000) (df:759) Note: ndcates that the varable s not estmated. Sgnfcant: p< 0.05 `*', p<0.001 `**'. Table 5: Summary of Maxmum Lkelhood Analyss of Varance In the destnaton choce models for Work-related and Recreaton actvty, there are three degrees of freedom for each ch-square because each varable has three coeffcents. So the null hypothess s that three coeffcents are zero. The log-lkelhood rato equals twce the postve dfference between the log-lkelhoods for the ftted model and the saturated model, and hgh p-values suggest a good ft. The p-value of n all two models assures that the models ftted well. The destnaton choce model for School and Shoppng actvty was not estmated snce the dataset s too small to estmate parameters. As shown n Table 5, the ch-square statstcs of some varables (Employment and Amode) were not estmated due to the lack of datasets that dd not nclude all possble cases. Interestngly, t s found that the varables representng personal characterstcs such as gender and auto ownershp are not statstcally sgnfcant. The passengers destnaton choce behavor seems to be affected by varables lke age and ncome rather than gender and auto ownershp.

9 Also, the varables representng travel characterstcs such as travel tme, transfer status, and date for travel are found to be sgnfcant n the model. The varables statstcally sgnfcant are ncluded n developng the destnaton choce models for both actvtes. Tables 6 shows the estmaton results for the MNL destnaton choce model of passengers for work-related and recreaton actvty. Ths analyss uses the largest value of dependent varables as a reference. Accordngly, Cheonan cty s the reference varable to estmate the parameters for three destnaton ctes. Cheonan s the closest destnaton from Seoul among four alternatve destnaton ctes. Varable Destnaton Work-related Recreaton Busan Daegu Daejeon Busan Daegu Daejeon Intercept (0.57) -.966** (11.45) (0.05) ** (16.48) ** (1.79) ** (14.4) Income (0.15) (0.35) (1.6) ** (8.13) (1.1) (1.58) Age (7.34) (.68) 0.106** (11.56) Ttme ** (5.35) ** (8.77) (1.9) 0.03** (39.74) 0.015** (30.99) * (4.73) Transfer * (9.07) (.86) ** (14.91) (0.03) * (5.4) (1.01) Date (.69) 1.603* (4.00).5356** (10.33) (.18) (0.09) (0.03) Note: ndcates that the varable s not ncluded n the model. () represents Ch-square value. Sgnfcant: p< 0.05 `*', p<0.001 `**'. Table 6: MNL Destnaton Choce Model for Actvty Type Wth respect to work-related actvty, t s nterestngly found that the varable, ncome, s not statstcally sgnfcant wth a 5% error level but, t shows that Busan seems less lkely chosen for work-related actvty as ncome ncreases compared wth Cheonan as a reference cty. However, the ncome was not seen to be a crtcal effect on destnaton choce. Travel tme s found to be statstcally sgnfcant and the sgns of parameters are all postve. Ths s because Cheonan s closest cty from Seoul and more travel tme s requred to get to other three ctes. The transfer status s also found to be sgnfcant and the sgns of parameters are negatve so that t would be explaned by that the more transfer, the lower the propensty toward three ctes. The varable, date, s found to be postvely affectng on the choce of destnaton snce the work-related actvty s commonly happened n weekday rather than weekend. Ths s reasonable snce more work-related actvtes are happened n Busan, Daegu, and Daejeon compared to Cheonan. As shown n Table 6, a destnaton choce model for the recreaton actvty, t s found that as passenger has more ncome, they are less lkely choosng the destnaton ctes located faraway from Seoul. Ths would be a reasonable result, snce the rch people would lke to choose automoble for makng a lengthy trp rather than choosng a ral especally for recreaton actvty. The older the passenger s more lkely to be nvolved n recreaton actvtes n all three ctes compared wth Cheonan. A related effect s seen for the transfer varable, n whch the more transfer s demanded, the lower the propensty toward Busan and Daegu. The older are less lkely to transfer compared wth the younger generally snce t s not convenent to travel by takng a ral. It s also shown that the dstance from Seoul affects on the travel tme and the most of lengthy trp for recreaton are happened n Busan, Daegu, and Daejeon compared to

10 Cheonan. However, travel tme s not seen to be a crtcal effect on destnaton choce compared to the varables as transfer status and date. The varable, date, s found to be negatvely affectng on the choce of destnaton snce the recreaton actvty s commonly happened n weekend. Ths sgns are all opposte drecton aganst the sgns of parameters for work-related actvty. Conclusons Ths study analyzes the ral passengers travel behavor by mplementng actvty-based modelng approach whch wll become a major approach n transportaton demand estmaton n near future. As frst step n the process of actvty-based travel demand model for ralways, ths study does nvestgate the travel behavor of ral passenger n terms of departure and arrval tme and destnaton choce by actvty type such as school, work-related, recreaton, and other. Accordng to the survey result, the trps for dong recreaton actvty were seen to be hghest (61.1%) n Seoul-Busan and work-related trps (41.3%) are secondly happened n Daegu-Busan. It s also found that the shorter the travel dstance between orgn and destnaton, the more the work-related and commute trps were generated. On the other hand, the longer the travel dstance, the more the recreaton trps were generated. From analyzng passengers departure and arrval tme, t s found that many passengers choose ther departure tme before 8am (17.1%) and arrve at ther destnaton around 1pm (11.%). They also take ther departure tme around 4pm (.0%) and arrval tme around 10pm (14.%). Ths explans that the passengers would lke to fnsh ther journey wthn 4 hours and would lke to have more tme to do ther prvate and work-related actvtes at ther destnaton. The departure tme for recreaton looks smlar to the one for work-related actvty. Ths would be hghly dependng on ralway schedule whch s fxed most of day. However, the departure tme for work-related actvty was found to be begnnng earler than the recreaton actvty. Ths study successfully developed the destnaton choce model by actvty type. However, the model developed here only provded some nformaton because the data was not desgned for mplyng actvty-based approaches. Consequently, not enough data are avalable to develop models for the departure and arrval tme choce. In the destnaton choce model, the choce set for both work-related and recreaton actvtes conssts of the four ctes such as Busan, Daegu, Daejeon, and Cheonan. From the results, t s found that the varables representng personal characterstcs such as age and ncome seem to affect on destnaton choce behavor. However, the ncome was not seen to be a crtcal effect on destnaton choce. The varables representng travel characterstcs such as travel tme, transfer status, and date for travel seem to be affectng on destnaton choce for both work-related and recreaton actvty. However, travel tme s not seen to be a crtcal effect on destnaton choce compared to the varables as transfer status and date. However, the destnaton choce would be affected by the sze of cty and economc relatonshp between Seoul and all four destnaton ctes. Ths study provdes some valuable nsghts for ral passengers travel behavor by mplementng actvty-based approaches. However, further nvestgaton should be requred based on the survey data obtaned from actvty-travel survey. In many respects, the actvty-based modelng approach s expected to be useful n measurng the changes n ral travel demand affected by the dversty of ralway busness. By dong that, new ralway plans n terms of determnng ralway route, locaton of ralway statons, tran operaton, ralway servce, and so on would be approprately made. Acknowledgements The authors acknowledge Hyun-woong Km of the Department of Polcy and Logstcs Research at Korea Ralroad Research Insttute for assstng wth the Kyeongbu ral passengers travel survey data.

11 References [1] Model Valdaton and Reasonableness Checkng Manual, TMIP, (1997). Accessed [] Actvty and Tour-Based Model Semnar, Cambrdge Systematcs, Inc, (004). [3] Ortuzar and Wllumsen, Modelng Transport nd edton, Wley, New York, (1994) [4] Algers, S., Elasson, J. and Mattsson, L., Actvty-based model development to support transport plannng n the Stockholm regon.presented at the 5 th Workshop of the TLE Network, Nynashamn, September 8-30, (001). [5] McNally, M. G. "The Actvty-Based Approach", In Handbook of Transport Modellng. Edted by Hensher, D.A. and Button, pp , (000). [6] A study on developng a plan of ntermodal system for Kyeonbu (Seoul-Busan) Hgh-speed ral, Korea Ralroad Research Insttute, (003). [7] Ettema, D.F. and Tmmermans, H.J.P. Actvty-Based Approaches to Travel Analyss, Elsever Scence Ltd, (1997).