Applying the theory of reasoned action to understanding consumers’ intention to adopt hybrid electric vehicles in Saudi Arabia

To satisfy the global energy demand while accommodating the rapidly increasing consumption rate in its domestic market, Saudi Arabia must develop and implement fuel efficiency programs in many sectors. In the transportation sector, which is a major contributor to fuel consumption and emissions, hybrid electric vehicles (HEVs) could provide a viable solution, but they are not yet available in the Saudi market. Applying the theory of reasoned action (TRA) and an online questionnaire instrument (N = 847), this paper aims to identify the factors that could drive Saudi citizens’ intention to adopt such technology. We find that the TRA is appropriate to describe intention to adopt HEVs in the Saudi context, and that both subjective norms and attitudes are significant in explaining Saudi consumers’ intention, with subjective norms having three times as strong an effect as attitudes. The findings should be useful to relevant Saudi government officials as they develop and implement transportation-related initiatives and policies, as well as to global auto manufacturers and dealers seeking to tap into Saudi Arabia’s prospective HEV market.

     

American and European transportation responses to the 1973–74 oil embargo

A survey of U.S. and European responses to the energy crises engendered by the Arab boycott of October 1973–April 1974 is presented. U.S. Government policy actions with respect to allocation, rationing, price, and conservation measures are reviewed and contrasted with those of affected European governments. The results of those policies and their effects on transportation energy supply and demand behavior with respect to both commodity and passenger movement are described.Based on short- and long-term impact assessments of the policies considered, the authors identify significant research requirements.The authors were heavily engaged in U.S. responses to the energy emergency, and were asked to undertake study missions to those European nations significantly affected by the crisis to seek out avenues for mutual aid, and joint research, in resolving common energy problems. This paper is one product of those study missions.     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.