Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level

Barabino, Benedetto; Salis, Sara; Useli, Bruno

doi:10.1016/j.trb.2014.08.001

Cited by 50 publications

(28 citation statements)

References 8 publications

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“…The possibilities of fare evasion exist and thus the passenger demand is underestimated [7]. Exact origin and destination of the passengers cannot be identified [7,20].…”

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

“…The possibilities of fare evasion exist and thus the passenger demand is underestimated [7]. Exact origin and destination of the passengers cannot be identified [7,20]. Matching data to the exact bus stops, data validation and anomalies in the data [7] have to be addressed.…”

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

“…Exact origin and destination of the passengers cannot be identified [7,20]. Matching data to the exact bus stops, data validation and anomalies in the data [7] have to be addressed.…”

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

“…However, in the proof-of-payment type AFC systems, the possibilities of fare evasion exist and thus the passenger demand is underestimated. Hence, there is a need to establish a procedure to tackle the fare evaders to ensure that the demand data of all the passengers [7].…”

Section: Introductionmentioning

confidence: 99%

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Bus Passenger Demand Modelling Using Time-Series Techniques- Big Data Analytics

Cyril¹,

Mulangi²,

Varghese³

2019

TOTJ

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Background: Public transport demand forecasting is the fundamental process of transport planning activity. It plays a pivotal role in the decision making, policy formulations and urban transport planning procedures. In this paper, public bus passenger demand forecasting model is developed using a novel approach. The empirical passenger demand for a bus depot is modelled and forecasted using a data-driven method. The big data generated by Electronic Ticketing Machines (ETM) used for issuing tickets and collecting fares is sourced as the data for demand modelling. This big data is time indexed and hence has the potential for use in time-series applications which were not previously explored. Objectives: This paper studies the application of time-series method for forecasting public bus passenger demand using ETM based time-series data. The time-series approach used is the four Holt-Winters’ modeling methods. Holt-Winters’ additive and multiplicative models with and without damping have been empirically compared in this study using the data from the inter-zonal buses. The data used in the study is a part of the transaction on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at the Trivandrum City depot of an Indian state Kerala, for the period between 2010 and 2013. The forecasting performance of four time-series models is compared using Mean Absolute Percentage Error (MAPE) and the model goodness of fit is determined using information criteria. Conclusion: The forecasts indicate that multiplicative models with and without damping, which better account for seasonal variations, outperform the additive models.

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“…The possibilities of fare evasion exist and thus the passenger demand is underestimated [7]. Exact origin and destination of the passengers cannot be identified [7,20].…”

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

“…Exact origin and destination of the passengers cannot be identified [7,20]. Matching data to the exact bus stops, data validation and anomalies in the data [7] have to be addressed.…”

Section: Data Source-the Electronic Ticketing Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bus Passenger Demand Modelling Using Time-Series Techniques- Big Data Analytics

Cyril¹,

Mulangi²,

Varghese³

2019

TOTJ

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“…Because each card is uniquely numbered, the transaction information could be used to statistic transit trips. According to existing research studies [34,35], AFC data are not free from systematic error causing factors, such as fare evaders; thus, the calculated ridership is underestimated. Considering that the purpose of this paper is to test the correlation between distribution proportions and land use difference which is a relative relationship, this systematic error would not make a significant difference in this relationship.…”

Section: Transit Ridership Data Based On Automatic Fare Collection Datamentioning

confidence: 99%

Transit trip distribution model considering land use differences between catchment areas

Wang

Sun

Rong

et al. 2016

J of Advced Transportation

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Sustainable land use planning and advanced public transport system are believed to be effective solutions to traffic congestion. To this end, it is important to reveal the relationship between transit ridership and land use. However, current Direct Ridership Models only focus on the relationship between single station's boarding volume and the corresponding catchment area land use. This paper analyzed the ridership distribution between transit stations by considering the land use difference between catchment areas. Land use difference was calculated from point of interest (POI) data extracted from an electronic map of Beijing; transit trip distribution volume was obtained from 'automatic fare collection' facility. After data specification, a transit ridership distribution model was proposed and calibrated. The calibration results suggest that land use difference has a directly proportional correlation with transit ridership distribution. The research findings build a bridge between detailed urban form and public transport, which is of significance for the further research of sustainable urban planning.

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Motive des Fahrens ohne (gültigen) Fahrschein

Schwerdtfeger¹

2019

Fahren Ohne (Gültigen) Fahrschein

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Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level

Cited by 50 publications

References 8 publications

Bus Passenger Demand Modelling Using Time-Series Techniques- Big Data Analytics

Bus Passenger Demand Modelling Using Time-Series Techniques- Big Data Analytics

Transit trip distribution model considering land use differences between catchment areas

Motive des Fahrens ohne (gültigen) Fahrschein

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