Transport Modelling and Operations - Traffic Survey - Engineering Assignment Help

Assignment Task :

Problem 1: Speed, Flow and Density Modelling using Tube Count Data (50 marks) 

A traffic survey has been conducted on Gardeners Road (highlighted in red), between the intersection of Botany Road and O’Riordan Street, as shown in Figure 1. The survey used automatic tube vehicle counts (pneumatic tubes) which collected speed and volume data between 30/08/2020 and 5/09/2020 (7 day survey period). The data is presented in the associated spreadsheet

Traffic Survey 

Analyse the provided data and answer the following questions; 

a) Within the data set are there observations where zero vehicles were recorded (Flow = 0)? Hypothetically if we did observe zero vehicle data, do we include this data in our assessment? Discuss the potential issues of including and excluding these observations within our data set. 

b) Determine the peak flow periods during the AM and PM time periods. What activities would create these peak conditions? Consider the surrounding land uses when answering the question.  

For the remainder of the question, remove the observations where zero flow occurs and assess conditions for BOTH eastbound and westbound directions of travel. 

c) Determine the daily time mean speeds and the time mean speed for the entire survey period. 

d) Determine the daily space mean speeds and the space mean speed for the entire survey. 

e) A Traffic Engineer used the data obtained from traffic survey, traffic flow theory and linear regression to estimate the Greenshields model for the speed-density relationship of Gardeners Road (for both directions of travel) using the following methodology. 

1. Use speed data observations from the traffic survey as “speed measurements” for the model. 

2. Estimate observed density values for each corresponding speed observation, by using the fundamental relationship k = q/vs?. Flow (q) is considered to be the hourly observed flows, while speed (vs?) is assumed to be the speed observations from the data collection. 

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