Traffic data obtained in the field usually have some errors. For instance, traffic volume data on the various links of a network must be consistent and satisfy flow conservation, but this rarely occurs. This paper presents a method for using fuzzy optimization to adjust observed values so they meet flow conservation equations and any consistency requirements. The novelty lies in the possibility of obtaining the best combination of adjusted values, thereby preserving data integrity as much as possible. The proposed method allows analysts to manage field data reliability by assigning different ranges to each observed value. The paper is divided into two sections: The first section explains the theory through a simple example of a case in which the data is equally reliable and a case in which the observed data comes from more or less reliable sources, and the second one is an actual application of the method in a freeway network in southern Spain where data were available but some data were missing.
a b s t r a c tObtaining data to use in an urban public transport operation planning and analysis is problematic, particularly in urban bus transit lines. In an urban environment and for bus services, most ticketing methods can be used to record passengers getting on board but not getting off, and current methods are unable to make a proper adjustment of boardings and alightings based on the available data unless they do alighting counts. This paper presents a method whereby counts are made at fewer stops and qualitative information on alightings and/or vehicle loads between consecutive stops is used to make the boarding and alighting adjustment as a previous step to obtain the real origin and destination (O/ D) of passengers allowing the O/D matrix calibration by using the loads between stops. Qualitative information can be obtained by the vehicle's driver or an on board observer, avoiding the necessity of counting many stops in planning period. The method is applied to a real bus transit line in Malaga (Spain) and to a set of 50 different bus transit lines with number of stops ranging from 10 to 75. The results show that the proposed method reduces the adjustment errors with regard to traditional methods, such as Least Square Method, even in the situation where no qualitative information is used. When qualitative data is used on alightings and loadings, the reduction of the average error is over 50%.
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