“Optimization of Employee Transport Facility”

“Optimization of Employee Transport Facility”

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Atul Ashok Kamble
Executive -GIS Data Analyst
Central Mechanical Engineering Research Institute
Lavasa Corporation Ltd. (An HCC Group Company)
Mumbai, India
[email protected]

Yogesh Suhas Kale
Executive – GIS Analyst
Lavasa Corporation Ltd. (An HCC Group Company)
Mumbai, India
[email protected]

Introduction:
GIS has often been effectively used for Route Optimization. Unlocking new age technology such as Google Earth and Google Map applications in conjunction with traditional methods for data collection and generation, allows us to reach building level information and avail the advantages of precise location at an affordable price.

The following case study discusses how Route Optimization of Employee Transport Facility was extended to the individual level, thanks to new age technology and GIS, which is otherwise seldom possible for a public transport system.

Context:
LAVASA (An HCC Group Company) is an upcoming ex-urban town planned for the upwardly mobile population. It is a part of 25,000 acres of land declared as “Hill Station”, located in the western mountain ranges of India, very near to the two major metropolises, Mumbai and Pune. The developed road via Chandni Chowk (Pune) makes LAVASA approximately an hour’s drive, approximately 65 km, from Pune.

Transport facility for about 160 employees staying in Pune had to be provided by the company. Formulating an optimized vehicle route for the “Pick Up and Drop” of all employees in the organization was a complex task for the Administration Department, especially when several constraints existed, such as:

  1. The route should facilitate the pickup of maximum number of employees
  2. The distance to the pick up point for every employee must be as less as possible
  3. The time taken by the vehicle to reach each pick up point should be minimal
  4. The distance traveled by every vehicle should be optimized
  5. From the starting point of route, the vehicle should reach the decided common destination within approximately 25 minutes, before going to LAVASA site

Prior to this, GIS in LAVASA had been effectively used for site suitability analyses, for Environment and Engineering applications, for automation of mapping and facilities management, database creation and management, to name a few. However, it was a special opportunity when the organization decided to start its own bus service for employees and wanted GIS to be used for its route optimization.

Brief Methodology Flowchart:

Description:

  1. Detailed addresses of employees were collected.
  2. These addresses were located and marked on Google Earth as new place marks at building level. These place marks were later saved into Google Earth’s standard .kml/.kmz files.
  3. Google Maps was used to extract street details and these were updated with the existing surveyed route data in the GIS.
  4. Thus, using the existing surveyed route data, Google maps and Google Earth, the GIS ready, network enabled data was created. Lavasa Corporation Limited (An HCC Group Company) Optimization of Employee Transport Facility

  5. Cluster of employees were visually identified and dense areas were demarcated.
  6. Considering every employee as a point, a buffer of 2 km for every employee location was created in ArcGIS. This was the maximum distance an employee would travel to reach the probable bus stop.
  7. Intersection of nearest main road with the 2km buffer gave the road of interest
  8. The data for important locations, gathered from ground survey and Google Earth communities was used to locate important landmarks within the buffer and on the road of interest
  9. The above procedure gave the probable bus stop locations.
  10. The collection of bus stops intern, defined the probable routes for the buses.
  11. At an average speed of 50kmph the bus would cover 21 km in 25 minutes. Accordingly, the approximate starting point for each route was decided.
  12. Impedance per route of 5minutes was distributed over 5 stops, making it 1 min per stop. A buffer time of 10 minutes was added, making it an approximately 40 minutes journey.
  13. Some minor adjustments were made for location of bus stops to reduce the travel time for certain routes. This led to minor increase in ‘distance to bus stop’ for a few employees.

Result:

Six routes were successfully derived for busses with the above methodology. 1 route remained an exception though.

The output of this was an optimized route map that considered the convenience of almost all of the 160 employees and satisfied the stipulated conditions given by the Administration Department as well.

Conclusion:

The GIS fraternity has often used Network Analyst for route optimization. Generally it has been used for a City Level Public Transport Facility. However, such an endeavor of taking into consideration every individual employee is seldom made. That is one rare case presented above.

Also, it has been long since people in GIS domain have been struggling unavailability of desired data at the right time. The above case study presents an example of unlocking modern, cheap, easily available technology to the fullest for arriving at a practical solution.

Google Earth and Google maps were readily used for collecting data, right upto building level of every employee. Precise location was available at affordable price. The above endeavor is replicable over all other case studies. Further accuracy in data can be obtained by using the Professional version of Google Earth. It is also extendible to location based solutions.