Some pay much but many don’t: Vehicle TCO imputation in travel surveys

Costs of cars are among the most relevant factors influencing travel behavior. However, there is a lack of data about the true costs of car ownership and specifically on how these costs are distributed across different vehicles and across the population. This paper presents a multistage method for imputing car costs by cost item in a German national travel survey data set. Based on vehicle information reported by survey participants, we assign costs to each of the three thousand cars in the data set using the most comprehensive German vehicle cost data base. In addition to combining different data sets, we use model based imputation methods. In order to validate the average costs for private vehicles we analyze the German income and expenditure survey EVS. The average total cost of ownership for a private car in Germany is about 310 Euros per month. This translates to about 30 Eurocents per auto-km. About one third of the costs are fuel, another third is depreciation, and the rest are other mainly fixed costs (insurance, tax, repair and maintenance). However, the cost distribution is strongly skewed with a long tail to the right. Hence, the majority of motorists pay less than average for their private vehicles while few pay more and evidently some pay a lot more. This imputation approach delivers unprecedented vehicle cost information in particular with regard to the distribution of vehicle costs. Such data is key for understanding the fundamentals of mobility choices

Modelling the impact of automated driving

The paper presents projections for the impact of private autonomous vehicles on destination choice, mode choice and thus overall travel demand for Germany and the USA. These results were obtained by combining a vehicle technology diffusion model and a travel demand model

Quo Vadis Europe? Mobility trends, future perspectives & implications for urban transit in Spain

Overall, Spain´s mobility patterns develop along a relatively typical European path. Therefore, developments in other countries help understand developments in Spain. But travel demand in metropolitan areas in Spain is likely to turn out lower and more transit oriented than in other European metro areas.There will be growth of total travel in some metropolitan areas due to population growth. But overall, everyday travel of Europeans shows signs of saturation. The group of captive transit riders will continue to decrease in the future. In the future, urban travel will be more multimodal, more diverse and increasingly shaped by non-routine travel. If urban transit adapts to this situation, there will be substantial opportunities

Assessment of real-world vehicle data from electric vehicles – potentials and challenges

This paper introduces benefits and challenges related to collecting and analyzing vehicle data, in particular relating to electric vehicles. We use data from a current research project as an example. We address both technical and legal issues related to the data collection. On the technical side, pre-processing steps are needed to enhance data quality because the measured data are not faultless. On the legal side, data-privacy issues arise since precise GPS locations of the vehicles are captured. Two data collection methods are introduced and compared with each other. The advantages of vehicle data collection over other data sources for addressing various research questions are discussed

Mobility-on-demand pricing versus private vehicle TCO: how cost structures hinder the dethroning of the car

This study uses a unique dataset on the cost of motoring in Germany to analyse cost competitiveness of emerging mobility-on-demand (MOD) services. Previous studies have focused on comparing current and projected MOD prices with the average cost of private motoring. This study quantifies which proportion of private car travel would actually turn out to be costlier than MOD given that MOD costs drop below certain levels relative to the cost of private motoring. In this context, not the average but the distribution of the costs of motoring are the key issue. These costs are strongly skewed across the cars in private households when including new and old vehicles: a large proportion of private car kilometres are driven at relatively low cost. The study uses simplified scenario settings with MOD price levels ranging from 0.1 €/km to 1.5 €/km to make predictions of hypothetical modal shifts under the assumption that car user switch to the most economic mode of travel. These modal shifts serve as an indicator of MOD cost competitiveness. The results indicate that MOD prices would have to drop to 0.5 €/km or lower to have a notable impact on use of the private car if cost was the key mode choice criterion. Only if MOD prices drop down to a level of about 0.3 €/km—quite possibly a lower boundary for automated MOD—MOD-enabled mobility packages would be the less costly alternative to the private car for a substantial proportion of mileage. However, even at that MOD price level, the private car would still be the most economic option for the majority of today’s car user kilometres. Our findings illustrate that the skewed distribution of the cost of owning and running private cars—where many of those who drive much drive inexpensively—substantially dampens the disruptive potential of MOD. While we use data from Germany to illustrate this, many of our findings are more widely applicable

Some pay much but many don’t: Vehicle TCO imputation in travel surveys

Costs of cars are among the most relevant factors influencing travel behavior. However, there is a lack of data about the true costs of car ownership and specifically on how these costs are distributed across different vehicles and across the population. This paper presents a multistage method for imputing car costs by cost item in a German national travel survey data set. Based on vehicle information reported by survey participants, we assign costs to each of the three thousand cars in the data set using the most comprehensive German vehicle cost data base. In addition to combining different data sets, we use model based imputation methods. In order to validate the average costs for private vehicles we analyze the German income and expenditure survey EVS. The average total cost of ownership for a private car in Germany is about 310 Euros per month. This translates to about 30 Eurocents per auto-km. About one third of the costs are fuel, another third is depreciation, and the rest are other mainly fixed costs (insurance, tax, repair and maintenance). However, the cost distribution is strongly skewed with a long tail to the right. Hence, the majority of motorists pay less than average for their private vehicles while few pay more and evidently some pay a lot more. This imputation approach delivers unprecedented vehicle cost information in particular with regard to the distribution of vehicle costs. Such data is key for understanding the fundamentals of mobility choices

Identifying and understanding long-distance travel demand by combining official transport statistics and survey data

While much is known about everyday travel of the German population, long-distance travel is still underreported. The main data source, the national travel survey “Mobility in Germany (MiD)”, cannot simply be used to describe the demand: complex extrapolations and complementary data are necessary to obtain a consistent picture. The presented approach of ‘data fusion’ integrates different data sources to provide the overall long-distance travel demand. The result reveals that almost half of the total transport performance of the residential population in Germany (46 % of passenger kilometers) is accounted for by trips of at least 100 km (one-way distance)