SFCTA Regional Transportation Model Summary
San Francisco County Transportation
Authority Transportation Modeling System
Overview and Summary
Introduction
One of the first tour-based micro-simulation models that is being used extensively in planning is the model system created by Cambridge Systematics and Parsons Brinckerhoff for the San Francisco County Transportation Authority, completed in 2000. The model system was designed to use the “full day pattern” modeling approach, first introduced by Bowman and Ben-Akiva at MIT (1). The main feature of the “full day pattern” approach is that it simultaneously predicts the main components of all of a person’s travel across the day. The concept of the tour is used to represent travel. A tour is a sequence of trips that begin and end either at home (Home-Based Tour) or work (Work-Based Sub-Tour). A synthesized population of San Francisco residents is input to the component models of vehicle availability, day pattern choice (tour generation), tour time of day choice, destination choice and mode choice. Destination and mode choice are predicted at both the tour and the trip level. The synthesized tours and trips are aggregated to represent flows between traffic analysis zones before traffic assignment. The model system predicts the choices for a full, representative sample of residents of San Francisco County, almost 800,000 simulated individual person-days of travel.
In the San Francisco Model, a micro-simulation framework is applied to individuals and households making vehicle ownership, trip pattern, and trip destination and mode choices; many of these models are logit formulations. A Monte Carlo method is used select outcomes according to these logit model probabilities based on random number draws. Each time the sequence of random numbers used to simulate choices is varied, the model result, or ‘end state’ of the model may change.
The SF Model predicts demand for SF County residents only. The SF Model relies on the Bay Area regional travel demand model, developed and maintained by Metropolitan Transportation Commission (MTC Model), for non-resident travel demand, including non-home-based trips made entirely within SF County by non-SF County residents. The MTC model is an aggregate trip-based model with three trip purposes (HBW, HBO, NHB) run for approximately 2,000 TAZs.
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FIGURE 1 San Francisco model components.
Population Zonal Synthesizer Data Person All Models Records Workplace Vehicle Location Availability Model Model
Full-Day Tour Accessibility Logsum Variables Pattern Models Measures
Time of Day Models
Nonwork Tour Network Level Destination Trip Diary of Service Variables Records Choice Models Logsum
All Remaining Tour Mode Models Choice Models
Intermediate Visitor Trip Stop Location and Destination Choice Choice Model
Visitor Trip Trip Mode Mode Choice Choice Regional Trip Tables Model for NonSF Trips
Trip Tables Transit Highway Assignment by Assignment by Time Period (5) Time Period (5)
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Travel Survey Database
The SF Model was estimated based on the 1990 MTC Household Survey. The 1990 survey collected single weekday travel data from nearly 9,400 Bay Area households and multiple- weekday travel data from nearly 1,500 Bay Area households. The 1990 survey effort also included a separate sub-project, funded by the Bay Area Rapid Transit District, to collect multiple-weekday travel data from 1,000 BART-using households. (The BART-using households were identified and contacted based on responses to on board surveys conducted by the BART District in 1988 and 1989). The BART survey and the MTC multiple-weekday survey were completed in the spring of 1990; the MTC single-weekday survey was continued and completed during the autumn of 1990. Only data for SF residents were used for model estimation, about 1,500 households. The survey was a trip-based survey, not activity based. Only information on each trip, including from and to purpose, was collected. There were 3,100 approximately person-days and 4,200 tours upon which to estimate the SF Model.
The lack of an on-board survey presented some challenges in the model development process. Transfer rates for various modes had to be inferred from total expanded trips by mode and total boardings by mode, and significant effort was required to adjust alternative-specific constants to match observed transit boardings. SFCTA has recently engaged consultants support and will be designing and conducting the first MUNI on-board transit survey in 27 years. This survey should be complete by the end of 2003 and provide data upon which to re-calibrate mode choice models.
Transportation Networks
Highway and transit networks are maintained in the TP+ software package. This software is also used for building highway and transit level-of-service skims and for assigning trip tables to transport networks. The MTC models are also implemented in the TP+ package, but rely on separate networks. The SF Model networks offer a very high level of detail in SF County but are similar to the MTC level of detail in the other 8 counties in the Bay Area. There are approximately 750 TAZs in SF County in the SF Model, and only 127 TAZs in SF County in the MTC model. The SF Model links represent every street in SF County, whereas the MTC model only represents major arterials or greater link classification. There are 51,000 links and 1,739 zones in the full system.
The high level of network and TAZ detail in the SF Model precludes the need for any walk market segmentation within a TAZ. Almost all TAZs in the SF Model have walk-access to transit as a result of the automated walk link coding procedure offered in the TP+ transit path builder. There is only one PNR lot in SF County, at the SF/San Mateo border.
There are five (5) time periods in the SF Model. Each time period has a separate transit network, but shares a single highway network. They are:
· Early A.M. (3 A.M. to 6 A.M.) · A.M. Peak (6 A.M. to 9 A.M.) · Midday (9 A.M. to 3:30 P.M.) · P.M. Peak (3:30 P.M. to 6:30 P.M.) · Evening (6:30 P.M. to 3 A.M.)
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There are four ‘primary’ transit modes (Local Bus, MUNI Rail, Premium, and BART) and a number of modal combinations (Local Bus to MUNI Rail, etc.) possible in the SF Transit networks. Each mode is disaggregated by mode of access and mode of egress. Auto access/egress is only allowed for premium transit or BART. Note that because these are tour- based models, the tradition of assuming building transit skims in production-attraction format is not observed. All highway and transit skims are build in origin-destination format by time period.
The ‘combined’ modes can be collapsed with the ‘primary’ modes by imposing a ‘hierarchy’ of modes on the skim-building process. The hierarchy is determined by the modes available for each set of skims, which are listed below. This method requires relying on the transit path- builder to determine which modes are actually utilized for each zone-pair and skim set. For example, when building BART skims, some portion of zones will have direct walk access to BART, while others will require walk to bus with a transfer to BART. Table 1 shows the hierarchy of modes used in the SF Model.
To ensure that the path-builder will include the primary mode where it is available, the primary mode in-vehicle time is weighted in the path-building process to give it an advantage over the other available modes. In application, the mode choice program will test the skims to make sure that the primary mode was included in the path.
Table 1: Hierarchy of Modes
Primary Mode Available Modes
Local Bus Local Bus Only MUNI Rail MUNI Rail and Local Bus Premium Transit Premium Transit, Local Bus, MUNI Rail BART BART, Local Bus, MUNI Rail, Premium Transit
Land Use and Demographic Data Input
Socioeconomic data were developed from parcel-level data aggregated to traffic analysis zones and adjusted to match control totals, as follows:
· The San Francisco Planning Department provided a current parcel database and a current business and employment database. The parcel database provides current estimates of residential units at the block and lot level and the business and employment database contains current estimates of employment by type at the block and lot level. These are aggregated to the traffic analysis zones. · The Planning Department and the Port of San Francisco maintain lists of new development projects under construction, approved, and under review, as well as information on development potential for major area plans. These are used to allocate forecast data by traffic analysis zone. · The Association of Bay Area Governments’ Projections ‘98 is used as a control total for county-wide forecasts of population and employment. The employment data in San Francisco uses a different categorization compared to the MTC data. The original MTC databases classified employment by six categories – retail, service, pbConsult, Inc. Page 4 SFCTA Regional Transportation Model Summary other, agricultural, manufacturing and trade. The new San Francisco socioeconomic databases classified employment by a different set of six categories – CIE (Cultural, Institutional, and Educational), MED (medical), MIPS (Management, Information and Professional Services), PDR (Production, Distribution and Repair), Retail, and Visitor. Most models retain the distinctive employment categories, but some use a common set of categories across all areas. Basic information on the SIC codes falling under each category was used to regroup these 12 fields into four categories – PDR, MIPS, Retail, and Service.
Pedestrian environment factors were developed to evaluate urban design projects and estimate changes in pedestrian and bicycle modal options. Eight members of the San Francisco Pedestrian Advisory Group, made up of staff from local agencies and private enterprises, collected relevant data and allocated the results to the PEF traffic analysis zone (TAZ) system established for the effort. PEFs will allow local planners to:
· Quantify base year variables related to the pedestrian environment by geographic area (traffic analysis zone, area type) that can be used for transportation, transit, and land use planning and modeling; · Develop a policy variable to measure the potential impacts of improved pedestrian systems on future travel demand; and · Incorporate pedestrian factors into the travel demand modeling process to assess integrated land use and transportation policies/alternatives.
Parking cost and availability have a significant impact on vehicle availability and mode choice for travel to destinations in San Francisco. To support this analysis, the supply, cost, and availability of parking were developed from a variety of sources, including parking surveys, a small sample stated preference survey, parcel data and aerial photographs of on-street parking.
Area type was used as an aggregate zonal variable in a number of the model components, as well as a network variable. Area type was derived from the MTC regional TAZs and is classified into six categories – Core Central Business District (CBD), CBD, Urban Business District (UBD), Urban, Suburban, and Rural. Within San Francisco County, all the TAZs fall within the first four categories, implying that all suburban and rural classifications within the study area are outside the city.
Model Description
The following section describes each component of the SF Model in detail. Table 2 lists the sequence of models and gives an idea of the key endogenous variables in each one. The first is the workplace location model, which predicts the work locations for any workers in the household based on the residence location and variables such as household size and income. These feed into a model to predict the number of vehicles available to the household. Both workplaces and vehicle availability feed into the “full-day tour pattern” model. The pattern model predicts the main components of the entire day’s travel for the individual, and provides key inputs to the subsequent models: primary tour time-of-day, tour and trip destination choice, and tour and trip mode choice. The method of applying the models in forecasting is described in the final section of this paper.
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A noteworthy feature of this model system is that workplace location is modeled at the “top” of the decision tree. This structure reflects the fact that work location is a longer-term decision relative to choosing time-of-day or mode or non-work travel destinations. By putting workplace location at the top of the structure, the location of workplaces relative to home can be used as input in other models such as vehicle ownership and frequency of travel.
TABLE 2 Sequence of Models for SF County Residents (I = input, O = output, (W) = work purpose only)
Primary home-based tours Secondary & work-based tours Trips (tour segments) Model purp chain time Dest mode num chain time dest mode num time dest mode Workplace location choice O (W) Household vehicle I availability choice (W) Full day tour pattern choice O O I O O (W) Primary tour time period I I O I choice Secondary and work-based I I I O O tour chain type Secondary and work-based I I I O tour time periods Trip time period I I I I O Non-work tour primary I I I I (W) I I I O destination choice O Tour main mode choice I I I I O I I I I O I Intermediate stop location I I I I I I I I I I I I O choice Trip mode choice I I I I I I I I I I I I I O
Tour purposes Tour chain types # tours or stops Trip time periods Destinations Tour modes (purp) (chain) (num) (time) (dest) (mode)
Work No intermediate stops None Early (3-6 a.m.) 1,728 traffic Car drive alone Education Stop(s) before primary 1 a.m. peak (6-9 a.m.) analysis zones Car passenger Other Stop(s) after primary 2 Midday (9 a.m.-3:30 p.m.) (For each tour/trip, Walk Stop(s) both ways 3 p.m. peak (3:30-6:30 p.m.) a stratified sample Bike 4 or more Evening (6:30 p.m.-3 a.m.) of 40 zones is used Walk to transit for estimation) Drive to transit
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Synthetic Sample Generation
A full sample of persons and households is generated for San Francisco County using three primary data sources: the U.S. Census Public Use Microdata Sample (PUMS), the population and employment data developed for San Francisco County, and socioeconomic data developed for the MTC by the Association of Bay Area Governments (ABAG). There is a hierarchy of zonal systems for these three datasets: · Six Public Use Microdata Areas (PUMAs), containing · 127 MTC Traffic Analysis Zones (MTAZs), containing · 766 San Francisco Traffic Analysis Zones (STAZs). The prototypical sample contains marginal distributions across three dimensions: · Household size and number of workers (nine categories); · Household income (four categories); and · Age of head of household (three categories). There are a total of 108 possible combinations of the above dimensions (9x4x3). The nine categories for household size/number of workers were chosen because they efficiently distinguish between important household life-cycle groups. The specific breakdowns for income and age were chosen because they correspond to categories that are available in the MTC future year land use files (zonal marginals from ABAG), so updating the populations to future years can be kept consistent with MTC breakdowns within zones. Also, all of these categorizations are compatible with the Census tables available in the Census Transportation Planning Package (CTPP) Urban Element.
Workplace location model
Destination choice models perform the same general function that trip distribution models, such as the gravity model, do in the traditional four-step modeling process. Unlike traditional models, trip attractions are not determined explicitly and instead are represented using size or opportunity indicators such as employment by category. Thus, destination choice models determine not only the trip interchanges but also the total attractions for each zone.
Two types of models were estimated – tour level models that determine the primary destination, and trip level models that capture the choice of intermediate stops on a tour, with the latter made dependent on the former. Each tour leaving home (home-based) or work (work- based) is modeled to have a number of stops ranging from one to nine – the primary destination and a maximum of four stops on each half tour. Following the hierarchy of trip purposes, and depending upon the time or distance traveled, one of these stops is classified as the primary destination. All other stops on the tour are considered to be intermediate stops made on the way to or from the primary destination. In the logit-based destination choice models, each Transportation Analysis Zone (TAZ) is a potential alternative, whose attributes and accessibility determines its utility, and therefore its probability of selection as the chosen alternative. A tour- level and trip-level model was estimated for each purpose, for a total of eight separate models. For the purpose of developing the tour level models, no differentiation is made among primary and secondary tours.
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The work location choice model is at the “top” of the decision tree. Therefore, this model is conditional only on the variables in the PUMS-based sample, including residence location, household characteristics, and person characteristics. The primary destination choice models for the other purposes come further down the decision tree, and are conditional on the predicted vehicle availability, tour type (number of intermediate stops), and times of day (the time periods of the forward and backward half tours) for the tour. The trip level intermediate stop location models are applied after all other tour level models are applied.
For each worker in the synthetic sample, a workplace zone is drawn from forty sampled zones according to multinomial logit model probabilities based on worker characteristics, mode choice accessibility logsums from work tour mode choice and work zone attributes. Since the time period of the tour is not known, peak levels of service are used to estimate a mode choice logsum for each alternative.
In order to provide for a good representation of the observed behavior, the ‘stratified importance sampling’ technique is utilized for sampling the 40 potential alternatives from among the available 1,728 TAZs. This technique has been successfully applied previously for models of Portland , Boise, and New Hampshire. The choice set is first divided into a number of strata and each stratum was assigned a different level of importance, which determines the number of alternatives to be sampled from that stratum. The strata are defined by the San Francisco County boundary (whether the trips are internal or external to the city limits), the origin and destination TAZ area type, and travel time to the destination. The import assigned to each stratum is based on the observed distribution of the trips. The sampling approach used for Work Location Choice is based on the area type distribution within San Francisco County and on travel time outside San Francisco County.
Recent analysis of the workplace location results indicated that TAZs with relatively lower levels of employment were overestimated (too many workers chose those TAZs with few jobs) and TAZs with relatively high levels of employment are underestimated. A shadow pricing mechanism was recently implemented to address this problem. The work location choice model is run iteratively. At each iteration, the number of workers estimated by the model is compared to the input employment for each TAZ. A shadow price is computed for TAZs that are over- estimated, and this price is added to the utility for the next iteration. The shadow price is computed as:
Price = damping factor * ln(total estimated workers/total observed employees)
The shadow pricing mechanism is run to close within a reasonable tolerance, approximately 50 iterations to ensure that no TAZs are over 10% over/under-estimated and nearly all TAZs are within 5% over/under-estimated. An additional four hours of runtime is required to iterate the work destination choice model to convergence.
Table 3 provides the results of the Home-based Work tour primary destination model, including the estimates of the coefficients, the corresponding t-statistics and some summary statistics. Overall, the model has a number of key attributes with the proper signs and providing an expected range of explanatory power. The three key types of variables contributing significantly to the model power are the employment data, area type, and LOS data:
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· Zonal employment information was incorporated into the Work location choice model in a variety of forms. After a number of trials, employment variables were included in the model using a size function. A number of size functions were tested, which mainly differed in the specification of the base size variable. The most significant SF or common employment variable when used caused problems either with estimation or with interpretation of results. The size variable specification that is reported with the final model specification uses the total employment as the base size variable. · The logsum multiplier represents a scaling factor for the size variable and is significantly different from one. The coefficients of the individual size variables capture the importance of the attribute relative to the first size variable, the total employment. The size coefficients in the table are exponents of the actual values reported by Alogit, and hence the t-statistics cannot be reported. · Area type and location variables play a significant role in explaining destination choice. San Francisco residents prefer to work within the City and as expected, areas with major employment concentration are also attractive work locations. This is illustrated by large positive values on related dummy variables for the Core CBD and CBD. Also, the gradual decrease in the level of attraction as the concentration level goes down is illustrated by the relative size of the UBD and Urban coefficients. · Outside of San Francisco, employment centers such as Silicon Valley and Oakland are major attractions for work location as proven by strongly positive coefficients for the dummy variables. · All models include a dummy variable to capture the preference to choose destinations within the zone of residence (Home Zone dummy). For work tours, this positive coefficient reinforces the simultaneous choice of residential and work locations along with the need to keep the daily commute as short as possible. · The probability of choosing a destination also increases if it is in the same area type as the origin – all dummy variables representing intra-Core CBD, intra-CBD, intra-UBD, and intra- Urban travel have positive coefficients. · The influence of the ease of travel in the choice of a destination is captured using the mode choice logsum variable. This is a single measure representing the relative utility of travel between and origin-destination pair across all modes. The hypothesis is that easy access makes a destination more attractive for travel. This is confirmed by positive coefficients that are significantly less than one in value in all models. · In order to capture and better match the distribution of trips, distance to the destination is used in a piece-wise linear form to minimize the interaction with the mode choice logsum variable. Ideally, we would like to match the travel time distribution (using travel time data in model estimation instead), but it is highly correlated with the logsum and takes away essential explanatory power of the logsum variable. The coefficent values for distance are all negative, significant, and in proper relative magnitude, showing an decreasing sensitivity as trip length increases.
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TABLE 3 Work Location Choice Model Estimation Results
Attribute Coefficient Standard Error t-statistic
Destination Size Attributes Size Specification Log-Sum-Multiplier 0.6534 0.0223 15.52 Total Employment (All Zones) 1.0000 – – SF CIE Employment (Zones in SF County)* 2.7037 – – SF MIPS Employment (Zones in SF County)* 1.4717 – – SF PDR Employment (Zones in SF County)* 1.9357 – – SF Retail Employment (Zones in SF County)* 2.5079 – – MTC Other Employment (Zones outside SF County)* 0.9736 – – Destination Characteristics Average Household Income in Thousands of Dollars 0.0020 0.0012 1.57 Destination is in Core or CBD 1.4664 1.0252 1.43 Destination is in UBD 1.1603 1.0239 1.13 Destination is in an Urban or Suburban area 0.8707 1.0170 0.86 Destination Zone is in SF County 1.4758 0.4500 3.28 Destination Zone is in Silicon Valley (Santa Clara) 2.1118 0.3760 5.62 Destination Zone is in Oakland (Alameda) 0.9627 0.3502 2.75 Southern Destinations (San Mateo) 1.9281 0.3466 5.56 Northern Destinations (Marin + Sonoma + Napa) 1.0739 0.3817 2.81 Origin-Destination Characteristics Destination Zone is Home Zone Dummy 4.4950 0.1651 27.22 Origin & Dest are in Core Dummy 0.7094 0.2031 3.49 Origin & Dest are in CBD Dummy 0.4593 0.1285 3.57 Origin & Dest are in UBD Dummy 0.3352 0.1376 2.44 Origin & Dest are in an Urban area Dummy 0.7243 0.1283 5.65 Origin-Destination Level of Service Piecewise linear distance 0-3 miles -0.4735 0.1658 -2.86 Piecewise linear distance 3Plus miles -0.0551 0.0068 -8.08 Mode Choice Logsum 0.0921 0.0336 26.99 Missing Mode Choice Logsum -1.6603 0.7712 -2.15 Summary Statistics Number of Observations 1,627 Log-Likelihood with Zero Coefficients -6,001.8 Initial Log-Likelihood -6,726.9 Log-Likelihood at Convergence -5,010.9 Rho-squared at Convergence 0.255 Adjusted Rho-squared at Convergence 0.252 Rho-squared w.r.t. Zero at Convergence 0.165 Adjusted Rho-squared w.r.t. Zero at Convergence 0.161
* The reported coefficients are exponentiated to obtain the actual value. Therefore, the reported t-values are not useful in these cases.
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Vehicle Availability Model
The vehicle availability model is a multinomial logit model that predicts the vehicles available in each household for each SF resident. Given the location of the household, the characteristics of the household members, and the primary work place location of each of its workers, the model estimates the probabilities of having none, one, two, or three or more vehicles available. The number of vehicles is defined as automobiles plus trucks; also available in the survey data are the numbers of motorcycles, mopeds and bicycles owned by the household, but these were not included in the number of vehicles available for household travel. The model was limited to four alternatives (0, 1, 2, or 3+ vehicles available) because of the relatively small number of households with four or more vehicles available (1.8 percent). The average number of vehicles in the fourth alternative (households with three or more vehicles available) was 3.36.
Table 4 presents the estimation results for the vehicle availability model. The utility function for each vehicle availability alternative is shown separately. A separate constant was estimated for each of the alternatives except for a single “base” alternative. The base alternative was defined as having no vehicles available. The alternative for households with one vehicle available is the most likely alternative and has a positive constant. Households with more than one vehicle available have negative constants, increasing with more vehicles available because they are less likely alternatives.
Coefficients can be compared across alternatives to judge the reasonableness of these values. For example, households with two adults are most likely to have two vehicles available (coefficient of 1.924) and slightly more likely to have three or more vehicles available (coefficient of 0.806) than one vehicle available (coefficient of 0.642). Since all three coefficients are positive, the 0-vehicle alternative is least likely for two-adult households (all else equal).
Some significant results from the vehicle availability model estimation are as follows: · A large number of the available household attributes provided significant explanatory power to the models, including the number of adults and workers and the number of people aged 18-24 (the adults least likely to have their own vehicle). · With respect to travel time to work, the model uses congested a.m. peak highway travel time to the workplace location. For households with multiple work locations, we tested the minimum, maximum and average times for all work locations and retained the maximum travel time. The result indicates that a longer travel time to work is related to owning more vehicles for travel. Given that vehicles tend to be generally affordable in the U.S., we expect that people will buy a car if they need one to get to work, rather that they will choose to work farther away because they already own a car. (In reality, both cases will exist, but we postulate that the first is more common.) · The “transit/auto accessibility ratio” measures the number of jobs that can be reached by transit from the residence zone, divided by the number of jobs that can be reached by car in a similar travel time. The better the transit service, the less likely is the household to own more than one auto.
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· Households with people working in areas with high parking costs are less likely to have multiple vehicles. Long-term parking costs and parking availability are highly correlated, but the parking cost gives a better fit for the work zone. Generally, in work zones where commute parking costs are highest, residents also have to pay for parking, either directly or indirectly. · Households that live in areas with high residential densities, low parking availability and high “vitality” in terms of the pedestrian environment, are less likely to own vehicles.
TABLE 4 Vehicle Availability Model
SUMMARY STATISTICS Observations 1,244 Final likelihood -1,209.49 Rho-squared (0) 0.2987 Rho-squared (c) 0.2200
Alternative: 0 veh.(base) 1 veh. 2 veh. 3+ veh. Chosen obs.: 323 529 303 89
Variable Coefficient T-statistic Coefficient T-statistic Coefficient T-statistic
Household Variables Household income (000) 0.0262 5.8 0.0366 7.5 0.0398 6.8
2 adults in household 0.642 3.7 1.924 7.7 0.806 2.1 3 adults in household 1.874 6.0 1.917 4.5 # adults over 3 in household 0.714 2.9 1.005 2.9 Full time workers in household 0.361 2.6 0.490 2.9 0.946 4.6 Part time workers in household 0.722 3.3 1.293 4.4 # household members age 18-24 -0.317 -2.1 -0.381 -2.2 -0.381 -2.2
Level of Service Variables Max. auto time to work (min.) 0.0144 2.3 0.0273 4.0 0.0273 4.0 Transit/auto accessibility ratio -0.128 -0.5 -0.641 -2.0 -0.641 -2.0 Work zone parking cost ($) -0.250 -2.0 -0.359 -2.3 -0.832 -3.3
Locational Variables Households within half mile (000) -0.145 -5.5 -0.185 -4.9 -0.310 -4.3 Home zone parking availability index 0.469 1.8 0.469 1.8 0.469 1.8 Home zone vitality index -0.218 -1.6 -0.432 -1.9
Constants Residual constant 0.909 1.4 -0.527 -0.7 -1.324 -1.6
Full-day Tour and Trip Pattern Models
For each synthetic person, the probability of each full-day pattern (comprised of tours and trips), including “no travel,” is predicted for each person. A random Monte Carlo procedure is used to select a single pattern. The main feature of the “full-day pattern” approach is that it simultaneously predicts the main components of all of a person’s travel across the day. This includes the frequency of five types of tours: pbConsult, Inc. Page 12 SFCTA Regional Transportation Model Summary
· Home-based work primary tours; · Home-based education primary tours; · Home-based other primary tours; · Home-based secondary tours; and · Work-based subtours.
A home-based tour includes the entire chain of trips made between leaving home and arriving back at home. The “primary” home-based tour is defined as the main home-based tour made during the day. If a worker makes a work tour or a student makes an education tour, then that is always the primary tour. If there are no work or education tours, the primary tour is the tour with the highest priority activity at the destination (shopping/personal business, followed by social/recreation, followed by serve passenger). If there are two or more tours with the same activity priority, then the one with the longest duration of stay at the destination is the primary tour. Note that in San Francisco, approximately one-half of all tours are either work tours or work-based tours. All other home-based tours are designated as “secondary” tours. A special type of tour is a work-based “sub-tour”, defined as the entire chain of trips made between leaving the primary workplace and returning back to that workplace in the same day. There is one full-day pattern model for each of four person types (children under the age of 16, working adults, student adults, and other adults). The day-pattern model predicts:
· The purpose class of the primary home-based tour (work, education, other, or none); · The trip chain type of the primary home-based tour (one or more stops before, after, neither, or both); and, · The number of home-based secondary tours (0, 1, or 2+). After the day-pattern is chosen for each person, the time of day of the primary tour is chosen (see below). A series of classification models is then run to “fill in the details” about the rest of the day pattern including:
· The exact number of secondary tours (2, 3, or 4) if 2+; · The exact number of work-based subtours (1, 2, 3, or 4) if 1+; · The trip chain types for any secondary or work-based tours, conditional on the predicted purpose and chain type for the primary tour; · The time periods for any secondary or work-based tours, conditional on the predicted time periods for the primary tour; · The exact number of intermediate stops (1, 2, 3, or 4) for any half-tours where 1+ stops are predicted; and, · The departure time period from any intermediate stop, conditional on the predicted time periods for the tour and the predicted number of stops on the half-tour. Table 5 presents the results of the full-day pattern choice models estimated for working adults. (The corresponding models for students/children and other adults are not reported in this paper due to space constraints) The model contains a number of different utility function components, corresponding to:
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· The utility of making a primary tour for each purpose class; · The utility of making intermediate stops on primary tours for each purpose; · The utility of making work-based subtours during work tours; and · The utility of making secondary tours.
Each of these components is shown separately in the table. A positive coefficient indicates a higher probability of making a particular type of tour or pattern, while a negative coefficient indicates a lower probability – all relative to the “base” alternative of not traveling at all. There may be a number of different alternative-specific constants within a single component. For example, most of the variables in the utilities for intermediate stops apply to both stops before and stops after the primary destination, but there are separate constants for the two cases. The utility for stops both ways is equal to the utility of stops before plus the utility of stops after, plus an additional constant term. Similarly, the constants for work-based tour and secondary tour utilities can vary depending on what type of primary tour they are combined with.
For the worker pattern model, a nested structure was estimated, nesting all alternatives within each primary tour purpose separately, and then nesting all of the travel alternatives separately from the “no travel” alternative.
Without space for an exhaustive description of the estimation results, some important points to note are: · The segmentation into the three person types (workers, students/children, others) itself accounts for much of the difference in tour patterns. There is not much residual systematic variation in terms of who chooses to travel versus not travel, or travel for work versus other purposes. There is more systematic variation as to who makes intermediate stops and secondary tours. · Compared to full-time workers, part-time workers are more likely to make primary non- work tours and to make secondary tours. · Not having a car in the household is often related to fewer tours and/or stops. For those who can drive and have one or more cars, competition for cars in the household (fewer cars than adults) is sometimes related to fewer intermediate stops but more secondary tours – i.e., less trip chaining. · For workers (and students), the logsum coefficients are significantly lower than 1.0, supporting the nesting structure tested: i.e., people are more likely to shift between different activity patterns within the same over type (e.g., more or less trip chaining as part of a work tour pattern) than they are to shift to another primary purpose or to not traveling at all. · Many of the land use and accessibility variables are at least marginally significant: This group of variables is critical, as they provide the feedback from the lower levels of the model system that determine network loadings and speeds. As service levels change, they influence accessibility measures such as the numbers of jobs accessible by road and transit from the home and work zones during peak and off-peak periods, as well as travel times between home and work. Because we are not using a full nested structure with logsums from lower levels, these simpler accessibility measures are necessary to provide feedback from networks to activity/tour patterns. · Being able to reach more jobs by car somewhat increases the likelihood of a work pattern.
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· Being able to reach more retail and service businesses by car increases the probability of making intermediate stops on a primary tour, but having those same businesses within walking distance of home decreases the probability of intermediate stops – people are more likely to go to those places as part of separate home-based tours instead. · Work-based tours are more likely if the work place is in a location with retail and service businesses nearby. These tend to be instead of stops on the way to or from work. · Having more retail and service employment within 15 minutes by car in the off-peak increases the probability of making primary non-work tours and stops and secondary tours. TABLE 5 Working Adult Full-Day Pattern Choice Model
Variable Coefficient T-statistic Work primary tour pattern utility - Age 25-34 2.716 2.2 - No car in household 2.528 1.0 - Less than 1 car per adult in household .543 0.8 - Total employment within 15 minutes by car in PM peak (000) .0324 1.5 - Constant 18.58 1.9 Work tour intermediate stop utility - No kids in household -.405 -4.6 - Female, kids under 5 .318 2.1 - Couple, non-worker in household -.414 -4.9 - No car in household -.375 -3.1 - Retail + service emp within 15 minutes by car in PM peak (000) .00957 1.9 - Retail + service employment within half mile (000) -.0313 -1.5 - Retail + service employment within half mile of work (000) -.0174 -2.6 - Stop before constant -1.538 -10.5 - Stop after constant -.841 -6.4 - Stops both ways constant 1.141 7.8 Work-based subtour utility - Income under $30,000 -.609 -3.0 - No car in household -.981 -4.4 - Less than 1 car per adult in household -.747 -5.6 - Retail + service employment within half mile of work (000) .0239 2.4 - Constant -1.336 -10.9 - Combine with stop before work -1.359 -5.8 - Combine with stop after work -.743 -5.0 - Combine with stops both ways -.766 -4.1 Other primary tour pattern utility - Part time worker 4.601 2.2 - No car in household 1.718 2.5 - Retail + service emp within 15 minutes by car in off-peak (000) .0197 1.9 - Constant 9.167 1.1 Other tour intermediate stop utility - No car in household -.485 -1.6 - Less than 1 car per adult in household -.508 -2.2 - No kids in household -.339 -1.5 - Retail + service emp within 15 minutes by car in off-peak (000) .0197 1.9 - Retail + service employment within half mile (000) -.1195 -1.6 - Stop before constant -1.570 -4.1 - Stop after constant -1.198 -3.3 - Stops both ways constant 1.496 4.0 Secondary tours utility - Single adult in household .535 4.1 - Part time worker .732 4.1 - Age 25-34 .338 3.0 - No car or no license -.782 -4.5 - Retail + service emp within 15 minutes by car in off-peak (000) .0114 3.3 - One with a work tour constant -1.962 -14.8 pbConsult, Inc. Page 15 SFCTA Regional Transportation Model Summary
- One with a work tour and subtour constant .458 3.2 - Two with a work tour constant -3.706 -19.6 - Two with a work tour and subtour constant .441 1.5 - One with an “other” primary tour constant -1.195 -5.8 - Two with an “other” primary tour constant -1.853 -7.7 Logsums (T vs. 1.0) - Across all travel alternatives .358 4.8 - All alternatives within each primary purpose .246 7.5 Observations 2170 Final log-likelihood -5450.1 Rho-squared with respect to 0 coefficients .305
Time-of-day Models
The Time of Day Models predict the period when the traveler leaves home to begin the primary tour, simultaneously with the period when the traveler leaves the primary destination to return home. The periods used for the models are defined as:
· Early A.M. (3:00 a.m. to 5:59 a.m.) · A.M. Peak (6:00 a.m. to 8:59 a.m.) · Midday (9:00 a.m. to 3:29 p.m.) · P.M. Peak (3:30 p.m. to 6:29 p.m.) · Evening (6:30 p.m. to 2:59 a.m.)
Excluding overnight tours there are 15 possible combinations of these five periods. The time-of- day choice is influenced by individual and household characteristics, tour characteristics, and simple accessibility measures. Table 6 shows estimation results for primary Work tours. The utility function for each time period combination is shown separately. In cases where there are very few observations, only a constant term was estimated, and these alternatives are grouped in the table (e.g., the “Early period”). Otherwise, a separate constant was estimated for each of the 15 alternatives except for a single “base” alternative. The base alternative was defined as the one with the most demand – a.m. peak-p.m. peak for Work, a.m. peak-Midday for Education, and Midday-Midday for Other. As a result, all of the constants for the other periods have negative coefficients. Some important results to note are:
· Part time workers are more likely to choose the a.m. peak-Midday, Midday-p.m. peak, p.m. peak-Late and Late-Late combinations. · Those with secondary tours during the day are more likely to return from work during the Midday, and less likely to return Late. · Those who make one or more intermediate stops on the way home from work are most likely to be coming home from work in the Midday period, and more likely to be coming home in the p.m. peak than in the Late period. · Those with high incomes, those under age 35, and those making stops on the way to work are more likely to work long hours (the a.m. peak-late combination). · Those under age 20 are more likely to work evenings. · Those with a work-based subtour in the pattern are more likely to work a.m. peak to p.m. peak. · Higher network accessibility to employment during slightly increases the probability of traveling in that period. This is more true for the outbound period than for the return period, and more true for auto accessibility than for transit accessibility. pbConsult, Inc. Page 16 SFCTA Regional Transportation Model Summary
In general, there is strong relationship between the day pattern type (primary tour purpose, stops before and after the main destination, secondary tours) and the time periods during which the tour is made. This result reinforces the importance of modeling pattern choice in order to predict realistic shifts in time-of-day distributions.
TABLE 6 Work Primary Tour Time of Day Choice Model
Variable Coefficient T-statistic Early period utilities - Early-Early constant -6.734 -6.6 - Early-a.m. peak constant -5.869 -8.1 - Early-p.m. peak constant -3.521 -12.6 - Early-Late constant -6.734 -6.6 - a.m. peak-a.m. peak constant -5.671 -8.0 Early-Midday utility - Constant -3.187 -11.7 - Secondary tours in pattern .889 3.1 - Stop after work 1.130 3.5 a.m. peak-Midday utility - Constant -2.377 -14.1 - Part time worker 2.122 8.0 - Secondary tours in pattern .629 3.4 - Stop after work 2.088 9.9 a.m. peak-p.m. peak utility - 2+ secondary tours in pattern -1.244 -4.0 - Work-based subtour in pattern .724 4.7 - Stop after work .826 5.4 - Female .375 3.7 - Couple, with non-working adult in household .242 2.2 a.m. peak-Late utility - Constant -2.061 -9.9 - Age under 35 .638 3.7 - Income over $60,000 .882 4.8 - Secondary tours in pattern -1.435 -5.0 - Stop before work .568 2.7 Midday-Midday utility - Constant -3.899 -13.2 - Part time worker 2.760 7.8 - Secondary tours in pattern .859 2.8 - Stop after work 2.173 6.7 Midday-p.m. peak utility - Constant -1.027 -5.4 - Part time worker 1.346 4.4 - No intermediate stops -.668 -3.4 Midday-Late utility - Constant -1.900 -11.1 - Part time worker 1.201 3.3 Late period utilities - p.m. peak-p.m. peak constant -10.0 Fixed - p.m. peak-Late constant -3.504 -13.8 - Late-Late constant -4.154 -12.5 - Age under 20 1.560 2.4 - Part time worker 1.726 3.8 Accessibility variables (included in all utilities) - Total employment within 15 minutes by auto, outbound period (000) .00234 2.4 - Total employment within 15 minutes by auto, return period (000) .00130 1.9 - Total employment within 30 minutes by transit, outbound period (000) .00081 0.9 - Total employment within 30 minutes by transit, return period (000) .00010 0.2 pbConsult, Inc. Page 17 SFCTA Regional Transportation Model Summary
Observations 1729 Final log-likelihood -2486.8 Rho-squared with respect to 0 coefficients .4689
Tour Primary Destination
As previously stated, the tour primary destination choice model predicts the TAZ location of the primary destination for every tour except for work tours, whose location is already chosen by the work location choice model. Stratified importance sampling is used to construct a sample of 40 TAZs. School tours tend to be destined to zones with schools, and therefore use student enrollment and travel time as the basis for sampling zones. Travel times from survey responses (excluding external destinations) were grouped into quartiles, and zones were sampled equally from each quartile range. Tours made with the work location as the start and end points (Work-Based Tours) are typically made in the middle of the day for work-related purposes or errands. These tours tend to be short and to locations that are in close proximity to the work place location. Hence the sampling procedures for Work-Based Tours are based on their observed travel time distribution. Again, survey data was used to define quartile ranges and 10 zones were sampled from each quartile.
The logsum from the mode choice model, defined as the logarithm of the sum of the exponents of the individual modal utilities, captures the travelers’ perceptions of the level-of-service characteristics of the various modes and is traditionally used for this purpose. Additional accessibility variables that are useful in matching the observed trip length distributions include travel time and distance. These were used in non-linear forms (piece-wise and step-wise) to reduce the interaction with the logsum variable.
For School tour models, the main determinants of the size are related variables such as the school area and enrolment. Zonal enrolment values for school and college are applied only to relevant individuals. . For School tours, zones with a presence of schools and colleges have a higher probability of choice compared to those that do not. Accordingly, the coefficients for related dummy variables are positive and significant. Similarly, zones with a higher presence of schools both in terms of school area and number of school buildings attract more school trips. For Other and Work-based Tours, zonal employment becomes the main determinant of size. In all models, the size-related variables have a positive effect as expected. All models include a dummy variable to capture the preference to choose destinations within the zone of residence (Home Zone dummy). School tours are an exception because they are constrained by the presence of a school or college. For work tours, this positive coefficient reinforces the simultaneous choice of residential and work locations along with the need to keep the daily commute as short as possible. For non-work tours, it reflects the tendency to travel to areas with the most familiarity and awareness. For the work subtour destination choice model, this hypothesis of short commutes is also substantiated by positive and significant coefficients on the Work Zone dummy variable.
The probability of choosing a destination also increases if it is in the same area type as the origin – all dummy variables representing intra-Core CBD, intra-CBD, intra-UBD, and intra- Urban travel have positive coefficients. Work-based Tours originating outside San Francisco have a strong tendency to stay outside the City reflecting the difficulty to travel across the bridges.
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Tour Mode Choice Models
The mode choice models developed for the SFCTA Model determine the mode for tours, and also for all trips made as part of tours, and are the basis for the accessibility measure (logsum) used in the tour primary destination choice models. The mode choice models differ from traditional “trip-based” mode choice models in that there are two distinct sets of mode choice models. The tour mode choice model determines the primary mode for the tour, while the trip mode choice models determine the mode for each individual trip made on that tour, based on the mode chosen for the tour.
An analysis of trips by mode revealed a significant percentage of transit trips and non- motorized (walk and bike) trips made by SF residents. It also showed that there are a number of transit trips made by more than one transit mode; i.e., local bus access to BART. San Francisco can be considered a ‘transit-rich’ environment, where most residents have walk- access to transit, and the cost of parking is relatively high, accompanied by a limited supply. Based on this data, the following modes were defined and coded for trip mode choice model estimation:
· Drive-Alone · Shared-Ride 2 · Shared-Ride 3+ · Walk · Bike · Walk-Local-Walk · Walk-MUNI-Walk · Walk-Premium-Walk · Walk-Premium-Auto · Auto-Premium-Walk · Walk-BART-Walk · Walk-BART-Auto · Auto-BART-Walk
These modes are used to model individual trips that occur on a tour. A more general ‘tour mode’ was also defined, to control for the possible combinations of modes used for trips made on a tour. An analysis of the combinations of modes that occur on tours revealed that just one mode is used for most tours, and that much of the mode-switching that occurs on tours occurs in modal combinations including walk or auto-passenger with transit. This analysis was used to guide the definition of the tour modes and the structure of the tour and trip models. Tour modes were defined which allow the traveler to switch between modes where such behavior is most common. The tour modes were coded according to the following rules:
· Auto Driver – Tours that consist of trips primarily made by the driver of an automobile. If any trip on a tour was auto driver, the tour mode was coded as Auto Driver. · Auto Passenger – Tours that consist of trips made entirely by passengers of automobiles. Walk trips on Auto Passenger tours were maintained. · Walk – Tours that consist entirely of trips whose mode is walk. · Bike – Tours that consist entirely of trips whose mode is either walk or bike. pbConsult, Inc. Page 19 SFCTA Regional Transportation Model Summary
· Walk-Transit – Tours that consist of trips made by transit passengers or combinations of transit and auto passengers. Walk trips on Walk-Transit tours were also maintained. · Drive-Transit – Tours that consist of trips made by transit passengers where the access mode or egress mode is auto, or combinations of drive-transit, walk-transit, and Auto Passenger trips.
The tour mode definitions listed above allow the traveler to use walk as a mode for trips on any tour, and allow the traveler to switch between transit modes and auto-passenger modes for trips on transit tours. Table xx shows the trip modes allowed for each type of tour mode.
TABLE 7 Trip Modes Allowed by Tour Mode
Trip Mode Tour Mode Walk- Drive- Driver Walk Bike Passenger Transit Transit
Drive Alone X Share-2 X X X X Share-3+ X X X X Walk X X X X X X Bike X X X X Walk-Local X X Walk-MUNI X X Walk-Premium X X Walk-BART X X Drive- X Premium Drive-BART X
Tour-based model estimation also differs from traditional trip-based model estimation in at least two distinct ways. First, the models go beyond the traditional assumption that all work travel occurs in the peak period and non-work travel occurs in the off-peak period. The SF Models use the actual time period (early a.m., a.m. peak, midday, p.m. peak and evening) that travel occurred, in an attempt to more accurately reflect the travel conditions and modes available during that time period.
Furthermore, the tour models include the round-trip travel characteristics for the tour; that is, they include outbound LOS to the primary destination and the return LOS back to the tour origin. The LOS characteristics for these legs of the tour are based on the tour origin TAZ and the tour primary destination TAZ, but do not include trips made to intermediate stops on the tour, since the location of these stops is not known when the tour mode choice model is applied. pbConsult, Inc. Page 20 SFCTA Regional Transportation Model Summary
An innovative aspect of the mode choice models is the potential inclusion of reliability and crowding as explicit variables in the transit utility functions. These variables were included in a Stated Preference telephone survey of 407 transit users in San Francisco. Logit analysis was used to estimate tradeoffs between in-vehicle time, frequency of service, reliability (defined as the percent of days that the vehicle arrives 5 or more minutes late), and crowding ("low" = plenty of seats available, "medium" = few seats available, but plenty of room to stand, "high" = no seats available, standing room is crowded). It was estimated that improving the percent of vehicles arriving on time by 10% (e.g. once every two weeks) is equivalent to reducing the typical wait time (half the headway) by 4 minutes for commuters, or 3 minutes for non- commuters. It was also estimated that improving the level of crowding from "high" to "low" is equivalent to reducing the typical wait time by 5 minutes for commuters and 9 minutes for non- commuters. Thus, relative to commuters, non-commuters are less sensitive to delay but more sensitive to crowding, on average.
Table 8 shows the estimation results for the Work tour mode choice model. A summary of the estimation process and results follows:
· Overall, LOS variable coefficient values for tours are very similar to values typically obtained in a trip-based model. This indicates that the average relative importance given to the various components of travel time and cost for a tour are not significantly different from those in a trip-based model. All of the coefficients are correctly signed, and are generally statistically significant. The ratios of the various components of travel time to in-vehicle time are reasonable. · The in-vehicle time coefficients are generally lower for tour-based models than for trip- based models. This could be due to the inclusion of other variables not typically included in trip-based models, particularly the number of stops on the tour. The second wait coefficient for Work tours is significantly higher than the first wait coefficient. This reflects the relatively high transit LOS in San Francisco, particularly serving employment centers such as the financial district. Additionally, this relationship probably also reflects the large percentage of ‘choice’ transit riders in the Work tour market. · Attempts to estimate coefficients on the number of stops for each mode were successful. The coefficient on the walk mode takes the greatest negative value for all of the tour modes. The coefficient on stops for the transit mode is also negative for all tour purposes, indicating a lack of convenience associated with choosing transit when intermediate stops are required. · Pedestrian Environment Factors (PEFs) were tested and included in the mode choice model specifications. Five PEF variables, including pedestrian network continuity/integrity, ease of street crossing, perception of safety and personal security, urban vitality, and topological barriers, were constructed by a delphi panel familiar with the San Francisco and urban form. The PEFs were originally qualitatively assessed by panel members on a scale of 1 to 3 for each TAZ in the SF Model, with higher values indicating a ‘better’ score for the zone. The PEFs were converted to a dummy variable, where a value of 1 can be considered ‘bad’ for the characteristic measured, after analysis using the APPLY function of ALOGIT. The PEFs that were the most significant were generally associated with the walk mode, followed closely by walk-transit. The coefficient on neighborhood vitality was significant, indicating a strong relationship between non-auto modes and urban form in San Francisco. · A household type variable was tested associating household size with the propensity to ride-share. This coefficient was significant, indicating a higher probability, likely due to a pbConsult, Inc. Page 21 SFCTA Regional Transportation Model Summary
greater opportunity, to choose auto-passenger in larger households. Two stratifications of household-related alternative-specific constants were tested; autos per household (0, 1, 2+), and autos per worker (autos=0, autos
FIGURE 2 Tour Mode Choice Model Nesting Structure.
Choice
Non- Auto Transit Motorized
Walk- Auto - Driver Passgr Walk Bike Transit Transit
TABLE 8 Work Tour Mode Choice Model Estimation Results
Attribute Coefficient Standard Error t-statistic pbConsult, Inc. Page 22 SFCTA Regional Transportation Model Summary
In-Vehicle Time -0.0134 0.0059 -2.28 First Wait -0.0144 0.0149 -0.97 Second Wait -0.0411 0.0105 -3.90 Walk Time -0.0377 0.0042 -9.02 Walk Mode Time -0.0377 0.0042 -9.02 Bike Mode Time -0.0536 0.0135 -3.97 OPC, Income 0-30k -0.0021 0.0010 -2.08 OPC, Income 30-60K -0.0014 0.0009 -1.56 OPC, Income 60k+ -0.0012 0.0009 -1.29 Walk Mode Number of Stops -0.9387 0.2176 -4.31 Bike Mode Number of Stops -0.4748 0.2747 -1.73 Auto Passenger Number of Stops -0.2109 0.1004 -2.10 Walk-Transit Number of Stops -0.4576 0.0740 -6.18 Drive-Transit Number of Stops -0.8646 0.3100 -2.79 Walk Mode – Destination Network Connectivity -1.0697 0.3841 -2.79 Walk Mode – Destination Vitality -0.4945 0.4936 -1.00 Walk Mode – Destination Topology -0.9686 0.3096 -3.13 Walk-Transit Destination Network Connectivity -0.6019 0.1865 -3.23 Walk-Transit Destination Vitality -0.0675 0.2106 -0.32 Walk-Transit Destination Topology -0.6219 0.1707 -3.64 Auto Passenger - Household Size = 1 -0.0551 0.0068 -8.08 Walk Mode ASC, Autos=0 4.0410 0.3654 11.06 Walk Mode ASC, Autos
Summary Statistics Log-Likelihood at Convergence -1,360.87 Rho-Squared with respect to Zero 0.4664 Rho-Squared with respect to Constants 0.1638 First Wait/In-Vehicle Time 1.07 Second Wait/In-Vehicle Time 3.06 Walk Time/In-Vehicle Time 2.80 Value of Time, 0-30k $3.83 Value of Time, 30-60k $5.82 Value of Time, 60k+ $6.91
Intermediate Stop Location Choice
The intermediate stop location choice model chooses a TAZ for every stop on each tour. Intermediate stops are defined as activity locations that are between the tour anchor location pbConsult, Inc. Page 23 SFCTA Regional Transportation Model Summary
(home or work) and the tour primary destination. For each intermediate stop on each tour, an intermediate stop zone is chosen from forty sampled zones according to multinomial logit probabilities based on characteristics of the person and tour, zone size, and the additional cost of travel between the tour origin and destination imposed by the sampled stop. The cost of travel for each intermediate stop location is based on the additional highway time required to access the intermediate stop location given the tour anchor location and primary destination. Random numbers are used to control the selection of the sampled zones and the selection of an intermediate stop.
The data sets for estimating trip-level intermediate stop location choice models are prepared using the individual trip records from the survey. All trips on a half-tour are used except the last one – the trip to the primary destination on the forward half-tour, and the trip to the home/work location on the backward half-tour. Even though each trip has its own reported purpose, they are grouped together using the main purpose of the tour for ease of estimation and application. For example, the intermediate stop location choice model for work tours includes all trips made on work tours, regardless of the purpose of each trip. The actual purpose may in fact be dropping a child at school or making errands. This assumption was necessary because separate models are not estimated to predict the purpose of the individual trips on the tour.
Table 9 provides the results of the intermediate stop location choice models estimated for Work tours. The forward and backward half-tours exhibit significantly different characteristics, and these are captured by estimating half-tour-specific coefficients wherever possible. This is a simplification over estimating a separate model for each type of half-tour. A summary of the estimation results is provided below:
· Because the trip purpose is not the same as the purpose assigned for model estimation (tour purpose), no specific employment category can be attributed to attract trips, making it difficult to construct a suitable size variable. Individual employment categories are therefore used in model estimation. A higher level of employment increases the probability of choosing the zone as shown by the positive coefficients on employment variables. · A majority of the tours have both the main ends of the tour within San Francisco, so locations inside the City are preferred for intermediate stops, as illustrated by a strong positive coefficient on the corresponding dummy variable. · The preference to keep the tours short, visit locations that are familiar and on the original path is shown by the inclination to stop in either the tour origin or primary destination zone. · For home-based tours, the propensity to stop in the home zone is greater than that to stop in the primary destination zone on both directions – probably reflecting a higher level of familiarity with the locality. As observed with the tour destination models, the trips tend to remain within the same area type, again showing an inclination to keep travel times and distance short. · Auto travel time is used to capture the relative ease of travel to the potential choice of destinations. The coefficients are negative and significant and exhibit reasonable sensitivity in all models. TABLE 9 Intermediate Stop Location Choice Model Estimation Results for Work Tours
Attribute Coefficient Standard Error t-statistic
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Destination Size Attributes Logarithm of Health Services Employment (Zones in SF County) 0.1434 0.0235 6.09 Logarithm of MIPS Employment (Zones in SF County) 0.1088 0.0277 3.93 Logarithm of Retail Employment (Zones in SF County) 0.2333 0.0338 6.91 Logarithm of Service Employment (Zones in SF County) 0.0625 0.0300 2.08 Logarithm of Retail Employment (Zones outside SF County) 0.4149 0.1165 3.56 Logarithm of Service Employment (Zones outside SF County) 0.2360 0.1249 1.89 Destination Characteristics Destination Zone is in SF County 2.0915 0.6929 3.02 O-D Characteristics for First Half-Tour (From Origin/Home to Primary Destination) Stop Zone is Origin Zone Dummy 1.1558 0.3815 3.03 Stop Zone is Destination Zone Dummy 0.9733 0.3411 2.85 Origin Zone & Stop Zone are in CBD – Dummy 0.3926 0.2788 1.41 Origin Zone & Stop Zone are in UBD – Dummy 0.3497 0.2274 1.54 Origin Zone & Stop Zone are in an Urban area – Dummy 0.4848 0.2018 2.40 Stop Zone & Destination Zone are in Core – Dummy 0.9558 0.2494 3.83 Stop Zone & Destination Zone are in UBD – Dummy 0.5144 0.2821 1.82 Stop Zone & Destination Zone are in an Urban area – Dummy -0.8718 0.3781 -2.31 O-D Characteristics for Latter Half-Tour (From Primary Destination to Home) Stop Zone is Origin Zone Dummy 0.6492 0.2590 2.51 Stop Zone is Destination Zone Dummy 1.2026 0.2765 4.35 Origin Zone & Stop Zone are in Core – Dummy 0.2400 0.1982 1.21 Stop Zone & Destination Zone are in Core – Dummy -0.7299 0.3642 -2.00 Origin Zone & Stop Zone are in CBD – Dummy 0.3493 0.1859 1.88 Stop Zone & Destination Zone are in an Urban area – Dummy 0.3224 0.1416 2.28 Origin-Destination Level of Service Auto Travel Time -0.0287 0.0046 -6.29 Summary Statistics Number of Observations 976 Initial Log-Likelihood -4,068.5 Log-Likelihood at Convergence -3,721.5 Rho-squared at Convergence 0.085 Adjusted Rho-squared at Convergence 0.080
Trip Mode Choice Estimation
For each tour, a mode is chosen from eleven possible modes (Figure 3) according to logit probabilities based on characteristics of the person and tour and levels of service between the trip origin and destination zone conditioned by the chosen tour mode. A random number is used to select the chosen mode.
The estimation dataset for the trip mode choice model contains one record for each trip in origin-destination format, with household and person attributes appended. The trip mode choice models are applied to each intermediate stop on the tour, including the stop at the primary destination, and are conditional on the mode chosen for the tour, as shown in Table xx. For the intermediate stop location choice models, the travel time information was used to create quartile ranges from each of which 10 zones were sampled. For these models, the travel time pbConsult, Inc. Page 25 SFCTA Regional Transportation Model Summary
refers to the additional time incurred in traversing the extra stop on the way to or from the origin.The alternative-specific constants reflect the model structure, as they are stratified by tour mode. This structure makes it possible to calibrate the trip models by tour mode, ensuring the proper distribution of trips by both trip mode and tour mode.
Table 10 shows the estimation results for the Work trip mode choice model. The table shows ‘traditional’ LOS variables as well as those variables that pertain to the tour chain type (number of stops), pedestrian environment factor variables, and household variables. The originally estimated alternative-specific constants are given, as well as summary statistics describing goodness-of-fit. For each estimated coefficient, the standard error as well as the t-statistic (the coefficient value divided by the standard error) is given. A summary of the estimation process and results follows:
· The LOS coefficients (in-vehicle time, first wait time, transfer wait time, and out-of- pocket cost) all have the correct signs, are within reasonable ranges of value, and are significant. They are generally greater in absolute value than those estimated in the tour mode choice models, indicating higher elasticities with respect to time and cost for each trip mode given the tour mode. This may be due to the presence of the autos per worker stratification in the tour models, which has a major influence on the choice of motorized versus non-motorized, or transit versus non-transit, modes of travel. · The number of stops on a tour makes a significant contribution in determining the type of trip mode utilized for trips on the tour. They are logically negative and significant, indicating that drive-alone is preferred when stops are required, consistent with the tour mode choice model estimation. Pattern analysis of the sequence of trips by mode on tours (not shown) revealed little consistent behavior among travelers. That is, it was not possible to determine when a certain trip mode was likely to occur on any given tour. Therefore, no ‘trip sequence’ coefficients were tested for the trip mode choice models. · Household variables and PEF variables were tested, as for the tour mode choice models, with similar results. The alternative-specific constants shown in the tables are stratified by tour mode. As previously mentioned, this model structure allows calibration of alternative-specific constants to match observed trips by mode and tour mode. · The nested model structure for trip mode choice is shown in Figure 3. The structure is consistent with that of the tour mode choice models; that is, auto alternatives are nested separately from transit. The access mode in the transit nests (walk vs. drive) is ‘higher’ in the nesting structure than are the transit sub-modes, indicating a higher elasticity between transit modes given the mode of access. The estimated nesting coefficient is 0.7011.
TABLE 10 Work Trip Mode Choice Estimation Results
Attribute Coefficient Standard Error t-statistic
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In-Vehicle Time -0.0220 0.0073 -3.01 First Wait -0.0550 0.0215 -2.56 Second Wait -0.0800 0.0129 -6.19 Out-of-Pocket Cost -0.0077 0.0018 -4.38 Walk Time -0.0877 0.0064 -13.78 Walk Mode Time -0.0877 0.0064 -13.78 Bike Mode Time -0.1156 0.0250 -4.62 Number of stops for walk mode, where tour mode is not walk 0.7083 0.0914 7.75 Number of stops for shared-ride modes, where tour mode is transit 0.4585 0.0742 6.17 Night indicator for walk mode -0.5721 0.2657 -2.15 Drive Alone-Low Income -0.4159 0.1920 -2.17 Shared-Ride 2, Household Size=1 -0.8003 0.2123 -3.77 Shared-Ride 3+, Household Size<=2 -1.5691 0.2122 -7.39 Walk mode – Destination Network Connectivity -0.4983 0.2084 -2.39 Walk mode – Destination Crossing -0.6599 0.2508 -2.63 Walk mode – Destination Vitality -0.7239 0.2572 -2.81 Walk mode – Destination Topology -0.1334 0.2595 -0.51 Transit Mode - Destination Network Connectivity -0.3974 0.1721 -2.31 Transit Mode – Destination Crossing -0.7164 0.2133 -3.36 Transit Mode – Destination Vitality -0.1608 0.1820 -0.88 Shared-Ride 2 ASC, Tour mode is Driver -1.5407 0.1010 -15.26 Shared-Ride 3+ ASC, Tour mode is Driver -2.3020 0.1267 -18.17 Walk mode ASC, Tour mode is Driver -0.8890 0.3449 -2.58 Shared-Ride 3+ ASC, Tour mode is Auto Passenger -1.2905 0.1833 -7.04 Walk mode ASC, Tour mode is Auto Passenger 1.4914 0.3623 4.12 Bike mode ASC -1.0876 0.3273 -3.32 Shared-Ride 3+ ASC, Tour mode is walk-transit -1.1198 0.2395 -4.68 Walk ASC, Tour mode is walk-transit 4.1943 0.3310 12.67 Walk-Local ASC, Tour mode is walk-transit 4.9448 0.2741 18.04 Walk-Muni ASC, Tour mode is walk-transit 4.2186 0.2769 15.23 Walk-Premium ASC, Tour mode is walk-transit 3.1882 0.3708 8.60 Walk-BART ASC, Tour mode is walk-transit 4.7910 0.3427 13.98 Shared-Ride 3+ ASC, Tour mode is drive-transit -0.6673 0.8209 -0.81 Walk ASC, Tour mode is drive-transit 4.4489 0.8124 5.48 Walk-Local ASC, Tour mode is drive-transit 4.0467 0.6634 6.10 Walk-Muni ASC, Tour mode is drive-transit 3.4963 0.9216 3.79 Walk-Premium ASC, Tour mode is drive-transit 2.4499 1.1922 2.05 Walk-BART ASC, Tour mode is drive-transit 4.8443 0.7835 6.18 Drive-Premium ASC, Tour mode is drive-transit 3.5757 0.8816 4.06 Drive-BART ASC, Tour mode is drive-transit 5.6775 0.6581 8.63 Summary Statistics Log-Likelihood at Convergence -2,266.79 Rho-Squared with respect to Zero 0.4157 Rho-Squared with respect to Constants 0.2573 First Wait/In-Vehicle Time 2.50 Second Wait/In-Vehicle Time 3.63 Walk Time/In-Vehicle Time 3.98 Value of Time $1.70
FIGURE 3 Trip Mode Choice Model Nesting Structure
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Highway Assignment
In the SF Model, the highway and transit assignment steps are executed in the TP+ transport modeling software package. The results of the previous model components are summarized by time period (Early A.M., A.M. Peak, Midday, P.M. Peak, and Night), trip mode, and origin- destination TAZ, and assigned to either a highway network or transit network depending on mode. Before the trip tables are assigned to networks, trips made by non-SF residents (from disaggregated MTC models) are added to the SF resident trip tables. Transit and highway assignment takes approximately 16 hours of processor time (skim building takes the same amount of time).
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REFERENCES
1. John Bowman and Moshe Ben-Akiva. “The day activity schedule approach to travel demand analysis.” Paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, 1999.
2. San Francisco Travel Model Development Executive Summary Final Report, San Francisco County Transportation Authority, June 2001.
3. Mark Bradley, Maren Outwater, Nageswar Jonnalagadda, Earl Ruiter, “Estimation of an Activity-based Microsimulation Model,” Paper presented at the 79th Annual Meeting of the Transportation Research Board, Washington, 2000.
4. Nageswar Jonnalagadda, Joel Freedman, William Davidson and John Douglas Hunt, “Estimation of Destination and Mode Choice Activity-Based Microsimulation Models for San Francisco,” In Transportation Research Record 1777, TRB, National Research Council, Washington, D.C. 2001, pp 25-35.
5. Joseph Castiglione, Joel Freedman and Mark Bradley, “A Systematic Investigation of Variability due to Random Simulation Error in an Activity-Based Micro-Simulation Forecasting Model,” In Transportation Research Record, 2003 – pending publication.
6. Joel Freedman, Joseph Castiglione, and Mark Bradley, “A Systematic Investigation of Variability in Highway and Transit Assignments due to Random Simulation Error in an Activity-Based Micro-Simulation Forecasting Model,” Paper presented at the 2003 Transportation Planning Applications Conference, Baton Rouge, LA, April 2003.
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