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Approach of deriving statistical prediction model to reduce railway station ticket counter queue length with optimized infrastructure cost

IP.com Disclosure Number: IPCOM000237501D
Publication Date: 2014-Jun-19
Document File: 7 page(s) / 81K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a proposed model for statistical prediction of mass inflow rate in a railway station and accordingly to optimize both queue length as well as infrastructure cost incurred. This model will address the existing issues of high queue length in ticket counters due to insufficient number of workers or counters and idle time due to less number of mass inflow and higer processing capability. This prediction model and cost optimization model together will also address the problems of time wastage, revenue loss and decreased customer satisfaction by considering various influenting factors that cause the variation of mass inflow rate at stations and to predict them with utmost accuracy and thus opening virtual ticketing counters as needed with optimized infrasture cost.

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Approach of deriving statistical prediction model to reduce railway station ticket counter queue length with optimized infrastructure cost

Problem statement


In every railway station there are two types of problems often observed:

Huge queue in front of ticket counters and queue is mostly increasing in length


Very less number of passengers and one or more ticket counters are having no customer

The first category problem causes further effect as:

Wastage of time for the passengers. Time is valuable and delay in getting the tickets cause passengers to miss the train at right time thus causing further harms.

Overload on specific counters resource s: Both the persons and machinery operating in those counters will be overloaded and exhausted.

Revenue loss: If an alternate root of commutation is available, passengers will avail them and hence the railway company will lose revenue

The second category of problem results in the following effects:


Wastage of time: For the railway ticket counter operator
Revenue loss: As idle resource cost are to be considered, hence infrastructure cost and labor cost will cause less profitability.

Known Solution


Unplanned increment or decrement of manually operated ticket counter numbers at the time of specific times in a day based on a overall average traffic of passenger inflow rate.

Drawbacks of Existing Solution


This partially solves the above problems but not at all times provides optimal benefit.

Example: The number of manual operated counter is kept proportional to the average mass inflow in the station over a certain period of time. This average is calculated

Either on empirical basis


Or on observation basis
Or on some standard random days different hour's data point.

Hence this does not take into account various parameters which affect the mass inflow rate in the station properly. Thus it will not give optimal queue length at most of the times and also will not give a cost benefit from ticket counter infrastructure perspective.

Need for New Solution


As described in earlier sections the problems which need to resolved are:

Queue length and waiting time: This has to be reduced so that people do not have to wait for long in a counter to get ticket
Idle resource time and associated cost: It has to be reduced when mass inflow rate is lower than expected and ticket counters are having no/less traffic than it's capacity.

Customer satisfaction: Has to be improved. The low waiting time will improve the customer satisfaction and more people will start availing the train route as preferred mode of communication Better modeling for mass inflow prediction: A better statistical approach is required so that rail companies can predict the mass inflow rate more accurately considering all the parameters that affect the mass inflow rate. This way the requirement for number of open ticket counters will be also predictable in better way.

Cost effectiveness: The concept of virtual counter (automated) will be proposed and a...