Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model

Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model Erik Sabina – Regional Modeling Manager Suzanne Childress – Senior Modeler Transportation Research Board Planning Applications Conference Reno, Nevada, May 10th, 2011

A Bit About Denver 2010 Population 2.9m Employment 1.6m 2035 Planning Goals Urban Centers Urban Growth Boundary New regional light rail TODs

The Question: why this outcome? COMPASS 2010 2035 Difference Average Speed 36 28.8 -20% Congested Lane Miles 9,828 25,147 156% VMT 74,704,607 114,873,487 54% FOCUS 2010 2035 Difference Average Speed 37.4 32 -14% Congested Lane Miles 7817 22131 183% VMT 73,879,832 113,387,559 53% Observations: Congested lane-miles is lower and speed is higher for both years in Compass. % growth in lane-miles of congestion is higher in FOCUS (but not total number of add’l congested lane-miles.) this is probably due to arbitrary nature of “congested” (.95 v/c.) VMT differences are modest. In any case, I told my transportation planning folks not to go around telling people that the congestion apocalypse isn’t coming after all. I’M GOING TO HAVE TO ADDRESS SHORTLY AFTER THIS HOW WELL THE FOCUS model matches FRTC and TBI, since these figure raise that question: is FOCUS doing better in 2010 or is COMPASS? This is something we were focusing on before: how well we did in calibration. I should have that data available somewhere. See if I can find it, and if not, ask suzanne. Why focus growth in congested lane miles greater? Probably because this is just a threshold stat, and when you’re approaching the steep part of the curve, but start at a lower level, more links cross the threshold. ANOTHER NOTE TO SAY: I told my transp planners that they shouldn’t assume the congestion apocalypse isn’t coming after all! Still digging into this, and it is quite subtle.

The answer: probably two threads Expected time of day effects of the activity-based model. Interaction effects between disaggregate demand-side model and aggregate supply side (static equilibrium) This paper started out as a fun ABM analysis. But the dirty old aggregate model isn’t entirely dead yet, and it came back from the grave (sort of the living dead, really, like a zombie or something) to bite us and eat our brains. Calibration of choice models looks fine (not a source of error.) we’ll show some of that in a minute. So the effects are being produced by something else. At this point, a digression into model structure seems in order.

The new FOCUS Model Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation THIS IS JUST FOR THE NOTES SECTION BELOW. In thinking through the “peak spreading” effects of this model, one can trace chains of causation. Example (next slide.) Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

The new FOCUS Model Population Synthesis Work-based Sub-tour Generation worker? Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Popsyn produces workers (and if we control the number of them, we affect that outcome overall.) Worker status is a strong variable in dap: if a worker, then likely to have a work tour (and this affects likelihood of other tours) DAP for a person generates a set of tours by purpose, and these will have different tour tod outcomes Tour tod outcomes have strong effect on intermediate stop tod. Also important to note that worker variables occur in many other components. Note no control on workers in popsyn right now, which produces too many workers right now as a result. Pretty sure this isn’t the right place to fix it, though. Just intervene after it is run, make some people unemployed. Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

The new FOCUS Model Population Synthesis Work-based Sub-tour Generation worker? Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Previous slide was just the main causative chain, but we’re really still evaluating where the main chains of effect are. Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

Key variables: accessibility Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation THIS IS JUST FOR THE NOTES SECTION BELOW. Time of day effects from the interaction of many of these. Maybe show a specific example of that (some vars from some model upstream that affect the outcome of the time of day model. Example: worker status affects DAP outcome, which then passes a different set of tours to the time of day model. Different purposes have different outcomes, so the overall outcome will change.) Pop syn really does, when you have an integrated lu/transp model, use accessibility! And we do it now for the scores for the parcels! Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

Key variables: age Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

Our Trip-Based Model 1. Trip Generation 2. Trip Distribution i. Network Processing ii. Area Type 1. Trip Generation i. Highway/Transit Skims 2. Trip Distribution 3. Mode Choice i. Parking Cost ii. Time-of-Day 4. Highway/Transit Assignment TOD Sensitivity notes: One of the things we’re going to address is the complexity of evaluating the ABM’s sensitivities/elasticities. want to emphasize that such calculations (especially end to end, such as demographic data to congestion outcomes) are far from easy for trip-based models too. also want to note that our old trip-based model (with its rather common structure) has tod sensitivity to demographic inputs. I don’t know about you, but I never even thought to examine them before I started this analysis for our abm, and have concluded that there’s no reason to believe that those sensitivities are correct either in direction or magnitude. Explain: are the trip gen variables the right ones to use (production rates as a fn of income and hh size, and attraction rates as a fn of emp type and area type), to influence tod dist? Income – probably not (except very low, where unemployment is high) Hh size. Very weak correlation, it seems to me. Emp type (somewhat better, with retail being a decent variable, but only at a sub-regional level) Area type – perhaps CBD as a rough proxy for peak spreading, but this has not been examined and validated, to my knowledge. Did anyone intend to build trip gen models so as to appropriately affect tod? I sure don’t think so! What are the chances that these sensitivities are appropriate? Not too good. Some difference in trip production rate balance by income and hh size level (minimal sensitivity effect on TOD, since different trip purposes have different tod distributions.) Trip dist – no effect, because for any given balance of trip purposes, time of day is not affected by anything. Model choice – same basic comment.

Our old time of day “model” Just show very quickly

Basic Results: 2010 and 2035 trips by time of day   2010 2035 Peak 49.6% 48.0% Off-Peak 50.4% 52.0% May need to explain the AM higher than the PM. VMT versus our weird trip times (arrival time on the outbound half tour and departure time on the return.) I FORGET: DID WE DO ARRIVAL TIME OR DEPARTURE TIME FOR THIS GRAPH?

Basic Results: 2035 – 2010 time of day by purpose Period Start Time Work School Escort PerBus Shop Meal Soc/Rec Modeled Total 4:00 AM -0.001% 0.000% 0.004% -0.008% -0.005% -0.009% -0.006% -0.015% 5:00 AM 0.104% 0.034% -0.120% -0.091% -0.025% -0.117% -0.017% 6:00 AM -0.109% 0.024% -0.164% -0.127% -0.147% -0.179% -0.198% 7:00 AM -0.170% -0.013% -0.319% -0.270% -0.336% -0.193% -0.447% 8:00 AM -0.207% -0.123% 0.122% -0.257% -0.252% -0.203% -0.100% -0.403% 9:00 AM 0.180% 0.161% 0.124% 0.091% -0.049% 0.138% 0.167% 0.121% 10:00 AM 0.075% 0.066% 0.149% 0.220% 0.354% 0.147% 0.270% 0.283% 11:00 AM 0.099% 0.112% 0.256% 0.436% 0.409% 0.314% 0.334% 12:00 PM 0.126% 0.025% 0.095% 0.238% 0.029% 0.236% 0.246% 1:00 PM 0.105% 0.020% 0.115% 0.193% 0.540% 0.117% 0.247% 0.276% 2:00 PM 0.012% 0.262% 0.318% 0.481% 0.068% 0.322% 0.240% 3:00 PM -0.066% 0.049% 0.229% 0.062% -0.174% -0.245% -0.099% -0.106% 4:00 PM -0.061% -0.081% -0.012% -0.220% -0.113% -0.033% -0.157% 5:00 PM -0.107% -0.047% -0.249% -0.168% -0.378% -0.143% -0.334% -0.256% 6:00 PM -0.071% -0.184% -0.124% -0.377% -0.649% -0.506% -0.177% 7:00 PM 0.028% -0.018% -0.058% 0.010% 0.065% 0.421% 0.103% 0.170% 8:00 PM -0.042% -0.045% -0.004% 0.110% -0.062% 0.074% 9:00 PM 0.002% -0.029% -0.014% 0.019% 0.023% 0.063% 10:00 PM -0.032% -0.034% -0.037% -0.020% 0.008% 11:00 PM -0.031% 0.015% 12:00 AM -0.002% 0.005% -0.003% 1:00 AM -0.007% 2:00 AM 0.006% -0.010% 3:00 AM

Drivers of those results 1 Trips Per Person 2010 2035 Work 0.95 0.91 School 0.51 0.49 Escort 0.58 0.57 Personal Business 0.61 Shop 0.54 Meal 0.25 0.26 Social Recreation 0.47 0.48 Total 3.85 Percent of people who are…   2010 2035 Workers 53% 51% Students 26% 24% I think we can say with confidence that a closed-form calculation of any useful elasticities (that is, the ones we want!) is impossible (mainly because the models are estimated sequentially). Ask Cathy! Also useful to note that, for example, we have quite a few different types of accessibility variables. This might start a discussion over greater consistency in the variables used throughout the model system (I’d be in favor of that!) We think this indicates that we need to do arc-elasticities through sensitivity runs.

Driver of Results 2

Validation Challenges: peak spreading?   1997 TBI 2010 FRTC Peak 48.1% 50.5% Off-Peak 51.9% 49.5% Are there some other cities that really increased in congestion between surveys (and that data could be used for validation of such models?) 2010 survey timing obviously a bit unfortunate for this issue, of course (2010 congestion dropped considerably compared to about 2007.)

Return to the question: why this outcome? COMPASS 2010 2035 Difference Average Speed 36 28.8 -20% Congested Lane Miles 9,828 25,147 156% VMT 74,704,607 114,873,487 54% FOCUS 2010 2035 Difference Average Speed 37.4 32 -14% Congested Lane Miles 7817 22131 183% VMT 73,879,832 113,387,559 53% However, all that fun ABM stuff doesn’t seem to answer the question “why is base year FOCUS congestion lower than COMPASS?” Modest difference in VMT may explain a bit of it, but not all, we believe. After all, we thought we’d calibrated FOCUS correctly, so base year should be good.

Calibration Outcome: trip time of day However: When reviewing the calibration process, we realized that we calibrated our trip-based model against VHT distribution. Built that from survey, splitting each trip into the time of day slices that it covered, and summing. Daily trip table was subdivided SO AS TO give us this outcome OF VHT WHEN WE ASSIGNED (not on some other basis of, say, the time period when trips began, or ended.) VHT peak sharper because of longer trips during the peaks (which spill over more than one period.) So this is a whole issue of dealing with the approximations inherent in handing off to a static equilibrium assignment model. Note that we have issues with this, and we have 10 periods (more than maybe anyone!) Trip time-of-day Period 2010 model 1997 TBI peak 50.55% 50.02% off-peak 49.45% 49.98%

Calibration Outcome: trip time of day versus VHT However: When reviewing the calibration process, we realized that we calibrated our trip-based model against VHT distribution. Built that from survey, splitting each trip into the time of day slices that it covered, and summing. Daily trip table was subdivided SO AS TO give us this outcome OF VHT WHEN WE ASSIGNED (not on some other basis of, say, the time period when trips began, or ended.) VHT peak sharper because of longer trips during the peaks (which spill over more than one period.) So this is a whole issue of dealing with the approximations inherent in handing off to a static equilibrium assignment model. Note that we have issues with this, and we have 10 periods (more than maybe anyone!) Trip time-of-day VHT Period 2010 model 1997 TBI peak 50.55% 50.02% off-peak 49.45% 49.98% Period % VHT Peak 52.1% Off-peak 47.9%

Possible Remedies Interfere with the calibration outcomes of the time-of-day choice models – REALLY BAD IDEA! Adjust “WriteTripsToTransCAD” module Module that writes from database into TransCAD matrices for assignment Outbound half-tour trip times are arrival times – bias backward in time. Return-bound half-tour trip times are departure times – bias forward in time. Key point – distribution INSIDE each hour. Proper remedy – DTA? That is, bias time of day choice models to produce the VHT distribution we want. Bad idea because it causes all sorts of pathologies to models downstream (tour time of day), and produces a deliberately wrong outcome that then lives in our database. Example: wrong tour time of day equals wrong mode choice, so then we have to fiddle that! Don’t forget: even what we’ll do will be approximate, and have known errors in it (we can’t REALLY make the trips spill over from one period to a another, so we’re just fiddling with the trip tables to get the right base year outcomes.) Future year “fiddle” should be different, but we don’t know how.

Modify WriteTripsToTransCAD

Conclusions Interaction issues between static assignment and ABMs can significantly affect congestion outcomes. ABMs do show “peak spreading” in forecast years (at this point, not appearing to be huge.) Demographic and accessibility variables appear key. Calculate arc elasticities via scenario runs to better evaluate sensitivities. Validation will be a challenge in any case. Once we sharpen this pencil as described here, I think I’ll be feeling comfortable enough to tell my tranp planning folks that these congestion outcomes (and the differences between them and the old trip-based model) are meaningful. Need more data for validation.

Contact Information Suzanne Childress – schildress@drcog.org Erik Sabina – esabina@drcog.org 303-455-1000

Influence of components Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation THIS IS JUST FOR THE NOTES SECTION BELOW. Time of day effects from the interaction of many of these. Maybe show a specific example of that (some vars from some model upstream that affect the outcome of the time of day model. Example: worker status affects DAP outcome, which then passes a different set of tours to the time of day model. Different purposes have different outcomes, so the overall outcome will change.) HOW are they important: Popsyn – changes in age and worker strongly influence tod (hh size too?) Workplace location – home work, but it isn’t d Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

Calibration Outcome: trip time of day versus VHT Period 2010 model 1997 TBI peak 50.55% 50.02% off-peak 49.45% 49.98% Looking at one model only here (in the interest of time): A few little issues here But overall, time day calib looks good. So it doesn’t seem we can attribute the effects we’re seeing (differences in base year especially) to choice model calibration problems