MODELLING REAL CHOICES BETWEEN CONVENTIONAL AND ELECTRIC CARS FOR HOME- BASED JOURNEYS

内容摘要

Over the last decades, several studies have focused on understanding what drives the demand for electric vehicles (EVs) and to what extent the difference in several characteristics – especially the limited driving range and limited charging options - makes it a feasible transport alternative compared to conventional internal combustion engine vehicles (ICVs). A large range of studies analyse the potential use of EVs for everyday mobility in households, based on data on ICV  journeys from national travel surveys (Hjorthol et al. 2014; Christensen et al. 2010). Greaves et al. 2014 used a model for energy consumption and recharging to assess the extent to which current conventional car journeys measured with GPS could be met by an EV. All studies above found that a large share of the revealed journeys are short journeys that potentially could be conducted with an EV. However such studies do not reveal to what extent households would actually use an EV for travel when the driving range is not a problem. This is relevant as the choice between an EV and an ICV may not be always in favour of an EV as other variables may also influence the choice between these vehicle options. literature contains a large number of consumer choice studies, where it has been shown that a number of factors, e.g. purchase price, driving range and charging infrastructure has an effect on individual’s stated preferences for EVs (see e.g. Bunch et al. 1993; Potoglou & Kanaroglou 2007; Glerum et al. 2014). Another approach to analyse what vehicle type would be preferred applies Total Cost of Ownership (TCO) models. This approach uses travel surveys or GPS data (e.g. Plotz et al. 2014) to calculate the lowest cost for a range of alternatives over some period of time. While costs are certainly important variables to consider, there are still other factors that should be considered when studying the potential of EVs. Here we advance this research through an analysis of what factors that are important in the choice between an EV and an ICV for households that in a period of three months had access to both vehicle types. This allows us to make a more complete analysis compared to the analyses mentioned above that only look at the marginal effects of either range or cost. We utilize a dataset describing household travel with either a private ICV or an EV that was available to the household for three months in connection with a large-scale EV demonstration project in Denmark. In this period, the household had access to both cars but they were encouraged to use the EV as the primary car. ICV trips were logged with a GPS device one month before and after the EV was received while the EV trips were logged during the full three months where the household was participating in the project. These data allow us to address the question: What are the factors that influence the choice between an EV and an ICV for home-based journeys? As it is usually not possible to change to the other car on the different trip-legs of a journey, we merge the observed trips into home-based journeys. After a data cleaning process and the merging of the trips into journeys, the data used consisted of 11107 journeys of which 10554 journeys were conducted within the same day. In table 1 below we show the descriptive statistics for journeys that begin and end on the same day for the two alternatives. For the ICV we also show the description of the journeys that took place before the EV was received. As expected, it is seen that the household journeys conducted with the ICV after the EV becomes available on average have the most stops, are longest, and take the most time whereas the journeys conducted with the EV have the fewest stops, are shortest, and have the shortest duration. In between these are the ICV journeys before the EV was available. Tabel 1: Within one day journeys for the ICV alternative before and after the EV was received and for the EV. ICV no EV ICV with EV EV Observations 2514 1312 9242 Variable Min Mean Max Min Mean Max Min Mean Max Number of triplegs 2.00 3.04 21.00 2.00 3.14 21.00 2.00 2.87 19.00 Number of charges 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 4.00 Number of triplegs above 100 km 0.00 0.02 3.00 0.00 0.04 2.00 0.00 0.00 2.00 Journey time (hours) 0.11 3.80 17.60 0.12 4.11 16.77 0.08 3.90 17.03 Net drivetime (minutes) 2.03 38.05 463.52 2.63 46.60 384.15 2.13 32.30 283.98 Distance (km) 0.63 32.93 680.84 0.79 43.83 563.97 0.47 23.96 417.22 In principle, a household member who was going for a journey in the period where the EV was available had the choice between the EV and the ICV. In order to model this choice, we set up a discrete choice model. We assume that for each journey, the distance and the number of activities (i.e. trips) is fixed. This means that these will not change if the non-observed alternative was chosen. In order to calculate the remaining variables for the non-observed alternative, we calculated average values from those journeys where this alternative was chosen within specific intervals of the distance of the journey. results of the model estimation are reported in table 2. All parameters are seen to have the expected sign. For the total journey time we did not find any difference in preferences between the alternatives. However, the net drivetime has a smaller marginal disbenefit for the EV compared to the ICV. This might be due to the circumstances of the demonstration project that the participants are accepting a longer driving time because they were told to use the EV as the primary car. Furthermore, we find a lower preference for EV as the number of triplegs, the average tripleg length, and the necessary number of charges increases. We also find a disbenefit if the journey takes place in the winter months which is most probably due to the reduced EV driving range in cold conditions. Finally we obtain a positive parameter for the households located in cities. These initial results show have our data can add to the current research on the use of EVs for everyday mobility. We plan to extend the current work with models that include more variables that can explain the choice between EV and ICV, e.g. related to weather conditions. Furthermore, we plan to investigate the journeys that took place over several days and to model journeys separately where the EV and the ICV were used at the same time by the same household. Tabel 2: Discrete choice model for the choice between ICV and EV for a household journey Name Value Robust t-statistic Alternative Specific Constant, EV 2.43 23.19 Total journeytime -0.175 -7.21 Net drivetime, ICV, min -0.061 -4.77 Net drivetime, EV, min -0.049 -4.27 Number of triplegs, EV -0.08 -2.19 Average tripleg length, EV, km -0.021 -2.97 Number of charges, EV -1.08 -4.78 Winter dummy -0.162 -2.65 City dummy 0.466 3.7 Number of estimated parameters 9 Number of observations 10554 Final log-likelihood -3717 References Bunch, Bradley, Golob, Kitamura & Occhiuzzo 1993, for clean-fuel vehicles in California: a discrete-choice stated preference pilot project, Transportation Research, Part A: Policy and Practice, vol. 27, no. 3, pp. 237-253. Christensen, L., Kveiborg, O. & Mabit, S.L. 2010, The Market for electric vehicles–what do potential users want., 12th World Conference on Transportation Research . 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