A multiple regression analysis shows the following relationship for the number of trips per household.  T = 0.82 + 1.3P + 2.1A Where       T = number of trips per household per day   P= number of persons per household                     A= number of autos per household If a particular TAZ contains 250 households with an average of 4 persons and 2 autos for each household, determine the average number of trips per day in that zone

hree routes connect an origin and a destination with performance functions: ?1=8+0.5?1; ?2=1+2?2; and ?3=3+0.75?3; with the x’s being the traffic volume expressed in thousands of vehicles per hour and t’s being the travel time expressed in minutes. If the peak hour traffic demand is 3400 vehicles, determine user equilibrium traffic flows. [Hint: Note that one of the paths will not be used under the equilibrium condition

Q1. The following is the number of trips and the number of households by the number of persons per household and the level of household income in a given zone. Persons/hh 1 2 3 4 5 or above Low Persons/household 1 2 3 4 5 or more No. of hh Trip No. No. of hh 93 222 149 72 341 138 59 417 125 120 1010 109 13 107 37 Income Medium The forecasted number of households in the study area for a target year is shown below. Low 120 100 90 150 30 Trip No. 616 853 1025 1186 457 High No. of hh Trip No. 96 360 27 205 33 381 40 471 33 423 Income Medium 280 220 190 180 60 High 130 40 50 70 60 (a) Calculate the forecasted number of trips for each combination of the number of persons per household and the level of household income. (b) Alternatively, trip rate can be estimated using the following linear regression equation. Trip rate=0.46+ 1.96 × NPERSON + 1.66 x HINCOME Where NPERSON = no. of persons per household (= 5 for 5 or more persons in household); HINCOME = level of household income (= 0 for low…

8.18 Two routes connect an origin and a destination. Routes 1 and 2 have performance functions t₁ = 2 + x₁ and t₂ = 1 + x2, where the 's are in minutes and the x's are in thousands of vehicles per hour. The travel times on the routes are known to be in user equilibrium. If an observation for route 1 finds that the gaps between 30% of the vehicles are less than 6 seconds, estimate the volume and average travel times for the two routes. (Hint: Assume a Poisson distribution of vehicle arrivals, as discussed in Chapter 5.)

The following 3 Travel Demand Forecasting models were created to estimate the number of peak-hour trips in the suburb of Croydon: T1 = 1.0 + 0.3(household size) + 0.01(household income in thousands of $) T2 = 1.5 + 0.2(household size) + 0.01(household income in thousands of $) T3 = 0.5 + 0.5(household size) + 0.01(household income in thousands of $) The suburb has a total of 3500 households with an average of 4 people per household, an average household income of $90,000 and survey data shows that it generates a total of 11,550 trips in the peak-hour. Which of the above models is the most accurate?   A. T1 B. T2 C. T3 D. Can't say as 2 or more models are equally accurate.

The following cross-classification data have been developed for a European city transportation study area. House Hold (HH) Autos/HH (%) Trip Rate/Auto (%) Income Trips (%) ($) High Med Low O1+2 0 1 +2 HBW HBO NHB 10,000 0 30 70 48 48 4 2.0 6.00 28.5 38 34 28 50 | 4 72| 24 2.5 7.5 30.0 38 2 53 45 20,000 30,000 40,000 50,000 50 50 00 19 81 7.5 12.0 39.0 20 50 34 28 10 70 20 4.0 9.0 33.0 35 34 31 20 75 5 1 32 57 5.5 10.5 36.0 27 35 38 37 60,000 70 30 43 40 0010 90 8.0 13.0 41.0 16 44 Develop the family of cross-classification curves and determine the number of trips produced (by purpose) for traffic zone containing 1000 houses with an average household income of $30,000. (Use high = 55,000; medium 25,000; low = 15,000.)

A zone has been divided into four large districts (traffic zones). The following data has been collected for those districts. Provide a trip distribution calculation (single constrained) using the gravity model. Assume Kij = 1 (Check balancing of Tij = A after the first iteration) District 1 2 3 4 Travel Time Fij Productions 3400 6150 3900 2800 1 2.0 Attractions 2800 6500 2550 4400 4 1.6 6 1.0 1 4 11 15 10 9 10 0.9 0.86 Travel time (min) 2 3 11 15 6 6 6 6 9 11 11 12 0.82 0.80 4 10 9 11 4 15 20 0.68 0.49

Consider the following plot of cumulative arriving and departing vehicles at a freeway bottleneck location: Vehicles 20,000 15,000 10,000 5,000 0 0 30 60 90 120 150 180 210 240 270 300 330 360 Time (mins) Arrivals (vehs) Departures (vehs) From this plot, determine the following: A. What is the capacity of the bottleneck location? 9000 whole number, i.e. X000.) B. What is the maximum size of the queue that develops? 3250 whole number, i.e. X000.) C. What is the longest wait time that any vehicle experiences during the breakdown? to the nearest whole number, i.e. XX.) (Hint: Provide the answer as a numerical number only in units of vehicles per hour and rounded to the nearest thousand (Hint: Provide the answer as a numerical number only in units of vehicles and rounded to the nearest thousand (Hint: Provide the answer as a numerical number only in units of minutes and rounded

Q1. The following is the number of trips and the number of households by the number of persons per household and the level of household income in a given zone. Persons/hh 1 2 3 4 5 or above Low No. of hh 93 72 59 120 13 Persons/household 1 2 3 4 5 or more Trip No. 222 341 417 1010 107 Income Medium Low 120 100 90 150 30 No. of hh 149 138 125 109 37 Trip No. 616 853 1025 1186 457 The forecasted number of households in the study area for a target year is shown below. High Income Medium 280 220 190 180 60 No. of hh 96 27 33 40 33 Trip No. 360 205 381 471 423 High 130 40 50 70 60

Determine the share (proportion) of person-trips by each of two modes (private auto and mass transit) using the multinomial logit model and given the following informa- tion: Utility function: U = A - 0.05 T – 0.04 T, – 0.03 T, –0.014 C Parameter Private Auto Mass Transit = access time (min.) T. T = waiting time (min.) T, = riding time (min.) C = out-of-pocket cost (cents) Calibration constant, A 14 20 25 %3D 50 70 250 100 -0.012 -0.068