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• 1.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
Five papers on large scale dynamic discrete choice models of transportation2018Doctoral thesis, comprehensive summary (Other academic)

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.

• 2.
KTH, School of Architecture and the Built Environment (ABE), Transport Science.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
Optimal pedestrian evacuation using Model Predictive Control2013In: 2013 European Control Conference, ECC 2013, IEEE , 2013, p. 1224-1229Conference paper (Refereed)

During an emergency in a building complex, an effective evacuation is essential to avoid crowd disasters. This article presents a route guiding that minimize the evacuation time during the evacuating of pedestrians from a building.

• 3.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. Royal Institute of Technology.
KTH, Superseded Departments (pre-2005), Infrastructure and Planning.
A joint between-day and within-day activity based travel demand with forward looking individualsManuscript (preprint) (Other academic)

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.

• 4.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. Royal Institute of Technology.
KTH, Superseded Departments (pre-2005), Infrastructure and Planning. KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
Discount factors greater than or equal to one in infinite horizon dynamic discrete choice modelsManuscript (preprint) (Other academic)

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.

• 5.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. Royal Institute of Technology.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Transport Science. KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
A dynamic discrete choice activitybased travel demand modelManuscript (preprint) (Other academic)

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.

• 6. Glerum, Aurélie
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. Royal Institute of Technology.
A dynamic discrete-continuous choice model of car ownership, usage and fuel typeManuscript (preprint) (Other academic)

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.

• 7.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
An Activity Based Demand Model for Large Scale Simulations2018In: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops, Elsevier, 2018, Vol. 130, p. 920-925Conference paper (Refereed)

This paper presents the ongoing development of SCAPER, a random utility based travel demand model that consistently incorporates time decisions. The paper focusses on how SCAPER can be used for large scale simulations, and more specifically: 1. How computational speed of SCAPER is improved using sampling of locations, and how it influences the simulation results. 2. Interfacing SCAPER to MATSim simulation framework, and estimating the SCAPER model with travel times and travel costs produced by the simulation of Stockholm demand (simulated) over Stockholm network using MATSim. 3. Preliminary results from 1 and 2.

• 8.
Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ, Canada.;CIRRELT Interuniv Res Ctr Entreprise Networks Log, Montreal, PQ, Canada..
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ, Canada.;CIRRELT Interuniv Res Ctr Entreprise Networks Log, Montreal, PQ, Canada.. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
Capturing correlation with a mixed recursive logit model for activity-travel scheduling2018In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 93, p. 273-291Article in journal (Refereed)

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 of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual 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 build on previous work by Blom Vastberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. 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.

• 9.
Department of Computer Science and Operations Research, Universit\'e de Montr\'eal, QC, Canada.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics. Royal Institute of Technology. Department of Computer Science and Operations Research, Universit\'e de Montr\'eal, QC, Canada.
Capturing correlation with a mixed recursive logit model for activity-travel schedulingManuscript (preprint) (Other academic)

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.

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