A German Passenger Car and Heavy Vehicle Stock Model: Towards an Autonomous Vehicle Fleet. Issue 46 (December 2018)

• Record Type: Journal Article
• Authors: Hartmann, Martin; Vortisch, Peter
• Is Part Of: Transportation research record. Volume 2672:Issue 46(2018)
• Page Start: 55
• Page End: 63
• Publication Date: 2018-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198118782042
• Languages: English
• ISSNs: 0361-1981

Abstract

Automated vehicles are becoming a reality. Many pilot projects have already begun demonstrating the technological capabilities, as public authorities now allow the testing of automated vehicles in real traffic. To smooth the transition from a conventional to an automated fleet, effective fiscal and regulatory policies must be developed by governmental agencies. But at what rate will automated vehicles actually be adopted, and what automation technology will be available for use in new cars joining the national fleet? A national vehicle stock model can be used to answer these questions and to observe the aggregate impact of governmental policies on individual vehicle purchase decisions. In this paper, we present a passenger car and heavy vehicle stock cohort model that forecasts the diffusion of automation technology in Germany. The model uses national data on vehicle stock and vehicle utilization patterns on German freeways and predicts market shares of generic automation levels in predefined instances of a trend scenario. Results point toward market saturation of automated vehicles beyond 2050, with almost 90% of the passenger car fleet being classified as at least partially automatized by this date. The results also suggest that technology diffusion will be faster in the heavy vehicle fleet than in the passenger car fleet. This implies a positive correlation between emission-linked road user charges for heavy vehicles on the freeway network and the renewal rate of the heavy vehicle fleet. The forecast shares of automated vehicles can be used as an input for traffic flow simulations or as a basis for those infrastructure measures and traffic policies that are sensitive to the share of automated vehicles.