Cognitive Cost in Route Choice with Real-Time Information: An Exploratory Analysis

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

Capturing correlation in large-scale route choice models

When using random utility models for a route choice problem, choice set generation and correlation among alternatives are two issues that make the modeling complex. In this paper we discuss different models capturing path overlap. First, we analyze several formulations of the Path Size Logit model proposed in the literature and show that the original formulation should be used. Second, we propose a modeling approach where the path overlap is captured with a 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 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 model. Moreover, the correlation parameters are significant

Practical solutions for sampling alternatives in large-scale models

Many large-scale real-world transport applications have choice sets that are so large as to make model estimation and application computationally impractical. The ability to estimate models on subsets of the alternatives is thus of great appeal, and correction approaches have existed since the late 1970s for the simple multinomial logit (MNL) model. However, many of these models in practice rely on nested logit specifications, for example, in the context of the joint choice of mode and destination. Recent research has put forward solutions for such generalized extreme value (GEV) structures, but these structures remain difficult to apply in practice. This paper puts forward a simplification of the GEV method for use in computationally efficient implementations of nested logit. The good performance of this approach is illustrated with simulated data, and additional insights into sampling error are also provided with different sampling strategies for MNL

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior

Route Choice Models with Subpath Components

The problem of route choice is critical in many contexts, for example in intelligent transport systems, GPS navigation and transportation planning. In order to capture the complexity of the decision process, disaggregate models, such as discrete choice models are required. In the Multinomial Logit (MNL) model, the alternatives are assumed to be independent. This assumption is not valid in a route choice context due to overlapping paths. Several adaptations of the MNL model have therefore been proposed in the literature, thereof the Path Size Logit model. In this paper we show that, except the original formulation, all Path Size formulations presented in the literature show counter intuitive results regarding the correction of the independence assumption. Furthermore, the generalized Path Size formulation fails its original purpose of penalizing longer paths in favor of shorter ones. There is however an interesting behavioural interpretation of the Path Size attribute. Namely, overlapping paths are attractive since travellers have the possibility of switching between routes. A Path Size attribute (original formulation) could therefore be included in the deterministic part of the utility with a behavioural interpreta

Capturing correlation with subnetworks in route choice models

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 a 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 on 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 applications, 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 Borlänge. 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.

Adaptive route choices in risky traffic networks: A prospect theory approach

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Adaptive Route Choice Models in Stochastic Time-Dependent Networks

We study adaptive route choice models 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 where a sequence of path choice models are applied at intermediate decision nodes; and a routing policy choice model where the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this paper is a decision rule that maps from all possible (node, time) pairs to next links out of the node. A policy-size Logit model is proposed for the routing policy choice, where policy-size is a generalization of path-size in path choice models to take into account the overlapping of routing policies. 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 - non-adaptive 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 non-adaptive model is larger in terms of expected travel time, if the network is more stochastic, indicating that the benefit of being adaptive is more significant in a more stochastic network