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Envisioning an emission diet: application of travel demand mechanisms to facilitate policy decision making


Emission reduction strategies are gaining attention as planning agencies work towards adherence to air quality conformity standards. Policymakers struggling to reduce greenhouse gases (GHG) must grapple with a growing number of travel demand policies. To consider any of these emerging demand mechanisms as a viable option to meet emission targets, planners and policymakers need tools to better understand the implications of such policies on travel behavior. In this paper we present an integrated multimodal travel demand and emission model of four policy strategies; presenting GHG and air pollutant reduction results at a very detailed level. Multiple policy outcomes are compared within a single modeling framework and study area. The results reveal that while no one demand mechanism is likely to reduce emissions to a level that meets policy-maker’s goals; a first-best pricing strategy that incorporates marginal social costs is the most effective emission reduction mechanism. Implementing such a mechanism may offer total emission reductions of up to 24 %. However, the efficacy of this strategy must be weighed against difficulties of establishing efficient pricing, a costly implementation, and substantial negative impacts to non-highway facilities. Decision makers must select a mixture of pricing and land use strategies to achieve emission goals on all road facilities.

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C 1 :

The average commute cost from the commute optimization operation

C 1 :

Average commute before optimization

C 2 :

Average commute cost after optimization

C a :

The capacity for link a

C excess :

The excess commute derived from commute optimization

\(D_{ij}^{k}\) :

Various distance terms (linear, log, squared, cubed and square root)

e a :

Emission price

\(f_{ij}^{r}\) :

Flow on path r, connecting each origin–destination (O–D) pair (ij)

l a :

Distance for link a

q ij :

Demand between each origin–destination (O–D) pair (ij)

t a :

Travel time for link a

t a (x a ):

Travel cost on link a as a function of flow

t ij :

Travel cost between origin i and destination j

\(u_{a}^{I} \left( {x_{a} ,e_{a} ,} \right)\) :

Travel time function for Model-1 which incorporates emission pricing term e a

\(u_{a}^{II} \left( {x_{a} ,\theta_{a} ,} \right)\) :

Travel time function for Model-1 which incorporates VMT tax term θ a

\(u_{a}^{III} \left( {x_{a} ,\sigma } \right)\) :

Travel time function for Model-1 which incorporates gas tax term σ

u a :

User cost for link a

\(u_{ij}^{c}\) :

Least cost path between O–D pairs i–j

x a :

Flow for link a

α a :

Constant, varying by facility type (BPR function)

β a :

Constant, varying by facility type (BPR function)

β k :

Weights for each term in the size variable (S j )

γ c :

Value of time (VOT) for user class c

\(\delta_{a,ij}^{r}\) :

Flow on link a, a subset of path r, connecting each origin–destination (O–D) pair (ij)

τ a :

Toll value for link a

Φ a :

Emissions cap for each link a

ϕ a :

Total emissions for link a

c :

User class

d ij :

The number of commuter trips between i and j

n :

Assignment iteration number

T :

The total number of commuters

t o :

Free flow time on link a

φ :

Emissions charge per gram of emissions, in cents

ω :

A positive constant (exponential demand function)


  • Ahn, K., Rakha, H.: The effects of route choice decisions on vehicle energy consumption and emissions. Transp. Res. Part D 13, 151–167 (2008)

    Article  Google Scholar 

  • Anderson, W.P., Kanaroglou, P.S., Miller, E.J.: Urban form, energy and the environment: a review of issues, evidence and policy. Urban Stud. 33, 7–35 (1996)

    Article  Google Scholar 

  • Beevers, S.D., Carslaw, D.C.: The impact of congestion charging on vehicle emissions in London. Atmos. Environ. 39, 1–5 (2005)

    Article  Google Scholar 

  • Ben-Akiva, M., Bowman, J.L.: Activity based travel demand model systems. In: Marcotte, P., Nguyen, S. (eds.) Equilibrium and Advanced Transportation Modeling, pp. 27–46. Kluwer, Dordrecht (1998)

    Chapter  Google Scholar 

  • Bolbach, C.J.: Land use controls under the clean air act. Seton Hall Law Rev. 6, 413 (1974)

    Google Scholar 

  • Bowman, J., Ben-Akiva, M.: Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. Part A 35, 1–28 (2001)

    Article  Google Scholar 

  • Camagni, R., Gibelli, M.C., Rigamonti, P.: Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion. Ecol. Econ. 40, 199–216 (2002)

    Article  Google Scholar 

  • Carroll-Larson, J., Caplan, A. J.: Estimating the effectiveness of a vehicle miles travelled tax in reducing particulate matter emissions. J. Environ. Plan. Manag. 52(3), 315–344 (2009). doi:10.1080/09640560802703223

    Google Scholar 

  • Chin, A.T.H.: Containing air pollution and traffic congestion: transport policy and the environment in Singapore. Atmos. Environ. 30, 787–801 (1996)

    Article  Google Scholar 

  • Citilabs. Cube Voyager (2013)

  • Clean Air Act of 1970, 42 USC § 7401 (1970)

  • Daniel, J.I., Bekka, K.: The environmental impact of highway congestion pricing. J. Urban Econ. 47, 180–215 (2000)

    Article  Google Scholar 

  • Deakin, E., Harvey, G., Pozdena, R., Yarema, G.: Transportation pricing strategies for California: an assessment of congestion, emissions, energy. And equity impacts (University of California Transportation Center, Working Paper). University of California Transportation Center (1996)

  • Deysher, B., Pickrell, D.: Emissions reductions from vehicle retirement programs. Transp. Res. Rec. 1587, 121–127 (1997)

    Article  Google Scholar 

  • Dill, J.: Estimating emissions reductions from accelerated vehicle retirement programs. Transp. Res. Part D 9, 87–106 (2004)

    Article  Google Scholar 

  • Fullerton, D., West, S.E.: Can taxes on cars and on gasoline mimic an unavailable tax on emissions. J. Environ. Econ. Manag. 43, 135–157 (2002)

    Article  Google Scholar 

  • Greene, D.L.: What is greener than a VMT tax? The case for an indexed energy user fee to finance us surface transportation. Transp. Res. Part D 16, 451–458 (2011)

    Article  Google Scholar 

  • Hamilton, B.W.: Wasteful commuting again. J. Political Econ. 97, 1497–1504 (1989)

    Article  Google Scholar 

  • Hamilton, B.W., Röell, A.: Wasteful commuting. J. Political Econ. 90(5), 1035–1053 (1982)

    Article  Google Scholar 

  • Hatzopoulou, M., Miller, E., Santos, B.: Integrating vehicle emission modeling with activity-based travel demand modeling: case study of the Greater Toronto, Canada, Area. Transp. Res. Rec 2011, 29–39 (2007)

    Article  Google Scholar 

  • He, B.Q., Shuai, S.J., Wang, J.X., He, H.: The effect of ethanol blended diesel fuels on emissions from a diesel engine. Atmos. Environ. 37, 4965–4971 (2003)

    Article  Google Scholar 

  • Horner, M.W.: Extensions to the concept of excess commuting. Environ. Plan. A 34, 543–566 (2002)

    Article  Google Scholar 

  • Horner, M.W.: Optimal’ accessibility landscapes? Development of a new methodology for simulating and assessing jobs—housing relationships in urban regions. Urban Stud. 45, 1583–1602 (2008)

    Article  Google Scholar 

  • Horner, M.W., O’Kelly, M.E.: Is non-work travel excessive? J. Transp. Geogr. 15, 411–416 (2007)

    Article  Google Scholar 

  • Ichinohe, M., Endo, E.: Analysis of the vehicle mix in the passenger-car sector in Japan for CO2 emissions reduction by a MARKAL model. Appl. Energy 83, 1047–1061 (2006)

    Article  Google Scholar 

  • Johansson-Stenman, O., Sterner, T.: What is the scope for environmental road pricing? In: Button, K.J., Verhoef, E.T. (eds.) Road Pricing. Traffic Congestion and the Environment: Issues of Efficiency and Social Feasibility. Edward Elgar, Cheltenham (1997)

    Google Scholar 

  • Johnston, R.A., De La Barra, T.: Comprehensive regional modeling for long-range planning: linking integrated urban models and geographic information systems. Transp. Res. Part A 34, 125–136 (2000)

    Google Scholar 

  • Kitamura, R.: An evaluation of activity-based travel analysis. Transportation 15, 9–34 (1988)

    Article  Google Scholar 

  • Kitamura, R.: Applications of models of activity behavior for activity based demand forecasting. In: Activity-based travel forecasting conference, New Orleans, Louisiana (1996)

  • Layman, C.C., Horner, M.W.: Comparing methods for measuring excess commuting and jobs-housing balance. Transp. Res. Rec. 2174, 110–117 (2010)

    Article  Google Scholar 

  • Li, S., Linn, J., Muehlegger, E.: Gasoline taxes and consumer behavior. National Bureau of Economic Research Working Paper Series, No. 17891 (2012). Retrieved from

  • Loo, B.P., Chow, A.S.: Jobs-housing balance in an era of population decentralization: an analytical framework and a case study. J. Transp. Geogr. 19, 552–562 (2011a)

    Article  Google Scholar 

  • Loo, B.P., Chow, A.S.: Spatial restructuring to facilitate shorter commuting an example of the relocation of Hong Kong international airport. Urban Stud. 48, 1681–1694 (2011b)

    Article  Google Scholar 

  • Ma, K.-R., Banister, D.: Excess commuting: a critical review. Transp. Rev. 26, 749–767 (2006)

    Article  Google Scholar 

  • Mishra, S., Welch, T.: Joint travel demand and environmental model to incorporate emission pricing for large transportation networks. Transp. Res. Rec. 2302, 29–41 (2012)

    Article  Google Scholar 

  • Muniz, I., Galindo, A.: Urban form and the ecological footprint of commuting. The case of Barcelona. Ecol. Econ. 55, 499–514 (2005)

    Article  Google Scholar 

  • Nagurney, A.: Congested urban transportation networks and emission paradoxes. Transp. Res. Part D 5, 145–151 (2000)

    Article  Google Scholar 

  • Niedzielski, M.A.: A spatially disaggregated approach to commuting efficiency. Urban Stud. 43, 2485–2502 (2006)

    Article  Google Scholar 

  • Nordhaus, W.D., Boyer, J.: Warming the World: Economic Models of Global Warming. MIT Press, Cambridge (2003)

    Google Scholar 

  • Parry, I.W., Small, K.A.: Does Britain or the United States have the right gasoline tax? Am. Econ. Rev. 95, 1276–1289 (2005)

    Article  Google Scholar 

  • Parry, I.W.H., Walls, M., Harrington, W.: Automobile externalities and policies. SSRN eLibrary (2007)

  • Rakha, H., Ahn, K.: Integration modeling framework for estimating mobile source emissions. J. Transp. Eng. 130, 183–193 (2004)

    Article  Google Scholar 

  • Roth, K.W., Rhodes, T., Ponoum, R.: The energy and greenhouse gas emission impacts of telecommuting in the US. In: IEEE International Symposium on Electronics and the Environment, 2008. ISEE 2008. Presented at the IEEE International Symposium on Electronics and the Environment, 2008. ISEE 2008, pp. 1–6

  • Schmidt, K., Van Gerpen, J.: The effect of biodiesel fuel composition on diesel combustion and emissions. Society of Automotive Engineers, 400 Commonwealth Dr, Warrendale, PA, 15096, USA (1996)

  • Scott, D.M., Kanaroglou, P.S., Anderson, W.P.: Impacts of commuting efficiency on congestion and emissions: case of the Hamilton CMA, Canada. Transp Res Part D 2, 245–257 (1997)

    Article  Google Scholar 

  • Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall, Englewood Cliffs (1985)

    Google Scholar 

  • Shiftan, Y., Suhrbier, J.: The analysis of travel and emission impacts of travel demand management strategies using activity-based models. Transportation 29, 145–168 (2002)

    Article  Google Scholar 

  • Sioshansi, R., Denholm, P.: Emissions impacts and benefits of plug-in hybrid electric vehicles and vehicle-to-grid services. Environ. Sci. Technol. 43, 1199–1204 (2009)

    Article  Google Scholar 

  • Skene, P., Hanslip, R.: Employer-Based Rideshare Program For Adelaide. Presented at the 17th Arrb Conference, Gold Coast, Queensland, Proceedings; Volume 17, Part 6, 15–19 Aug 1994

  • Steadman, P., Lautso, K., Wegener, M., Spiekermann, K., Sheppard, I., Martino, A., Domingo, R., Gayda, S.: PROPOLIS: Planning and Research of Policies for Land Use and Transport for Increasing Urban Sustainability. Kluwer Academic Publishers, Dordrecht (2004)

    Google Scholar 

  • Sterner, T., Dahl, C., Franzen, M.: Gasoline tax policy, carbon emissions and the global environment. J. Transp. Econ. Policy 26(2), 109–119 (1992)

    Google Scholar 

  • Tol, R.S.J.: The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33, 2064–2074 (2005)

    Article  Google Scholar 

  • Tzeng, G.H., Chen, C.-H.: Multiobjective decision making for traffic assignment. Eng. Manag. IEEE Trans. 40, 180–187 (1993)

    Article  Google Scholar 

  • US EPA, Clean Air Act of 1990, (1990)

  • Walters, A.A.: The theory and measurement of private and social cost of highway congestion. Econometrica 29(4), 676–699 (1961)

    Article  Google Scholar 

  • Williams, K., Burton, E., Jenks, M. (eds.): Achieving sustainable urban form: an introduction. In: Achieving sustainable urban form, pp. 1–6, E & FN Spon, London (2000)

  • Yin, Y., Lawphongpanich, S.: Internalizing emission externality on road networks. Transp. Res. Part D 11, 292–301 (2006)

    Article  Google Scholar 

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Correspondence to Timothy F. Welch.




This principle is based on the fact that individuals choose a route in order to minimize their travel time or travel cost and such a behavior on the individual level creates an equilibrium at the system (or network) level over a long period of time (Sheffi 1985). Simply, for each origin–destination (O–D) demand pair, the travel-cost/travel-time on all used routes of the road network should be equal.

$$Minimize \mathop \sum \limits_{a} \mathop{\int}\limits_{0}^{{x_{a} }} \left( {t_{a} (x_{a} )} \right)$$

Subject to:

$$\mathop \sum \limits_{r} f_{ij}^{r} = q_{ij}$$
$$x_{a} = \mathop \sum \limits_{i} \mathop \sum \limits_{j} \mathop \sum \limits_{r} f_{ij}^{r} \delta_{a,ij}^{r}$$
$$f_{ij}^{r} ,q_{ij}^{r} \ge 0$$

Equation (1) represents that at equilibrium the network will satisfy the UE condition, i.e. travel time on all the used routes connecting any given i-j pair will be equal. The term, t a , is the travel time for link a, which is a function of link flow x a . Equation (2) is a flow conservation constraint to ensure that flow on all paths r, connecting each O–D pair (ij) is equal to the corresponding demand. In other words, all O–D trips must be assigned to the network. Equation (3) represents the definitional relationship of link flow from path flows. Equation (4) is a non-negativity constraint for flow and demand. The travel time function t a (.) is specific to a given link ‘a’ and the most widely used model is the Bureau of Public Roads function given by

$$t_{a} \left( {x_{a} } \right) = t_{o} \left( {1 + \alpha_{a} \left( {\frac{{x_{a} }}{{C_{a} }}} \right)} \right)^{{\beta_{a} }}$$

where to(.) is free flow time on link ‘a’, and α a and β a are constants (and vary by facility type). C a is the capacity for link a. In the base model the objective is minimization of TST.

First-best emission pricing

The emissions cap for each link is:

$$\Phi_{a} = \left[ {\frac{1}{N}\mathop \sum \limits_{a = 1}^{N} \left( {\frac{{\phi_{a} }}{{l_{a} }}} \right)} \right]*l_{a}$$

where ϕ a is the total emissions for link a calculated for each link in the base model and l a is the link distance. Once the cap is determined, the emission price (e a ) can be incorporated into the travel demand model. The emission price can be converted to travel time units with appropriate factor (γ c) representing VOT in monetary terms as cents per minute for travellers of five income categories c. The revised user cost function for link based emission is

$$u_{a}^{I} \left( {x_{a} ,e_{a} } \right) = t_{a} \left( {x_{a} } \right) + \frac{{\varphi e_{a} \left( {x_{a} } \right)}}{{\gamma^{c} }}$$

where \(u_{a}^{I} \left( {x_{a} ,e_{a} ,} \right)\) is the travel cost function for Model-1, which incorporates emission pricing term e a . The objective function for Model-1 is similar to base case with the exception that the third term from Eq. (7) \((\frac{{\varphi e_{a} \left( {x_{a} } \right)}}{{\gamma^{c} }})\) is added to Eq. (1) which is the total emissions e produced on link a, which is a function of link flow x a multiplied by charge per gram of emissions, φ.

Second-best emission pricing (VMT Tax)

Analytically, the user cost function can be stated as the following to incorporate the VMT based tax.

$$u_{a}^{II} \left( {x_{a} ,\theta_{a} } \right) = t_{a} \left( {x_{a} } \right) + \frac{{\theta_{a} l_{a} }}{{\gamma^{c} }}$$

where, θ a is the VMT tax in $/mile for link a, l a is the link length in miles, and γ c is the VOT in $/hour. In traffic assignment procedure, the user cost shown in Eq. (8) can be used in Eq. (1). The advantage of VMT based tax is to encourage travelers to use transit as an alternate mode if the tax appears too onerous. Equation (8) refers to VMT based tax associated with value of time (VOT).

Gas tax

The effect of gas price on user behavior can be implemented as follows:

$$u_{a}^{III} \left( {x_{a} ,\sigma } \right) = t_{a} \left( {x_{a} } \right) + \frac{{\sigma l_{a} }}{{\gamma^{c} \vartheta }}$$

where σ is the gas price in dollars per mile (as a ration of dollars per gallon and fleet-wide efficiency of 24.5 mpg), l a is the link length in miles, γ c is the VOT in $/hr, and ϑ is the automobile gasoline efficiency in miles per gallon. Auto Operating Cost (AOC) is another component which is considered in the mode choice model. A higher gas price will result in a higher AOC and therefore will make auto travel more expensive.

Commute efficiency

$$\mathop {Minimize}\limits_{{}} \mathop \sum \limits_{i} \mathop \sum \limits_{j} u_{ij} d_{ij}$$

where u ij  = t ij and t ij is the travel cost between origin i and destination j and d ij is the number of commuter trips between i and j. The constraints for the optimization problem are subject to

$$\mathop \sum \limits_{j} d_{ij} = O_{i}$$
$$\mathop \sum \limits_{i} d_{ij} = D_{j}$$
$$d_{ij} \ge 0$$

The average commute cost from the optimization can be interpreted as

$$C_{1} = \frac{1}{T}\mathop \sum \limits_{i} \mathop \sum \limits_{j} u_{ij} d_{ij}^{o}$$

where T is the total number of commuters, and \(d_{ij}^{o}\) is the optimal number of commuters between ij pair. Before optimization, the average commuter cost was:

$$C_{2} = \frac{1}{T}\mathop \sum \limits_{i} \mathop \sum \limits_{j} u_{ij} d_{ij}^{{}}$$

The excess commute can be defined as the percentage difference between cost before and after optimization. This can be represented as

$$C_{excess} = \frac{{\left( {C_{2} - C_{1} } \right)*100}}{{C_{1} }}$$

The excess commute can be considered as a dis-benefit from the planner’s viewpoint.

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Welch, T.F., Mishra, S. Envisioning an emission diet: application of travel demand mechanisms to facilitate policy decision making. Transportation 41, 611–631 (2014).

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