Pub Date : 2013-05-29DOI: 10.1109/ICADLT.2013.6568437
M. Driss, Thierry Saint-Gérand, A. Bensaid, K. Benabdeli, M. Hamadouche
The significant growth generally observed in road transportation has led to serious human and economic losses as a result of road accidents. This observation calls for considerable attention from civil security policies and requires a precise and rigorous identification of public action priority sectors. In this paper, we propose a traffic accident prediction system based on fuzzy logic which allows to identify “the degree of exposure to road accidents' risk”, and to analyze the level of complexity of the factors involved. We focus our study on the possible influence of a series of local criteria observed and selected for each kilometer per segment of the road network studied. The study was conducted on a road network within the rural area of the Wilaya of Mascara in the northwestern region of Algeria. After data analysis and simulation conducted using Matlab/Simulink, a series of logical rules using multiple fuzzy membership functions were implemented on the evaluation criteria observed. The evaluation system has an adaptive capacity and an automatic learning advantage and provides a very important contribution as a treatment system contributing to measure the risk of road accidents to improve the level of safety on the roads. A Geographic Information System (GIS) was integrated into the analysis process to enable a spatial visualization of the degrees of exposure to road accidents' risk, providing a cartographically measurable solution to establish and attenuate accident risk. Results show that the developed system can be effectively applied as a useful Road Safety tool capable of identifying risk factors related to the characteristics of the road.
{"title":"A fuzzy logic model for identifying spatial degrees of exposure to the risk of road accidents (Case study of the Wilaya of Mascara, Northwest of Algeria)","authors":"M. Driss, Thierry Saint-Gérand, A. Bensaid, K. Benabdeli, M. Hamadouche","doi":"10.1109/ICADLT.2013.6568437","DOIUrl":"https://doi.org/10.1109/ICADLT.2013.6568437","url":null,"abstract":"The significant growth generally observed in road transportation has led to serious human and economic losses as a result of road accidents. This observation calls for considerable attention from civil security policies and requires a precise and rigorous identification of public action priority sectors. In this paper, we propose a traffic accident prediction system based on fuzzy logic which allows to identify “the degree of exposure to road accidents' risk”, and to analyze the level of complexity of the factors involved. We focus our study on the possible influence of a series of local criteria observed and selected for each kilometer per segment of the road network studied. The study was conducted on a road network within the rural area of the Wilaya of Mascara in the northwestern region of Algeria. After data analysis and simulation conducted using Matlab/Simulink, a series of logical rules using multiple fuzzy membership functions were implemented on the evaluation criteria observed. The evaluation system has an adaptive capacity and an automatic learning advantage and provides a very important contribution as a treatment system contributing to measure the risk of road accidents to improve the level of safety on the roads. A Geographic Information System (GIS) was integrated into the analysis process to enable a spatial visualization of the degrees of exposure to road accidents' risk, providing a cartographically measurable solution to establish and attenuate accident risk. Results show that the developed system can be effectively applied as a useful Road Safety tool capable of identifying risk factors related to the characteristics of the road.","PeriodicalId":269509,"journal":{"name":"2013 International Conference on Advanced Logistics and Transport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130043126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-01DOI: 10.5220/0004832804250433
Younes Rahmani, A. Oulamara, Wahiba Ramdane-Chérif
An extended variant of Location-Routing Problem namely LRP with Multi-Product and Pickup and Delivery (LRPMPPD) is considered in this study. The proposed model deals with simultaneously selecting (locating) one or more facilities from a set of potential hub (locations), assigning customers to the selected hubs and defining routes of the vehicles for serving multi-product customers demand in such way that each selected hub must be visited once for delivering, though they can be visited many times for picking up. We propose a mixed integer linear programming formulation and a heuristic approach for this problem. Since there is not any instance compatible with LRP-MPPD in the literature, we have extended known LRP instances to evaluate the performance of the proposed approach. A comparison with CPLEX shows that the proposed algorithm is a viable approach to solve small and large size LRP-MPPD instances.
{"title":"Multi-products Location-Routing Problem with Pickup and Delivery","authors":"Younes Rahmani, A. Oulamara, Wahiba Ramdane-Chérif","doi":"10.5220/0004832804250433","DOIUrl":"https://doi.org/10.5220/0004832804250433","url":null,"abstract":"An extended variant of Location-Routing Problem namely LRP with Multi-Product and Pickup and Delivery (LRPMPPD) is considered in this study. The proposed model deals with simultaneously selecting (locating) one or more facilities from a set of potential hub (locations), assigning customers to the selected hubs and defining routes of the vehicles for serving multi-product customers demand in such way that each selected hub must be visited once for delivering, though they can be visited many times for picking up. We propose a mixed integer linear programming formulation and a heuristic approach for this problem. Since there is not any instance compatible with LRP-MPPD in the literature, we have extended known LRP instances to evaluate the performance of the proposed approach. A comparison with CPLEX shows that the proposed algorithm is a viable approach to solve small and large size LRP-MPPD instances.","PeriodicalId":269509,"journal":{"name":"2013 International Conference on Advanced Logistics and Transport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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