Exam four-step-procedure for travel demand estimation Flashcards

Theory questions about the four-step-procedure

1
Q

In what step should a 10% growth in population be added as input? Which model parts will it affect? How is the data used?

March 21, 2022

A

Add as input in trip generation. Higher population will generate more trips. It will then affect all the steps after.

March 21, 2022

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2
Q

Where to introduce a new mode comparable to car?

March 21, 2022

A

Mode choice. Hard to calibrate without observations but start similar to car with parameters and attributes.

March 21, 2022

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3
Q

Stochastic route choice

March 20, 2023

A

Logit-based model where T_ij is the number of travellers in OD-pair (i,j), V_ijr is the utility for OD-pair (i,j) route r and R_pq are alla possible routes for OD-pair (p,q). Lower utility will result in less flow. The traveller will chose route based on perceived value.

March 20, 2023

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4
Q

Wardrop user equilibrium

March 20, 2023

A

Everyone travels according to their best possible choice where the system is in equilibrium when no traveller can change route to get a lower cost. All travel times (cost) will be equal for the OD-pair. h_ijr is the number of travellers choosing route r for OD-pair (i,j) and h_ijr > 0 if route is in use. The travel times for OD-pair (i.j) is given by pi_ij = sum over all links a, (delta^a)ijr ta(va) where ta(va) is the volume delay function, v is the flow and delta indicates links a used in route r for OD-pair (i,j).

March 20, 2023

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5
Q

Differences between stochastic route choice and Wardrop equilibrium.

March 20, 2023

A

There will be a difference in result since congestion is handled different in the models. Wardrop equilibrium is computationally more difficult and is based on obsereved costs while Logit models are based on what is perceived and is easier to compute once the parameters are estimated.

March 20, 2023

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6
Q

Information going from right to left

ExampleExam

A

Travel times (flows and costs data). The rational part is that it may affect mdoe choice and trip distribution. People usually prefer short trips.

ExampleExam

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7
Q

Shortcoming for trip generation

ExampleExam

A

Often based on survey data and statistical data that shows a relation between landuse and trips. For example, households and work locations. But this does not cover every relation.

ExampleExam

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8
Q

Shortcoming for trip distribution

ExampleExam

A

Mode choice is not considered in this step by “default”. The aggregations on zones will also affect the result.

ExampleExam

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9
Q

Shortcoming for mode choice

ExampleExam

A

Logit models require a large amount of data which is expensive and it is hard to collect.

ExampleExam

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10
Q

Shortcoming for route choice

ExampleExam

A

VDFs are hard to formulate, the do not recreate the reality fully since real behaviour is more dynamic than what a function can be. It also asumes static networks where nothing changes even temporarily.

ExampleExam

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11
Q

OD-calibration

March 19, 2021

A

OD-calibration adds data to the OD-matrix. The input is an OD-matrix and traffic counts. The output is a new OD-matrix. It is useful to estiamte and describe the current situation. Since it requires traffic counts, it cannot be used for forecasting becasue we cannot do traffic counts in the future. The behaviour assumption connections made in the four steps will be lost.

March 19, 2021

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12
Q

Input and output for route choice

March 19, 2021

A

Input: Description of network (the transport infrastructure), volume delay functions, demand matrix specific for a certain travel mode and time period.

Output: Flows on the links in the network and costs (travel times) associated to the flows.

March 19, 2021

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13
Q

What happens wehn the number of attracted trips increase?

June 8, 2023

A

It will change the input to the trip generation step. These trips will have to be produced somewhere so the trip production must be updated as well. Likely the numbers must be scaled to match. This will change the output which then goes in to the next step.

June 8, 2023

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14
Q

Improving PT with increased service

June 8, 2023

A

Introduce the change in the mode choice where it will have the most significant effect. Attribute values, for example waiting time, will change. If the total change in mode choice is large, this will affect the route choice and the travel times which should be fed back to the trip distribution.

June 8, 2023

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15
Q

Input in trip generation

March 18, 2019

A

Landuse, for example workplaces and residents.

March 18, 2019

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16
Q

Output of trip generation

March 18, 2019

A

Produced number of trips O_i in zone i and the attracted number of trips D_j in zone j

March 18, 2019

17
Q

Input on trip distribution

March 18, 2019

A

O_i and D_j from trip generation, then we connect the trips with some gravit model kind of optimization problem.

March 18, 2019

18
Q

Output of trip distribution

March 18, 2019

A

Demand matrix T_ij where T is the number of trips between zone i and j

March 18, 2019

19
Q

Input for mode choice

March 18, 2019

A

T_ih from the trip distribution as well as data and observations about different modes.

March 18, 2019

20
Q

Output for mode choice

March 18, 2019

A

Demand matrices T^m_ij where we get the trips made with mode m between zone i and j

March 18, 2019

21
Q

Input for route choice

March 18, 2019

A

T^m_ij from mode choice and then we need network information (transport infrastructure), volume delay functions

March 18, 2019

22
Q

Output for route choice

March 18, 2019

A

Number of travellers or vehicles (the volume) on each route and link in the graph and the corresponding travel times

March 18, 2019