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  • 1. Bierlaire, Michel
    et al.
    Frejinger, Emma
    Ecole Polytechnique Federale de Lausanne.
    Technical note: A Stochastic Choice Set Generation Algorithm2007Report (Other academic)
  • 2. Bierlaire, Michel
    et al.
    Frejinger, Emma
    Stojanovic, Jelena
    A latent route choice model in Switzerland2006In: Proceedings of the European Transport Conference, 2006Conference paper (Refereed)
    Abstract [en]

    Rature, for example the C-Logit (Cascetta et al., 1996), Path Size Logit (Ben-Akiva and Bierlaire, 1999), Link-Nested Logit (Vovsha and Bekhor, 1998) and subnetwork (Frejinger and Bierlaire, 2006) models. A latent cho- sen route corresponds to an unobserved choice where only an approximate choice description is available. Instead of an exact route description, trav- ellers describe their choice in terms of a sequence of locations and cities that they have traversed, without the need to relate the actual network used by the analyst. We compute the probability of an (aggregate) observation with an un- derlying route choice model using a detailed network description and actual paths. In this context, not only several routes can correspond to the same observation, but the exact origin-destination pair is not necessarily known. We therefore consider several possible origin-destination pairs and their as- sociated set of routes, generated by a choice set generation algorithm. We derive from this list the probability of each observation, in order to perform the maximum likelihood estimation of the route choice model. The methodology is illustrated by estimating Path Size Logit and sub- network models using a dataset collected in Switzerland. This application is one of few based on revealed preferences (RP) data that are presented in the literature. In addition, the network used here (39411 unidirectional links and 14841 nodes) is to our knowledge the largest network used for evaluation of route choice models based on RP data. The estimation results are very sat- isfactory. Indeed, they do not only show that it is possible to estimate route choice models based on aggregate observations, but also that the parameter estimates are stable across different types of models and that the standard errors are small.

  • 3.
    Fosgerau, Mogens
    et al.
    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, Transport and Location Analysis.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Karlström, Anders
    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, Transport and Location Analysis.
    A dynamic discrete choice approach for consistent route choice model estimation2011In: Proceedings of the Swiss Transport Research Conference, 2011Conference paper (Refereed)
    Abstract [en]

    We propose a dynamic discrete choice approach for consistently estimating route choice model parameters based on path observations using maximum likelihood. The approach is computationally efficient and does not require choice set sampling.

  • 4. Fosgerau, Mogens
    et al.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Karlström, Anders
    Consistent route choice model estimation without choice set sampling: a dynamic discrete choice approach2011In: Proceedings of the European Transport Conference, 2011Conference paper (Refereed)
  • 5.
    Fosgerau, Mogens
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Karlström, Anders
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Dynamically choosing routes: A dynamic discrete choice model using Krylov subspace methods and LU decomposition2010Conference paper (Other academic)
  • 6.
    Fosgerau, Mogens
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Frejinger, Emma
    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 and Economics (closed 20110301), Transport and Location Analysis (closed 20110301).
    Karlström, Anders
    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 and Economics (closed 20110301), Transport and Location Analysis (closed 20110301).
    Route choice modeling without route choice2009In: Proceedings of the European Transport Conference, 2009Conference paper (Refereed)
    Abstract [en]

    Route choice modeling is complex. The number of alternative paths is often very large, while the paths are likely to share unobserved attributes which induces correlation. When modelling this, we face a trade-off between using models that are simple enough to handle many alternative paths while at the same time being able to handle correlation. There is a substantial ongoing research effort seeking to resolve this dilemma, so far with limited success. For these reasons the multinomial logit model (path size logit and c-logit proposed by Ben-Akiva and Bierliare, 1999, and Cascetta et. al., 1996, respectively) is widely used in spite of its known limitations.

    The main purpose of this paper is to present and test a dynamic discrete choice approach for the estimation of the parameters of a route choice model. In the dynamic modeling approach, the individual is seen as taking sequential decisions on which link to choose, and the choices are made at the nodes in the network. The obvious advantage with this approach is that the choice set at every stage is quite small and well defined, while a correlation structure is naturally imposed among different paths, even if each sequential decision follows a multinomial logit model. From an econometric point of view, the link choice model can be a lot simper to deal with.

    The utility maximising choice of path may be broken down into a sequence of link choices, where at each stage the individual considers the utility associated with downstream link choices accumulated into a value function. However, if we were to compute the value function associated with the available link choices at every stage, the complexity of the problem would be at least the same as the original path choice problem. An exact solution method to calculate the value function runs into the curse of dimensionality when solving a dynamic programming problem. Therefore, the computational burden may be prohibitive for large networks if one tries to solve the dynamic programming problem by brute force. This is probably why the sequential approach is not used for estimating route choice models in spite of having been around for many years (e.g., Dial, 1971).

    However, it is not strictly necessary to solve the dynamic programming problem in order to estimate the parameters of the route choice model consistently. It is sufficient to find a suitable approximation to the value function. So the objective of this paper is to test whether it is possible to generate good predictors for the value function such that the parameters of the route choice model may be estimated on link choices rather than path choices. If this turns out to be possible, then both the econometric and computational complexity of route choice modelling may be dramatically reduced.

    The paper therefore discusses the conditions under which the route choice model can be consistently estimated. We then test the approach using simulated data for a real network (Borlänge, Sweden), where route choice observations are generated using the exact model, i.e. solving the dynamic programming problem. This allows us to compare the exact value functions with the approximations. We show how the approximation can be defined using proxy variables such as direction and distance to destination. The paper concludes with a discussion on the use of the model for prediction (policy analysis) and related issues.

  • 7.
    Frejinger, Emma
    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, Transport and Location Analysis.
    A dynamic discrete choice approach for modeling route choice2009Conference paper (Other academic)
  • 8.
    Frejinger, Emma
    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, Transport and Location Analysis.
    A logit model for the choice among infinitely many routes in a network2011Conference paper (Refereed)
  • 9.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    A sampling of alternatives approach for route choice models2009Conference paper (Other academic)
  • 10.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Challenges and recent developments in route choice modeling2009Conference paper (Other academic)
  • 11.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Cognitive cost in route choice models2011Conference paper (Refereed)
  • 12.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Estimation of dynamic route choice models2009Conference paper (Other academic)
  • 13.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Introduction of clean cars in Sweden: an analysis of policies and their effects2011Conference paper (Refereed)
    Abstract [en]

    In Sweden there is a tradition of buying large, powerful and heavy carswith high fuel consumption and CO2emissions.  It is the heaviest car fleetin Europe.  Major measures have been taken in order to accelerate the in-troduction of “clean” cars in the fleet.  In response to these measures, therehas been a fast and significant change in the shares of different fuel typestowards higher use of alternative fuels.  In this talk I present the measuresand an analysis of the effects.A recently  started project  aims at developing  a dynamic  model of thecar  fleet based  on registry  data.   I present  the  data  and  some preliminarymodeling ideas as well as a review of relevant literature.This talk is to a large extent based on work by Staffan Algers and MurielBeser Hugosson, both working at the Royal Institute of Technology.

  • 14.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Modellering av ruttval2009Conference paper (Other academic)
  • 15.
    Frejinger, Emma
    Transport and Mobility Laboratory (TRANSP-OR), EPFL.
    Random Sampling of Alternatives in a Route Choice Context2007In: Proceedings of the European Transport Conference, 2007Conference paper (Refereed)
    Abstract [en]

    In this paper we present a new point of view on choice set generation and route choice modeling. Choice sets of paths need to be defined when modeling route choice behavior using random utility models. Existing approaches generate paths and assume that actual choice sets are found. On the contrary, we assume that actual choice sets are the sets of all paths connecting each origin-destination pair. These sets are however unknown and we propose a stochastic path generation algorithm that corresponds to an importance sampling approach. The path utilities should then be corrected according to the used sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm. Furthermore, based on the assumption that actual choice sets contain all paths, we argue that Path Size (or Commonality Factor) attributes should be computed on all paths (or as many as possible) in order to reflect the true correlation structure. We present numerical results based on synthetic data. The results show that models including a sampling correction are remarkably better than the ones that do not. Moreover, unbiased estimation results are obtained if the Path Size attribute is computed based on all paths and not on generated choice sets. In real networks the set of all paths is unknown, we therefore study how many paths are needed for the Path Size computation in order to obtain unbiased results. The parameter estimates improve rather rapidly with the number of paths which is promising for real applications.

  • 16. Frejinger, Emma
    Route choice analysis: data, models, algorithms and applications2008Doctoral thesis, comprehensive summary (Other academic)
  • 17.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Route choice modeling: background and recent developments2010Conference paper (Other academic)
    Abstract [en]

    Predicting traffic of individual drivers' behavior is an important part of many transport relatedapplications, for example, traffic simulation and intelligent GPS navigation. Travelers have differenttrip purposes, habits, preferences, et cetera, and do not necessarily choose the shortest path. For thisreason, random utility models (RUM) are used and the topic of this talk is the estimation of such modelsbased on trip observations in real networks (revealed preferences data) and their application. Using theRUM framework, there are two main issues that need to be addressed; definition of alternatives andmodeling correlation among alternatives. The talk has the following outline. First a background to routechoice modeling (static uni-modal networks) is given and an application in the Swedish city of Borlängeis used as an example. Second, a sampling of alternatives approach for the definition of choice sets ispresented (joint work with Moshe Ben-Akiva and Michel Bierlaire). Finally, some future researchdirections are discussed.

  • 18.
    Frejinger, Emma
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Algers, Staffan
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Beser Hugosson, Muriel
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Habibi, Shiva
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Introduction of clean cars in Sweden: a descriptive analysis2011Conference paper (Other academic)
  • 19.
    Frejinger, Emma
    et al.
    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 and Economics (closed 20110301), Transport and Location Analysis (closed 20110301).
    Bierlaire, M.
    Ben-Akiva, M.
    Sampling of alternatives for route choice modeling2009In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 43, no 10, p. 984-994Article in journal (Refereed)
    Abstract [en]

    This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin-destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm. Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS. Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.

  • 20.
    Frejinger, Emma
    et al.
    EPFL.
    Bierlaire, Michel
    EPFL.
    Capturing correlation in route choice models using subnetworks2006In: Proceedings of the Swiss Transportation Research Conference, 2006Conference paper (Refereed)
    Abstract [en]

    When using random utility models for a route choice problem, choice set generation and correlation among alternatives are two issues that make the modelling complex. In this paper we propose a modelling approach where the path overlap is captured with a subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviourally relevant roads. In practise, the subnetwork can easily be defined based on the route network hierarchy. We propose a model where the subnetwork is used for defining the correlation structure of the choice model. The motivation is to explicitly capture the most important correlation without considerably increasing the model complexity. We present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured both by a Path Size attribute and error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlänge. The results show a significant increase in model fit for the Error Component model compared to a Path Size Logit and Multinomial Logit models. Moreover, the correlation parameters are significant. We also analyse the performance of the different models regarding prediction of choice probabilities. The results show a better performance of the Error Component model compared to the Path Size Logit and Multinomial Logit models.

  • 21. Frejinger, Emma
    et al.
    Bierlaire, Michel
    Capturing correlation with subnetworks in route choice models2006In: 11th International Conference on Travel Behaviour Research (IATBR), 2006Conference paper (Refereed)
  • 22. Frejinger, Emma
    et al.
    Bierlaire, Michel
    Capturing correlation with subnetworks in route choice models2007In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 41, no 3, p. 363-378Article in journal (Refereed)
    Abstract [en]

    When using random utility models for a route choice problem, a critical issue is the significant correlation among alternatives. There are basically two types of models proposed in the literature to address it: (i) a deterministic correction of the path utilities in a Multinomial Logit model (Such as the Path Size Logit or the C-Logit models) and (ii) an explicit modeling of the correlation through assumptions about the error terms, and the use of advanced discrete choice models such as the Cross-Nested Logit or the Error Component models. The first is simple, easy to handle and often used in practice. Unfortunately, it does not correctly capture the correlation structure, as we discuss in details in the paper. The second is more consistent with the modeling objectives, but very complicated to specify and estimate. The modeling framework proposed in this paper allows the analyst to control the trade-off between the simplicity of the model and the level of realism. Within this framework, the key concept capturing the correlation structure is called subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviorally relevant roads. In practice, the subnetwork can easily be defined based oil the route network hierarchy. The importance and the originality of our approach lie in the possibility to capture the most important correlation without considerably increasing the model complexity. This makes it suitable for a wide spectrum of application,.;, namely involving realistic large-scale networks. As an illustration, we present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured by error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlange. The results show a significant increase in model fit and forecasting performance for the Error Component model compared to a Path Size Logit model. Moreover, the correlation parameters are significant.

  • 23.
    Frejinger, Emma
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Bierlaire, Michel
    On Path Generation Algorithms for Route Choice Models2010In: Choice Modelling: The State-of-the-Art and the State-of-Practice / [ed] S. Hess and A. Daly, Emerald Group Publishing Limited , 2010, p. 307-315Chapter in book (Refereed)
  • 24.
    Frejinger, Emma
    et al.
    EPFL.
    Bierlaire, Michel
    EPFL.
    Random Sampling of Alternatives for Route Choice Modeling2007In: Swiss Transport Research Conference, 2007Conference paper (Refereed)
    Abstract [en]

    In this paper we present a new point of view on choice set generation for route choice models. When modeling route choice behavior using random utility models choice sets of paths need to be defined. Existing approaches generate paths and assume that actual choice sets are found. On the contrary, we assume that actual choice sets are the sets of all paths connecting each origindestination pair. These sets are however unknown and we propose a stochastic path generation algorithm that corresponds to an importance sampling approach. The path utilities should then be corrected according to the used sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm. We present numerical results based on synthetic data. The results show that the model including sampling correction yields unbiased coefficient estimates but we also make important observations concerning the Path Size attribute. Namely, it biases the estimation results if it is not computed based on the true correlation structure. These results suggest that the Path Size attribute should be computed based on as many alternatives as possible, more than in the generated choice sets.

  • 25.
    Frejinger, Emma
    et al.
    École Polytechnique Fédérale de Lausanne, Transport and Mobility Laboratory.
    Bierlaire, Michel
    École Polytechnique Fédérale de Lausanne, Transport and Mobility Laboratory.
    Route choice modeling with network-free data2008In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 16, no 2, p. 187-198Article in journal (Refereed)
    Abstract [en]

    Route Choice models arc difficult to design and to estimate for various reasons. In this paper we focus on issues related to data. Indeed, real data in its original format are not related to the network used by the modeler and do therefore not correspond to path definitions. Typical examples arc data collected with the Global Positioning System (GPS) or respondents describing chosen itineraries to interviewers. Data manipulation is then necessary in order to obtain network compliant paths. We argue that such manipulations introduce bias and errors and should be avoided. We propose a general modeling framework that reconcile network-free data with a network based model without data manipulations. The concept that bridges the gap between the data and the model is called Domain of Data Relevance and corresponds to a physical area in the network where a given piece of data is relevant.

    We illustrate the framework on simple examples for two different types of data (GPS data and reported trips). Moreover, we present estimation results of Path Size Logit and Subnetwork models based on a dataset of reported trips collected in Switzerland. The network is to our knowledge the largest one used in the literature for route choice analysis based on revealed preferences data.

  • 26. Frejinger, Emma
    et al.
    Bierlaire, Michel
    Route choice models with subpath components2005In: Swiss Transportation Research Conference, 2005Conference paper (Refereed)
  • 27. Frejinger, Emma
    et al.
    Bierlaire, Michel
    Stochastic Path Generation Algorithm for Route Choice Models2007In: Sixth Triennial Symposium on Transportation Analysis (TRISTAN), 2007Conference paper (Refereed)
  • 28.
    Frejinger, Emma
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Bierlaire, Michel
    Ben-Akiva, Moshe
    Expanded path size attribute for route choice models including sampling correction2009In: International Choice Modelling Conference, 2009Conference paper (Refereed)
  • 29.
    Frejinger, Emma
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Gao, Song
    Ben-Akiva, Moshe
    Hur påverkar information bilisters val av resväg?2011Conference paper (Refereed)
  • 30. Gao, Song
    et al.
    Frejinger, Emma
    Ben-Akiva, Moshe
    Adaptive Route Choice Models in Stochastic and time dependent networks2008In: 10th International Conference on Applications of Advanced Technologies in Transportation, 2008Conference paper (Refereed)
  • 31. Gao, Song
    et al.
    Frejinger, Emma
    Ben-Akiva, Moshe
    Adaptive Route Choice Models in Stochastic Time-Dependent Networks2008In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, Vol. 2085, p. 136-143Article in journal (Refereed)
    Abstract [en]

    Adaptive route choice models are studied that explicitly capture travelers' route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next links out of the node. Existing route choice models that can be estimated on disaggregate revealed preferences assume a deterministic network setting from the traveler's perspective and cannot capture the traveler's proactive adaptive behavior under uncertain traffic conditions. The literature includes a number of algorithmic studies of optimal routing policy problems, but the estimation of a routing policy choice model is a new research area. The specifications of estimating the two adaptive route choice models are established and the feasibility of estimation from path observations is demonstrated on an illustrative network. Prediction results from three models-nonadaptive path model, adaptive path model, and routing policy model-are compared. The routing policy model is shown to better capture the option value of diversion than the adaptive path model. The difference between the two adaptive models and the nonadaptive model is larger in terms of expected travel time if the network is more stochastic, indicating that the benefit of adaptivity is more significant in a more unpredictable network.

  • 32. Gao, Song
    et al.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Ben-Akiva, Moshe
    Adaptive route choices in risky traffic networks: A prospect theory approach2010In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 18, no 5, p. 727-740Article in journal (Refereed)
    Abstract [en]

    This paper deals with route choice models capturing travelers' strategic behavior when adapting to revealed traffic conditions en route in a stochastic network. The strategic adaptive behavior is conceptualized as a routing policy, defined as a decision rule that maps from all possible revealed traffic conditions to the choices of next link out of decision nodes, given information access assumptions. In this paper, we use a specialized example where a variable message sign provides information about congestion status on outgoing links. We view the problem as choice under risk and present a routing policy choice model based on the cumulative prospect theory (CPT), where utility functions are nonlinear in probabilities and thus flexible attitudes toward risk can be captured. In order to illustrate the differences between routing policy and non-adaptive path choice models as well as differences between models based on expected utility (EU) theory and CPT, we estimate models based on synthetic data and compare them in terms of prediction results. There are large differences in path share predictions and the results demonstrate the flexibility of the CPT model to represent varying degrees of risk aversion and risk seeking depending on the outcome probabilities.

  • 33. Gao, Song
    et al.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Ben-Akiva, Moshe
    Cognitive Cost in Route Choice with Real-Time Information: An Exploratory Analysis2011In: Procedia Social and Behavioral Sciences 17 / [ed] Cassidy, M. J. and Skabardonis,A., Elsevier , 2011, p. 136-149Chapter in book (Refereed)
  • 34. Gao, Song
    et al.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Ben-Akiva, Moshe
    Cognitive cost in route choice with real-time information: An exploratory analysis2011In: Transportation Research Part A: Policy and Practice, ISSN 0965-8564, E-ISSN 1879-2375, Vol. 45, no 9, p. 916-926Article in journal (Refereed)
    Abstract [en]

    Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.

  • 35.
    Gao, Song
    et al.
    Univ Massachusetts, Marston Hall 214C,130 Nat Resources Rd, Amherst, MA 01003 USA..
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. Royal Inst Technol, Ctr Transport Studies, SE-10044 Stockholm, Sweden..
    Ben-Akiva, Moshe
    MIT, Cambridge, MA 02139 USA..
    Cognitive Cost in Route Choice with Real-Time Information: An Exploratory Analysis2011In: PAPERS SELECTED FOR THE 19TH INTERNATIONAL SYMPOSIUM ON TRANSPORTATION AND TRAFFIC THEORY / [ed] Cassidy, MJ Skabardonis, A, ELSEVIER SCIENCE BV , 2011, p. 136-149Conference paper (Refereed)
    Abstract [en]

    Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.

  • 36. Glerum, Aurélie
    et al.
    Frejinger, Emma
    Faculté des arts et des sciences, Department of Computer Science and Operations Research Université de Montréal Montréal, Canada.
    Karlström, Anders
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Beser Hugosson, Muriel
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Bierlaire, Michel
    A dynamic discrete-continuous choice model of car ownership and usage2013In: Proceedings of the 13th Swiss Transport Research Conference, 2013, p. 1-13Conference paper (Refereed)
    Abstract [en]

    In this paper we present the methodologicalframework of adynamic discrete-continuouschoicemodel (DDCCM)of car ownership, usage and fuel type. The approach consistsof embeddinga discrete-continuous choice model into adynamic programming (DP)framework. This workproposes the following novel features. First, decisions are modeled at a household level. Sec-ond, we consider an extensive choice variable which involves the car replacement decision,the annual driving distance, the fuel type, the decision to take a company car, or a new versussecond-hand car.

  • 37.
    Habibi, Shiva
    et al.
    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, Transport and Location Analysis.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Sundberg, Marcus
    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, Transport and Location Analysis.
    An Empirical study of aggregation of alternatives and its influence on prediction: case study of car type choice in Sweden2012Conference paper (Other academic)
  • 38.
    Karlström, Anders
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Fosgerau, Mogens
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Solving route choice models in a dynamic discrete choice framework2009In: Proceedings of the 13th Euro Working Group on Transportation Meeting, Italy, 2009Conference paper (Refereed)
  • 39.
    Karlström, Anders
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Frejinger, Emma
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Fosgerau, Mogens
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Transport and Location Analysis (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    A dynamic discrete choice approach for consistent route choice model estimation2009Conference paper (Other academic)
  • 40. Ramos, M
    et al.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Daamen, W
    Hoogendoorn, Serge
    A revealed preference study on route choices in a congested network with real-time information2012In: TransportationArticle in journal (Refereed)
  • 41. Ramos, M
    et al.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Daamen, W
    Hoogendoorn, Serge
    A revealed preference study on route choices in a congested network with real-time information2012In: Proceedings of the International Conference on Travel Behaviour Research, Toronto, Canada, 2012Conference paper (Refereed)
  • 42. Ramos, M
    et al.
    Frejinger, Emma
    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, Transport and Location Analysis.
    Daamen, W
    Hoogendoorn, Serge
    Route choice model estimation in dynamic networks based on GPS data2012Conference paper (Refereed)
1 - 42 of 42
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