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Professional Achievements

Significant Contributions to Research

The research to-date has produced a number of highly trained safety professionals (at the Masters and PhD levels) and help advance our state-of-knowledge internationally on how safety can be incorporated into the planning, design and operation of complex transportation systems. There are essentially five major areas of contribution from the research that has been carried out, thus far, in the area of transportation safety.


1) A comprehensive understanding of crash prediction models and how these models can be used to identify hotspots and how countermeasures affect safety at these hotspots. The methodology that has been adopted includes a wide range of observational crash prediction models such as, Poisson and Negative Binomial models, empirical Bayesian models, use of Collision Modification Factors, Zero- Inflated Negative Binomial models and full Bayesian models. This work has highlighted a number of deficiencies or shortcomings for these types of models that need to be addressed, and which have not been resolved satisfactorily in the existing literature. These problems include, regression-to-the-mean
bias, presence of ecological fallacy, treatment of confounding factors, large numbers of zero crash events in the observational data, etc. This has re-directed our research focus into the area of microscopic traffic simulation models.


2) Development of microscopic traffic simulation models. A number of widely distributed traffic simulation models were assessed including VISSIM, PARAMICS and INTEGRATION. The key contribution of this research has been the integration of safety performance into the framework of established traffic simulation models. A number of different safety performance measures were considered from the literature and were found to be lacking in their representation of vehicle interaction risks. This research ultimately lead to the development of a new and more promising indicator. The incorporation of safety performance into traffic simulation models requires a comprehensive calibration
procedure whereby input parameter values for the underlying car-following, gap-acceptance, and lane- changing driver behaviour models produce accurate traffic outputs over time.


3) Application of microscopic models of safety performance to truck speed limiters. Unfortunately, the safety implications of mandated truck speed limiters are not well understood and this has led to a divergence of views on their possible effectiveness. The Ontario Ministry of Transportation (MTO) has taken the position that mandated truck speed limiters (set at 105 km/h) will reduce crashes and the severity of crashes where large trucks are involved. This position is supported by a number of industry and regulatory agencies in Canada, such as, Ontario Trucking Association (OTA), Canadian Trucking Alliance (CTA), Canada Safety Council and the Canadian Lung Association. On the other side, a number of groups have taken the position that mandated truck speed limiters can lead to increased crash risk in terms of both frequency and severity.
A study carried out by the applicant, funded by Transport Canada, adopted a calibrated
microscopic simulation model to investigate the safety implications of mandated truck speed limiters on freeways. The model was applied to a number of maximum speed control strategies including 105 km/h as mandated recently in the Provinces of Ontario and Quebec. This study found that truck speed limiters produced positive safety gains for different assumed volumes and percentage trucks and different compliance levels. Under certain conditions, however, such as high volumes and high percentage of trucks, speed limiters set at 105 km/h were found to result in a reduction in safety, especially at on- and off-ramp segments of freeways. This study provided very useful information to policymakers in both the federal and provincial governments and has been presented by Transport Canada to the annual meeting of transportation ministers.

4) Grade crossing safety. This research consisted of three phases: 1) identification of hotspots among the 30,000 level crossings currently comprising the Canadian rail network; 2) estimation of countermeasure effects, in terms of both reductions in the likelihood of crashes at a given crossing and their corresponding consequences (e.g. personal injuries and property damages); 3) the development of a decision support manual for use by regional rail safety professionals to decide which crossings need
attention and how their safety can best be enhanced. The decision support model has been converted to a web-based application with a corresponding user manual under the title Grade-X. This package has already been applied by a number of rail regional branches across the country.


5) Risk-based placement of dangerous goods cars along a train consist. Train derailments are important safety issues, and they become even more critical when dangerous goods (DG) are involved. This research has been concerned with mitigating derailment risk through improved operational strategies with a specific focus on dangerous goods marshalling practice in the train assembly process. A new modeling framework has been proposed to determine how the position of DG railway cars in a train affects their chances of being involved in a derailment along a given track segment or route. The underlying problem was formulated as a linear integer programming problem. However, since this is
computationally intractable, a heuristic method was developed based on a genetic algorithm that produces a near optimum solution. The model was applied to a hypothetical rail corridor and this has demonstrated how effective marshalling of DG along a train can reduce overall derailment risks. The current status of this work is to apply the model to a realistic corridor data with representative DG and non-DG volumes. Such a corridor has been identified connecting Los Angeles to Chicago.