Integrating Mode Specific Attributes into the Transit Pathfinder in a Manner Consistent with the Multimodal Demand Model Richard Walker and Bud Reiff, Portland Metro Ben Stabler, Parsons Brinckerhoff, Inc. 2009 TRB Planning Applications Conference

Motivation New Starts – FTA encourages integration of mode choice time parameters and path choice variables to ensure consistency in user benefit calculations Pathfinder will choose most efficient transit submode (LRT, streetcar, bus) using criteria based upon system attributes There are many pathfinder levers in VISUM – gives the analyst more control in replicating real-world decision process

Project Goal Closely integrate transit mode choice time coefficients and path choice parameters Mode choice bring more explanatory power into the time variables, thus reducing (but not totally eliminating) the need for the modal preference to be expressed solely in the constant modal preference as a constant is hard to capture in the path choice Path choice bring mode choice coefficient values into path choice

Strategy Improve the modeling of the choice between the transit submodes Differentiate time effects for walk, wait, transfer, and in-vehicle time – variation due to: transit stop amenities transit submode characteristics Use VISUM to replicate the differentiated time parameters into the transit path choice

Transit Stop Amenities Traditional stop Small waiting area Near curb and busy street Presence of shelter unlikely Little or no schedule information

Transit Stop Amenities Improved stop Shelter Small platform Schedule information signs

Transit Stop Amenities Transit Center Large protected platform Shelters Message signs Vendors Transit supervisors Well lit Good accessibility to adjoining neighborhoods

Hypothesis and Implementation Characteristics of transit stop influence perception of walk time to transit and wait time (initial and transfer) for a transit vehicle Implementation Classify transit stop nodes in network Classification influences walk and wait time perception, thus path choice Incorporate the weighted perception value in mode choice

Network Implementation Concept Node classified as Transit Center Walk and wait time – less onerous than traditional stop Line 1 Line 2 Node classified as Traditional Stop Walk and wait time – more onerous than Transit Center

Strategy Improve the modeling of the choice between the transit submodes Differentiate time effects for walk, wait, transfer, and in-vehicle time – variation due to: transit stop amenities transit submode characteristics Use VISUM to replicate the differentiated time parameters into the transit path choice

Transit Vehicle Attributes Local service vehicle Frequent stops Route “uncertainty” Exhaust Noise Travel time uncertainty due to operation in mixed traffic

Transit Vehicle Attributes Fixed route circulator Frequent stops Route certainty (“branding”) Exhaust free Quiet Travel time uncertainty due to operation in mixed traffic

Transit Vehicle Attributes High capacity transit Fewer stops Route certainty (“branding”) Travel time certainty Quiet Smooth starts and stops

Hypothesis and Implementation Transit vehicle characteristics influence the perception of travel time Implementation Classify transit vehicles in the network Classification influences in-vehicle travel time perception, thus path choice Incorporate the time spent in each vehicle type into the mode choice model Weight each by differences in perception

Network Implementation Concept Transit vehicle classified as HCT In-vehicle time parameter – less onerous than a local service vehicle Line 1 Line 2 Transit vehicle classified as Local Service Vehicle In-vehicle time parameter – more onerous than HCT

Measuring Time Perceptions Stated preference survey currently underway PB, RSG, Bradley Will measure perceived time relativity between stop types and between vehicle types Target of 900 completes ~ 75% transit users and ~ 25% auto users Computer-assisted experiment combining text, photos, skims and GPS to create realistic choices Findings will be applied within the model

Transit Mode Choice Utility Expression Utl = f(demographics, urban form, costs, time, modal pref) Time and modal preference: c1a(walk – trad stop) + c1b(walk – imp stop) + c1c(walk – transit center) + c2a(wait – trad stop) + c2b(wait – imp stop) + c2c(wait – transit center) + c3a(IVT – local) + c3b(IVT – circulator) + c3c(IVT – HCT) + modal preference Walk times influenced by classification of transit stops Wait times influenced by classification of transit stops IVT by mode is retained in pathfinder and expressed in utility Preference value by submode and weighting it by the respective IVT by OD

Will the Approach Work? Metro Test Case In-vehicle time LRT: 80% of model estimation value Streetcar: 90% of model estimated value Bus: 100% of model estimated value Transit stop wait and walk time 100% for all stop types node classification feature recently incorporated into VISUM next test will use this enhancement

Transit Paths – Are They Reasonable? Before: In-Vehicle Time (1, 1, 1)  95% Rail After: In-Vehicle Time (1, 0.9, 0.8)  100% Rail Pioneer Courthouse Square  Lloyd Center (PM Peak)

Transit Paths – Are They Reasonable? Before: In-Vehicle Time (1, 1, 1)  82% Rail After: In-Vehicle Time (1, 0.9, 0.8)  97% Rail Lloyd Center  Pioneer Courthouse Square (PM Peak)

Transit Paths – Are They Reasonable? Before After Bus 81% 80% Rail 18% 19% Streetcar 1%

Conclusions Based on testing so far… Results are promising Transit submodes can be accounted for in a direct manner Less data management than traditional methods Decision processes can be represented in the route pathfinding algorithm

Next Steps Stop classification application Compare algorithm path choices with those revealed from on-board surveys Link procedure with DASH (dynamic tour-based model under development at PSU – John Gliebe) Use procedure in the estimation process using new household survey data (2010-2011)