Validating expert system prototypes using the turing test

High-performance, expert-level computer systems require that the expert system prototype be continously evaluated during its development. Expert system validation—that is, testing systems to ascertain whether they achieve acceptable performance levels—has with few exceptions been ad hoc, informal, and of dubious value. This paper describes a variant of the Turing Test technique that may be used to formalize the validation process. A microcomputer-based prototype expert system, the Hazardous Location Analyst (HLA), in the accident location analysis domain was used as a case study of the technique. Turing tests provide a blind method for multiple experts to assess expert system performance qualitatively, and provide a means of determining reasonable performance levels for the particular domain. The HLA has been modified based upon findings from the case study work done previously. The improved prototype was implemented in the City of Greensboro, North Carolina, and a set of 10 case studies are analyzed using the HLA, and by a Greensboro traffic engineer. The results were summarized in identical form and mailed to four traffic engineers outside of Greensboro. Their ratings of case study findings were summarized, and the results used to find a reasonable performance level for this application and to assess the performance of the HLA. The consistency of the expert ratings was also assessed. It was concluded that there exists excellent consistency among experts with regard to human performance, but not with regard to the HLA. This paper demonstrates that the Turing Test as a validation methodology provides an objective, quantitative way to assess system performance.     

Running for the 7:45: The effects of public transit improvements on commuter stress

This research project took advantage of the implementation of a major mass transit improvement by New Jersey Transit which provided a "one-seat ride" into New York City for many commuters who previously had to transfer in Hoboken in order to take Port Authority Trans Hudson (PATH) trains into New York City. The creation of this new service provided a natural experiment in which some riders switched to the new route, while others continued to use their previous route. We studied psychological and psychophysiological responses to these commuting options, using a quasi-experimental, pre-post change, field research design.We found that riders on this new line had lower levels of stress, as multiply measured, than they had earlier, before the advent of this new train, or than did other riders currently using the Hoboken-PATH option. The stress effects seemed to be mediated by the time of the trip – that is, the reduced trip time of the new, direct service seemed to be a primary factor in the reduced stress to riders. Predictability of the trip was also inversely correlated with stress, but did not distinguish between the commuter groups. These results were largely replicated with a student group who rode the same lines acting as simulated commuters.     

A statistical process control approach using cumulative sum control chart analysis for traffic data quality verification and sensor calibration for weigh-in-motion systems

Weigh-in-motion systems have been widely used by state agencies to collect the traffic data on major state roadways and bridges to support traffic load forecasting, pavement design and analysis, infrastructure investment decision making, and transportation planning. However, the weigh-in-motion system itself poses difficulties in obtaining accurate data due to sensor characteristics that can be sensitive to vehicle speed, weather conditions, and changes in surrounding pavement conditions. This study focuses on developing a systematic methodology to detect weigh-in-motion sensor bias and enhance current practices for weigh-in-motion calibration. A mixture modeling technique using an expectation maximization algorithm was developed to divide the vehicle class 9 gross vehicle weight into three normally distributed components: unloaded, partially loaded, and fully loaded trucks. Then the well-known statistical process control technique cumulative sum control chart analysis was applied to expectation maximization estimates of daily mean gross vehicle weight for fully loaded trucks to identify and estimate shifts in the weigh-in-motion sensor. Special attention was given to the presence of autocorrelation in the data by fitting an autoregressive time-series model and then performing cumulative sum control chart analysis on the fitted residuals. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the weigh-in-motion sensor accurately and also indicate the time point when the system went out of calibration. This methodology can be effectively implemented by state agencies, resulting in more accurate and reliable weigh-in-motion data.     

Panama Canal expansion: alleviating global climate change

This paper quantifies likely changes in greenhouse gas emissions which contribute to anthropogenic climate change, resulting from the expansion of the Panama Canal and the consequent increase in ship-borne commercial transportation from east Asia to the US east coast. Ocean transportation offers higher fuel economy and lower pollutant emissions compared with land transport. Additionally, truck and train transportation of cargo along the US land bridge threatens to overwhelm existing highway and rail systems and limit economic growth. The alternate transportation route for easterly transit of the Panama Canal will reduce overland traffic congestion and enhance economic development by maintaining freight flow efficiency.