Transportation Demand Analysis Mode Choice Mode Choice

Outline Introduction Binary Diversion Analysis Abstract Mode Models Behavioral Models Transportation Demand Analysis - Mode Choice

Transportation Demand Analysis Mode Choice Introduction

Third Step in Urban Transportation Modeling System Socio-economic Forecasts (Population, Employment, …) Trip Generation Trip Distribution Transportation Demand Analysis - Mode Choice Mode Choice Trip Assignment

Situation Trip-end models Trip interchange models Tim=Tif(m) Restricted to the characteristics of origin (socio-economic variables) Trip interchange models Tijm=Tijf(m) Restricted to the characteristics of origin-destination (socio-economic and LOS variables) Transportation Demand Analysis - Mode Choice

Situation Four-step model: Given Tij  Tijm= f(Tij, …m) Direct approach: Tijm = f(Ti,Aj,…m)=f(g(popi),h(popj),m(ttijm)) =Ψ(popi, popj, ttijm) More general: Tijm = f( …i, …j, …ij, …ijm) Example: Tijm = f(popi, popj, Number of callsij, ttijm) Transportation Demand Analysis - Mode Choice

Hierarchy of mode and route choice Simultaneous approach Useful in many application (e.g., policy making) Desirable, if the scope of the problem at hand is manageable Sequential approach It is generally agreed to be the mode followed by the route choice Preferable for large scale networks with excessive number of alternatives Transportation Demand Analysis - Mode Choice

Scale of mode and route choice problem Microscopic (individual based) due to policy making Mode choice examples Exclusive lanes for high occupancy vehicles Increased parking taxes for private automobiles … Route choice example Toll collection Metering on freeway ramps Transportation Demand Analysis - Mode Choice

Binary Diversion Analysis Transportation Demand Analysis Mode Choice Binary Diversion Analysis

Early developments Aggregate diversion models Binary choice case Splitting traffic flows into modes based on some simple formulation of their relative attributes Binary choice case Traffic splits into two modes Automobile (private) Transit (public) Transportation Demand Analysis - Mode Choice

Concept Breaking the traffic down based on the attributes of private and public transportation It has been used for modal split both for trip-end (before trip distribution analysis) trip interchanges (after trip distribution analysis) Transportation Demand Analysis - Mode Choice

Trip-end Models Placed before trip distribution step The accounted variables: Socio economic variables Accessibility ratio to each mode The model is independent of system variables The model is almost obsoleted due to increasing car usage after the 2nd world war Transportation Demand Analysis - Mode Choice

Example (Binary Diversion Curves) Accm= ∑Djfij, Dj: trip attraction of zone j fijm: distance between i and j for mode m Transportation Demand Analysis - Mode Choice Note: Curves may stratified on some socioeconomic characteristics (e.g., Cars per household, household size).

Examples of Binary Diversion Curves (Trip-end) Wisconsin Model (1963): Advanced trip-end model including trip type (purpose), individual characteristics (number of cars) and System characteristics limited to zone i (accessibility ratio in the form of private to public) Chicago Model (CATS): Including Land use, Gender, Occupation, Driver, Age, Car ownership Transportation Demand Analysis - Mode Choice

Example of Binary Regression Model (Trip-end) Pittsburg model (PATS): Trip type (purpose), car ownership, residential density, distance from CBD Ln(number of edu. trips by public/1000people)= 3.3-0.91 Ln (residential density) Ln(number of other trips by public/1000people with no car)= 84.02 (residential density) - 0.094 Ln (residential density)2 Ln(number of other trips by public/1000people with 1 car)= 3.04+3.20 (residential density) -0.026 Ln (residential density)2 Ln(number of other trips by public/1000people with 2+ car)= 0.604+3.6 (residential density) -0.0334 Ln (residential density)2 Generally, regression is avoided because of 0,1 boundaries of the probability Transportation Demand Analysis - Mode Choice

Trip Interchange Models Placed after trip distribution step The attributes are specific to origin-destination pairs More than a single measure of accessibility: travel time ratios travel cost ratios … Transportation Demand Analysis - Mode Choice

Examples of Binary Diversion Curves (Trip-interchange) Bay Area Rapid transit (BART) Trip type (purpose), peak and off-peak period, CBD and non-CBD, Travel time ratio Washington, Philadelphia and Toronto study: Cost ratio, Travel time ratio, out of vehicle time ratio, Income level Transportation Demand Analysis - Mode Choice

Washington, Philadelphia and Toronto Study Transportation Demand Analysis - Mode Choice

Washington, Philadelphia and Toronto Study CR: Ratio of transit to auto cost EC: Annual median income per worker L: Ratio of transit to auto service Transportation Demand Analysis - Mode Choice Range for (L) service time ratio: L4: 0.0 to 1.5 L3: 1.5 to 3.5 L2: 3.5 to 5.5 L1: 5.5 and over

Washington, Philadelphia and Toronto Study CR: Ratio of transit to auto cost EC: Annual median income per worker L: Ratio of transit to auto service Transportation Demand Analysis - Mode Choice TTR: Ratio of transit to auto time

Binary Diversion Analysis Limitations The method is too aggregate It is not applicable for policy-making (Policies affect different groups of urban travelers differently) Do not accounted for captive users Captive transit users Captives car users Limited to binary choice situations Nowadays, multiple choices are available in most transportation systems Transportation Demand Analysis - Mode Choice

Transportation Demand Analysis Mode Choice Abstract Mode Models

Assumptions Mode choice is based on the modes characteristics (not on their names) A mode characteristics are sufficient to find its transportation share A new mode usage can be determined by its characteristics General form (Gravity structure): T=αHβCγNθ H: travel time, C:Cost, N:Number of modes Transportation Demand Analysis - Mode Choice

Abstract Mode Example 1 Mode 3 Mode 2 Mode 1 3 2 1 Travel Time (hr) Ratio to best 5 Travel cost (100$) 1.5 2.5 Tk=1000-100Hb-60Cb-60Hrk-50Crk-50N Tk: Number of trips by mode k Hb: Best travel time Hrk: Travel time ratio of mode k to the best Cb: Best cost Crk: Travel cost ratio of mode k to the best N: Number of modes Tmode1=1000-100*1-60*2-60*1-50*2.5-50*3 Tmode1=445, Tmode2=435, Tmode3=400  TTotal=1280 Transportation Demand Analysis - Mode Choice

Abstract Mode Example 1 (Introduction of mode 4) 1.5 3 2 1 Travel Time (hr) Ratio to best 5 Travel cost (100$) Tk=1000-100Hb-60Cb-60Hrk-50Crk-50N Tk: Number of trips by mode k Hb: Best travel time Hrk: Travel time ratio of mode k to the best Cb: Best cost Crk: Travel cost ratio of mode k to the best N: Number of modes Tmode1=1000-100*1-60*1-60*1-50*5-50*4 Tmode1=330, Tmode2=370, Tmode3=360 Tmode4=500  TTotal=1560 Transportation Demand Analysis - Mode Choice

Abstract Mode Example 1 (Introduction of mode 5) 0.5 1.5 3 2 1 Travel Time (hr) 6 4 Ratio to best 5 Travel cost (100$) Introduction of Mode 5: Tmode1=385, Tmode2=315, Tmode3=220 Tmode5=520  TTotal=1440 Introduction of Mode 4 and Mode 5: Tmode1=270, Tmode2=250, Tmode3=180 Tmode4=410, Tmode5=480  TTotal=1590 Transportation Demand Analysis - Mode Choice

Abstract Mode Example 2 Tijk= α0(Pi)α1(Pj)α2(Yi)α3(Yj)α4(Mi)α5(Mj)α6 (Nij)α7 f1(H)f2(C)f3(D) f1(H)= (Hijb)β0 (Hijrk)β1 f2(C)= (Cijb)γ0 (Cijrk)γ1 f3(D)= (Dijb)δ0 (Dijrk)δ1 Hb: Best travel time Hrk: Travel time ratio of mode k to the best Cb: Best cost Crk: Travel cost ratio of mode k to the best Db: Best convenience Drk: Convenience ratio of mode k to the best N: Number of modes between i and j Y: Income M: Land-use index (Labor source) P:population Transportation Demand Analysis - Mode Choice

Abstract Mode Model Limitations The total number of trips depends on the number of modes (This may not be the case in work trips) Sometimes, it is difficult to find a corridor with flow similar to the studied model Ratios are more important than real values Transportation Demand Analysis - Mode Choice

Transportation Demand Analysis Mode Choice Behavioral Models

Variables Category Application Socioeconomic demand variables Level of service or supply variables Application Significant Can reflect the policy analysis or planning Transportation Demand Analysis - Mode Choice

Socio-economic Variables Income Age and role in household Car ownership Household size Residential location Profession … Transportation Demand Analysis - Mode Choice

Supply Variables In-vehicle travel time. Access, waiting, and transfer times. Travel cost. Qualitative and attitudinal variables … Transportation Demand Analysis - Mode Choice

Two Postulations in Choice Modeling Unlabeled Choice models (Mode-abstract) Consumers decide on the basis of characteristics of goods and services rather than the goods themselves (Lancaster, 1966) E.g., Two distinct modes which have the same level of service attributes would be treated as one Labeled choice (Mode-specific) Choices are influenced by the level of service attributes, but these influences will vary from one alternative to another Transportation Demand Analysis - Mode Choice

γi: a mode-specific constant term (different values for A and B) Mode-Abstract and Mode-Specific Comparison Unlabeled choice Choice function parameters have no relation to specific modes Labeled choice Choice function parameters would also vary depending on the alternatives They may have dummies or mode specific constants α,β: parameters i: A or B 𝑉 𝑖 =𝛼 𝑇 𝑖 +𝛽 𝐶 𝑖 Transportation Demand Analysis - Mode Choice 𝑉 𝑖 =𝛼 𝑇 𝑖 + 𝛽 𝑖 𝐶 𝑖 𝑉 𝑖 =𝛼 𝑇 𝑖 + 𝛽 𝑖 𝐶 𝑖 + 𝛾 𝑖 γi: a mode-specific constant term (different values for A and B)

Mode-Abstract and Mode-Specific Comparison Build on the framework of modern microeconomic theory Requiring a lower number of model parameters Mode-specific More consistent with choice behavior Transportation Demand Analysis - Mode Choice

Examples of Urban Mode Choice Models Stochastic binary model Earlier applications of stochastic choice models Due to the absence of efficient, computer-based estimation techniques for multinomial models Trinomial model Multimodal model Transportation Demand Analysis - Mode Choice

A Stochastic Binary Model (De Donnea, 1970) Choice between auto and bus transit in Rotterdam, the Netherlands. Calibrated both a logit and probit mode-specific model Insignificant difference in the results Transportation Demand Analysis - Mode Choice

A Stochastic Binary Model (De Donnea, 1970) 𝑉 1 = 𝛼 1 + 𝛼 2 𝑌 𝑡 1 + 𝛼 3 𝐻 𝑉 2 = 𝛼 2 𝑌 𝑡 2 V(1): Auto choice function V(2): Transit choice function Y: individual's income t: travel time H: a dummy variable (H = 1, if the individual is head of a household) α1,α2,α3 : model parameters Transportation Demand Analysis - Mode Choice

A Stochastic Binary Model (De Donnea, 1970) L= V(1) - V(2) = a1+ a2 Y (t1– t2 ) + a3 H Logit model: Probit model: Transportation Demand Analysis - Mode Choice

A Stochastic Binary Model (De Donnea, 1970) Insignificant difference in the results Logit Model Probit Model a1 0.0226 0.0117 a2 -0.0024 -0.0014 a3 1.0800 0.6600 Transportation Demand Analysis - Mode Choice

A Trinomial Model (Ganek and Saulino, 1976) A model calibrated for the Pittsburgh, Pennsylvania Three modes Automobile Transit Carpool a mode between drive alone and public transportation Transportation Demand Analysis - Mode Choice

Variables (Ganek and Saulino, 1976) In-vehicle time Access time and egress times Total cost Relative comfort and convenience Flexibility Mode reliability Car availability Location of work place Household income Sex Mode constants Transportation Demand Analysis - Mode Choice

A Multinomial Model (Train, 1976) A multinomial logit choice mode for San Francisco Bay Area households Four alternatives Auto alone Bus with walking access Bus with automobile access Carpool Transportation Demand Analysis - Mode Choice

Variables (Train, 1976) Cost divided by post tax wage (cents per cent per minute) On-vehicle time in minutes Walk time in minutes Transfer time in minutes Number of transfers Bus headways in minutes (below or above 8 minutes) Family income classes Length of residence in the community Number of persons in the household who can drive Mode dummy variables Transportation Demand Analysis - Mode Choice

A Multinomial Model (Ben Akiva and Richards, 1976) A multinomial choice mode for some Dutch cities Six alternatives Walking Bicycle Moped bus Train automobile Transportation Demand Analysis - Mode Choice

Variables (Ben Akiva and Richards, 1976) In-vehicle time Out-of-vehicle time Out-of-pocket cost Household income Car availability Occupation Mode-specific dummy variables Transportation Demand Analysis - Mode Choice

Notes Urban mode choice models can be used to evaluate Effects of minor and gradual changes In the area where they have been calibrated Mode-specific models are rather successful in predicting behavior than mode-abstract models According to the literature: Most of the variations of mode choice behavior: Mode-specific dummy variables Some socioeconomic variables (particularly income or car ownership) Transportation Demand Analysis - Mode Choice

New developments Mode specific models containing a number of alternatives Developing models for new urban types Relaxing the assumption of independency in IID and developing the nested structure Relaxing the assumption of independency and identically in IID and developing the mixed structure Relaxing the assumption of homogeneity of population and developing the latent class structure Transportation Demand Analysis - Mode Choice

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