Assessing Transit Oriented Development Strategies with a New Combined Modal Split and Traffic Assignment Model
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1 Assessing Transit Oriented Developent Strategies with a New Cobined Modal Split and Traffic Assignent Model Guangchao Wang, Ph.D. Anthony Chen, Ph.D. Ziqi Song, Ph.D. Songyot Kitthakesorn, Ph.D. Departent of Civil and Environental Engineering Utah State University Logan, UT 84322
2 Motivation Public transportation can itigate congestion and alleviate air pollution. Transit oriented developent (TOD) will proote public transportation. Traditional planning odels fail to consider users captivity in ode choice. Route overlapping is one of the ajor concerns in route choice odels. This study ais at developing a new cobined odal split and traffic assignent (CMSTA) odel to quantitatively assess TOD strategies. 2
3 TOD s Ipacts on Travel Behavior TOD boosts transit ridership. TOD induces higher transit ode share. TOD reduces travelers trips by autos. 3
4 Transit Captivity Auto ownership level has huge ipact on travelers ode choice behavior in TODs. Travelers with no vehicle available are ore likely to coute by transit. TOD residents tend to own fewer cars. 4
5 Evaluation of TOD Strategies Epirical study of travel survey data Statistical analysis/odeling of travel survey data Station level evaluation Transportation network odeling Overall ipacts of TODs at network/regional level 5
6 Dogit Model Choice odel Choice aong specific pairs of alternatives to be consistent with the independence fro irrelevant alternatives (IIA) property Allow choice aong other pairs not to possess the IIA property. Can be used to consider user captivity Mode choice Destination choice 6 Custoer loyalty in product shopping behaviors
7 Dogit Model Mode choice probability P = exp æ n ( V ) + h exp å ( V ) + h exp ö nîm n ç nîm nîm è å ø å n ( V ) () where P = probability that an individual will select travel ode between O-D pair. V = deterinistic coponent of the utility of travel ode between O-D pair. h = captivity paraeter related to ode choice,, h ³ " Î M. 7
8 Dogit Model Mode choice probability P h exp = + + h + h exp ( V ) ( V ) n n n nîm nîm nîm , (2) = + å å å Captive share Free-choice share Logit assignent hj Jk å,, nîm n where k =exp( V ) exp ( V ) n " ÎM Î IJ and J = ( h ) Probability ratio between two alternatives +å, " Î IJ nîm P P hj + Jk h + k = = hj + Jk h + k n n n n n, (3) n IIA holds only when h and h both be zero 8
9 Dogit-PSL Model in Z = Z + Z + Z + Z + Z + Z Logit SUE åå r ra a = f d v åå h w dw f f q ÎIJ rîr r r ò ( ) + ååå ( ln -) a ÎM aîa ÎIJ ÎM rîr r r - åååf lnv + ååéë q -Jh q q -Jh q q g ( ) ln ( ) ÎIJ ÎM rîr ÎIJ ÎM ln ln. ( ) ( ) ùû åå ( ) åå + q -Jh q q J - q q - - q Y q Î IJ Î M Î IJ Î M (4-a) Captive ode choices s.t. å q = q, " Î IJ, (4-b) ÎM Flow conservation r å f = q, " Î M, Î IJ, constraints (4-c) rîr q ³, " Î M, Î IJ, (4-d) r f ³, " r Î R, Î M, Î IJ, (4-e) Entropy related to overlapping 9
10 Nuerical Results Nyuyen and Dupius Network TOD iproves transit service Transit travel tie decreases by %, 2% and 5% Auto line Transit line Bike lane
11 Nuerical Results Modal flow ratio.5.5 (a) Modal split relative to the base case in the dogit-psl odel (Transit FFTTs iprove %) O-D O-D 2 O-D 3 O-D 4 Modal flow ratio (c) Modal split relative to the base case in the dogit-psl odel (Transit FFTTs iprove 2%) O-D O-D 2 O-D 3 O-D 4 Modal flow ratio (e) Modal split relative to the base case in the dogit-psl odel (Transit FFTTs iprove 5%) O-D O-D 2 O-D 3 O-D 4 Modal flow ratio (b) Modal split relative to the base case in the MNL-PSL odel (Transit FFTTs iprove %) O-D O-D 2 O-D 3 O-D 4 Modal flow ratio (d) Modal split relative to the base case in the MNL-PSL odel (Transit FFTTs iprove 2%) O-D O-D 2 O-D 3 O-D 4.75 Modal flow ratio (f) Modal split relative to the base case in the MNL-PSL odel (Transit FFTTs iprove 5%) O-D O-D 2 O-D 3 O-D 4 FIGURE 2 Modal Splits in Different Scenarios for the MNL-PSL and Dogit-PSL Models
12 Nuerical Results 5 (a) Modal split in the MNL-PSL odel (+.5) 5 (b) Modal split in the dogit-psl odel (+.973) Modal flow 5 (+.384) (+.88) (-.56) (-.26) (-.23) (-.476) (-.387) Base case % 2% 5% Transit FFTT scenarios (-.798) Modal flow 5 (+.326) (+.56) (-.54) (-.23) (-.93) (-.479) (-.375) Base case % 2% 5% Transit FFTT scenarios (-.794) FIGURE 3 Network Modal Splits in the MNL-PSL and the Dogit-PSL Models under Different Scenarios Ignorance of captive users tend to overestiate the ipact of TOD strategy on the ode split results 2
13 Nuerical Results Table 3. Mode and network VMTs under different transit FFTT iproveent scenarios Mode MNL-PSL Dogit-PSL Base case % 2% 5% Base case % 2% 5% Car 2,374.,649.5, ,373.9,38.9,47.7 6,4.6,992.6 (-.59)* (-.3) (-.485) (-.56) (-.27) (-.488) Transit 97.,89.3,269.4,93 49.,38., (+.88) (+.384) (+.5) (+.56) (+.326) (+.97) Bike 4, ,56.6 2, , ,47.9 (-.25) (-.389) (-.8) (-.94) (-.378) (-.796) Su 3, ,24.,76.7 6, ,89.7 4,37. 8, ,34.2 (-.9) (-.9) (-.482) (-.77) (-.6) (-.478) * This cell eans VMT for car decreases 5.9 percent copared with the base case after ipleenting the TOD strategy. Ignorance of captive users tend to overestiate the ipact of TOD strategy on the increase of transit VMTs 3
14 Concluding Rearks A new atheatical prograing forulation for the CMSTA proble is proposed. Captive travel behavior in ode choice and route overlapping in route choice are explicitly considered. Nuerical exaples show that ignoring ode captivity can lead to biased results in evaluating the ipacts of TOD strategies. 4
15 5
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