Tom Brijs

Using association rules for product assortment decisions: a case study

It has been claimed that the discovery of association rules is well-suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. Moreover, recent work indicates that the discovery of interesting rules can in fact only be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fullyautomated convenience store is used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.

Studying the effect of weather conditions on daily crash counts using a discrete time-series model

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

Benchmarking road safety: Lessons to learn from a data envelopment analysis

Road safety performance indicators (SPI) have recently been proposed as a useful instrument in comparing countries on the performance of different risk aspects of their road safety system. In this respect, SPIs should be actionable, i.e. they should provide clear directions for policymakers about what action is needed and which priorities should be set in order to improve a countrys road safety level in the most efficient way. This paper aims at contributing to this issue by proposing a computational model based on data envelopment analysis (DEA). Based on the model output, the good and bad aspects of road safety are identified for each country. Moreover, targets and priorities for policy actions can be set. As our data set contains 21 European countries for which a separate, best possible model is constructed, a number of country-specific policy actions can be recommended. Conclusions are drawn regarding the following performance indicators: alcohol and drugs, speed, protective systems, vehicle, infrastructure and trauma management. For each country that performs relatively poor, a particular country will be assigned as a useful benchmark.

Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model

In recent years, data mining researchers have developed efficient association rule algorithms for retail market basket analysis. Still, retailers often complain about how to adopt association rules to optimize concrete retail marketing-mix decisions. It is in this context that, in a previous paper, the authors have introduced a product selection model called PROFSET. This model selects the most interesting products from a product assortment based on their cross-selling potential given some retailer defined constraints. However this model suffered from an important deficiency: it could not deal effectively with supermarket data, and no provisions were taken to include retail category management principles. Therefore, in this paper, the authors present an important generalization of the existing model in order to make it suitable for supermarket data as well, and to enable retailers to add category restrictions to the model. Experiments on real world data obtained from a Belgian supermarket chain produce very promising results and demonstrate the effectiveness of the generalized PROFSET model.

An evaluation of graduated driver licensing programs in North America using a meta-analytic approach

Most jurisdictions in North America have some version of graduated driver licensing (GDL). A sound body of evidence documenting the effectiveness of GDL programs in reducing collisions, fatalities and injuries among novice drivers is available. However, information about the relative importance of individual components of GDL is lacking. The objectives of this study are to calculate a summary statistic of GDL effectiveness and to identify the most effective components of GDL programs using a meta-analytic approach. Data from 46 American States, the District of Columbia and 11 Canadian jurisdictions are used and were obtained from the Fatality Analysis Reporting System (FARS) for the U.S. and from Transport Canadas Traffic Accident Information Database (TRAID) for Canada. The timeframe of this evaluation is 1992 through 2006, inclusive. Relative fatality risks and their 95% confidence intervals were calculated using fatality counts and population data for target and comparison groups, both in a pre-implementation and post-implementation period in each jurisdiction. The target groups were 16-, 17-, 18- and 19-year-old drivers. The comparison group was 25-54-year-old drivers. The relative fatality risks of all jurisdictions were summarized using the random effects DerSimonian and Laird model. Meta-regression using Restricted Maximum Likelihood (REML) and Markov Chain Monte Carlo (MCMC) Gibbs sampling was also conducted. Strong evidence in support of GDL was found. GDL had a positive and significant impact on the relative fatality risk of 16-year-old drivers (reduction of 19.1%). Significant effects were found for meta-regression models with 16-, 18- and 19-year-old drivers. These effects include length of night restriction in the learner stage, country, driver education in the learner stage and in the intermediate stage, whether night restrictions are lifted in the intermediate stage for work purposes, passenger restriction in the intermediate stage, whether passenger restrictions in the intermediate stage are lifted if passengers are family members, and whether there is an exit test in the intermediate stage. In conclusion, several GDL program components have an important effect on the relative fatality risk of novice drivers. These results help understand how such effects are achieved.

Building an Association Rules Framework to Improve Product Assortment Decisions

It has been claimed that the discovery of association rules is well suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. However today, one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. Therefore, recent work indicates that the discovery of interesting rules can in fact best be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fully automated convenience store are used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.

Explaining variation in safety performance of roundabouts.

The conversion of an intersection into a roundabout has been proven to reduce generally the number of crashes with injuries or fatalities. However, evaluation studies frequently showed considerable individual differences in safety performance of roundabouts or particular groups of roundabouts. The main purpose in the present study was to explain the variance in safety performance of roundabouts through the use of state-of-the-art cross-sectional risk models based on crash data, traffic data and geometric data of a sample of 90 roundabouts in Flanders-Belgium. Poisson and gamma modelling techniques were used, the latter one since underdispersion in the crash data was observed. The results show that the variation in crash rates is relatively small and mainly driven by the traffic exposure. Vulnerable road users are more frequently than expected involved in crashes at roundabouts and roundabouts with cycle lanes are clearly performing worse than roundabouts with cycle paths. Confirmation is found for the existence of a safety in numbers-effect for bicyclists, moped riders and - with less certainty - for pedestrians at roundabouts.

Injury crashes with bicyclists at roundabouts: influence of some location characteristics and the design of cycle facilities

PROBLEM Previous research indicated that conversions of intersections into roundabouts appear to increase the number of injury crashes with bicyclists. However, it was assumed that the effectiveness of roundabouts could vary according to some differences in design types of cycle, facilities and other geometrical factors. METHOD Regression analyses on effectiveness-indices resulting from a before-and-after study of injury crashes with bicyclists at 90 roundabouts in Flanders, Belgium. RESULTS Regarding all injury crashes with bicyclists, roundabouts with cycle lanes appear to perform significantly worse compared to three other design types (mixed traffic, separate cycle paths, and grade-separated cycle paths). Nevertheless, an increase of the severest crashes was noticed, regardless of the design type of the cycle facilities. Roundabouts that are replacing signal-controlled intersections seem to have had a worse evolution compared to roundabouts on other types of intersections. IMPACT ON INDUSTRY The results might affect design guidelines for roundabouts, particularly for the accommodation of bicyclists.

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand ones own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a countrys road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the models input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.