Yoram Shiftan Transportation Research Institute, Technion - Israel Institute of Technology

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1 Yoram Shiftan Transportation Research Institute, Technion - Israel Institute of Technology

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3 The use of activity-based models for policy analysis Advances in ABM Emerging data Old challenges New challenges

4 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.

5 Schedule H Space H Tours Space H Trips Space W W W Time H H D S Time H H H D S Time H H H D D S S W H: Home W: Work S: Shop D: Dinner out

6 Pre-Toll Schedule Space (Home) Car Work Potential Responses to Toll (a) Change Mode & Pattern Space (b) Change Time & Pattern Car Space Bus Work Work (c) Work at Home Car Space Shop Time Shop Time Car Shop Time Shop Time = Peak Period

7 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

8 Pre-Toll Schedule Space (Home) Car Work Potential Responses to Toll (a) Change Mode & Pattern Space (b) Change Time & Pattern Car Space Bus Work Work (c) Work at Home Car Space Shop Time Shop Time Car Shop Time Shop Time = Peak Period

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10 Ability to analyze data by various categories: Income level Auto ownership Residential location

11 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

12 Highway Project travel time savings per 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 Transit Project fewer passengers will enjoy the improved service and accessibility revenue will be lower than estimated

13 The Assumption of Fixed Demand on Users Benefits D S 1 e a S 2 g h b f c

14 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% V/C E=(-0.25) E=(-0.5) E=(-0.75)

15 Household decisions Extending the Framework Urban development Residential choice Work place Auto ownership Kids errands Parking Transit ABA Shopping behavior Activity participation (location, sequence, scheduling, mode) Driver s decisions (route, parking) Transportation system performance

16 Value of Accessibility calculation process 1 CSn max j ( Unj j ) 1 ln( nj ) J n V ABAn e dunj dunj n dy j 1 n dcn J 1 1 Vnj E( CSn ) ln( e ) C j 1 n

17 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

18 Computation power continues to increase Advance in Econometric enables better behavioral representation/realism Advanced in data collecting methodologies contribute to improve data

19 Integrating with land use models Integration with longer-term decisions Explicit integration with micro-simulation/dynamic assignment parking choices and constrained parking equilibrium

20 Deriving activity demand from happiness/lifestyle Activity scheduling Meaning of activities Priority of activities/urgency Social network (ride-sharing)

21 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.

22 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

23 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

24 Funf in a box

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29 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

30 Behavioral Realism and Computational Complexity Behavioral Realism Computational complexity

31 Benefits from Behavioural Realism and Computational Simplicity Total Model Benefits Behavioral Realism Computational Simplicity

32 Total Model Benefits Behavioral Realism Computational Simplicity

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34 % Utilization ratio

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36 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

37 Policy needs continue to intensify Technology continues to develop Social changes Attitude and preference changes

38 Emergence of various services: Megabus CitiBike Zipcar Uber Lyft US Car sharing users has grown from 12K in 2002 to 900K in 2013.

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43 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

44 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

45 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

46 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.

47 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

48 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

49 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

50 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)

51 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?

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