References

Chapter 1

Chapter 2

Chapter 3

  • California Department of Transportation (Caltrans). (2020). Chapter 1000:– Bicycle Transportation Design, Highway Design Manual. Sacramento, CA.
  • National Association of City Transportation Officials (NACTO).
  • National Academies of Sciences, Engineering, and Medicine. (2022). Highway capacity manual 7th edition: A guide for multimodal mobility analysis.
  • AASHTO Task Force on Geometric Design (1999). AASHTO Guide for the Development of Bicycle Facilities. American Association of State Highway and Transportation Officials, Washington, DC.

Chapter 4

Chapter 5

  • Ramanathan, V., Aines, R., Auffhammer, M., Barth, M., Cole, J., Forman, F., et al. (2019). Bending the Curve: Climate Change Solutions. Location: Regents of the University of California. Editor: V. Ramanathan. Co-Editors: Adam Millard-Ball; Michelle Niemann; Scott Friese. Book published by the Regents of the Univ of California. Retrieved from https://escholarship.org/uc/item/6kr8p5rq. 815pp https://creativecommons.org/licenses/by-nc-sa/4.0/
  • Tanvir, Shams. Modeling and Simulation of Driving Activity from an Energy Use-Emissions Perspective. North Carolina State University, 2018.
  • Ahn, K., Rakha, H., Trani, A., & Van Aerde, M. (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering, 128(2), 182-190.
  • Alkidas, A. C. (2007). Combustion advancements in gasoline engines. Energy Conversion and Management, 48(11), 2751-2761.
  • An, F., Barth, M., Norbeck, J., & Ross, M. (1997). Development of comprehensive modal emissions model: Operating under hot-stabilized conditions. Transportation Research Record: Journal of the Transportation Research Board, 1587(1), 52-62.
  • Barth, M., & Boriboonsomsin, K. (2009). Traffic congestion and greenhouse gases. ACCESS Magazine, 1(35)
  • Bartin, B., Mudigonda, S., & Ozbay, K. (2007). Impact of electronic toll collection on air pollution levels: Estimation using microscopic simulation model of large-scale transportation network. Transportation Research Record: Journal of the Transportation Research Board, 2011(1), 68-77.
  • BTS. (2015). State transportation statistics 2015. Washington DC: US Department of Transportation, Bureau of Transportation Statistics.
  • Bureau of Transportation Statistics. (2013). National transportation statistics (table 1-11: Number of U.S. aircraft, vehicles, vessels, and other conveyances). Retrieved from http://www.bts.gov/publications/national_transportation_statistics/html/table_01_11.html
  • Cambridge Systematics. (1996). Quantifying air quality and other benefits and costs of transportation control measures. ( No. Task 1 Report. Project 8-33.). Washington D.C: National Cooperative Highway Research, Transportation Research Board.
  • Cappiello, A. (2002). Modeling traffic flow emissions (MS Thesis).
  • CARB. (2002). EMFAC 2002, california air resources board’s emission inventory . ( No. Series, September).California Air Resource Board.
  • Chamberlin, R., Swanson, B., Talbot, E., Dumont, J., & Pesci, S. (2011). Analysis of MOVES and CMEM for evaluating the emissions impact of an intersection control change. Paper presented at the Transportation Research Board 90th Annual Meeting, (11-0673)
  • Daganzo, C. F. (2006). In traffic flow, cellular automata = kinematic waves. Transportation Research Part B: Methodological, 40(5), 396-403. doi://dx.doi.org.prox.lib.ncsu.edu/10.1016/j.trb.2005.05.004
  • De Vlieger, I., De Keukeleere, D., & Kretzschmar, J. G. (2000). Environmental effects of driving behaviour and congestion related to passenger cars. Atmospheric Environment, 34(27), 4649-4655.
  • Eliasson, J., Hultkrantz, L., Nerhagen, L., & Rosqvist, L. S. (2009). The stockholm congestion–charging trial 2006: Overview of effects. Transportation Research Part A: Policy and Practice, 43(3), 240-250.
  • EPA. (1999). Regulatory announcements. Ann Arbor, MI: United States Environmental Protection Agency.
  • EPA. (2008). Integrated science assessment for oxides of nitrogen – health criteria (final report). ( No. EPA/600/R-08/071). Washington D.C.: U.S. Environmental Protection Agency.
  • EPA. (2010). Integrated science assessment for carbon monoxide (final report). ( No. EPA/600/R-09/019F). Washington D.C.: U.S. Environmental Protection Agency.
  • EPA. (2012). User guide for MOVES2010b. ( No. EPA-420-B-12-001b).United State Environmental Protection Agency.
  • EPA. (2014). Inventory of U.S. greenhouse gas emissions and sinks: 1990-2012. ( No. EPA 430-R-14-003). Washington D.C.: U.S. EPA.
  • Etheridge, D. M., Steele, L. P., Langenfelds, R. L., Francey, R. J., Barnola, J., & Morgan, V. I. (1996). Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in antarctic ice and firn. Journal of Geophysical Research: Atmospheres (1984–2012), 101(D2), 4115-4128.
  • Faiz, A., Weaver, C. S., & Walsh, M. P. (1996). Air pollutation from motor vehicles: Standards and technologies for controlling emissions World Bank-free PDF.
  • Frey, H. C., Zhai, H., & Rouphail, N. M. (2009). Regional on-road vehicle running emissions modeling and evaluation for conventional and alternative vehicle technologies. Environmental Science & Technology, 43(21), 8449-8455. doi:10.1021/es900535s; 10.1021/es900535s
  • Frey, H. C., & Liu, B. (2014). Development and evaluation of a simplified version of MOVES for coupling with a traffic simulation model. Proceedings, 91st Annual Meeting of the Transportation Research Board,
  • Frey, H. C., Rouphail, N. M., Unal, A., & Colyar, J. D. (2001). No title. Emissions Reduction through Better Traffic Management: An Empirical Evaluation Based upon on-Road Measurements,
  • Frey, H. C., Yazdani-Boroujeni, B., Hu, J., Liu, B., & Jiao, W. (2013). Field measurements of 1996 to 2013 model year light duty gasoline vehicles. Paper presented at the Proceedings, 106th Annual Conference, Air & Waste Management Association, Chicago, IL,
  • Fujita, E. M., Campbell, D. E., Zielinska, B., Chow, J. C., Lindhjem, C. E., DenBleyker, A., . . . Lawson, D. R. (2012). Comparison of the MOVES2010a, MOBILE6. 2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements. Journal of the Air & Waste Management Association, 62(10), 1134-1149.
  • Gordon, P., Kumar, A., & Richardson, H. W. (1990). Peak-spreading: How much? Transportation Research Part A: General, 24(3), 165-175.
  • Greene, D. L., & Plotkin, S. E. (2011). Reducing greenhouse gas emission from US transportation. Arlington: Pew Center on Global Climate Change,
  • Gualtieri, G., & Tartaglia, M. (1998). Predicting urban traffic air pollution: A GIS framework. Transportation Research Part D: Transport and Environment, 3(5), 329-336.
  • Health Effects Institute. Panel on the Health Effects of Traffic-Related Air Pollution. (2010). Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects Health Effects Institute.
  • Hurdle, V. F., & Son, B. (2000). Road test of a freeway model. Transportation Research Part A: Policy and Practice, 34(7), 537-564. doi://dx.doi.org.prox.lib.ncsu.edu/10.1016/S0965-8564(99)00031-2
  • Jimenez-Palacios, J. L. (1998). Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing
  • Karppinen, A., Kukkonen, J., Elolähde, T., Konttinen, M., Koskentalo, T., & Rantakrans, E. (2000). A modelling system for predicting urban air pollution: Model description and applications in the helsinki metropolitan area. Atmospheric Environment, 34(22), 3723-3733.
  • Koupal, J., Cumberworth, M., Michaels, H., Beardsley, M., & Brzezinski, D. (2002). Design and implementation of MOVES: EPA‘s new generation mobile source emission model. Ann Arbor, 1001, 48105.
  • Koupal, J., Michaels, H., Cumberworth, M., Bailey, C., & Brzezinski, D. (2002). (2002). EPA’s plan for MOVES: A comprehensive mobile source emissions model. Paper presented at the Proceedings of the 12th CRC on-Road Vehicle Emissions Workshop, San Diego, CA,
  • Krzyzanowski, M., Kuna-Dibbert, B., & Schneider, J. (2005). Health effects of transport-related air pollution World Health Organization Copenhagen.
  • Lautso, K., & Toivanen, S. (1999). SPARTACUS system for analyzing urban sustainability. Transportation Research Record: Journal of the Transportation Research Board, 1670(1), 35-46.
  • Ligterink, N. E., & Lange, R. d. (2009). Refined vehicle and driving-behaviour dependencies in the VERSIT emission model. Paper presented at the ETAPP Symposium,
  • Lin, J., Chiu, Y., Vallamsundar, S., & Bai, S. (2011a). Integration of MOVES and dynamic traffic assignment models for fine-grained transportation and air quality analyses. Paper presented at the Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum On, 176-181.
  • Lin, J., Chiu, Y., Vallamsundar, S., & Bai, S. (2011b). Integration of MOVES and dynamic traffic assignment models for fine-grained transportation and air quality analyses. Paper presented at the Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum On, 176-181.
  • Lomax, T., Turner, S., Shunk, G., Levinson, H. S., Pratt, R. H., Bay, P. N., & Douglas, G. B. (1997). NCHRP report 398: Quantifying congestion. Transportation Research Board, National Research Council, Washington, DC,
  • Mahmassani, H. S., & Liu, Y. (1999). Dynamics of commuting decision behaviour under advanced traveller information systems. Transportation Research Part C: Emerging Technologies, 7(2), 91-107.
  • Meyer, M. D. (1999). Demand management as an element of transportation policy: Using carrots and sticks to influence travel behavior. Transportation Research Part A: Policy and Practice, 33(7), 575-599.
  • Mitchell, G., Namdeo, A., Lockyer, J., & May, A. D. (2002). The impact of road pricing and other strategic road transport initiatives on urban air quality. Paper presented at the Proceedings of the 8th International Conference on Urban Transport and the Environment in the 21st Century, 481-490.
  • Nam, E. K., Brazil, H., & Sutulo, S. (2002) The  integration  of  a  fuel  rate  based modal emissions model  (CMEM modified)  into  the VISSIM microscopic  traffic model  – inventory  comparison with MOBILE6  in  southfield michigan. . Proceedings of the 12th CRC on-Road Vehicle Emissions Workshop,
  • Namdeo, A., Mitchell, G., & Dixon, R. (2002). TEMMS: An integrated package for modelling and mapping urban traffic emissions and air quality. Environmental Modelling & Software, 17(2), 177-188.
  • Newell, G. F. (1993). A simplified theory of kinematic waves in highway traffic: (I) general theory; (ii) queuing at freeway bottlenecks; (iii) multi-dimensional flows . Transportation Research Part B: Methodological, 27(4), 281-313.
  • Newell, G. F. (2002). A simplified car-following theory: A lower order model. Transportation Research Part B: Methodological, 36(3), 195-205.
  • Ni, D., Leonard, J. D., & Williams, B. M. (2006). The network kinematic waves model: A simplified approach to network traffic. Journal of Intelligent Transportation Systems, 10(1), 1-14.
  • Noland, R. B., & Quddus, M. A. (2006). Flow improvements and vehicle emissions: Effects of trip generation and emission control technology. Transportation Research Part D: Transport and Environment, 11(1), 1-14.
  • Ntziachristos, L., Samaras, Z., Eggleston, S., Gorissen, N., Hassel, D., & Hickman, A. J. (2000). Copert iii. Computer Programme to Calculate Emissions from Road Transport, Methodology and Emission Factors (Version 2.1), European Energy Agency (EEA), Copenhagen,
  • Oduyemi, K., & Davidson, B. (1998). The impacts of road traffic management on urban air quality. Science of the Total Environment, 218(1), 59-66.
  • Roughgarden, T. (2012). The price of anarchy in games of incomplete information. Paper presented at the Proceedings of the 13th ACM Conference on Electronic Commerce, 862-879.
  • Samaranayake, S., Glaser, S., Holstius, D., Monteil, J., Tracton, K., Seto, E., & Bayen, A. (2014). Real‐Time estimation of pollution emissions and dispersion from highway traffic. Computer‐Aided Civil and Infrastructure Engineering, 29(7), 546-558.
  • Schrank, D. L., & Lomax, T. J. (2007). The 2007 urban mobility report Texas Transportation Institute, Texas A & M University.
  • Schrank, D., Eisele, B., Lomax, T., & Bak, J. (2015). 2015 urban mobility scorecard. Texas: Texas A&M Transportation Institute and INRIX, Inc.
  • Sivak, M., & Schoettle, B. (2012). Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy. Transport Policy, 22, 96-99.
  • Sjodin, A., Persson, K., Andreasson, K., Arlander, B., & Galle, B. (1998). On-road emission factors derived from measurements in a traffic tunnel. International Journal of Vehicle Design, 20(1), 147-158.
  • Smit, R. (2006). An examination of congestion in road traffic emission models and their application to urban road networks
  • Song, G., Yu, L., & Zhang, Y. (2012). Applicability of traffic microsimulation models in vehicle emissions estimates. Transportation Research Record: Journal of the Transportation Research Board, 2270(1), 132-141.
  • Stathopoulos, F. G., & Noland, R. B. (2003). Induced travel and emissions from traffic flow improvement projects. Transportation Research Record: Journal of the Transportation Research Board, 1842(1), 57-63.
  • Tanvir, S., Karmakar, N., Rouphail, N. M., & Schroeder, B. J. (2016). Modeling freeway work zones with mesoscopic dynamic traffic simulator: Validation, gaps, and guidance. Transportation Research Record: Journal of the Transportation Research Board, (2567), 122-130.
  • Tonne, C., Beevers, S., Armstrong, B., Kelly, F., & Wilkinson, P. (2008). Air pollution and mortality benefits of the london congestion charge: Spatial and socioeconomic inequalities. Occupational and Environmental Medicine, 65(9), 620-627.
  • Twigg, M. V. (2007). Progress and future challenges in controlling automotive exhaust gas emissions. Applied Catalysis B: Environmental, 70(1), 2-15.
  • USEPA. (2007). MOBILE 6 vehicle emission modelling software and documentation. (). Washington, DC: US Environmental Protection Agency.
  • Vallamsundar, S., & Lin, J. (. (2011). MOVES versus MOBILE. Transportation Research Record: Journal of the Transportation Research Board, 2233(1), 27-35.
  • Xie, Y., Chowdhury, M. A., Bhavsar, P., & Zhou, Y. (2011). An integrated tool for modeling the impact of alternative fueled vehicles on traffic emissions: A case study of greenville, south carolina. Paper presented at the Transportation Research Board 90th Annual Meeting, (11-3880)
  • Yperman, I. (2007). The link transmission model for dynamic network loading (Ph.D. Thesis).
  • Zhai, H., Frey, H. C., Rouphail, N. M., Goncalves, G. A., & Farias, T. L. (2009). Comparison of flexible fuel vehicle and life-cycle fuel consumption and emissions of selected pollutants and greenhouse gases for ethanol 85 versus gasoline. Journal of the Air & Waste Management Association (1995), 59(8), 912-924.
  • Zhang, K., & Batterman, S. A. (2009). Time allocation shifts and pollutant exposure due to traffic congestion: An analysis using the national human activity pattern survey. Science of the Total Environment, 407(21), 5493-5500.
  • Zhou, X., Tanvir, S., Lei, H., Taylor, J., Liu, B., Rouphail, N. M., & Christopher Frey, H. (2015). Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies. Transportation Research Part D: Transport and Environment, 37, 123-136. doi://dx.doi.org/10.1016/j.trd.2015.04.013

Chapter 6

Chapter 7

Chapter 8

  • Greenblatt, J. B., & Shaheen, S. (2015). Automated vehicles, on-demand mobility, and environmental impacts. Current sustainable/renewable energy reports2(3), 74-81.
  • Walker, J., & Johnson, C. (2016). Peak car ownership: The market opportunity of electric automated mobility services. Rocky Mountain Institute.
  • SAE international. (2016). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE International,(J3016).
  • Hu, L., Dong, J., Lin, Z., & Yang, J. (2018). Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA. Transportation Research Part C: Emerging Technologies87, 91-104.
  • Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D. (2017). On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences, 114(3), 462-467.
  • Bischoff, J., & Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia computer science83, 237-244.
  • Wenzel, T., Rames, C., Kontou, E., & Henao, A. (2019). Travel and energy implications of ridesourcing service in Austin, Texas. Transportation Research Part D: Transport and Environment, 70, 18-34.
  • Komanduri, A., Wafa, Z., Proussaloglou, K., & Jacobs, S. (2018). Assessing the impact of app-based ride share systems in an urban context: Findings from Austin. Transportation Research Record2672(7), 34-46.
  • Castiglione, J., Cooper, D., Sana, B., Tischler, D., Chang, T., Erhardt, G., Roy, S., Chen, M., & Mucci, A. (2018, October). TNCs & congestion. Draft report, San Francisco County Transportation Authority.
  • Castillo, J. C., Knoepfle, D., & Weyl, G. (2017, June). Surge pricing solves the wild goose chase. In Proceedings of the 2017 ACM Conference on Economics and Computation (pp. 241-242). ACM.
  • Schaller, B. (2018). Making Congestion Pricing Work for Traffic and Transit in New York City. Schaller Consulting.
  • Xu, Z., Yin, Y., & Ye, J. (2019). On the supply curve of ride-hailing systems. Transportation Research Part B: Methodological.
  • Hall, J. D., Palsson, C., & Price, J. (2018). Is Uber a substitute or complement for public transit?. Journal of Urban Economics108, 36-50.
  • Henao, A., & Marshall, W. E. (2018). The impact of ride-hailing on vehicle miles traveled. Transportation, 1-22.
  • Bauer, G. S., Greenblatt, J. B., & Gerke, B. F. (2018). Cost, energy, and environmental impact of automated electric taxi fleets in Manhattan. Environmental science & technology52(8), 4920-4928.
  • Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice86, 1-18.
  • Meyer, J., Becker, H., Bösch, P. M., & Axhausen, K. W. (2017). Autonomous vehicles: The next jump in accessibilities?. Research in Transportation Economics62, 80-91.
  • Ramanathan, V., Aines, R., Auffhammer, M., Barth, M., Cole, J., Forman, F., et al. (2019). Bending the Curve: Climate Change Solutions. Location: Regents of the University of California. Editor: V. Ramanathan. Co-Editors: Adam Millard-Ball; Michelle Niemann; Scott Friese. Book published by the Regents of the Univ of California. Retrieved from https://escholarship.org/uc/item/6kr8p5rq. 815pp. https://creativecommons.org/licenses/by-nc-sa/4.0/

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