For decades, the technology of freight railcars has not changed significantly, mostly due to little or no incentive for significant investments in rolling stock. Taking into account the disruptive developments anticipated in automotive transportation, this approach appears no longer feasible, especially if regulatory agencies aim to reduce carbon dioxide emissions while maintaining economic growth. With the advent of telematics, on-board sensing and cloud-based analytics for control and condition based maintenance, high potential for efficiency improvements has become possible. Such technologies are de facto standards in automotive transport, which induces the need for implementation of similar technologies in rail transport as well. In addition to enabling efficiency gains, telematics, on-board sensing and cloud-based analytics also offer new means to approach pressing problems such as rail noise emission, train integrity and safety against derailment, while at the same time reducing maintenance cost and downtime. Furthermore, a connected wagon offers a seamless integration into current and future logistics systems, which are driven and controlled by the industrial Internet of Things to support the fourth industrial revolution. An important concept, introduced with theWagon 4.0, is standardized hardware, together with an open-source operating system based on prognostics and health management principles for predictive analytics. Thus, the Wagon 4.0 paves the way for new operations and maintenance concepts, user interfaces and value proposals. Additional economic advantages will be made possible from the self-organizing features of such vehicles, the ability to achieve mass customization and from a rise in efficiency in operation and maintenance. This paper describes the basis of such a system including the power supply, intra-train communications, sensing and cloud-based analytics. A study of use cases from railway operation illustrates the approach and highlights the opportunities of this novel system design. The paper concludes with a description of how the implantation enables the railcar operator to practice predictive maintenance and increase operational efficiency.
 Haug K. C., Kretschmer T. and Strobel T. 2016. Cloud adaptiveness within industry sectors - Measurement and observations. Telecommunications Policy, 40: 291 - 306
 Harris I., Wang Y. and Wang H. 2015. ICT in multimodal transport and technological trends: Unleashing potential for the future. Int. J. Production Economy, 159: 88-103
 Murgoitio J. et al. 2016. Spanish initiative for fully automated stowage on roll-on/roll-off operations. Transportation Research Procedia, 14: 173 - 182
 Marinov M. and Viegas J. 2009. A simulation modelling methodology for evaluating flat-shunted yard operations. Simulation Modelling and Practice, 17: 1106 – 1129
 Behrends V., Haunschild M. and Galonske N. 2016. Smart telematics enabling efficient rail transport - development of the ViWaS research and development project. Transportation Research Procedia, 14: 4430 - 4439
 Galonske N., Riebe E., Toubol A. and Weismantel S. 2016. The ViWaS project: future-proof solutions for wag-onload transport. Transportation Research Procedia, 14: 2850 – 2859
 Pfaff R. and Enning M. 2016. WagonEcosystem: Zeitgeme Automatisierung am Gterwagen. Bahntechnik Aktuell, 61: 23 - 32 (in German)
 Enning M. and Pfaff R. 2016. Digitalisierung bringt mehr Gter auf die Schiene. ATZ extra, (accepted for publication) (in German)
 Barke, D. and Chiu, W.K., 2005. Structural health monitoring in the railway industry: a review. Structural Health Monitoring, 4(1), pp.81-93.
 Lagnebck, R., 2007. Evaluation of wayside condition monitoring technologies for condition-based maintenance of railway vehicles. Lule: Lule University of Technology.
 Ward, C.P., Goodall, R.M., Dixon, R. and Charles, G., 2010, September. Condition monitoring of rail vehicle bogies. In Control 2010, UKACC International Conference on (pp. 1-6).
Ward, C.P.,Weston, P.F., Stewart, E.J.C., Li, H., Goodall, R.M., Roberts, C., Mei, T.X., Charles, G. and Dixon, R., 2011. Condition monitoring opportunities using vehiclebased sensors. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 225(2), pp.202-218.
 Hubbard, P., Ward, C., Goodall, R. and Dixon, R., 2013. Real time detection of low adhesion in the wheel/rail contact. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, p.0954409713503634.
 Mei, T.X. and Li, H., 2008. Measurement of vehicle ground speed using bogie-based inertial sensors. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 222(2), pp.107-116.
 Hopkins, B.M., Seidel, A., Maraini, D. and Shahidi, P. 2015. March. End-of-Car Device Condition Monitoring With Onboard Sensors. In 2015 Joint Rail Conference (pp. V001T06A005-V001T06A005). American Society of Mechanical
 Maraini D., Shahidi P., Hopkins M., and Seidel A. 2014. Development of a Bogie-Mounted Vehicle On-Board Weighing System. In 2014 Joint Rail Conference, pp. V001T02A001 -V001T02A001. American Society of Mechanical
 Li, P. and Goodall, R., 2004, September. Model-based condition monitoring for railway vehicle systems. In Proceedings of the UKACC international conference on control, Bath, UK
 Maraini, D. and Nataraj, C., 2015. Freight Car Roller Bearing Fault Detection Using Artificial Neural Networks and Support Vector Machines. In Vibration Engineering and Technology of Machinery (pp. 663-672). Springer International Publishing.
 Haykin, S. and Network, N., 2004. A comprehensive foundation. Neural Networks, 2(2004).
 Cortes, C. and Vapnik, V., 1995. Support-vector networks. Machine learning, 20(3), pp.273-297.
 Arulampalam, M.S., Maskell, S., Gordon, N. and Clapp, T., 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing, 50(2), pp.174-188
 Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82(1), pp.35-45.
 Shahidi P., Maraini D., Hopkins B. and Seidel A. 2015. Railcar Bogie Performance Monitoring using Mutual Information and Support Vector Machines. Annual Conference of the Prognostics and Health Management Society, Coronado, California. 2015.
 Scholten, H., Westenberg, R. and Schoemaker, M. Trainspotting, a WSN-based train integrity system. Eighth International Conference on Networks. Gosier, Guadeloupe, France. 2009.