Yoram Shiftan Transportation Research Institute, Technion - Israel Institute of Technology The use of activity-based models for policy analysis Advances in ABM Emerging data Old challenges New challenges Travel demand is derived from demand for activities. People face time and space constraints that limit their activity schedule choice. Activity and travel scheduling decisions are made in the context of a broader framework: Conditioned by outcome of longer term processes. Scheduling process interacts with the transportation system. Tours Schedule Space Trips Space H Space H H W W W S H H H D H Time S H H: Home W: Work H H H D Time Time S: Shop W S D: Dinner out S D D Pre-Toll Schedule Potential Responses to Toll (a) Change (b) Change Mode & Pattern Time & Pattern Space (Home) Space (c) Work at Home Space Space Car Car Bus Work Work Work Shop Car Shop Time Time = Peak Period Shop Time Time Car Shop Secondary effects - adjustment to the activity pattern that have to be made in response to the primary effect A more realistic presentation of trip purposes More detailed travel data: by tour, by trip, by individual, and by various variable for equity issues and other purposes Better input requires for externalities evaluation Ability to deal with “induced demand” Measure of overall accessibility Pre-Toll Schedule Potential Responses to Toll (a) Change (b) Change Mode & Pattern Time & Pattern Space (Home) Space (c) Work at Home Space Space Car Car Bus Work Work Work Shop Car Shop Time Time = Peak Period Shop Time Time Car Shop Ability to analyze data by various categories: Income level Auto ownership Residential location VMT Travel by mode and time of day Fraction of cold/hot starts Time and location of starts More accurate estimate of emissions Exposure measures Speed/Acceleration/Driving Profile Travel by Vehicle Class and Model Highway Project Transit Project travel time savings per fewer passengers will vehicle will be less than estimated vehicle kilometers of travel will be more than estimated emissions and other externalities will be higher than estimated benefits for new riders are ignored enjoy the improved service and accessibility revenue will be lower than estimated The Assumption of Fixed Demand on Users’ Benefits D S1 a e S2 b h g f c Bias from the Assumption of Fixed Demand Fixed Demand Bias by V/C and Demand Elasticity 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 0.25 0.50 0.75 1.00 1.25 V/C E=(-0.25) E=(-0.5) E=(-0.75) 1.50 Extending the Framework Household decisions Urban development Work place Residential choice Auto ownership Kids errands Shopping behavior Parking Transit ABA Activity participation (location, sequence, scheduling, mode) Driver’s decisions (route, parking) Transportation system performance Value of Accessibility calculation process CSn 1 max j (U nj j ) n dU nj dU nj n dYn dCn E (CSn ) 1 ABAn 1 n J ln( e j 1 1 J ln( e Vnj Vnj j 1 )C ) Provide details on tours not just trips Provide better output for externality calculations They are disaggregate, therefore can provide detailed travel data: By socio-economic By auto ownership By type of tours By type of trips (cold/hot starts) Ability to deal with Induced demand Provide accessibility measure to feed into long term choice decision models, and to economic evaluation Computation power continues to increase Advance in Econometric enables better behavioral representation/realism Advanced in data collecting methodologies contribute to improve data Integrating with land use models Integration with longer-term decisions Explicit integration with micro-simulation/dynamic assignment parking choices and constrained parking equilibrium Deriving activity demand from happiness/lifestyle Activity scheduling Meaning of activities Priority of activities/urgency Social network (ride-sharing) Models of household interaction Car allocation More detailed definition of activities In-home and out-of-home activity trade-off Fine level of time resolutions Representing time as continuous variable Fine level of spatial resolution. Multi day/weekly ABM Seasonality/Special events Non-residents/visitors Learning models: day to day/Spatial learning Improvement of accessibility variables Flexible model structure Choice set generation Rule-based vs. RUM models Need for much more detailed and higher resolution data. New data collection techniques . Some advances in data collection: Panel surveys Multiple-day surveys Revealed/Stated preference surveys Efficient use of different source of data Maximize use of existing data Big Data Sophisticated processing algorithm Machine learning algorithms Use of GPS/data-loggers/Apps Funf in a box Preferences, Hobbies, Needs, Abilities Well being? Detailed of home activities Household member task substitution The attractiveness of different locations Substitution possibilities Which activities are available where? Constrains: operating hours At what spatial resolution? Activity based SP Behavioral Realism and Computational Complexity Behavioral Realism Computational complexity Benefits from Behavioural Realism and Computational Simplicity Total Model Benefits Behavioral Realism Computational Simplicity Total Model Benefits Behavioral Realism Computational Simplicity 50 45 40 35 % 30 25 20 15 10 5 0 0.0-0.8 0.8-1.2 Utilization ratio 1.2-2.0 Time schedule – planning vs. construction Skewed distribution of demand/cost/ construction time of actual vs. estimated – indication of underlying systematic bias. The role of modeling in decision making Interest groups Institutional barriers (choose easy to implement/less controversial projects) Incentives for local governments to opt for high investment projects Policy needs continue to intensify Technology continues to develop Social changes Attitude and preference changes Emergence of various services: Megabus CitiBike Zipcar Uber Lyft US Car sharing users has grown from 12K in 2002 to 900K in 2013. Safety Increase speed/reduce travel time Capacity increase, reduce headway and lane width Reduce driver burden (stress/fatigue/productive time) travel time budget, VOT Cost increase - technology, reduce – sharing, insurance, parking, fuel efficiency Travel money budget Parking requirements New opportunities for young/elderly/disabled Task allocation – the car take the kids to school Reduce transit cost, allow more flexibility Reduce car-sharing/ride-sharing barriers No need to walk to them/park them. Allow repositioning vehicles to better response to demand. New modes – public/private combination Can be both – as in the auto mate Privacy/flexibility/productive time use Value of travel time Longer commute/other travel distances Activity travel patterns change? Time of travel – sleep at night….. Access more desirable activities further away Land use impacts Would value of agglomeration economy diminish? Value of land? Car type purchase Larger cars - conduct more activities while driving Reduce walking and bicycling / health effect North American car-sharing members reduce their driver distance by 27%, with approximately 25% of members selling a vehicle and another 25% forgoing a vehicle purchase (Shaheen and Cohen, 2013). This will be more attractive with autonomous vehicles. Potential behavioral shift following autonomous vehicles received little attention so far. What’s different: Demand Supply: capacity, safety, reliability What can be captured with existing models Analogs of existing modes Would preference/attitudes change What structural changes are necessary New modes, attributes, choice set, decisions Behavioral change How utility of different mode change Substitution between private and public modes transformation via changes at the levels of institutions and societies The role of societal and cultural contextual factors New mobility paradigm – the increasing link between travel and new technologies, and the primacy of social networks in influencing travel decisions. The penetration phase Ford (2012) – Shared autonomous taxi model (but travelers had to walk to fix taxi stands) Kornhauser (2013)/Burns et. al. (2013)- dynamic ride sharing implications (focus on cost and benefits estimates) Fagnant and Kockelman (2014) How much new travel may be induced (from lower perceived travel time cost and by those without drive licenses) BUT assumed trip rates and attraction rates Walker (2014) – MTC 4-8% increase in VMT for moderate scenarios 15% increase in VMT for most extreme scenarios The impacts can be numerous A lot of uncertainty!!! “California could explicitly accept that the future for which is it planning is highly uncertain” (Wachs, 2012) Activity based models are continuously improving in providing better behavioral realism given advances in data collection, computational power, and econometric and simulation methods. Do ABM respond to old policy making challenges? Are they ready to new challenges: technology and social changes? Are they capable to provide the forecasts needed for major investments in transportation?