Hichem Omrani

Application of fuzzy TOPSIS in evaluating sustainable transportation systems

Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope us this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards environment. The challenge before the transportation decision makers is how to evaluate and select such sustainable transportation systems. In this paper, we present a multicriteria decision making approach for selecting sustainability transportation systems under partial or incomplete information (uncertainty). The proposed approach comprises of three steps. In step 1, we identify the criteria for sustainability assessment of transportation. In step 2, experts provide linguistic ratings to the potential alternatives against the selected criteria. Fuzzy TOPSIS is used to generate aggregate scores for sustainability assessment and selection of best alternative. In step 3, sensitivity analysis is performed to determine the influence of criteria weights on the decision making process. A numerical illustration is provided to demonstrate the applicability of the approach. The strength of the proposed work is its practical applicability and the ability to generate good quality solutions under uncertainty.

A hybrid approach based on AHP and belief theory for evaluating sustainable transportation solutions

This paper presents a hybrid approach based on AHP and belief theory for evaluating sustainable transport solutions. The approach consists of four steps. In the first step, we identify the criteria for evaluating the transport measure. In the second step, we collect data on the criteria from multiple sources and perform data fusion using belief theory. In the third step, we evaluate the state of sustainability of the city. Finally, in the fourth step, we propose five IF-THEN rules to generate recommendations for implementing the transportation measure. An application on carsharing is provided to illustrate our approach.

Prediction of Individual Travel Mode with Evidential Neural Network Model

An evidential neural network (ENN) for predicting individual travel mode is presented. This model can be used to support management decision making and to build predictions under uncertainty related to changes in peoples behavior, the economic context, or the environment and policy. The presented model uses individuals’ characteristics, transportation mode specifications, and data related to places of work and residence. The data set analyzed was taken from a survey conducted in 2007 and contains information on the daily mobility (e.g., from home to work) of individuals who either lived or worked in Luxembourg. Individual characteristics were extracted to relate daily mobility (journeys between home and work, in particular) to the characteristics of working individuals. Information about public transportation specification and some geographical particularities of residential areas and workplaces were used. Rates of successful prediction obtained by the ENN and several alternative approaches were compared by cross-validation. The results showed that the ENN was superior to the studied alternatives.

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.

An Approach for spatial and temporal data analysis: Application for mobility modeling of workers in Luxembourg and its bordering areas

In this paper, we propose two general analytic methods for building cartographical representations of large amounts of spatial data collected in the form of Origin-Destination (OD) matrices. The use of classical techniques, such as the Linear Directional Mean (LDM), for the mapping of OD data, may lead to cartographical representations which are difficult to interpret visually, especially when the size of the OD matrix is large. The two methods that we propose, which are extensions of Toblers LDM method, overcome this limitation and allow the discovery of interesting mobility patterns: a first extension, the Weighted Linear Directional Mean (WLDM), computes the mean direction of movement by weighting the volumes of mobility flows. The second extension, the Dempster-Shafer Weighted Linear Directional Mean (DS-WLDM), takes into account ignorance (which corresponds to a missing origin and/or destination for a particular movement), as well as incertitude, which may both occur in real OD data. The paper also presents an example in which the two proposed methods are applied to administrative data, in order to evaluate the spatial and temporal aspects of daily and residential mobility of workers between Luxembourg and its bordering areas. The methods we propose are generic and allow the use of multiple spatial scales (e.g. locality, district, municipality etc.), with potential fields of application including the mapping of social and demographic information (e.g. mobility of people, goods and information) as well as the cartographic representation of traffic flows.

A method and a tool for geocoding and record linkage

For many years, researchers have presented the geocoding of postal addresses as a challenge. Several research works have been devoted to achieve the geocoding process. This paper presents theoretical and technical aspects for geolocalization, geocoding, and record linkage. It shows possibilities and limitations of existing methods and commercial software identifying areas for further research. In particular, we present a methodology and a computing tool allowing the correction and the geo-coding of mailing addresses. The paper presents two main steps of the methodology. The first preliminary step is addresses correction (addresses matching), while the second caries geocoding of identified addresses. Additionally, we present some results from the processing of real data sets. Finally, in the discussion, areas for further research are identified.

A Hybrid Approach for Evaluating Environmental Impacts for Urban Transportation Mode Sharing

The current paper presents an AHP based approach for evaluating sustainable transport solution measures like car-sharing, park and ride, access control zones etc. In the first stage, we identify the indicators (criteria) for evaluating the transportation solution measure. These indicators (criteria) can be divided into several sub-indicators (sub-criteria). In the second stage, we allot weights to the indicators and sub-indicators using AHP. The values for the sub-indicators are measured using multiple sources and multiplied with their weights in order to compute state change variable values that form part of the city state equation. The respective values of the state change variables in the city state equation are then used to evaluate the efficiency of the transportation solution measure. Finally, we illustrate our approach by giving an example of car-sharing and measuring its impact on city environmental conditions.

A goal-oriented approach based on fuzzy axiomatic design for sustainable mobility project selection

ABSTRACTSustainable mobility project evaluation is a challenging problem for transportation decision-makers. The specific context of each city, involvement of multiple objectives of stakeholders, g...

A scenario simulation approach for sustainable mobility project evaluation based on fuzzy cognitive maps

Abstract Sustainability evaluation of new urban mobility projects is a challenging decision due to the presence of multiple objectives (social, economic, environmental), limited data availability, presence of multiple stakeholders, and specific context of each city. Scenario modeling and simulation is a useful tool to address such situations. In this paper, we present a fuzzy cognitive mapping-based scenario simulation approach for evaluating sustainable mobility projects. Linguistic assessments and fuzzy set theory are used to address the uncertainty arising from lack of quantitative data. The criteria for sustainability evaluation are obtained using fuzzy Delphi technique. A numerical application is provided for the city of Luxemburg. The strength of the proposed approach is the ability to aid in decision-making of new sustainable mobility project evaluation and selection under limited or no quantitative data availability. In addition, it is able to deal with multiple, co-related and conflicting criteria in evaluation.

Musicological indices for soundscape ecological analysis

Soundscape ecologists have collected sound recordings from large-scale studies that are difficult to analyze with traditional approaches and tools. Natural soundscapes are complex and contain a diverse mixture of biological, geophysical, and anthropogenic sources that span similar frequency bands and often lack a discernible fundamental frequency. Selecting features that are responsive to signals without fundamental frequencies and that are capable of classification for multi-layer signals, or polyphonic textures, is a challenging task in soundscape ecology. Spectral timbral features in various combinations have been shown to discriminate in music classification problems, and lend support to our hypothesis; timbral features in soundscape analysis may detect and identify patterns that are inherently related to order-specific communication in frequency bands shared by biological, geophysical, and anthropogenic sounds. Combined timbral feature extractions provides a new level of information about acoustic ac...