Abstract
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 policymakerâ€™s goals; a firstbest 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 24Â %. However, the efficacy of this strategy must be weighed against difficulties of establishing efficient pricing, a costly implementation, and substantial negative impacts to nonhighway facilities. Decision makers must select a mixture of pricing and land use strategies to achieve emission goals on all road facilities.
This is a preview of subscription content, access via your institution.
Abbreviations
 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 originâ€“destination (Oâ€“D) pair (iâ€“j)
 l _{ a } :

Distance for link a
 q _{ ij } :

Demand between each originâ€“destination (Oâ€“D) pair (iâ€“j)
 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 Model1 which incorporates emission pricing term e _{ a }
 \(u_{a}^{II} \left( {x_{a} ,\theta_{a} ,} \right)\) :

Travel time function for Model1 which incorporates VMT tax term Î¸ _{ a }
 \(u_{a}^{III} \left( {x_{a} ,\sigma } \right)\) :

Travel time function for Model1 which incorporates gas tax term Ïƒ
 u _{ a } :

User cost for link a
 \(u_{ij}^{c}\) :

Least cost path between Oâ€“D pairs iâ€“j
 x _{a} :

Flow for link a
 Î± _{ a } :

Constant, varying by facility type (BPR function)
 Î² _{ a } :

Constant, varying by facility type (BPR function)
 Î² ^{k} :

Weights for each term in the size variable (S _{ j })
 Î³ ^{c} :

Value of time (VOT) for user class c
 \(\delta_{a,ij}^{r}\) :

Flow on link a, a subset of path r, connecting each originâ€“destination (Oâ€“D) pair (iâ€“j)
 Ï„ _{ a } :

Toll value for link a
 Î¦_{ a } :

Emissions cap for each link a
 Ï• _{ 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
 Ï† :

Emissions charge per gram of emissions, in cents
 Ï‰ :

A positive constant (exponential demand function)
References
Ahn, K., Rakha, H.: The effects of route choice decisions on vehicle energy consumption and emissions. Transp. Res. Part D 13, 151â€“167 (2008)
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, 7â€“35 (1996)
Beevers, S.D., Carslaw, D.C.: The impact of congestion charging on vehicle emissions in London. Atmos. Environ. 39, 1â€“5 (2005)
BenAkiva, M., Bowman, J.L.: Activity based travel demand model systems. In: Marcotte, P., Nguyen, S. (eds.) Equilibrium and Advanced Transportation Modeling, pp. 27â€“46. Kluwer, Dordrecht (1998)
Bolbach, C.J.: Land use controls under the clean air act. Seton Hall Law Rev. 6, 413 (1974)
Bowman, J., BenAkiva, M.: Activitybased disaggregate travel demand model system with activity schedules. Transp. Res. Part A 35, 1â€“28 (2001)
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, 199â€“216 (2002)
CarrollLarson, J., Caplan, A. J.: Estimating the effectiveness of a vehicle miles travelled tax in reducing particulate matter emissions. J. Environ. Plan. Manag. 52(3), 315â€“344 (2009). doi:10.1080/09640560802703223
Chin, A.T.H.: Containing air pollution and traffic congestion: transport policy and the environment in Singapore. Atmos. Environ. 30, 787â€“801 (1996)
Citilabs. Cube Voyager (2013)
Clean Air Act of 1970, 42 USC Â§ 7401 (1970)
Daniel, J.I., Bekka, K.: The environmental impact of highway congestion pricing. J. Urban Econ. 47, 180â€“215 (2000)
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, 121â€“127 (1997)
Dill, J.: Estimating emissions reductions from accelerated vehicle retirement programs. Transp. Res. Part D 9, 87â€“106 (2004)
Fullerton, D., West, S.E.: Can taxes on cars and on gasoline mimic an unavailable tax on emissions. J. Environ. Econ. Manag. 43, 135â€“157 (2002)
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, 451â€“458 (2011)
Hamilton, B.W.: Wasteful commuting again. J. Political Econ. 97, 1497â€“1504 (1989)
Hamilton, B.W., RÃ¶ell, A.: Wasteful commuting. J. Political Econ. 90(5), 1035â€“1053 (1982)
Hatzopoulou, M., Miller, E., Santos, B.: Integrating vehicle emission modeling with activitybased travel demand modeling: case study of the Greater Toronto, Canada, Area. Transp. Res. Rec 2011, 29â€“39 (2007)
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, 4965â€“4971 (2003)
Horner, M.W.: Extensions to the concept of excess commuting. Environ. Plan. A 34, 543â€“566 (2002)
Horner, M.W.: Optimalâ€™ accessibility landscapes? Development of a new methodology for simulating and assessing jobsâ€”housing relationships in urban regions. Urban Stud. 45, 1583â€“1602 (2008)
Horner, M.W., Oâ€™Kelly, M.E.: Is nonwork travel excessive? J. Transp. Geogr. 15, 411â€“416 (2007)
Ichinohe, M., Endo, E.: Analysis of the vehicle mix in the passengercar sector in Japan for CO_{2} emissions reduction by a MARKAL model. Appl. Energy 83, 1047â€“1061 (2006)
JohanssonStenman, 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)
Johnston, R.A., De La Barra, T.: Comprehensive regional modeling for longrange planning: linking integrated urban models and geographic information systems. Transp. Res. Part A 34, 125â€“136 (2000)
Kitamura, R.: An evaluation of activitybased travel analysis. Transportation 15, 9â€“34 (1988)
Kitamura, R.: Applications of models of activity behavior for activity based demand forecasting. In: Activitybased travel forecasting conference, New Orleans, Louisiana (1996)
Layman, C.C., Horner, M.W.: Comparing methods for measuring excess commuting and jobshousing balance. Transp. Res. Rec. 2174, 110â€“117 (2010)
Li, S., Linn, J., Muehlegger, E.: Gasoline taxes and consumer behavior. National Bureau of Economic Research Working Paper Series, No. 17891 (2012). Retrieved from http://www.nber.org/papers/w17891
Loo, B.P., Chow, A.S.: Jobshousing balance in an era of population decentralization: an analytical framework and a case study. J. Transp. Geogr. 19, 552â€“562 (2011a)
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, 1681â€“1694 (2011b)
Ma, K.R., Banister, D.: Excess commuting: a critical review. Transp. Rev. 26, 749â€“767 (2006)
Mishra, S., Welch, T.: Joint travel demand and environmental model to incorporate emission pricing for large transportation networks. Transp. Res. Rec. 2302, 29â€“41 (2012)
Muniz, I., Galindo, A.: Urban form and the ecological footprint of commuting. The case of Barcelona. Ecol. Econ. 55, 499â€“514 (2005)
Nagurney, A.: Congested urban transportation networks and emission paradoxes. Transp. Res. Part D 5, 145â€“151 (2000)
Niedzielski, M.A.: A spatially disaggregated approach to commuting efficiency. Urban Stud. 43, 2485â€“2502 (2006)
Nordhaus, W.D., Boyer, J.: Warming the World: Economic Models of Global Warming. MIT Press, Cambridge (2003)
Parry, I.W., Small, K.A.: Does Britain or the United States have the right gasoline tax? Am. Econ. Rev. 95, 1276â€“1289 (2005)
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, 183â€“193 (2004)
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. 1â€“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, 245â€“257 (1997)
Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. PrenticeHall, Englewood Cliffs (1985)
Shiftan, Y., Suhrbier, J.: The analysis of travel and emission impacts of travel demand management strategies using activitybased models. Transportation 29, 145â€“168 (2002)
Sioshansi, R., Denholm, P.: Emissions impacts and benefits of plugin hybrid electric vehicles and vehicletogrid services. Environ. Sci. Technol. 43, 1199â€“1204 (2009)
Skene, P., Hanslip, R.: EmployerBased Rideshare Program For Adelaide. Presented at the 17th Arrb Conference, Gold Coast, Queensland, Proceedings; Volume 17, Part 6, 15â€“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)
Sterner, T., Dahl, C., Franzen, M.: Gasoline tax policy, carbon emissions and the global environment. J. Transp. Econ. Policy 26(2), 109â€“119 (1992)
Tol, R.S.J.: The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33, 2064â€“2074 (2005)
Tzeng, G.H., Chen, C.H.: Multiobjective decision making for traffic assignment. Eng. Manag. IEEE Trans. 40, 180â€“187 (1993)
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), 676â€“699 (1961)
Williams, K., Burton, E., Jenks, M. (eds.): Achieving sustainable urban form: an introduction. In: Achieving sustainable urban form, pp. 1â€“6, E & FN Spon, London (2000)
Yin, Y., Lawphongpanich, S.: Internalizing emission externality on road networks. Transp. Res. Part D 11, 292â€“301 (2006)
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Basecase
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 originâ€“destination (Oâ€“D) demand pair, the travelcost/traveltime on all used routes of the road network should be equal.
Subject to:
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 ij 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 Oâ€“D pair (iâ€“j) is equal to the corresponding demand. In other words, all Oâ€“D trips must be assigned to the network. Equation (3) represents the definitional relationship of link flow from path flows. Equation (4) is a nonnegativity constraint for flow and demand. The travel time function t _{ a } (.) is specific to a given link â€˜aâ€™ and the most widely used model is the Bureau of Public Roads function given by
where to(.) is free flow time on link â€˜aâ€™, and Î± _{ a } and Î² _{ 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.
Firstbest emission pricing
The emissions cap for each link is:
where Ï• _{ 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 (Î³ ^{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
where \(u_{a}^{I} \left( {x_{a} ,e_{a} ,} \right)\) is the travel cost function for Model1, which incorporates emission pricing term e _{ a }. The objective function for Model1 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, Ï†.
Secondbest emission pricing (VMT Tax)
Analytically, the user cost function can be stated as the following to incorporate the VMT based tax.
where, Î¸ _{ a } is the VMT tax in $/mile for link a, l _{ a } is the link length in miles, and Î³ ^{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:
where Ïƒ is the gas price in dollars per mile (as a ration of dollars per gallon and fleetwide efficiency of 24.5Â mpg), l _{ a } is the link length in miles, Î³ ^{c} is the VOT in $/hr, and Ï‘ 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
where u _{ ij }Â =Â 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
The average commute cost from the optimization can be interpreted as
where T is the total number of commuters, and \(d_{ij}^{o}\) is the optimal number of commuters between iâ€“j pair. Before optimization, the average commuter cost was:
The excess commute can be defined as the percentage difference between cost before and after optimization. This can be represented as
The excess commute can be considered as a disbenefit from the plannerâ€™s viewpoint.
Rights and permissions
About this article
Cite this article
Welch, T.F., Mishra, S. Envisioning an emission diet: application of travel demand mechanisms to facilitate policy decision making. Transportation 41, 611â€“631 (2014). https://doi.org/10.1007/s1111601395114
Published:
Issue Date:
DOI: https://doi.org/10.1007/s1111601395114