GeoTrans Lab. Department of Geography Santa Barbara, CA 93106, USA Phone: , Fax: *Corresponding Author

Transcription

1 Feasibility of using time-space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments Seo Youn Yoon* Kathleen Deutsch Yali Chen Konstadinos G. Goulias GeoTrans Lab. Department of Geography Santa Barbara, CA 93106, USA Phone: , Fax: *Corresponding Author ABSTRACT: Hägerstrand s original framework of time geography and the subsequent timespace prism computational methods form the foundation of a new computational method for potential path areas (PPA) in a realistic representation of dynamic urban environments. In this paper the time-space prism framework is used to assess sensitivity of PPA size to different parameters and to build choice sets for regional destination choice models. We explain the implication of different parameters to choice set formation in a step-wise manner and illustrate not only the complexity of the idea and the high computational demand but also behavioral realism. In this context, this paper tests the feasibility of using constraint-based time-space prism to find the choice sets for a large-scale destination choice model, and identifies a variety of implementation issues. Computational demand is estimated based on a household travel survey for the Southern California Association of Government, and the feasibility of using timespace prisms for destination choice models is assessed with different levels of information on the network and destinations available. The implications of time of day effects and flexibility in scheduling on choice set development due to time-of-day varying level of service on the network and availability of activity opportunities are discussed and numerically assessed. KEYWORDS: time-space prism, choice set building, location choice, flexibility in scheduling, dynamic urban environment 1

2 1. INTRODUCTION Since Hägerstrand (1970) offered a framework of time geography that considers temporal and spatial dimensions of behavior, a variety of conceptual models have been developed (e.g., Miller 1991; Kwan 1998; Weber and Kwan 2002; Kim and Kwan 2003), and the time-space prism has been computationally operationalized (e.g., Kwan and Hong 1998; Lee et al. 2009; Auld and Mohammadian 2011). In parallel, in the field of random utility models that are disaggregate (individual) discrete choice models one thorny issue emerged. This is the choice set identification for an individual decision maker. Thill (1992) discusses choice set misspecification and the consequences of an ill-defined model and points out to the need for eliminating regression coefficient biases by identifying the right choice set. This is also compounded by the number of alternatives in space that are often large (e.g., the store to choose for clothing shopping in a large metropolitan area). Often, the sheer number of alternatives in the universal choice set poses a computational problem, causing the need for methodologies to reduce the choice set to a computationally manageable level. Methods to deal with the large number of alternatives include a deterministic approach in which a researcher specifies a set of rules by which to reduce the number of alternatives. These rules include distance and time thresholds (Black 1984; Termansen et al. 2004; Scott 2006), observed choices by similar sample members (Miller and O Kelly 1998), or a combination of activity type/trip purpose and distance (Bowman and Bradley 2006). Considering the uncertainties residing in all these methodologies, stochastic methods were also developed and they include Manski s two-stage method for estimating choice models (Manski 1977) and its broadening in spatial contexts (Zheng and Guo 2008), the joint estimation of choice set and alternative selection using dominance criteria as in Cascetta et al. (2007), and the process that implicitly considers choice set formation rather than in two stages (Bierlaire et al. 2009). Another approach that is able to describe the dynamically changing urban environment a person faces in her daily scheduling of activities and travel and can also help us handle the 2

3 large number of possible alternatives is the incorporation of time-space concepts into choice set formation. Using Hägerstrand s ideas from the early 1970s as the foundation, Kwan (1998) suggested a method for measuring point based accessibility using the feasible opportunity set that is found using the time-space prism. Later on, Weber and Kwan (2002) brought travel time variation and facility opening hours into the measurement method to account for the dynamics of congestion level and temporal availability of activity opportunities. Kim and Kwan (2003) further developed the methodology, and included the idea of a time window during which each facility can be enjoyed and the traveling environment, which is provided by transportation network (i.e., one-way streets, turn prohibitions, congestion, and segment specific travel speeds) enables. This line of studies tested their measurement methodology using travel diaries combined with datasets of activity opportunities and detailed network data. The results showed that a measurement method based on time geography provides accessibility measures that are able to account for individual heterogeneity in spatio- temporal constraints and dynamics in transportation network and opportunity supply. In this paper we use this approach of modeling the dynamics of an urban environment to demonstrate incorporation of the time-space prism concept in choice set building using the data sets that are widely available and being used by transportation planning agencies. The methodology presented here includes an algorithm to find potential path area (PPA) that is programmable and readily applicable for a travel time matrix, and that can use data from existing regional models suitably modified to increase spatial detail. We also identify implementation issues and estimate the computational requirements to describe an area within a large region, show the time of day changes in availability of opportunities, and find choice sets using time-space prisms for a large-scale destination choice model based on data from a household travel survey for the Southern California Association of Governments (SCAG). Since pinpointing of destination choice within a relatively small area is desired in more advanced travel models, we used US Census blocks as the spatial units. This is enabled by the disaggregation 3

4 method of Chen et al. (2011). Using these units, the time of day effects on choice set building due to varying level of service on the network and availability of activity opportunities are explained, and their implication for destination choice models are discussed as well. First we describe the data used here followed by the method with a selection of results. The paper concludes with a summary and next steps. 2. DATA USED AND METHOD One objective in this study is to identify the activity opportunities (or a proxy of this) that a person can reach within a given amount of available time. The amount of opportunities one can reach depends on the amount of time available (time window), the space one can reach within that time available (which depends on speed of travel), and the density of opportunities (which depends on spatial organization and opening and closing hours of businesses). To compute these we use a variety of data that are: (1) The Household Travel Survey for SCAG that was conducted from spring 2001 to spring 2002 and contains 16,939 households and 40,376 individuals. Information from this survey is extracted to develop profiles of workers presence at job sites by industry type that we explain later. We also use this source of information to estimate the available time during which activities can be engaged in based on different types of activities. Most of the trip destinations in the survey have been geocoded, so that the trip destinations can be overlaid with the other geographic data sets used in this paper using Geographic Information Systems for further computation and analyses; (2) Roadway network including time dependent travel speed/travel time that was created by merging the network of a regional transportation model that simulates every day traffic with a very detailed network used in navigation systems and includes the entire hierarchy of roadways in the region; 4

5 (3) Employment data of many industry types that combine the US Census Transportation Planning Package (CTPP) of year 2001 and Dun & Bradstreet (D&B) to create the spatial distribution of number of persons employed in each industry that were postproceesed to be allocated to each of the 203,191 US Census blocks (for details, see Chen et al. (2011)); (4) Geographic information of the census blocks and the block groups in SCAG region (also available from the Census website, (5) InfoUSA that is a database enumerating all the business establishments in the region that provides business type, number of employees and other characteristics with some duplication with item 3) above but with the added advantage of offering point data; and (6) A land parcel database that allows the identification of locations by the type of land use (in this case we use this information to locate and enumerate major shopping centers). As mentioned earlier the analysis of this paper uses the smallest feasible spatial aggregation units available in order to represent the interaction between space and time in high spatial resolution. The SCAG region is divided into 203,191 blocks and the roadway network is created to connect them. Travel speed on the network is derived from a regional travel model and assumptions about the predominant speed on local roadways that are not in the travel model. The regional model uses four time periods in a day, which are also used to derive travel speeds (and associated travel from each origin to each destination among the 203,191 blocks) in this paper. The four periods are AM peak (6am-9am), PM peak (3pm-7pm), midday (9am- 3pm), and night time (7pm-6am) periods. Another component of a dynamically changing urban environment we considered is time-dependent availability of opportunities. To capture this we first create counts of employees in each of 15 different industry types that are: a) Agriculture, forestry, fishing and hunting and mining; b) Construction; c) Manufacturing; d) Wholesale trade; e) Retail trade; f) Transportation and warehousing and utilities; g) Information; h) Finance, insurance, real estate and rental and 5

6 leasing; i) Professional, scientific, management, administrative, and waste management services; j) Educational; k) Health; l) Arts, entertainment, recreation, accommodation and food services; m) Other services (except public administration); o) Public administration; p) Armed forces). This enumeration captures the spatial distribution of different opportunities in the region. Since we also need to estimate availability by time of day to capture the opening and closing of businesses and the different level of service offered by different industries during a day we need to also use time of day profiles of workers that are present for each industry at their job sites using survey data. Table 1 illustrates the number of retail workers present at work in Los Angeles County by time of the day. This profile shows that the best service is offered during midday, which is also the period with less congestion. More details of the entire process are offered in Chen et al. (2011), which also provides a description of regression based methods to perform spatial allocation of entities computed at different geographic levels. 6

7 Table 1 Number of retail workers present at work in Los Angeles County (Chen et al. 2011) Time Period Time of Day Number of Workers Present at Work Percentage of Workers Present at Work AM Peak 6-7 am % 7-8 am % 8-9 am % Midday 9-10 am % am % 11 am-12 Noon % 12-1 pm % 1-2 pm % 2-3 pm % PM peak 3-4 pm % 4-5 pm % 5-6 pm % 6-7 pm % Night Time 7-8 pm % 8-9 pm % 9-10 pm % pm % 11 pm-12 Mid % 12-1am % 1-2 am % 2-3 am % 3-4 am % 4-5 am % 5-6 am % Time available during which a person can participate in activities can be computed by considering the fixity and flexibility of different activities; based on this, time windows are found when individuals can have access to activity opportunities. In Table 2, the activity types defined for the SCAG travel survey are listed, with the selection of the skeletal activities appearing in bold italic. The skeletal activities are defined based on their spatial and temporal nature. Workand school-related activities (type 7~11) are usually fixed (this is relative fixity in the sense that is harder to move them in space and time or their scheduling modification requires transactions) in space and time becoming major spatio-temporal constraints in daily activity participation and 7

8 travel. All the home activities (types 23 and 24) are also defined as skeletal activities considering that activities pursued at home (i.e., eating meal, sleeping, and household maintenance) are usually fixed in time and happens strictly at home location, and arrival time and departure time at home are major determinants of the amount of opportunity reachable in a day. Being picked up and dropped off at home, work, and school location (a part of types 2 and 3) are also defined as skeletal activities because they are associated with other home activities or work/school-related activities. Activities related to medical, community meetings, political/civic event, public hearing, and religion (types 13, 18 and 20) are considered as skeletal activities since the decision about the location and the schedule for those activities are made by some external entities. Table 2 activity classification and skeletal activities (bold italic) 1 Change mode of transportation 2 Pick up someone or get picked up (activities that happened at home, work and school) 3 Drop off someone or get dropped off (activities that happened at home, work and school) 4 ATM, buy gas, quick stop for coffee, newspaper, etc. 5 Shopping 6 Banking, post office, pay bills 7 Work (include regular scheduled volunteer work) 8 Work-related (sales call, meeting, errand, etc.) 9 School (attending classes) 10 Other school activities (sports, extra-curricular) 11 Childcare, daycare, after school care 12 Eat meal (restaurant, drive through, take out) 13 Medical 14 Fitness activity (playing sports, gym, bike ride) 15 Recreational (vacation, camping, etc.) 16 Entertainment (watching sports, movies, dance, bar, etc.) 17 Visit friends/relatives 18 Community meetings, political/civic event, public hearing 19 Occasional volunteer work 20 Church, temple, religious meeting 21 With another person at their activity out of home 22 Other personal (specify) 23 Working at home (related to main or second job) 24 Other at home activities 97 Other activity 8

9 99 Don t know/refused to answer Based on the skeletal activities listed above, 81,317 time windows (or time budgets) are found from the entire travel survey. For each of these 81,317 time windows, a potential path area (PPA) is computed based on the origin and destination skeletal activities and the time budget between them to find the choice set for each time window. This provides insight regarding the amount of computation needed to implement the time-space prism concept. The skeletal activity locations are spatially joined with the census blocks, and the unique identification number (ID) of the block that each skeletal activity belongs to is assigned to each skeletal activity as an attribute. The travel time matrices computed for the 203,191 by 203,191 pairs of blocks are then used to compute the travel time between any two locations. We used four travel time matrices from the SCAG four step model that were generated by Chen et al. (2011), and each matrix contains the travel time between the blocks in one of the four periods (AM peak (6am-9am), PM peak (3pm-7pm), off-peak (9am-3pm), and night time (7pm-6am)). Depending on the departure time from the origin skeletal activity, different travel time matrices are used to compute the PPA to account for congested travel time and resulting contraction of the PPA or less congested travel time and resulting expansion of the PPA. Figure 1 illustrates how a PPA is computed using the travel time matrices. Figure 1-A shows an example of computing the PPA between home and work with a given time budget. The home is located in block i and the work is located in block j. The objective is to find all the blocks that can be reached on the way from home to work within the time budget. In order to do this, the travel time from block i to block j through each block k needs to be computed. In Figure 1-B, each row has the travel time from an origin block to all the destination blocks and each column has the travel time from all the blocks to a destination block. We obtain the travel time from block i to each block k by selecting the i th row in the matrix and the travel time from each block k to block j by selecting the j th column. The j th column is then transposed and added to the 9

10 i th row producing total travel time from block i to j through each k (Figure 1-C). By selecting only the cells that have total travel time less than a given time budget, the block IDs belonging to the PPA corresponding to the time budget are obtained. Figure 1 Computation of PPA from travel time matrix 10

11 3. PPA ANALYSIS AND CHOICE SETS In this section, we take a time window with skeletal activities located at home and work and apply several variations in the settings that control the size of PPA (i.e. time budget, time of day and flexibility in departure and arrival time). For selected combinations of the settings, PPAs are computed using the algorithm described in the previous section, and they are overlaid on the spatial distribution of available opportunities at the specific time of day to show how the attractiveness of each location changes depending on the settings that we assumed in the computation of PPA. Following these steps, the types of variability that can be captured when we use time-space prisms and consider dynamically changing urban environments in choice set formation of destination choice models are illustrated. As the first comparison, Figure 2 shows the PPAs for different time budgets with an assumed existence of a minimum activity duration. We consider a trip from home to work during the AM peak. In the upper part of Figure 2, the conceptual illustration of the time-space prisms without considering the network or network speed is given, and in the lower part the actual projections of the time-space prisms (PPA) considering the network and the travel speed on the network model are given. From A to C, we can see the expansion of the potential path area with the increasing time budget from 55 minutes to 75 minutes. The intensity of the shades show the amount of time a person can dedicate to activities at each location. In the central part of the PPAs, it is obvious that the time that can be spent for an activity at the location increases as the time budget increases. Locations that are closer to the most direct route between home and work allow longer duration of activity. The further one travels from this direct route, the less amount of time is available for activity participation. Similarly, the added size of a time window increases the size of PPA and also the potential to increase activity duration as Figure 2-C shows. Figure 2-D goes one step further from C and shows the PPA with a minimum stay requirement to participate in an activity. We tested with 20 minutes of 11

12 minimum stay and the blocks in the outer rim with less than 20 minutes available for activity participation were excluded from the choice set. Figure 2 Potential path areas for different time windows with minimum activity duration In Figure 3, the boundaries of Figure 2-C and D are overlaid with the available opportunities in the AM peak to illustrate how the choice set is narrowed down by applying timespace prism concept and the minimum activity duration assumption. The grey shades show the block density of the workers present at work between 8am and 9am in retail trade industry (A) and finance industry (B). They are considered as a proxy of the amount of opportunities provided within each block for purchasing activities or financing/banking activities. The dashed boundary is the extent of the PPA of Figure 2-C with 75 minute time budget and the solid boundary is the extent of the PPA of Figure 2-D with the added condition of the minimum activity duration (20 minutes). The opportunities outside of the dashed boundary are excluded from the choice set because it is not feasible to reach them within the given time budget, and the opportunities between the dashed boundary and the solid boundary can be excluded as well because it is not feasible to participate in an activity that is long enough in these blocks although it is possible to reach them. Figure 2 shows that the time-space prism narrows the choice set to an area that offers travel time and activity duration that satisfy personal constraint conditions and activity type-specific minimum activity duration. It is possible to develop further on this and 12

13 to use estimated (minimum) activity duration that considers individual heterogeneity or situational impacts (such as short grocery shopping on the way from work to home or longer shopping activities with family in weekends) in the duration of a certain type of activities to find the choice set. By using time-space prism, the choice set can reflect the way that each type of activity is associated with the individual characteristics and situated within the behavioral context as well as how each individual is constrained in time and space. 13

14 A) Purchasing activity/retail industry B) Banking activity/finance industry Area PPA without minimum duration (Dashed): 2,223 km 2 PPA with minimum duration (Solid): 1,120 km 2 Difference: 1,103 km 2 (49.6%) Number of Businesses PPA without minimum duration (Dashed) Accessible: 40,593 Accessible: 13,110 Open: 32,377 Open: 10,881 PPA with minimum duration (Solid) Accessible: 26,768 Open: 21,334 Difference [Dashed] [Solid] Accessible: 13,825 Open: 11,043 (34.1%) Number of Employees at Work PPA without minimum duration (Dashed) Accessible: 8,528 Open: 7,078 Accessible: 4,582 Open: 3,803 (35.0%) 143, ,299 PPA with minimum duration (Solid) Difference [Dashed] [Solid] 52,755 (36.8%) 90,697 97,416 44,883 (31.5%) Figure 3 Available opportunities within PPA for different types of activities A) For purchasing activities: available workers in retail industry from 8am to 9am B) For banking and financing activities: available workers in finance industry from 8am to 9am 14

15 A summary of accessible opportunities is given in Figure 3 as well. We used the time-ofday availability of employees (shown in Table 1 for retail) to compute the number of open businesses at each time of day, considering that all the businesses are open when the availability is the maximum and only part of them are open in proportion of the availability at the time to the maximum availability. For example, in the 8am-9am time period, 42.14% of the total retail employees are at work and this is 79.76% of the maximum (52.83% at 11am-12noon). We consider this as the percentage of the businesses that are open. This is an approximation of the open businesses to provide an idea about the time-of-day variation of choice sets. Figure 3 also shows the percentage difference in area when one computes PPA with and without the minimum activity duration and the different industries that can be reached as well as the drastic reduction of reachable opportunities when a minimum activity duration and opening and closing hours are incorporated in the analysis. This motivates our use of duration based models in activity-based simulation models for travel demand forecasting. To relate activity purposes and choice set more specifically, the retail industry can be further classified into different types. In this section, we show how alternatives in destination choice set can be narrowed down to a smaller number for a specific activity type by using time-space prism and more detailed business classification. Figure 4 shows grocery stores and regional shopping centers, which serve different purposes of purchasing, overlaid with the PPA for 75 minute time budget during the AM peak. Each dot and polygon represents one grocery store or one regional shopping center respectively, and as specified in the legend, different formatting indicates where the potential destinations belong: PPA with the minimum activity duration (solid), PPA without the minimum activity duration (dashed), and neither of the two. In Figure 4-A, there are 288 grocery stores within the PPA for 75 minute time budget (dashed) and 230 of them are open. With the condition of 20 minute minimum duration (solid), one can access only 189 grocery stores and 151 of them are open. In Figure 4-B, 35 regional shopping centers are accessible and about 28 of them are open within the PPA without the condition of minimum 15

16 duration (dashed), but the numbers reduces to 25 and 20 with the condition of 20 minute minimum duration (solid). A) Grocery store B) Regional shopping center Figure 4 Grocery stores and regional shopping centers that are accessible 16

17 Now we move on to illustrate choice sets in a dynamically changing environment. When we make a decision for activity participation and associated trips in an urban environment, time of day is a very important factor. At different times in a day, travel time on a network varies, impacting expected travel time to destinations. In addition, the spatial distribution of available opportunities changes as well, depending on the opening and closing hours of businesses. All this impacts the attractiveness of locations for activity participation. In this study, the travel time variations by time of day are represented by the 203,191 by 203,191 travel time matrices for the four time periods, and the availability of opportunities is represented by the proxy of workers present at work at each hour. Based on the temporal resolution of the dynamics data we have, we computed a PPA for each time period. The four PPAs are overlaid with the map of available opportunities at the corresponding time of day to see the impact of time of day on the choice set and the attractiveness of alternatives within the choice sets. Figure 4 illustrates the results. The extent of the PPA for 75 minute time budget and available time for activity participation at each location during each time period is shown on the left hand side, and the boundaries of a 75 minute time budget with and without a 20 minute minimum activity duration is shown with the block density of retail workers present at work in the middle column. The extent of PPAs for the AM peak and PM peak is smaller compared to that for Midday and Night reflecting the traffic congestion during the AM and PM peaks. In this example, the travel time in the PM peak constrains the ability to reach opportunities at different locations more than the travel time in the AM peak does (see the areas of solid boundary given on the right hand side of Figure 5). During both AM and PM peaks, the amount of opportunity that are reachable within the PPA is limited as well because not all of the businesses are open yet in the AM peak and some of the businesses that were open during midday are closed in the PM peak. During the Midday period, one can reach farther opportunities than during either the AM or PM peaks and more opportunities than during any other time periods because he/she experiences less congestion and businesses are fully open. On the other hand, during the Night time one 17

18 can reach the farthest extent within a given time budget (75 minutes in this example) but available opportunity options are very limited because most of the businesses are closed after a certain time of day. The numbers of accessible businesses and open businesses are presented in the right hand side in Figure 5, illustrating the time-of-day variation of accessibility and choice alternatives in terms of the number of businesses accessible (and open). Time period Potential activity duration Retail workers present at work Number of businesses and area of solid PPA A) AM peak (7am to 8am) Accessible: 26,768 Open: 14,506 Area:1,120km 2 Midday (12noon to 1pm) Accessible: 30,434 Open: 29,627 Area: 1,310km 2 PM peak (5pm to 6pm) Accessible: 13,426 Open: 5,901 Area: 412km 2 18

19 Night time (10pm to 11pm) Accessible: 41,745 Open: 5,427 Area: 2,137km 2 Figure 5 PPA, potential activity duration, density of retail workers at work and accessible opportunities of retail industry by time of day 19

20 This comparison clearly shows how the spatial extent of the choice set, and the attractiveness of each alternative change by time of day can be incorporated into the generation of choice sets using the time-space prism concept. The last analysis in this paper is on flexibility in scheduling activities and its impact on choice set formation. A certain level of flexibility is assumed for the departure from and arrival to skeletal activities, and the implication of the assumed flexibility on the choice set and the alternatives is explained in the presence of all the considerations discussed previously (available time budget, minimum activity duration, opportunities within PPA, and time of day variation of travel time and available opportunity). A person s schedule with a Home-Work tour during the AM peak and a Work-Home tour during the PM peak is taken as an example to show the implication of flexibility and the corresponding time-space prisms are illustrated in Figure 6. As the baseline of the comparison (Figure 6-A), he/she departs from home at 7:15 am and arrives at work at 8:00 am in the Home- Work tour, and departs from work at 5:00 pm and arrives home at 5:45pm in the Work-Home tour with very constrained time-space prisms. The Home-Work tour is represented by a timespace prism in the lower part of Figure 6-A, and the Work-Home tour is represented by another time-space prism in the upper part of Figure 6-A. Then, we allow 30 minute flexibility in arrival/departure at home and work, and they are shown as time-space prisms in Figures 5-B and C. Figure 6-B illustrates the flexibility to depart early from work in the afternoon (4:30 pm) and to arrive late at work in the morning (8:30 am), and Figure 6-C illustrates the flexibility to depart early from home in the morning (6:45 am) and to arrive late at home in the evening (6:15 pm). 20

21 DRAFT for 2012 TRB special issue of Transportation Figure 6 Flexibility in arrival and departure time and resulting variation in reachable opportunities in the finance industry 21

22 On the right hand side, the boundaries of the PPAs for 75 minute time budget (with and without 20 minute minimum activity duration) in the AM and PM peaks are shown with the available opportunities provided by the finance industry at the corresponding time of day. In the baseline case of Figure 6-A, a person experiences the opportunities available between 7 and 8 am in the morning on the way to work and the opportunities available between 5 and 6 pm in the evening on the way home. By allowing 30 minute flexibility for arrival at work, the time-space prism expands, allowing him/her to experience the opportunities available only after 8 am (especially around the destination (Work)). The area where he/she can enjoy the increased opportunities that open after 8am is marked with grey color in the Home-Work prism of Figure 6- B. The total number of open finance businesses within the solid PPA is 3,530 between 7am to 8am and 7,074 between 8am and 9am. By allowing 30 minute flexibility for arrival at work, he/she can enjoy the increased accessibility around the work location. However, when 30 minute flexibility is allowed for the departure from home in the morning, the opportunity that he/she can enjoy by departing early is equal to or less than the opportunities of 7-8 am (Figure 6-C). The area where he/she experiences fewer opportunities by arriving late is marked with grey color as well in the Home-Work prism of Figure 6-C. In this case, the total number of finance businesses that are open within the solid PPA is 3,530 between 7am to 8am and 1,377 between 6am and 7am, and this person experiences this decreased accessibility especially around the home location. The flexibility allowed in the evening also shows similar patterns. Early departure from work allows a person to enjoy more opportunities around the work location but late arrival at home leads to fewer opportunities around home location than the base line case due to opening and closing hours of businesses. This analysis shows that the opportunities in each alternative (block) that a person faces change as this person departs from the origin or plans to arrive at the destination early or late. It also indicates that available opportunities at a destination are accurately measured by the 22

23 arrival time and not by the departure time from the origin in a dynamically changing environment. People have a certain extent of flexibility in scheduling activities and the flexibility is different by activity type. This analysis shows that the number of alternatives and the characteristics of alternatives (attractiveness as destination in this example) change depending on when, where, and how flexibility is allowed, and this has to be accounted for in activity scheduling and destination choice models to achieve behavioral realism and to enhance the ability to predict the impact of policies related to time such as staggered arrival at work, flexible work hours and extended opening hours. 4. SUMMARY AND DISCUSSION We offer a methodology to apply the concept of time-space prism for PPA computation and choice set formation in location choice models explicitly considering the dynamic nature of urban environment. The methodology uses the datasets that are being used by transportation planning agencies and travel time information that was developed based on those datasets in Chen et al, (2011). The algorithm that we suggest is programmable and readily applicable in regional models. The sample size of the SCAG travel survey and the number of time-space prism estimations provided in the data section give us an idea about how much computation is required to incorporate this algorithm in choice set building demonstrating its feasibility. The analysis of this paper confirms that the time-space prism concept is able to account for the variations of choice sets that are ignored otherwise. Based on the time-geographical concepts that have been developed (Miller, 1991; Kwan, 1998; Weber and Kwan, 2002; Kim and Kwan, 2003), we identified several factors of dynamically changing environment and individual temporal decisions that have major impacts on choice set formation. The components that we identified are size of time budget, minimum activity duration, daily profile of available opportunities, travel times varying by time of day, and temporal flexibility in scheduling activities. We illustrated their coordination in temporal and spatial dimensions, and confirmed 23

24 that not only the choice set itself but also the characteristics of the destination alternatives change according to the factors that we identified. To show that the variation of choice set according to the factors is actually countable and computable, we explicitly presented the number of employees at work and the number of accessible and open businesses by business type, and compared them with the area of potential path areas. All these considerations are significant in all behavioral models that are based on dynamically changing environments and flexible temporal decisions,, including regional simulation models. This paper also provides a framework that enables the consideration of heterogeneous dynamic characteristics of environments in the destination choice model and travel demand models, for example differences between an area that provides opportunities during 24 hours and another area where opportunities are provided only during limited time of day. The techniques used in this paper can also aid the geovisualization of land use and transportation policies as well as the output of land use and activity simulation models. As a further extension of this paper, estimation of location choice models using the choice set generation method suggested in this paper and its comparison with location choice model estimation using other choice set generation methods are necessary. In addition, although the interactions within household were not addressed in this paper, our previous research offers evidence that these interactions are important in destination choice (Yoon and Goulias 2010) and this may be the immediate next step of our research inquiry here. Moreover, the impact of using finer spatial units as opposed to the TAZs on model estimation and estimation results is another facet to be analyzed in future extensions of this paper. Yet another research direction is the exploration of information provision and change of the choice sets within the context of this paper as well as the role of attitudes in the form of sense of place (Deutsch et al. 2011). 24

25 ACKNOWLEDGEMENT Funding for this project was provided by the University of California Transportation Center, the United States Department of Transportation Eisenhower Fellowship program, the University of California Office of the President UC Lab Fees Program, the University of California Office of the President Multicampus Research Program Initiative Sustainable Transportation, and the Southern California Association of Governments. The contents of this paper do not constitute a policy at any level of government. REFERENCES Auld J. and Mohammadian A (2011) Planning Constrained Destination Choice in the ADAPTS Activity-Based Model. Paper presented at the 90 th annual Transportation Research Board Meeting, Washington D.C. Bierlaire M, Hurtubia R, and Flötteröd G (2009) An analysis of the implicit choice set generation using the constrained multinomial logit model. Paper presented at the 88 th Annual Transportation Research Board Meeting, Washington D.C. Black WC (1984) Choice set definition in patronage modeling. Journal of Retailing 60, Bowman J and Bradley M (2006) Activity Based Travel Forcasting Model for SACOG. Intermediate Stop Location Models. Technical Memo, July, Cascetta E, Pagliara F, Axhausen K (2007) Dominance attributes for alternatives perception in choice set formation: an application to spatial choices. Paper presented at the 86 th Annual Transportation Research Board Meeting, Washington D.C. Chen Y, Ravulaparthy S, Deutsch KE, Dalal P, Yoon SY, Lei T, Goulias KG, Pendyala RM, Bhat CR, Hu H-H (2011) Development of Opportunity-based Accessibility Indicators, Transportation Research Record: Journal of the Transportation Research Board,2255:

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