A portfolio theory of route choice

Although many individual route choice models have been proposed to incorporate travel time variability as a decision factor, they are typically still deterministic in the sense that the optimal strategy requires choosing one particular route that maximizes utility. In contrast, this study introduces an individual route choice model where choosing a portfolio of routes instead of a single route is the best strategy for a rational traveler who cares about both journey time and lateness when facing stochastic network conditions. The proposed model is compared with UE and SUE models and the difference in both behavioral foundation and model characteristics is highlighted. A numerical example is introduced to demonstrate how such model can be used in traffic assignment problem. The model is then tested with GPS data collected in metropolitan Minneapolis–St. Paul, Minnesota. Our data suggest there is no single dominant route (defined here as a route with the shortest travel time for a 15 day period) in 18% of cases when links travel times are correlated. This paper demonstrates that choosing a portfolio of routes could be the rational choice of a traveler who wants to optimize route decisions under variability.     

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.     

The influence of travel time variability on the value of time

Current benefits from travel time savings have only been related to the benefits from reducing mean travel time. Some previous attempts of including variability in the generalised cost function have mainly assumed commuters with fixed arrival time. This paper presents a comprehensive framework for valuing travel time variability that allows for any journey purpose and arrival time constraint. The proposed model is based on the expected utility approach and the mean-standard deviation approach. Stated Preference methods are considered the best technique for providing the data for calibrating the models. The values of time derived from the models are highly influenced by the value of travel time variability and it strongly depends on the probability distribution function travellers are faced with.     

Investigating the effect of travel time variability on drivers' route choice decisions in Shanghai,China

Abstract

This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty.     

Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?

This paper deals with the question of whether the capability of car drivers to estimate the cost of a new hypothetical, highly differentiated congestion charge influences their decision to change travel behaviour. The analysis makes use of an integrated choice and latent variable model (ICLV) which merges classic choice models with the structural equation approach (SEM) for latent variables. This hybrid model improves the explanatory power considerably compared with a conventional discrete choice model. The results suggest that charge complexity decreases the resistance in considering behavioural changes. Car drivers tend to avoid a travel option where the price is not known beforehand, a phenomenon known as ambiguity avoidance.     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

On the cost of misperceived travel time variability

Because individuals may misperceive travel time distributions, using the implied reduced form of the scheduling model might fall short of capturing all costs of travel time variability. We reformulate a general scheduling model employing rank-dependent utility theory and derive two special cases as econometric specifications to study these uncaptured costs. It is found that reduced-form expected cost functions still have a mean–variance form when misperception is considered, but the value of travel time variability is higher. We estimate these two models with stated-preference data and calculate the empirical cost of misperception. We find that: (i) travelers are mostly pessimistic and thus tend to choose departure times too early to achieve a minimum cost, (ii) scheduling preferences elicited using a stated-choice method can be relatively biased if probability weighting is not considered, and (iii) the extra cost of misperceiving the travel time distribution might be nontrivial when time is valued differently over the time of day and is substantial for some people.     

Route choice decision under travel time uncertainty

We study route choice behavior when travel time is uncertain. In this case, users choice depends both on expected travel time and travel time variability. We collected survey data in the Paris area and analyzed them using a method based on the ordered probit. This leads to an ordinal as well as to different cardinal measures of risk aversion. Such an approach is consistent with expected and with non-expected utility theory. Econometric estimates suggest that absolute risk aversion is constant and show that risk aversion is larger for transit users, blue collars and for business appointments.     

Bayesian predictive travel time methodology for advanced traveller information system

Travellers can benefit from the availability of point‐to‐point driving time estimates on a real time basis for making travel decisions such as route choice at strategic locations (e.g. junctions of major routes). This paper reports a predictive travel time methodology that features a Bayesian approach to fusing and updating information for use in advanced traveller information system. The methodology addresses the issue that data captured in real time on travel conditions becomes obsolete and has archival value only unless it is used as an input to a predictive travel time method for updating the information. The need for fusing real time data with other factors that influence travel time is defined and the concept of predictive travel time is discussed. The methodological framework and its components are advanced and an example application is provided for illustrating the fusion of data captured by infrastructure‐based and mobile technology with model‐based predictions in order to produce expected travel times. Copyright © 2010 John Wiley & Sons, Ltd.     

A study of carpooling behaviour using a stated preference web survey in selected cities of India

This paper analyses how people perceive the idea of carpooling and evaluate preferences while making a decision to join a carpool. Analysing data from a web-based stated preference survey in India reveals that cognitive attitudes play a significant role in evaluating the perceived advantages and disadvantages of carpooling whereas intentions to carpool are associated with perceived negative evaluations. A factor analysis identifies two latent attitudinal factors: a ‘time–convenience’ factor that discourages carpooling and a ‘private–public cost’ factor that encourages carpooling. The study analyses the influential attributes – extra travel time, walking time to reach meeting point, waiting time at pickup point and cost savings – as explanatory variables for the utility of carpooling. Cost savings prove to be the most significant attribute when combined with other attributes, followed by extra travel time. The study provides the implications to policy-makers of designing promotional tools to improve the propensity of carpooling among single occupant vehicle drivers.     

Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings

In recent years we have seen important extensions of logit models in behavioural research such as incorporation of preference and scale heterogeneity, attribute processing heuristics, and estimation of willingness to pay (WTP) in WTP space. With rare exception, however, a non-linear treatment of the parameter set to allow for behavioural reality, such as embedded risk attitude and perceptual conditioning of occurrence probabilities attached to specific attributes, is absent. This is especially relevant to the recent focus in travel behaviour research on identifying the willingness to pay for reduced travel time variability, which is the source of estimates of the value of trip reliability that has been shown to take on an increasingly important role in project appraisal. This paper incorporates, in a generalised non-linear (in parameters) logit model, alternative functional forms for perceptual conditioning (known as probability weighting) and risk attitude in the utility function to account for travel time variability, and then derives an empirical estimate of the willingness to pay for trip time variability-embedded travel time savings as an alternative to separate estimates of time savings and trip time reliability. We illustrate the richness of the approach using a stated choice data set for commuter choice between unlabelled attribute packages. Statistically significant risk attitude parameters and parameters underlying decision weights are estimated for multinomial logit and mixed multinomial logit models, along with values of expected travel time savings.     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

A bi-objective cyclist route choice model

It is widely acknowledged that cyclists choose their route differently to drivers of private vehicles. The route choice decision of commuter drivers is often modelled with one objective, to reduce their generalised travel cost, which is a monetary value representing the combined travel time and vehicle operating cost. Commuter cyclists, on the other hand, usually have multiple incommensurable objectives when choosing their route: the travel time and the suitability of a route. By suitability we mean non-subjective factors that characterise the suitability of a route for cycling, including safety, traffic volumes, traffic speeds, presence of bicycle lanes, whether the terrain is flat or hilly, etc. While these incommensurable objectives are difficult to be combined into a single objective, it is also important to take into account that each individual cyclist may prioritise differently between travel time and suitability when they choose a route.This paper proposes a novel model to determine the route choice set of commuter cyclists by formulating a bi-objective routing problem. The two objectives considered are travel time and suitability of a route for cycling. Rather than determining a single route for a cyclist, we determine a choice set of optimal alternative routes (efficient routes) from which a cyclist may select one according to their personal preference depending on their perception of travel time versus other route choice criteria considered in the suitability index. This method is then implemented in a case study in Auckland, New Zealand.The study provides a starting point for the trip assignment of cyclists, and with further research, the bi-objective routing model developed can be applied to create a complete travel demand forecast model for cycle trips. We also suggest the application of the developed methodology as an algorithm in an interactive route finder to suggest efficient route choices at different levels of suitability to cyclists and potential cyclists.     

Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves their estimate of the expected travel time, thereby reducing scheduling costs. In order to assess the negative effects of uncertainty and the benefits of travel time information, this paper proposes a conceptual model of departure time choice under travel time uncertainty and information. The model is based on expected utility theory, and includes the variation in travel time, the quality of travel time information and travellers’ perception of the travel time. The model is illustrated by an application to the case of the A2 motorway between Beesd and Utrecht in the Netherlands.     

Traffic dynamics in a bi-modal transportation network with information provision and adaptive transit services

This paper has two major components. The first one is the day-to-day evolution of travelers’ mode and route choices in a bi-modal transportation system where traffic information (predicted travel cost) is available to travelers. The second one is a public transit operator adjusting or adapting its service over time (from period to period) based on observed system conditions. Particularly, we consider that on each day both travelers’ past travel experiences and the predicted travel cost (based on information provision) can affect travelers’ perceptions of different modes and routes, and thus affect their mode choice and/or route choice accordingly. This evolution process from day to day is formulated by a discrete dynamical model. The properties of such a dynamical model are then analyzed, including the existence, uniqueness and stability of the fixed point. Most importantly, we show that the predicted travel cost based on information provision may help stabilize the dynamical system even if it is not fully accurate. Given the day-to-day traffic evolution, we then model an adaptive transit operator who can adjust frequency and fare for public transit from period to period (each period contains a certain number of days). The adaptive frequency and fare in one period are determined from the realized transit demands and transit profits of the previous periods, which is to achieve a (locally) maximum transit profit. The day-to-day and period-to-period models and their properties are also illustrated by numerical experiments.     

Effects of incorporating latent and attitudinal information in mode choice models

Abstract

Hybrid choice modelling approaches allow latent variables in mode choice utility functions to be addressed. However, defining attitude and behavior as latent variables is influenced by the researcher's assumptions. Therefore, it is better to capture the effects of latent behavioral and attitudinal factors as latent variables than defining behaviors and attitudes per se. This article uses a hybrid choice model for capturing such latent effects, which will herein be referred to as modal captivity effects in commuting mode choice. Latent modal captivity refers to the unobserved and apparently unexplained attraction towards a specific mode of transportation that is resulting from latent attitude and behavior of passengers in addition to the urban transportation system. In empirical models, the latent modal captivity variables are explained as functions of different observed variables. Empirical models show significant improvement in fitting observed data as well as improved understanding of travel behavior.     

Modelling travellers’ risky choice in a revealed preference context: a comparison of EUT and non-EUT approaches

Recent work on risky choice modelling has sought to address the shortcomings of expected utility theory (EUT) by using non-expected utility theoretic (non-EUT) approaches. However, to date these approaches have been merely tested on stated choice data which is flexible and cheap. In this study, we empirically investigate the feasibility and validity of non-EUT approaches in a revealed preference (RP) context in which travel time distribution is extracted from observed historical travel time data, and subsequently present systematic comparisons between EUT, weighted utility theory, rank-dependent expected utility theory, and prospect theory (PT). The empirical evidence indicates that each non-EUT model has important behavioural insights to offer, moreover, EUT as well as non-EUT models can be applied to the RP context. However, the EUT and non-EUT model fits are generally similar with only PT providing a marginally improved model fit over EUT. The key findings presented in this study reinforce the importance of exploring non-EUT models within a revealed preference context before they can be applied reliably to modelling risky choices in the real world.     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.     

Validation of a multimodal travel simulator with travel information provision

This paper presents a computer based travel simulator for collecting data concerning the use of next-generation ATIS and their effects on traveler decision making in a multimodal travel environment. The tool distinguishes itself by presenting a completely abstract multimodal transport network, where knowledge levels are fully controlled for in terms of awareness of mode-route combinations as well as in terms of variability of characteristics of known alternatives. Furthermore, it contains an information service-module that provides a variety of advanced types of travel information, with controlled for levels of reliability. These novel features warrant an extensive validation of the travel simulator. Such a validation is performed, using two datasets (one with revealed data, one with data from an experiment held in the simulator) obtained among 264 participants. Results suggest that the simulator succeeds in collecting useful data on multimodal travel choice making under provision of advanced types of travel information.     

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

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.