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Five papers on large scale dynamic discrete choice models of transportation
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0001-7379-8840
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

Travel demand models have long been used as tools by decision makers and researchers to analyse the effects of policies and infrastructure investments. The purpose of this thesis is to develop a travel demand model which is: sensitive to policies affecting timing of trips and time-space constraints; is consistent with microeconomics; and consistently treats the joint choice of the number of trips to perform during day as well as departure time, destination and mode for all trips. This is achieved using a dynamic discrete choice model (DDCM) of travel demand. The model further allows for a joint treatment of within-day travelling and between-day activity scheduling assuming that individuals are influenced by the past and considers the future when deciding what to do on a certain day.

Paper I develops and provides estimation techniques for the daily component of the proposed travel demand model and present simulation results provides within sample validation of the model. Paper II extends the model to allow for correlation in preferences over the course of a day using a mixed-logit specification. Paper III introduces a day-to-day connection by using an infinite horizon DDCM. To allow for estimation of the combined model, Paper III develops conditions under which sequential estimation can be used to estimate very large scale DDCM models in situations where: the discrete state variable is partly latent but transitions are observed; the model repeatedly returns to a small set of states; and between these states there is no discounting, random error terms are i.i.d Gumble and transitions in the discrete state variable is deterministic given a decision.

Paper IV develops a dynamic discrete continuous choice model for a household deciding on the number of cars to own, their fuel type and the yearly mileage for each car. It thus contributes to bridging the gap between discrete continuous choice models and DDCMs of car ownership.

Infinite horizon DDCMs are commonly found in the literature and are used in, e.g., Paper III and IV in this thesis. It has been well established that the discount factor must be strictly less than one for such models to be well defined.Paper V show that it is possible to extend the framework to discount factors greater than one, allowing DDCM's to describe agents that: maximize the average utility per stage (when there is no discounting); value the future greater than the present and thus prefers improving sequences of outcomes implying that they take high costs early and reach a potential terminal state sooner than optimal.

##### Abstract [sv]

Modeller för reseefterfrågan har länge använts av besultsfattare såväl somforskare för att analysera effekterna av transportpolitiska åtgärder. Avhandlingenshuvudsakliga syfte har varit att bidra till utvecklandet av modellerför reseefterfrågan som är: känsliga för åtgärder som påverkar tidsvalför resor eller tids-rums begränsningar; och konsistent behandlar valet avantalet resor, avresetid, destination och färdmedel för en individ. Dettauppnås genom användandet av en dynamisk diskret valmodell (DDCM) förreseefterfrågan. Modellen klarar vidare av att gemensamt modellera bådedagligt resande med hänsyn till hur det påverkar behovet av andra resoröver en längre tidshorisont, där individer antas ta hänsyn till både när desenaste utfört olika aktiviteter samt framtida effekter av sina besult.

Papper I utvecklar den dagliga komponenten i den föreslagna modellenför reseefterfrågan, presenterar en estimeringsteknik samt resultat från simuleringarmed valideringsresultat. Papper II förbättrar modellen genom attinkludera korrelation i preferenser under dagen med hjälp av en mixed-logitspecifikation. Papper III introducerar en koppling mellan dagar genom enDDCM med oändlig tidshorisont. För att den kombinerade modellen skullevara möjlig att estimera härleddes vilkor under vilka sekvensiell estimeringvar möjlig. Dessa vilkor möjligör därmed estimering av en specific typ avstorskaliga DDCM modeller i situationer när: den diskreta tillståndsvariabelnär delvis latent men där val observeras; där modellen återkommer tillett mindre tillståndrum; och där det mellan återkomsten till detta mindretillståndrum inte sker någon diskontering, nyttofunktionernas feltermer gesav i.i.d Gumble termer och övergångarna mellan disrekta tillståndsvariablerär deterministisk givet valet.

Papper IV utvecklar en dynamiskt diskret-kontinuerlig valmodell för etthushålls beslut gällande antalet bilar att äga, deras bränsletyp samt årligamiltal för varje bil. Det därmed till att komibinera dynamiska och diskretkontinulerligavalmodeller för bilägande.

DDCM med oändliga tidshorisonter är vanligt förekommande och användsi bland annat Papper III och IV i den här avhandlingen. Det harvarit väl etablerat att diskonteringsfaktorn måste vara strikt mindre än ettför att sådana modeller ska vara väldefinerade. Papper V visar hur det ärmöjligt tillåta diskonteringsfaktorer större än eller lika med ett, och därmedbeskriva agenter som: maximerar den genomsnittliga nyttan per steg (närdet inte sker någon diskontering); värderar framtiden högre än nutiden ochdärmed föredrar förbättrande sekvenser vilket också implicerar att de tarhöga kostnader så tidigt som möjligt och når ett potentiellt sluttillståndtidigare än optimalt.

##### Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018.
##### National Category
Transport Systems and Logistics
##### Research subject
Transport Science
##### Identifiers
OAI: oai:DiVA.org:kth-219882DiVA, id: diva2:1165859
##### Public defence
2017-01-19, Kollegiesalen, Brinellvägen 8, Stockholm, 13:00 (English)
##### Supervisors
Available from: 2017-12-14 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved
##### List of papers
1. A dynamic discrete choice activitybased travel demand model
Open this publication in new window or tab >>A dynamic discrete choice activitybased travel demand model
##### Abstract [en]

During the last decades, many activity-based models have been developed in the literature. However, especially in random utility based models timing decisions are often treated poorly or inconsistently with other choice dimensions. In this paper we show how dynamic discrete choice can be used to overcome this problem. In the proposed model, trip decisions are made sequentially in time, starting at home in the morning and ending at home in the evening. At each decision stage, the utility of an alternative is the sum of the one-stage utility of the action and the expected future utility in the reached state.

The model generates full daily activity schedules with any number of trips that each is a combination of one of 6 activities, 1240 locations and 4 modes. The ability to go from all to all locations makes evaluating the model very time consuming and sampling of alternatives were therefore used for estimation. The model is estimated on travel diaries and simulation results indicates that it is able to reproduce timing decisions, trip lengths and distribution of the number trips within sample.

To explain when people perform different activities, two sets of parameters are used: firstly, the utility of being at home varies depending on the time of day; and secondly, constants determine the utility of arriving to work at specific times. This was enough to also obtain a good distribution of the starting times for free-time activities.

##### Keywords
Travel demand, Activity based model, dynamic discrete choice model
##### National Category
Transport Systems and Logistics
##### Research subject
Transport Science
##### Identifiers
urn:nbn:se:kth:diva-219815 (URN)
##### Note

QC 20171214

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved
2. Capturing correlation with a mixed recursive logit model for activity-travel scheduling
Open this publication in new window or tab >>Capturing correlation with a mixed recursive logit model for activity-travel scheduling
##### Abstract [en]

Representing activity-travel scheduling decisions as path choices in a time-space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters. Relaxing the independence from irrelevant alternatives (IIA) assumption of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize the correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to extend previous work that allow to model complex and realistic correlation patterns in this type of network. The resulting model is a mixed recursive logit which keeps the advantages of the recursive logit for prediction. We use sampled choices sets in order to estimate the model in reasonable time for large-scale, dense time-space networks. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.

##### Keywords
travel demand modeling, activity-travel scheduling, mixed recursive logit, activity network, mode choice
##### National Category
Transport Systems and Logistics
##### Research subject
Transport Science
##### Identifiers
urn:nbn:se:kth:diva-219879 (URN)
##### Note

QC 20171214

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved
3. A joint between-day and within-day activity based travel demand with forward looking individuals
Open this publication in new window or tab >>A joint between-day and within-day activity based travel demand with forward looking individuals
##### Abstract [en]

Including day-to-day planning to account for systematic variability in activity participation has the potential to further improve travel demand models. This paper introduce a dynamic discrete choice model of day-to-day and within-day planning in a joint framework. No model up to date jointly treats within-day and day-to-day planning with individuals that take future days into account. The model is estimated using a combination of a small survey with week long data and a larger single day travel survey. A static, myopic and forward looking version of the model is estimated. There is a big improvement in model fit when moving from a static to a dynamic model, but allowing forward-looking behaviour gives a relatively small additional improvement. As a policy test, grocery stores are closed on Sundays. The myopic model predicts that people as a consequence will shop more on Mondays-Thursdays and therefore unintuitively also less on Saturdays. The forward looking model also predicts increased shopping on weekdays but mainly that people will shop more on Saturdays anticipating that stores are closed on Sundays.

##### National Category
Transport Systems and Logistics
##### Research subject
Transport Science
##### Identifiers
urn:nbn:se:kth:diva-219877 (URN)
##### Note

QC 20171214

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved
4. A dynamic discrete-continuous choice model of car ownership, usage and fuel type
Open this publication in new window or tab >>A dynamic discrete-continuous choice model of car ownership, usage and fuel type
##### Abstract [en]

This paper presents a dynamic discrete-continuous choice model of car ownership, usage, and fuel type that embeds a discrete-continuous choice model into a dynamic programming framework to account for the forward-looking behavior of households in the context of car acquisition. More specifically, we model the transaction type, the choice of fuel type, and the annual driving distance for up to two cars in the household. We present estimation and cross-validation results based on a subsample of the Swedish population that is obtained from combining the population and car registers. Finally we apply the model to analyze a hypothetical policy that consists of a subsidy that reduces the annual cost of diesel cars.

##### National Category
Transport Systems and Logistics
##### Research subject
Transport Science
##### Identifiers
urn:nbn:se:kth:diva-219880 (URN)
##### Note

QC 20171214

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved
5. Discount factors greater than or equal to one in infinite horizon dynamic discrete choice models
Open this publication in new window or tab >>Discount factors greater than or equal to one in infinite horizon dynamic discrete choice models
##### Abstract [en]

In this paper, the theory on infinite horizon DDCM's is extended to allow for discount factors greater than or equal to one. The proposed methods are applied to Rust's (1987) bus engine replacement model, where a discount factor of 1.075 is identified using grid search. The infinite horizon problem with and without a terminal state are treated separately. Sufficient conditions are given for the existence of solutions to Bellman's equation in the terminal state problem and to a normalized version of Bellman's equation in the non-terminal state setting. If a terminal state exists, acting according to Bellman's equation still yields the maximum expected total utility under derived conditions on the one-stage utility functions and reachability of the terminal state. In the non-terminal state problem, $\beta=1$ implies that individuals maximize the average cost per stage, but for $\beta>1$ no rationale for acting according to Bellman's equation, even when it has a solution, has been found.

##### National Category
Transport Systems and Logistics Economics
##### Research subject
Transport Science; Economics
##### Identifiers
urn:nbn:se:kth:diva-219878 (URN)
##### Note

QC 20171214

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-14Bibliographically approved

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Cite
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• ieee
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