One route or the other? : development and evaluation of a day-to-day route choice model incorporating the principljes of inertial behavior and quantification of the indifference band based on a real-world experiment

Essen, Mariska van (2014) One route or the other? : development and evaluation of a day-to-day route choice model incorporating the principljes of inertial behavior and quantification of the indifference band based on a real-world experiment.

Abstract:Background Nowadays, traffic management is very important in daily life. Traffic management measures are developed based on extensive analyses mainly on travel behavior. The main interest of this report is route choice behavior, which is an important part of travel behavior, and route choice modeling. The most commonly used route choice theory is the utility maximization theory, which is based on the assumption that all travelers are optimizers with perfect knowledge about their choice set, presuming perfect information, rationality and homogeneity. However, there still exist some discrepancies between real-world route choice behavior and modeled route choice behavior. Therefore, the behavioral aspects of route choice have gained more and more attention in the transportation research field. Many researchers have proposed adaptations to the current modeling practice in order to include behavioral principles that are more reality alike and therewith reduce the gap between model results and in reality observed behavior. However, only a few of these studies are based on a real-world data. This gave reason for the Virginia Tech Transportation Institute to perform a large scale real-world experiment on this issue in which they asked 20 individuals to complete 20 driving sessions containing five different trips. Based on this experiment, Vreeswijk, Rakha, Van Berkum, and Van Arem (n.d.) identified four choice strategies and found that a significant number of choices concern route alternatives with the non-shortest travel time. The obtained data is used in this research to improve the understanding of route choice behavior and develop a new route choice model using the four choice strategies. Most researches focus on route switching, while examining the behavior of individuals not changing their route choice is just as valuable. This non-switching behavior is caught in the term inertia, which represents the tendency of individuals to continue choosing their current path. As a result, this research will focus on inertial behavior and the corresponding inertia thresholds in route choice behavior. Research objective and relevance The objective of this research is to develop and evaluate a route choice model based on the notions of inertia and the indifference band in order to improve predictions on daily route choices of individuals and to quantify the indifference band. The focus of the research will lie on pre-trip route choices under day-to-day dynamics for the next day that a certain trip will be made. The four choice strategies as identified by Vreeswijk et al. (n.d.) will be used as a starting point. This research is important for the transportation research field as it aims at improving route choice predictions on daily route choices of individuals and therewith reduce the gap between observed real-world behavior and modeled route choice behavior. This gap reduction makes it possible for transport operators to apply their traffic management measures more effectively. These measures might be able to push individuals towards a system optimum, which realizes a more optimal use of the transportation network but is suboptimal on the individual level. It is believed that travel information can play an important role in this. However, insights in the effect of travel information on route choice behavior are necessary. In order to obtain these insights, first, a better understanding of route choice behavior in general is needed. Insights in the inertia thresholds can 5 | P a g e indicate to what extent individuals can be pushed into a specific direction. Besides this, research directions for further improvements in the field of route choice modeling can be identified. Research method In order to achieve the research objective, several steps are taken. Through a literature study the theoretical framework is shaped and the scope of the research is determined. Subsequently, the available data is analyzed. These first steps create initial feeling for the data and the principles of inertia and the indifference band. Together with a short analysis of the findings on explanatory attributes for inertial behavior and the corresponding indifference band within literature, the findings of this data-analysis is used to identify different variables that might be important in explaining inertial behavior. These variables are then used in a regression analysis in order to identify the most important explanatory variables. A regression model predicting certain choice behavior (i.e. the used choice strategy) is obtained, which is implemented within a model framework. This model is calibrated and validated using an enterwise regression method and a jack-knife cross-validation method. Subsequently, the model is extended using an agent-based approach based on Bayesian simulation in order to see the effect of this approach on the model performance. Then, the model is evaluated by executing a sensitivity analysis, followed by a comparison of the model performance to the model performance of five state-of-the-art models; the shortest path theory, the prospect theory, the regret theory, the fixed thresholds theory and the SILK-theory. Lastly, the indifference band is quantified by altering the model attribute related to travel time within the developed model. Besides this, the data-analysis and the fixed threshold theory are used to quantify the indifference band for comparison. Results This research resulted in a newly developed route choice model based on the principles of inertia, shown in figure 1. This 2-step-model consists of a Dynamic Expected Shortest Path Module and a Choice Strategy Module. The first module determines a preliminary choice based on a travel time updating process and the second module alters this preliminary choice based on the choice strategy predicted by the implemented regression model. An updating process for the expectation of the different route alternatives is based on a smoothing factor weighting the last experienced travel time in relation to previous experiences. 6 | P a g e The most suitable regression model turned out to be a combined model, based on the identification of four observed choice strategies; minimizing by switching (i.e. switches to shortest route alternative), minimizing by non-switching (i.e. sticks to shortest route alternative), inertia (i.e. sticks to longer route alternative) and compromising (i.e. switches to the longer route alternative). This combined model contains two sub-models; one that is applied if at day -1 the longer route alternative was chosen and an inertial choice strategy is possible (i.e. the inertia sub-model), and one that is applied if the shortest route alternative was chosen at day -1 and a compromising choice strategy is possible (i.e. the compromising sub-model). According to this combined model, individual characteristics and situation-specific characteristics where found to be most important in explaining exposed choice strategies, while variables on experience were found to be less important. The newly developed 2-step-model predicts the observed route choices of the available dataset in 75.35% of the cases correctly which places it among the highest of all state-of-the-art models. It is found that certain state-of-the-art models perform better on certain OD-pairs than others and vice versa. This indicates that in certain circumstances or choice situations a certain route choice model would be most suitable. Therefore, a hybrid model could significantly improve current modeling practice. The model performance of the prospect theory (43.17%) and the regret theory (65.88%) suggest that these choice models might not be that suitable in predicting route choices. On the contrary, the fixed threshold theory performs very well on capturing the day-to-day dynamics of route choices with 79.02% correctly predicted cases. Figure 1: Developed model framework ‘2-step-model’ Dynamic Expected Shortest Path Module Shortest expected travel time for day by individual i Choice Strategy Module Regression model prediction Regression model (i.e. combined model) Update expected travel time by individual for both route alternatives for day +1 Preliminary route choice day Final route choice day Update variables (e.g. # of switches) for day +1 Initialization Characteristics of individual + Route characteristics of choice situation Initial expected travel time (avg) by individual on both routes of choice situation 7 | P a g e In order to extend the 2-step-model transforming it into an agent-based route choice model, the Bayesian modeling approach is used to simulate 1000 individuals obtaining 1000 sets of parameter representations β. When these are applied on the available dataset observations 74.55% of the cases are correctly predicted if the correlations between the model parameters are considered using the Cholesky Decomposition tool. Ignoring these parameter correlations leads to a model performance of only 51.51%. This indicates that the explanatory variables of route choice behavior are strongly correlated and are therefore crucial in obtaining accurate model results in micro-simulations. Lastly, the indifference band is quantified using data-analysis, the fixed threshold theory and the 2-step-model. Inertia thresholds between 12.1% and 22.1% of the average trip travel time are found on an individual level. On the situational level (i.e. per OD-pair) this is 12.6% to 16.3% of the average trip travel time. Subconscious indifference bands based on perception errors (7.5%-8.7% of the average trip travel time) seem to be generally lower than conscious thresholds based on inertial behavior. These findings give an indication to what extent individuals can be pushed into a certain direction in order to realize a more optimal use of the transportation network. Data-analysis already showed that 1/3 of the observed choices contained, in terms of travel time, a suboptimal route choice. Based on this it seems that individuals do not necessarily (want to) use the optimal travel time alternative, emphasizing the potential of management measures pushing individuals into a certain suboptimal choice direction in order to establish a system optimum in the road network. Recommendations It is recommended to improve the current 2-step-model by further examining the effect of the travel time updating process, the determination of the initial expected travel time and how to reduce the available route alternatives to only two possible alternatives (as the current model can only be applied in the case that two choice options are available). Furthermore, it might be useful to apply the 2-step-model as well as the state-of-the-art models on other datasets in order to gain some more insights on the model performance in different choice situations. Eventually, it might be possible to determine which model would be best applicable in which situation, leading towards the development of a hybrid model. In addition, it is interesting to examine how travel time information affects the model performance of the developed model. Finally, if the model is improved and further investigation is conducted, the 2-step-model can be employed in the route choice modeling practice.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
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