Study and Analysis of Delay Factors of Delhi Metro Using Data Sciences and Social Media: Automatic Delay Prediction System for Delhi Metro

Study and Analysis of Delay Factors of Delhi Metro Using Data Sciences and Social Media: Automatic Delay Prediction System for Delhi Metro

Arun Solanki, Ela Kumar
Copyright: © 2018 |Pages: 15
DOI: 10.4018/978-1-5225-3176-0.ch009
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Abstract

Delhi Metro passengers had a difficult time mostly on Monday morning as trains on the busy corridors are delayed due to technical problems or track circuit failure. This study found different factors like power failure, weather, rider load, festive season, etc. which are responsible for the delay of Delhi Metro. Due to these factors, Metro got delayed and run at a reduced speed causing much inconvenience to the people, who are hoping to reach their offices on time. Delhi Metro data are received from different sources which may be structured (timings, speed, traffic), semi-structured (images and video) and unstructured (maintenance records) form. So, there is heterogeneity in data. Except for this data, the feedback or suggestion of a rider is vital to the system. Nowadays riders are using social media like Facebook and Twitter very frequently. Three-tier architecture is proposed for the delay analysis of Delhi Metro. Different implementation techniques are studied and proposed for the social media module and delay prediction modules for the proposed system.
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Delay Factors In Delhi Metro

Identification of the delay factors and their reasons helps the future planning of service, and it can be used for traffic forecast. The delay factors can be broadly divided into two parts:

  • 1.

    Operational Side: Fan and Weston (2012) discuss operational side algorithms like brute force, first-come-first-served, Tabu search, simulated annealing, genetic algorithms, ant colony optimization, dynamic programming and decision tree based elimination are already examined

  • 2.

    Passenger Side: Nagy and Csiszar (2014) in their work discuss the specified factors (weather conditions, lines, service type, etc.) and the punctuality of vehicles (departure and arrival time) is predictable. These values can be used for passenger information on stations as well as on personalized travel information applications like journey planners.

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