Modelling real choices between conventional and electric cars for home-based journeys
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1 Modelling real choices between conventional and electric cars for home-based journeys Anders Fjendbo Jensen Stefan Lindhard Mabit DTU Transport Technical University of Denmark 1 / 17
2 Outline Background Data and descriptive statistics Discrete choice model on car choice for home based journeys Results 2 / 17
3 Background Several studies have focused on the demand for electric vehicles (EVs) and to what extent it is a feasible alternative to conventional internal combustion engine vehicles (ICVs). Analyses of potential use of EVs for everyday mobility needs, e.g. Greaves et al. (2014), Hjorthol et al. (2014), Christensen et al. (2010) A large share of revealed journeys are short journeys that could potentially be met with an EV 3 / 17
4 Background Several studies have focused on the demand for electric vehicles (EVs) and to what extent it is a feasible alternative to conventional internal combustion engine vehicles (ICVs). Analyses of potential use of EVs for everyday mobility needs, e.g. Greaves et al. (2014), Hjorthol et al. (2014), Christensen et al. (2010) A large share of revealed journeys are short journeys that could potentially be met with an EV Stated Preferences experiments, Discrete choice models, e.g. Bunch et al. (1993), Potoglou & Kanaroglou (2007) Driving range is a major limiting factor 3 / 17
5 Background Several studies have focused on the demand for electric vehicles (EVs) and to what extent it is a feasible alternative to conventional internal combustion engine vehicles (ICVs). Analyses of potential use of EVs for everyday mobility needs, e.g. Greaves et al. (2014), Hjorthol et al. (2014), Christensen et al. (2010) A large share of revealed journeys are short journeys that could potentially be met with an EV Stated Preferences experiments, Discrete choice models, e.g. Bunch et al. (1993), Potoglou & Kanaroglou (2007) Driving range is a major limiting factor Real life trials, e.g. Jensen et al. (2014), Franke & Krems (2013), Gärling & Johansson (1998) EVs are fun and comfortable to drive Purhase intentions seem to decrease after long duration trial, Jensen et al. (2014), Franke & Krems (2013), Gärling & Johansson (1998) 3 / 17
6 Klöckner et al. (2013): Survey information from EV and non-ev households 50% of non-ev housesholds only had one car 9.5% of EV households only had one car One car EV households drive less than one car ICV households No significant difference in mileage between multi car EV households and multicar ICV households 4 / 17
7 C.A. Klöckner et al. / Transportation Research Part D 21 (2013) Fig. 3. Self-reported percentage of car use for different trip types against car type. 5 / 17
8 Motivation What are the important factors for home-based EV journeys? Journey characteristics Household characteristics Weather characteristics How are the effect of these factors influenced by real life experience with an EV? 6 / 17
9 Data 1. EV and ICV GPS data from household trips during a large scale real-life trial of EVs EV available in the households for 3 months, all trips logged (70,000 journeys from 500+ households) Conventional car logged one month before and after EV is received (only 100 households) Households encouraged to use the EV as the primary car 2. Survey on household characteristics collected in the trial application process 3. Hourly weather data from nationwide weather stations 7 / 17
10 In this study we focus on the journeys from households from which we have data for both alternatives Period 1: The month before the EV was received (ICV data) Period 2: First month of the EV trial (EV and ICV data) Period 3: Second and third month of the EV trial (EV data) Period Choice Total ICV EV Total Italics are journeys conducted within the same day 8 / 17
11 4,5 Average number of trips per day 4 3,5 3 2,5 2 1,5 1 0, ,1 2,2 3 ICV Weekday ICV Weekend EV weekday EV weekend 9 / 17
12 45 Average number of km per day ,1 2,2 3 ICV Weekday ICV Weekend EV weekday EV weekend 10 / 17
13 Method In principle, a household member who was going to use a car in Period 2, had the choice between the EV and an ICV. Single trips are merged into home-based journeys as it is usually not possible to switch between alternatives during a journey. Weather data from the nearest weather station is joined to each journey (hourly data) Only data from households from which data is available for both alternatives are used. 11 / 17
14 We use a discrete choice model to describe the situation where a household member is at home and needs to conduct a car journey. U jnt = ASC j + βj S S n + βj X X jnt + µ jn + ε jnt where S n : Vector of household characteristics X jnt : Vector of journey characteristics including weather variables ε w jnt : Extreme value distributed error term µ w jn : Error terms, normally distributed across individuals βj S, βj X : Vectors of parameters to be estimated 12 / 17
15 The values for the attributes of the non-observed alternative j is based on mean values for the observed journeys within a high number of journey distance intervals d: x jd,u = x jd,o x jd,u x jd,o Simple method that does not take into account that another route might have been chosen with the unobserved alternative, but travel time and number of stops can differ 13 / 17
16 Results Several specifications were tested for all the available attributes Testing interactions with first week dummy and weekend dummy 15% of the journeys where conducted when the other alternative was also used at some point during the journey. Estimating separately on these two types of data, LR test show that a better model is obtained if separate parameters for all attributes are estimated. 14 / 17
17 Name Value Robust t-stat. Alternative Specific Constant, EV SIGMA_EV Total journeytime Net drivetime Number of triplegs Journey distance, km, EV At least one charge, EV Windspeed, m/s, EV Precipitation, mm, EV Temperature, C, EV First week dummy, EV First week * Number of triplegs, EV First week * Precipitation, EV First week * Windspeed, EV First week * Journey time, EV Weekend dummy, EV Weekend * Temperature, EV Citroën dummy, EV Number of driving licences, EV City dummy, EV Obs: 4295, Rho bar wrt.0: 0.461, LL: , EV chosen: 3245 (76%) 15 / 17
18 Name Value Robust t-stat. Alternative Specific Constant, EV SIGMA_EV Total journeytime Net drivetime Number of triplegs Journey distance, km, EV At least one charge, EV Windspeed, m/s, EV Precipitation, mm, EV Temperature, C, EV First week dummy, EV First week * Number of triplegs, EV First week * Precipitation, EV First week * Windspeed, EV First week * Journey time, EV Weekend dummy, EV Weekend * Temperature, EV Citroën dummy, EV Number of driving licences, EV City dummy, EV Obs: 4295, Rho bar wrt.0: 0.461, LL: , EV chosen: 3245 (76%) 15 / 17
19 Name Value Robust t-stat. Alternative Specific Constant, EV SIGMA_EV Total journeytime Net drivetime Number of triplegs Journey distance, km, EV At least one charge, EV Windspeed, m/s, EV Precipitation, mm, EV Temperature, C, EV First week dummy, EV First week * Number of triplegs, EV First week * Precipitation, EV First week * Windspeed, EV First week * Journey time, EV Weekend dummy, EV Weekend * Temperature, EV Citroën dummy, EV Number of driving licences, EV City dummy, EV Obs: 4295, Rho bar wrt.0: 0.461, LL: , EV chosen: 3245 (76%) 15 / 17
20 Name Value Robust t-stat. Alternative Specific Constant, EV SIGMA_EV Total journeytime Net drivetime Number of triplegs Journey distance, km, EV At least one charge, EV Windspeed, m/s, EV Precipitation, mm, EV Temperature, C, EV First week dummy, EV First week * Number of triplegs, EV First week * Precipitation, EV First week * Windspeed, EV First week * Journey time, EV Weekend dummy, EV Weekend * Temperature, EV Citroën dummy, EV Number of driving licences, EV City dummy, EV Obs: 4295, Rho bar wrt.0: 0.461, LL: , EV chosen: 3245 (76%) 15 / 17
21 Conclusion The model is useful to explain everyday travel behaviour with an EV 16 / 17
22 Conclusion The model is useful to explain everyday travel behaviour with an EV This is relevant, as many studies assumes that travel behaviour of EV users can be predicted by ICV travel behaviour 16 / 17
23 Conclusion The model is useful to explain everyday travel behaviour with an EV This is relevant, as many studies assumes that travel behaviour of EV users can be predicted by ICV travel behaviour Whether charging is necessary has a significant effect on the choice of EV for a journey 16 / 17
24 Conclusion The model is useful to explain everyday travel behaviour with an EV This is relevant, as many studies assumes that travel behaviour of EV users can be predicted by ICV travel behaviour Whether charging is necessary has a significant effect on the choice of EV for a journey Experience changes the preference for specific factors In the first week the choice is more affected by journey time but number of trip legs is not important 16 / 17
25 Conclusion The model is useful to explain everyday travel behaviour with an EV This is relevant, as many studies assumes that travel behaviour of EV users can be predicted by ICV travel behaviour Whether charging is necessary has a significant effect on the choice of EV for a journey Experience changes the preference for specific factors In the first week the choice is more affected by journey time but number of trip legs is not important Weather information drastically improved the model. Higher windspeed decrease EV preference, especially first week More precipitation increase EV preference, but not in the first week So far no clear result for temperature 16 / 17
26 Tak for opmærksomheden Tak til ForskEL/energinet.dk 17 / 17
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