Integrating Land Use and Socioeconomic Factors into Scenario-Based Travel Demand and Carbon Emission Impact Study

Heng Wei , Ting Zuo , Hao Liu , Y. Jeffrey Yang

Urban Rail Transit ›› 2017, Vol. 3 ›› Issue (1) : 3 -14.

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Urban Rail Transit ›› 2017, Vol. 3 ›› Issue (1) : 3 -14. DOI: 10.1007/s40864-017-0056-2
Original Research Papers

Integrating Land Use and Socioeconomic Factors into Scenario-Based Travel Demand and Carbon Emission Impact Study

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Abstract

Integration of land use and transportation planning with current and future spatial distributions of population and employment is a challenge but critical to sustainable planning outcomes. The challenge is specific to how sustainability factors (e.g., carbon dioxide emission), and land use and socioeconomic changes are considered in a streamlined manner. To address the challenge, this paper presents an integrated modeling and computing framework for systemic analysis of regional- and project-level transportation environmental impacts for land use mix patterns and associated transportation activities. A synthetic computing platform has been developed to facilitate the scenario-based quantitative analysis of cause-and-effect mechanisms between land use changes and/or traffic management and control strategies, their impacts on traffic mobility and the environment. Within the integrated platform, multiple models for land use pattern, travel demand forecasting, traffic simulation, vehicle and carbon emission, and other operation and sustainability measures are integrated using mathematical models in a Geographical Information System environment. Furthermore, a case study of the Greater Cincinnati area at regional level is performed to test the integrated functionality as a capable tool for urban planning, transportation and environmental analysis. The case study results indicate that such an integration investigation can help assess strategies in land use planning and transportation systems management for improved sustainability.

Keywords

Integration / Land use / Socioeconomic factor / Travel demand / Transportation environmental sustainability / Carbon emission

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Heng Wei, Ting Zuo, Hao Liu, Y. Jeffrey Yang. Integrating Land Use and Socioeconomic Factors into Scenario-Based Travel Demand and Carbon Emission Impact Study. Urban Rail Transit, 2017, 3(1): 3-14 DOI:10.1007/s40864-017-0056-2

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References

[1]

Arts J, Hanekamp T, Dijkstra A (2014) Integrating land-use and transport infrastructure planning: towards adaptive and sustainable transport infrastructure. In: Transport research arena 2014, Paris

[2]

Heeres N, Tillema T, Arts J. Duurzame Planning Van Weginfrastructuur: Een International Perspectief (Sustainable Planning of Road Infrastructure an International Perspective), 2012, Groningen: Research report Faculty of Spatial Sciences, University of Groningen

[3]

Wei H, Liu H, Lu M, Coifman B (2014) Traffic data and integrated project-level PM2.5 conformity analysis. Research report no.: FHWA/OH-2014/9

[4]

Wei H, Yang YJ, Wang X, Yao Z, Liu H, Liang S (2012) Framework for integrating traffic-source emission estimates into sustainability analysis. In: ASCE proceedings of 12th COTA international conference of transportation professionals (CICTP 2012), Beijing, China, August 3–6, 2012

[5]

Handy S, Tal G, Boarnet MG (2013) Policy brief on the impacts of regional accessibility based on a review of the empirical literature. California Air Resources Board, California. https://arb.ca.gov/cc/sb375/policies/regaccess/regional_accessibility_brief120313.pdf. Accessed July 2016

[6]

U.S. Environmental Protection Agency (U.S. EPA) (2015) Social Cost of Carbon. https://www.epa.gov/climatechange/social-cost-carbon. Accessed July 2016

[7]

Dasigi S (2015) An integrated approach linking land use and socioeconomic characteristics for improving travel demand forecasting. Master of Science Thesis (Advisor: Heng Wei). University of Cincinnati

[8]

Zuo T, Wei H, Liu H (2016) Impacts of compact development density on travel demand: a scenario-based analysis in Hamilton County, Ohio, U.S. (Paper No.: 16-4135). In: Compendium of papers for 95th transportation research board annual meeting, Washington, DC, January 10–14, 2016

[9]

Wu P (2007) Modeling transportation-related emissions using GIS. In: 86th annual meeting of the transportation research board, Washington, DC

[10]

Yao Z, Wei H, Rohne A, Corey J, Perugu H (2015) Connecting household socioeconomics and travel carbon footprint: empirical results from high-resolution GPS household travel survey (Paper No.: 15-5897). In: Compendium of papers for 94th transportation research board annual meeting, Washington, DC, January 11–15, 2015

[11]

Boarnet M, Crane R. The influence of land use on travel behavior: specification and estimation strategies. Transp Res A Policy Pract, 2001, 35(9): 823-845

[12]

Halat H, Saberi M, Mahmassani HS (2015) Land use and travel behavior: empirical evidence of the effects of mixed-use development on travel mode choice in Chicago. In: Transportation research board 94th annual meeting (No. 15-3950)

[13]

Litman T. Land use impacts on transport how land use factors affect travel behavior, 2015, Victoria: Victoria Transport Policy Institute

[14]

Maat K, Harts JJ. Implications of urban development for travel demand in the Netherlands. Transp Res Rec J Trans Res Board, 2001, 1780: 9-16

[15]

Miller SJ (2010) Feasibility of using jobs/housing balance in Virginia statewide planning (No. FHWA/VTRC 11-R1). Virginia Transportation Research Council

[16]

Transportation Research Board (TRB) (2009). Driving and the built environment, the effects of compact development on motorized travel, energy use, and CO2 emissions. Special Report 298. Washington, DC

[17]

Ewing R, Cervero R. Travel and the built environment: a meta-analysis. J Am Plan Assoc, 2010, 76(3): 265-294

[18]

Kockelman MK. Travel behavior as function of accessibility, land use mixing, and land use balance, evidence from San Francisco Bay Area. Transp Res Rec J Transp Res Board, 1997, 1607: 116-125

[19]

Boarnet MG, Hsu H-P, Handy S. Draft policy brief on the impacts of jobs-housing balance based on a review of the empirical literature, 2011, Irvine: University of California

[20]

Crane R, Chatman DG. Traffic and sprawl: evidence from U.S. commuting, 1985 to 1997. Plan Mark, 2003, 6(1): 14-22.

[21]

Zhang L, Hong J, Nasri A, Shen Q. How built environment affects travel behavior: a comparative analysis of the connections between land use and vehicle miles traveled in UC cites. J Transp Land Use, 2012, 5(3): 40-52.

[22]

Cervero R, Duncan M. Which reduces vehicle travel more: jobs-housing balance or retail-housing mixing?. J Am Plan Assoc, 2006, 72(4): 475-490

[23]

Jones PM, Koppelman FS, Orfeuil JP (1990) Activity analysis: state-of-the-art and future directions. In: Jones PM (ed) Developments in dynamic and activity-based approaches to travel analysis, Gower, Aldershot, pp 34–55

[24]

Ferdous N, Bhat C, Vana L, Schmitt D, Bowman JL, Bradley M, Pendyala R (2011) Comparison of four-step versus tour-based models in predicting travel behavior before and after transportation system changes: results interpretation and recommendations. Report FHWA/OH-2011/2014. Office of Research and Development, Ohio Department of Transportation, Columbus

[25]

Shan R, Zhong M, Lu C (2013) Comparison between traditional four-step and activity-based travel demand modeling—a case study of Tampa, Florida. In: Proceedings of the second international conference on transportation information and safety ICTIS (2013), held in Wuhan, China, June 29–July 2. pp 627–633

[26]

Franco V, Kousoulidou M, Muntean M, Ntziachristos L, Hausberger S, Dilara P. Road vehicle emission factors development: a review. Atmos Environ, 2013, 70: 84-97

[27]

Liu H, Wei H, Yao Z (2012) Modeling ITS data sources for generating realistic traffic operating parameters for project-level conformity analysis (Paper No. 0401). In: Proceedings for 15th IEEE conference on intelligent transportation systems, Anchorage, Alaska, USA, Sept. 16–19, 2012. ISSN: 2153-0009. pp 1912–1917

[28]

Liu H, Wei H, Yao Z (2014a). Validating MOVES PM2.5 emission factor empirically by considering accumulative emission effect. In: ASCE sponsored proceedings for the 14th COTA international conference of transportation professionals (CICTP 2014), Changsha, China, July 4–7, 2014

[29]

Liu H, Wei H, Yao Z, Ren H, Ai Q (2014b) Modeling and evaluating short-term on-road PM2.5 emission factor using different traffic data sources (Paper No. 14-3253). In: Compendium of papers CD-ROM for 93rd transportation research board annual meeting, Washington, DC, January 12–16, 2014

[30]

Perugu H, Wei H, Yao Z (2016) Estimating the contribution of heavy-duty trucks to the Urban PM2.5 Pollution (Paper No.: 16-1406). In: Compendium of papers for 95th transportation research board annual meeting, Washington, DC, January 10–14, 2016

[31]

Perugu H, Wei H, Yao Z (2014) Modeling truck activity using short-term traffic counts for reliable estimation of heavy-duty truck emissions in Urban areas (Paper No. 14-4350). In: Compendium of papers CD-ROM for 93rd transportation research board annual meeting, Washington, DC, January 12–16, 2014

[32]

Yao Z, Wei H, Ma T, Yang J, Ai Q, Liu H (2013) Developing operating mode distribution inputs for MOVES using computer vision-based vehicle data collector. Transp Res Rec J Transp Res Board (2340):49–58

[33]

U.S. Environmental Protection Agency (U.S. EPA) (2014) User guide for MOVES2014. U.S. EPA, office of transportation and air quality (OTAQ). Report No.: EPA-420-B-14-055

[34]

Coelho MC, Farias T, Rouphail NM. Impact of speed control traffic signals on pollutant emissions. Transp Res D Transp Environ, 2005, 10(4): 323-340

[35]

Qu Y, Holmén B, Ravishanker N. Predicting light duty gasoline vehicle on road particle number concentrations from gas concentration using time series cross section regression, 2007, Storrs: Department of Statistics, University of Connecticut

[36]

Yao Z, Wei H, Wang XH, Liu H, Yang YJ (2014) Scenario-based carbon footprint inventory tool for urban sustainable development decision support: the Cincinnati case study (Paper No. 14-3242). In: Compendium of papers CD-ROM for 93rd transportation research board annual meeting, Washington, DC, January 12–16, 2014

[37]

Yao Z, Wei H, Perugu H, Liu H, Li Z. Sensitivity analysis of project level MOVES running emission rates for light and heavy duty vehicles. J Traffic Transp Eng, 2014, 1(2): 81-96.

[38]

Kim BY, Waysonb RL, Fleming GG (2002) Development of the traffic air quality simulation model (TRAQSIM). http://cat.inist.fr/?aModele=afficheN&cpsidt=18600460

[39]

Park S, Rakha H, Farzaneh M, Zietsman J, Lee DW (2010) Development of fuel and emission models for high speed heavy duty trucks, light duty trucks, and light duty vehicles. In: IEEE conference on intelligent transportation systems (ITSC), 2010 13th international IEEE, pp 25–32

[40]

Cervero R, Kockelman K. Travel demand and the 3Ds: density, diversity, and design. Transp Res D, 1997, 2(3): 199-219

[41]

Gomez-Ibanez JD Driving and the built environment, the effects of compact development on motorized travel, energy use, and CO2 emissions, 2009, Washington: Transp Res Board

[42]

Ewing R, Dumbaugh E, Brown M. Internalizing travel by mixing land uses study of master-planned communities in south Florida. Transp Res Rec J Transp Res Board, 2001, 1780: 115-120

[43]

Bordoloi R, Mote A, Sarkar PP, Mallikarjuna C. Quantification of land use diversity in the context of mixed land use. Soc Behav Sci, 2013, 104: 563-572

[44]

Bento AM, Cropper ML, Mobarak AM, Vinha K. The effects of urban spatial structure on travel demand in the United States. Rev Econ Stat, 2005, 87(3): 466-478

[45]

Zhou J, Wang Y, Schweitzer L. Jobs/housing balance and employer-based travel demand management program returns to scale: evidence from Los Angeles. Tranp Policy, 2012, 20: 22-25

[46]

Hamidi S, Ewing R. A longitudinal study of changes in urban sprawl between 2000 and 2010 in the United States. Landsc Urban Plan, 2014, 128: 72-82

[47]

Wargelin L, Stopher P, Minser J, Tierney K, Rhindress M (2012) GPS-based household interview survey for the Cincinnati, Ohio region. Report No.: FHWA/OH-2012/1

[48]

Castiglione J, Bradley M, Gliebe J (2015) Activity-based travel demand models: a primer (No. SHRP 2 Report S2-C46-RR-1)

[49]

Bowman JL, Ben-Akiva ME. Activity-based disaggregate travel demand model system with activity schedules. Transp Res A Policy Pract, 2001, 35: 1-28

[50]

McNally MG. Activity-based models, 2000, Irvine: University of California

[51]

Newbold KB, Scott DM, Spinney SEL, Kanaroglou P, Paez A. Travel behavior within Canada’s older population: a cohort analysis. J Transp Geogr, 2005, 13: 340-351

[52]

Vovsha P, Petersen E, Donnelly R. Impact of intra-household interactions on individual daily activity-travel patterns. Transp Res Rec J Transp Res Board, 2004, 1898: 87-97

[53]

U.S. Environmental Protection Agency (U.S. EPA) (2011) Guide to sustainable transportation performance measures

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