Leveraging Geospatial Analysis and Machine Learning for Optimal Green Vehicle Assignment
References
- (2021) A boost for urban sustainability: Optimizing electric transit bus networks in Rotterdam. INFORMS J. Appl. Anal. 51(5):391–407.Link, Google Scholar
- (2003) Operating cost, fuel consumption, and emission models in aaSIDRA and aaMOTION. Proc. 25th Conf. Australian Institutes Transport Res. (University of South Australia, Adelaide, Australia), 1–15.Google Scholar
- (2018) Heavy-duty trucks and new engine technology: Impact on fuel consumption, emissions and trip cost. Internat. J. Energy Production Management 3(3):167–178.Google Scholar
- (2018) Optimization of inventory routing problem to minimize carbon dioxide emission. Internat. J. Simulation Model 17(1):42–54.Google Scholar
- (1965) A novel method of data analysis and pattern classification. Technical Report NO. NTIS AD 699616, Stanford Research Institute, Menlo Park, CA.Google Scholar
- (2024) Innovative Integer programming software and methods for large-scale routing at DHL supply chain. INFORMS J. Appl. Anal. 54(1):20–36.Link, Google Scholar
- (2011) A comparative analysis of several vehicle emission models for road freight transportation. Transportation Res. Part D Transport Environment 16(5):347–357.Google Scholar
- (1999) Transportation GHG emissions in developing countries: The case of Lebanon. Transportation Res. Part D Transport Environment 4(4):251–264.Google Scholar
- EPA (2024) Inventory of U.S. greenhouse gas emissions and sinks: 1990–2022. Retrieved April 15, https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.Google Scholar
- EPA (2025) Greenhouse gases equivalencies calculator: Calculations and references. Retrieved December 12, https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator-calculations-and-references.Google Scholar
- EU (2017) Assessment of the modalities for LDV CO2 regulations beyond 2020. Retrieved April 1, https://climate.ec.europa.eu/system/files/2017-11/ldv_co2_modalities_for_regulations_beyond_2020_en.pdf.Google Scholar
- (2021) Heavy-duty trucks: The challenge of getting to zero. Transportation Res. Part D: Transport Environment 93:102742.Google Scholar
- (2012) Routes to 2050: Developing a better understanding of the secondary impacts and key sensitivities for the decarbonisation of the EU’s transport sector by 2050. http://www.eutransportghg2050.eu.Google Scholar
- (2022) CH Robinson uses heuristics to solve rich vehicle routing problems. INFORMS J. Appl. Anal. 52(2):173–188.Link, Google Scholar
- (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environment. Res. Lett. 16(7):073005.Google Scholar
- (1967) Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Sympos. Math Statist. Probability, 281–297.Google Scholar
- NTM (2018) Emission factors for greenhouse gas inventories: US EPA. Retrieved January 7, https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf.Google Scholar
- (2021) Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe. Res. Transportation Econom. 90:101074.Google Scholar
- (2019) Eco-friendly 3D-routing: A GIS based 3D-routing-model to estimate and reduce CO2-emissions of distribution transports. Comput. Environment. Urban Systems 73:40–55.Google Scholar
- (2011) Evolving limitations in k-means algorithm in data mining and their removal. Internat. J. Comput. Engrg. Management 12:105–109.Google Scholar
- (2016) A new statistical method of assigning vehicles to delivery areas for CO2 emissions reduction. Transportation Res. Part D Transport Environment 43:33–144.Google Scholar

