Reliance Fresh, Beauty parlous visits for girls, Barber visits for boys, etc. It is at these times that Autos become extremely important for transport. We took samples of data from the main gate, of entries of vehicles for 13 days. Total number of data points is 995, which we believe will be an adequate representation of average vehicle inflow at any given time of the year. We then proceeded to analyses the distribution of these autos, day-wise, destination-wise, hour-wise, etc. We also did Regression Analysis, Hypothesis testing on our sample of data. Various factors were analyzed and inferences were drawn.
Page 2 ANALYSIS : Given below is a snapshot of our sample data : Data till 8th August was compiled in the afore-mentioned manner. The following is the convention that we have followed during our analysis and in our graphs : Page 3 We then got some interesting observations from our data analysis. This is the snapshot of distribution of vehicles destination-wise: Page 4 Sorting the above data, we got the following average number of vehicles on Weekdays and Weekends : Observation: The average inflow of vehicles on Weekends is higher than that of Weekdays. Inference : Due to no classes on Weekends, Traveling by students increases.
Page 5 Auto volume by Destination : Page 6 Observations: 1. Hostels that see most travel volume are J and H. 2. Hostels that see least travel volume are B, F and K. 3. Hostels see much more travel volume than non-hostels. Inferences: 1 . The reason for J seeing maximum number of autos coming in could be because it has maximum number of inmates. It has x number of rooms, on a triple sharing basis. Since he number of people are more, naturally the number of travel instances are more. 2. The hostel H sees a lot of travel volume because it is a central point on the campus – close to the H mess, Madcap store and academic block.
T probably why people find it convenient to state H as their destination point. 3. The hostels B, F and K are single occupancy hostels. Also, they are occupied primarily by BGP students, who had their mid-term exams in the month on August. Many of them also have their own vehicles. This could be the reason for low volume of travel. 4. The reason for hostels seeing more auto traffic than non-hostels because the number of hostels is much greater than the number of non-hostels. GROUP 26 Page 7 II. Auto volume by Time Interval 1. Entry of autos by hour of the day 2.
Entry of autos by time period (dividing the entire day into 8 periods of three hours each) Page 8 3. Entry of autos by quarter (dividing the day into four quarters) Observations: 1. The most number of autos are seen at 16:00, 19:00 and 17:00 hours. The period of the day where most auto traffic is seen is also the third quarter of the day, from 12:00 hours in the afternoon to 17:00 hours in the evening. 2. The least number of autos are seen at wee hours of 2:00, 3:00 and 4:00 in the morning. The period of the day where least auto traffic is seen is also the first quarter of the day. . There is a sudden increase in auto traffic from 4:00 hours in the morning to 5:00 hours. Inferences: 1. Since on an average, classes for BGP and BGP end in the evening time from 16:00 to 19:00 hours, it is only logical that the number of autos in that period is maximum. 2. The least number of autos is in the first quarter of the day because that is the period where least activity is seen. Page 9 3. The sudden increase in auto traffic from 4:AMA to 5:00 AM can be attributed to he fact that a lot of overnight buses and trains arrive at that time in the morning.
Hence incoming traffic is only likely to see a sudden spike. Ill. Auto volume to specific destinations page 10 Observations: 1 . To Hostel J, we can see that maximum auto traffic is during the third quarter of the day from 16:00 hours to 19:00 hours as we saw in Part II. 2. To Hostel H, we can see that maximum auto traffic is during the period from 19:00 hours to 21 hours. Inferences: 1. As explained in Part II, J hostel sees most traffic in the typical quarter of the day. This is because of this period coinciding with the time when most classes get over. 2.
H hostel however, presents an interesting aberration of seeing most traffic of the day in a different period. This behavior however, is in sync with the analysis of why H hostel sees high traffic in the first place. As it is close to the H mess, folks who are coming from outside around those times find it easier to get dropped off near the H mess because this period coincides with dinner time. ‘V. Auto volume on Saturday and Sunday 1 . Auto traffic on Saturday (03/08/2013) Observations: GROUP 26 page 11 . As is shown from the full dataset, J and H hostels see the most traffic. . Least traffic is to hostels with single occupancy, like B, C, LA, etc. Inferences: 1 . We can see that the data of this particular Saturday, mirrors the data of the entire dataset. We can confidently say that this sample is a good representative of the entire dataset. IV. Standard normal distribution of auto volume, by destination page 12 page 13 Analysis: 1. From the above three graphs, we can infer that by excluding the mathematical outliers, the distribution tends to move more towards being a standard aroma distribution.