Qiong Bao

Serious Injuries: An Additional Indicator to Fatalities for Road Safety Benchmarking

Objectives: Almost all of the current road safety benchmarking studies focus entirely on fatalities, which, however, represent only one measure of the magnitude of the road safety problem. The main objective of this article was to investigate the possibility of including the number of serious injuries in addition to the number of fatalities for road safety benchmarking and to further illuminate its impact on the countries’ rankings. Methods: We introduced the technique of data envelopment analysis (DEA) to the road safety domain and developed a DEA-based road safety model (DEA-RS) in this study. Moreover, we outlined different types of possible weight restrictions and adopted 2 of them to indicate the relationship between road fatalities and serious injuries for the sake of rational benchmarking. One was a relative weight restriction based on the information of their shadow price, and the other was a virtual weight restriction using a priori knowledge about the importance level of these 2 aspects. Results: By computing the most optimal road safety risk scores of 10 European countries based on the different models, we found that United Kingdom was the only best-performing country no matter which model was utilized. However, countries such as The Netherlands, Sweden, and Switzerland were no longer best-performing when the serious injuries were integrated. On the contrary, Spain, which ranked almost at the bottom among all of the countries when only the number of road fatalities was considered, became a relatively well-performing country when integrating its number of serious injuries in the evaluation. In general, no matter whether the countrys road safety ranking was improved or deteriorated, most of the countries achieved a higher risk score when the number of serious injuries was included, which implied that compared to the road fatalities, more policy attention has to be paid to improve the situation of serious injuries in most countries. Conclusions: Given the importance of considering the serious injuries in addition to the fatalities for international benchmarking of road safety, the proposed model (i.e., the DEA-RS model with weight restrictions) turned out to be effective in deriving reasonable results. We are thereby also inspired to apply this kind of model to a more complete road safety benchmarking practice in the future when the data on, for example, the number of slight injuries, the degree of property damage, and the number of crashes are ready (i.e., comparable) to use.

Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices – average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations – are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs.

Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model

Nowadays, considerable attention has been paid to the activity-based approach for transportation planning and forecasting by both researchers and practitioners. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time, especially when large amount of population and detailed geographical unit level are taken into account. In this research, we investigated the possibility of restraining the size of the study area in order to reduce the computation time when applying an activity-based model, as it is often the case that only a small territory rather than the whole region is the focus of a specific study. By introducing an accuracy level of the model, we proposed in this research an iteration approach to determine the minimum size of the study area required for a target territory. In the application, we investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders, Belgium, with regard to two different transport modes, that is, car as driver and public transport. Afterwards, a validation analysis and a case study were conducted. All the experiments were carried out by using the FEATHERS, an activity-based microsimulation modeling framework currently implemented for the Flanders region of Belgium.

Travel Demand Forecasting Using Activity-Based Modeling Framework FEATHERS: An Extension

Activity‐based travel demand modeling is the approach with most relation and need for intelligent solutions as it directly aims at reproducing human decision making in daily life. Therefore, the way to implement the selected intelligent solution plays an important role in the successful application of the models. FEATHERS (the Forecasting 16 Evolutionary Activity‐Travel of Households and their Environmental RepercussionS) is an activity‐based microsimulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. For the sake of more detailed travel demand forecasting, we investigated in this study the extension of the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems was compared. The extended modeling system was further applied to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders.

Activity-Based Travel Demand Modeling Framework FEATHERS: Sensitivity Analysis with Decision Trees

The decision tree technique is increasingly applied in activity-based travel demand modeling. It has the strength of representing the full complexity of interactions between different variables. However, this complexity often hinders an interpretation of the relative effects of these variables on the activity travel choice. In this study, a sensitivity analysis is performed on decision trees in FEATHERS, an activity-based microsimulation modeling framework, with the purpose of quantitatively measuring the relative effect of input variables involved in the given decision trees on the choice variable. The local and global sensitivity analysis approaches are investigated: (a) a one-at-a-time approach that predicts the choice frequency distribution by varying selected input variables one after another and keeping all other variables as observed and (b) the improved Sobol method, which evaluates the effect of an input variable while all other variables are varied as well. With the application of these two approaches to two representative decision trees concerning work-related activity (i.e., commute trip) choice and transport mode choice for work-related activities in the FEATHERS framework, consistent results concerning the key input variables for these two decision trees are derived, and some extra insights are gained from each of the approaches.

Geographical Extension of the Activity-based Modeling Framework FEATHERS

Abstract FEATHERS is an activity-based micro-simulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km 2 . In this study, we investigated the transferability of applying the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km 2 . In this paper, we elaborated the data processing procedure in order to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems were compared. The results indicated the validity and also the necessity of this extension.

Research on Restrained Study Areas for Effective Activity-based Travel Demand Forecasting

Recently, considerable attention has been devoted to studying the activity-based approach for transportation planning and forecasting. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time. This research investigated the possibility of restraining the size of the study area to reduce the computation time when applying an activity-based model. By introducing an accuracy level of the model, the authors proposed an iterative approach to determine the minimum size of the study area required for a target territory. In the application, the authors investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders with regard to two different transport modes: car as driver and public transport. Additionally, a validation analysis was conducted. All of the experiments were carried out by using the Forecasting Evolutionary Activity-Travel of Households and their Environmental Repercussion (FEATHERS) framework, an activity-based micro-simulation modeling framework currently implemented for the Flanders region of Belgium.

TOPSIS and its Extensions: Applications for Road Safety Performance Evaluation

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the wellknown classical multi-criteria decision making (MCDM) techniques. In this chapter we illustrate the use of this method to combine individual safety performance indicators (SPIs) into an overall index of road safety performance for a set of European countries. In this respect, to deal with the subjective kind of uncertainty on data (such as linguistic variables given by experts) which are usually adopted to assess the weights of criteria/indicators, we explore an extension of the classical TOPSIS method to fuzzy environments. Moreover, due to the ever increasing number of SPIs used to reflect each road safety risk factor in a more comprehensive way, we consider a hierarchical structure of the indicators in this study. Accordingly, a hierarchical fuzzy TOPSIS model is realized and applied to combine the multilayer indicators into one overall index. Comparison of the results based on the three models (i.e. the classical TOPSIS, the fuzzy TOPSIS, and the hierarchical fuzzy TOPSIS) demonstrates the effectiveness of applying the hierarchical fuzzy TOPSIS method to handle the problem of linguistic expression instead of crisp values given by experts, and to take the layered hierarchy of the indicators into account which is seldom considered in the current road safety index research.

An Excellent Guest Professor in Hasselt University

Prof. Da Ruan died completely unexpected on July 31 2011. Since September 2005, Da Ruan was a guest professor at IMOB. As a guest professor he was involved in several teaching and research activities. As part of his teaching activities he lectured on Knowledge-Based Systems and Computational Intelligence. As part of his research activities he worked closely with several IMOB researchers where his outstanding expertise regarding fuzzy sets and multi criteria analysis proved valuable w.r.t. their doctoral research.