Adjustment boarding and alighting passengers on a bus transit line using qualitative information

Abstract

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%.

Redesigning infrastructure for autonomous vehicles and evaluating its impact on traffic

“…Buses with maximum capacity are used in the simulation model because it is considered that double-decked buses are most commonly used during peak hour. de Oña, Juan et al [21] suggested qualitative method of estimating boarding and alighting passenger volume. Our paper also mentions the lack of match between observed boarding and alighting counts and the lack of passenger alighting data in urban buses systems.…”

Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

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