Guest Editorial: Special Issue on Energy and Transportation

With the growing urgency to reduce our production of greenhouse gases, the transportation sector, which consumes 28% of the energy in the United States, has attracted increased attention in the push to find alternative energy sources. Personal vehicles consume more than 60% of the energy used in the transportation sector (NAE 2014). The natural question then is, how can we do better? And how can the transportation science community contribute, beyond our traditional strength in making transportation systems more efficient? Our special issue includes papers from four broad areas: • The efficient operation of electric vehicles given limits on range • Forecasting demand for electric vehicles • Planning biofuel supply chains • Meeting carbon emission targets These papers address a variety of analysis problems, including operational management of vehicles with limited range and battery management, forecasting demand for new technologies (an entirely new challenge in demand forecasting), handling feedstock uncertainty in the planning and management of biofuel supply chains, and finally the design of markets for meeting carbon emission targets. These important problems have drawn on all of the tools familiar to transportation scientists, but have introduced fresh challenges to motivate new research. We open the special issue with “Optimal Control of Plug-In Hybrid Electric Vehicles with Market Impact and Risk Attitude” by Lai Wei and Yongpei Guan. This paper addresses the problem of using batteries in electric vehicles as a form of storage which can be bid into both real-time and day-ahead markets by vehicleto-grid aggregators. Wei and Guan develop models and algorithms to solve the day-ahead and real-time bidding problems, considering both risk-neutral and risk-averse settings. They study the effect of storage on electricity prices, and show the benefits of allowing the aggregator to participate in both the day-ahead and real-time markets. Next, the paper “Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions” by Aurelie Glerum, Lidija Stankovikj, Michael Themans, and Michel Bierlaire address the problem of forecasting the demand for vehicle technologies that are only seeing very early adoption, limiting access to historical information. They address the problems of survey design, model estimation, and forecasting, developing in the process a method that corrects for selection biases due to the setting of a new technology. They develop a revealed preference model from stated preference data which required taking into account population structure to forecast demand for new products as they are entered. Not surprisingly, operational challenges remain. In “The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations,” Michael Schneider, Andreas Stenger, and Dominik Goeke consider the challenges of efficiently routing a fleet of limited range electric vehicles with the additional constraint of visiting recharging stations. A variableneighborhood search algorithm with a tabu search heuristic is designed and analyzed, and shown to produce high quality results. The hybrid approach outperforms competing algorithms on “green” vehicle routing problems as well as other vehicle routing problems. We anticipate that as this field matures, the adoption of new technologies will continue to challenge the routing and scheduling community. Jing-Quan Li, in “Transit Bus Scheduling with Limited Energy,” introduces the problems of scheduling battery-operated public transit buses given the option to either swap out a battery, or recharge it. A novel scheduling model is proposed which captures these new choices. The model is then solved using a multistart local-search heuristic.