Estimation of cost and CO2 emissions with a sustainable cross-border supply chain in the automobile industry: A case study of Thailand and neighboring countries

CO₂ emissions are increasing because of the growth in the cross-border supply chain, which is leading the locations of assembly plants and suppliers to spread across a wider area. Given that one passenger vehicle needs more than 20,000 components and parts, the automobile industry exploits the cross-border supply chain. Recently, the free cross-border movement of people, goods, capital, and information has accelerated in Asia. Therefore, a sustainable cross-border supply chain is required to reduce both CO₂ emissions and cost. This study estimates total CO₂ emissions per vehicle including production and transportation processes in Thailand and neighboring countries and the change in CO₂ emissions based on future policy scenarios that consider the automobile market and locational conditions in 2030. The results show that locating production close to the place of consumption and the electricity emissions factors in each country should be considered.     

Book review

TRANSPORTATION ENGINEERING, by Jason C. Yu. Elsevier North Holland, New York, 1982. 462 pp. ($32.50 U.S. and Canada, $55.75 elsewhere)

FUNDAMENTALS OF TRAFFIC ENGINEERING, 10th Edition by W. S. Hombur‐ger and James H. Kell. University of California, Institute of Transportation Studies, 1981.

DECISION THEORY AND INCOMPLETE KNOWLEDGE, by Z. W. Kmietowicz and A. D. Pearman. Gower Publishing Co., Aldershot, England, pp. 121. (£12.50)

URBAN PUBLIC TRANSPORTATION, by Vukan R. Vuchic. Prentice Hall Inc., Englewood Cliffs, N.J. 1981. 673 pp. (£27.20)

AUTOS, TRANSIT AND CITIES, by John R. Meyer and Jose A. Gomez‐Ibanez. Harvard University Press, Cambridge Mass., 1981. 359 pp. ($20.00)

PUBLICITY AND CUSTOMER RELATIONS IN TRANSPORT MANAGEMENT, by David W. Wragg. Gower. 144 pp. (£12.50 case)     

Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge

Short-term forecasting of traffic characteristics, such as traffic flow, speed, travel time, and queue length, has gained considerable attention from transportation researchers and practitioners over past three decades. While past studies primarily focused on traffic characteristics on freeways or urban arterials this study places particular emphasis on modeling the crossing time over one of the busiest US–Canada bridges, the Ambassador Bridge. Using a month-long volume data from Remote Traffic Microwave Sensors and a yearlong Global Positioning System data for crossing time two sets of ANN models are designed, trained, and validated to perform short-term predictions of (1) the volume of trucks crossing the Ambassador Bridge and (2) the time it takes for the trucks to cross the bridge from one side to the other. The prediction of crossing time is contingent on truck volume on the bridge and therefore separate ANN models were trained to predict the volume. A multilayer feedforward neural network with backpropagation approach was used to train the ANN models. Predicted crossing times from the ANNs have a high correlation with the observed values. Evaluation indicators further confirmed the high forecasting capability of the trained ANN models. The ANN models from this study could be used for short-term forecasting of crossing time that would support operations of ITS technologies.