System dynamics modelling for electric and hybrid commercial vehicles adoption

Problems caused by the increasing freight transportation demand in cities call for integrated solutions where all stakeholders' efforts are coordinated, in order to both reduce the negative impacts of freight transportation, such as pollution and congestion, and carry no disadvantages to public and private operators. Among the solutions that can be implemented for these purposes, one of the most studied and applied one is the partial or total substitution of commercial vans with low emission vehicles. Previous studies have been focused mainly on the vehicle-related factors that make such adoption sustainable for the private stakeholders. However, there is a lack of contributions that also take into account the operational aspects of a city logistics system. In order to contribute to this literature, our work develops a System Dynamics model that assesses the adoption of low emission vehicles by analysing the most important operational factors typical of a freight distribution system. Results of the simulation and the sensitivity analyses demonstrate that the adoption of low emission commercial vehicles is feasible within a reasonable time period if some strategies are put in place. For instance, public contribution including both incentives to low emission vehicles and disincentives to traditional ones could effectively increase the adoption process, along with effective advertising campaigns about the operational benefits given by such distribution model

[1]  Tugrul U. Daim,et al.  Technology diffusion: forecasting with bibliometric analysis and Bass model , 2009 .

[2]  Eiichi Taniguchi,et al.  Evaluation of Load Factor Control and Urban Freight Road Pricing Joint Schemes with Multi-agent Systems Learning Models☆ , 2014 .

[3]  Orestis Terzidis,et al.  Market Penetration of Alternative Power train Concepts in Heavy Commercial Vehicles : A System Dynamics Approach , 2014 .

[4]  Nigel Meade,et al.  Forecasting the Diffusion of Innovations: Implications for Time-Series Extrapolation , 2001 .

[5]  Jan Jacob Trip,et al.  Supporting electric vehicles in freight transport in Amsterdam , 2014 .

[6]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[7]  Miguel A. Figliozzi,et al.  Impacts of Congestion on Commercial Vehicle Tour Characteristics and Costs , 2010 .

[8]  Omer Tatari,et al.  A dynamic modeling approach to highway sustainability: Strategies to reduce overall impact , 2012 .

[9]  C. Ringle,et al.  Innovative methods in logistics and supply chain management : current issues and emerging practices , 2014 .

[10]  Yoshitsugu Hayashi,et al.  A system dynamics model of CO2 mitigation in China's inter-city passenger transport , 2008 .

[11]  J. Forrester.  Industrial Dynamics , 1997 .

[12]  Toshinori Nemoto,et al.  URBAN FREIGHT TRANSPORT POLICY AND PLANNING , 1999 .

[13]  Carlos Franco,et al.  Policy analysis to boost the adoption of alternative fuel vehicles in the Colombian market , 2013 .

[14]  F. Armah,et al.  A Systems Dynamics Approach to Explore Traffic Congestion and Air Pollution Link in the City of Accra, Ghana , 2010 .

[15]  Udo Lindemann,et al.  SYSTEM DYNAMICS MODELING OF NEW VEHICLE ARCHITECTURE ADOPTION , 2011 .

[16]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[17]  Maja Kiba-Janiak,et al.  Correlation Between City Logistics and Quality of Life as an Assumption for Referential Model , 2012 .

[18]  Simon Shepherd,et al.  Factors affecting future demand for electric vehicles: A model based study , 2012 .

[19]  J. Sterman,et al.  Transition challenges for alternative fuel vehicle and transportation systems , 2006 .

[20]  J. C. Fisher,et al.  A simple substitution model of technological change , 1971 .

[21]  John D. Sterman,et al.  Business dynamics : systems thinking and modelling for acomplex world , 2002 .

[22]  Steven J. Skerlos,et al.  Greenhouse gas mitigation policies and the transportation sector: The role of feedback effects on policy effectiveness , 2009 .

[23]  Michael Browne,et al.  The impact on urban distribution operations of upstream supply chain constraints , 2011 .

[24]  Sang Yong Park,et al.  Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects , 2011 .

[25]  Jafar Heydari,et al.  A system dynamics modeling for urban air pollution: A case study of Tehran, Iran , 2014 .

[26]  J Swanson,et al.  Business Dynamics—Systems Thinking and Modeling for a Complex World , 2002, J. Oper. Res. Soc..