Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes

In this paper we discuss the specification, covariance structure, estimation, identification, and point-estimate analysis of a logit model with endogenous latent attributes that avoids problems of inconsistency. We show first that the total error term induced by the stochastic latent attributes is heteroskedastic and nonindependent. In addition, we show that the exact identification conditions support the two-stage analysis found in much current work. Second, we set up a Monte Carlo experiment where we compare the finite-sample performance of the point estimates of two alternative methods of estimation, namely frequentist full information maximum simulated likelihood and Bayesian Metropolis Hastings-within-Gibbs sampling. The Monte Carlo study represents a virtual case of travel mode choice. Even though the two estimation methods we analyze are based on different philosophies, both the frequentist and Bayesian methods provide estimators that are asymptotically equivalent. Our results show that both estimators are feasible and offer comparable results with a large enough sample size. However, the Bayesian point estimates outperform maximum likelihood in terms of accuracy, statistical significance, and efficiency when the sample size is low.     

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications.     

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.     

Transportation agencies experiences with decision support systems for airport ground access planning

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior.     

Comparison of updating techniques in transferability analysis of work trip mode choice models in developing countries

This study analyzes the performances of updating techniques in transferability of mode choice models in developing countries. A model specification, estimated in Ho Chi Minh City, was transferred to Phnom Penh. Naïve transfer and four updating methods associated with small sized samples were used in the transfer process and were evaluated based on statistical perspective and predictive ability. The study also illustrates the problems faced in model transferability development, due to the lack of available and suitable data in Phnom Penh. This lack is strongly related to different methods and structures applied in collecting the data. Simplified approaches to the difficulties are proposed in the study. The results show that updating ASCs, updating both ASCs and scale parameter, and use of combined transfer estimators all produce significant improvement, both statistically and in predictability, in updating the model. The last two methods have proven to be superior to the first method, owing to the inclusion of transfer bias considerations in the estimations. However, small data samples should not have large transfer bias when using combined transfer estimators. It is also concluded that naïvely transferring a model is not recommended, and Bayesian updating should be avoided when transfer bias exists. Copyright © 2010 John Wiley & Sons, Ltd.     

Gravity model estimation with proxy variables and the impact of endogeneity on transportation planning

This study presents an alternative method for estimating gravity models by multiple linear regression that is based on proxy variables, thus circumventing the endogeneity problems arising when least-squares estimators are used. The proxy variable approach generates consistent estimators for a gravity model without endogeneity bias. The presence of endogeneity is tested for using statistical tests developed specifically for our application.We conclude that proxy variables eliminate the endogeneity and produce consistent estimators in gravity models estimated using least squares. We also find, however, that endogeneity bias has no significant impact either on gravity model prediction or on urban transportation system planning processes based on such models.     

Towards the analysis of attitudinal and behavioral responses to transportation system characteristics

This report treats the requirements of planning methods for short-range and low-capital transportation options within the context of two primary objectives. The objectives are (1) the presentation of a fundamental set of behavioral principles which are relevant to the planning process and (2) the discussion of alternative methods for assessing the behavioral consequences of transportation changes. The presentation of fundamental behavioral principles relies substantially on classical behavioral reinforcement theory, but reference is also made to attitude theory, econometrics, marketing, and psychometrics. The discussion of data collection procedures presents information on sample specification, and it illustrates a variety of questionnaire formats for the collection of perceptions and preferences from respondents. Advantages and disadvantages of the formats are mentioned. Based on the discussion of behavioral principles and methods, general guidelines are offered for the modeling and data collection requirements of planning methods for short-range and low-capital transportation options.     

Reconsidering the multinomial probit model

Recently developed computational methods have greatly reduced the difficulty of estimating multinomial probit models and may soon make multinomial probit a computationally feasible option in applied travel demand modeling. This paper discusses some of the benefits and costs that are associated with the use of multinomial probit in demand modeling. It is argued that although there are situations in which multinomial probit is essential for achieving a satisfactory model, most problems with existing demand models are unlikely to be mitigated by the use of multinomial probit.     

Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle

Uncertainty is inherent in major infrastructure projects, but public decision-making for such projects ignores it. We investigate the uncertainty about the future effects of tearing down the Alaskan Way Viaduct in downtown Seattle, using an integrated model of housing, jobs, land use and transportation, on outcomes including average commute times. Our methodology combines the urban simulation model UrbanSim with the regional transportation model. We assess uncertainty using Bayesian melding, yielding a full predictive distribution of average commute times on 22 different routes in 2020. Of these routes, 14 do not include the viaduct and eight do. For the 14 base routes that do not include the viaduct, the predictive distributions overlap substantially, and so there is no indication that removing the viaduct would increase commute times for these routes. For each of the eight routes that do include the viaduct, the 95% predictive interval for the difference in average travel times between the two scenarios includes zero, so there is not strong statistical support for the conclusion that removing the viaduct would lead to any increase in travel times. However, the median predicted increase is positive for each of these routes, with an average of 6 min, suggesting that there may be some measurable increase in travel time for drivers that use the viaduct as a core component of their commute.     

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.     

A probabilistic map matching method for smartphone GPS data

Smartphones have the capability of recording various kinds of data from built-in sensors such as GPS in a non-intrusive, systematic way. In transportation studies, such as route choice modeling, the discrete sequences of GPS data need to be associated with the transportation network to generate meaningful paths. The poor quality of GPS data collected from smartphones precludes the use of state of the art map matching methods. In this paper, we propose a probabilistic map matching approach. It generates a set of potential true paths, and associates a likelihood with each of them. Both spatial (GPS coordinates) and temporal information (speed and time) is used to calculate the likelihood of the data for a specific path. Applications and analyses on real trips illustrate the robustness and effectiveness of the proposed approach. Also, as an application example, a Path-Size Logit model is estimated based on a sample of real observations. The estimation results show the viability of applying the proposed method in a real route choice modeling context.     

Multi-class percentile user equilibrium with flow-dependent stochasticity

Travelers often reserve a buffer time for trips sensitive to arrival time in order to hedge against the uncertainties in a transportation system. To model the effects of such behavior, travelers are assumed to choose routes to minimize the percentile travel time, i.e. the travel time budget that ensures their preferred probability of on-time arrival; in doing so, they drive the system to a percentile user equilibrium (UE), which can be viewed as an extension of the classic Wardrop equilibrium. The stochasticity in the supply of transportation are incorporated by modeling the service flow rate of each road segment as a random variable. Such stochasticity is flow-dependent in the sense that the probability density functions of these random variables, from which the distribution of link travel time are constructed, are specified endogenously with flow-dependent parameters. The percentile route travel time, obtained by directly convolving the link travel time distributions in this paper, is not available in closed form in general and has to be numerically evaluated. To reveal their structural properties, percentile UE solutions are examined in special cases and verified with numerical results. For the general multi-class percentile UE traffic assignment problem, a variational inequality formulation is given and solved using a route-based algorithm. The algorithm makes use of the diagonal elements in the Jacobian of percentile route travel time, which is approximated through recursive convolution. Preliminary numerical experiments indicate that the algorithm is able to achieve highly precise equilibrium solutions.     

Statistical models of discrete choice processes

A number of problems studied in the transportation literature, such as automobile choice, motorist route choice, and transportation mode choice, involve an agent who makes a series of discrete choices over time. This paper presents statistical models and estimation methods for such discrete choice processes, and illustrates potential applications of these methods to the transportation literature.     

A multivariate ordered-response model system for adults’ weekday activity episode generation by activity purpose and social context

This paper proposes a multivariate ordered-response system framework to model the interactions in non-work activity episode decisions across household and non-household members at the level of activity generation. Such interactions in activity decisions across household and non-household members are important to consider for accurate activity-travel pattern modeling and policy evaluation. The econometric challenge in estimating a multivariate ordered-response system with a large number of categories is that traditional classical and Bayesian simulation techniques become saddled with convergence problems and imprecision in estimates, and they are also extremely cumbersome if not impractical to implement. We address this estimation problem by resorting to the technique of composite marginal likelihood (CML), an emerging inference approach in the statistics field that is based on the classical frequentist approach, is very simple to estimate, is easy to implement regardless of the number of count outcomes to be modeled jointly, and requires no simulation machinery whatsoever.The empirical analysis in the paper uses data drawn from the 2007 American Time Use Survey (ATUS) and provides important insights into the determinants of adults’ weekday activity episode generation behavior. The results underscore the substantial linkages in the activity episode generation of adults based on activity purpose and accompaniment type. The extent of this linkage varies by individual demographics, household demographics, day of the week, and season of the year. The results also highlight the flexibility of the CML approach to specify and estimate behaviorally rich structures to analyze inter-individual interactions in activity episode generation.     

Willingness to accept longer commutes for better salaries: Understanding the differences within and between couples

This paper reports on an analysis aiming to understand differences across individual people in their willingness to accept increased commuting time in return for higher salary, using Hierarchical Bayes (HB) analysis of a dataset collected in Sweden. We find that socio-demographic and attitudinal differences are significant in explaining the variations in values of time for individuals, in particular income, who drives when carpooling and hours worked per week. Additionally we also examine the values of individuals when their choices also impact on the salary and commute of their partner, finding that incomes, income differentials, driving behaviour when carpooling, division of housework and car user decisions significantly explain the values assigned to others and variations in an individual’s own values once their partner is affected. The overall richness of the results reflect the benefits that posterior analysis can bring, and highlight the computational efficiency of Bayesian methods in producing such conditionals at an individual level.     

Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis

As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.

     

The sequenced activity mobility simulator (SAMS): an integrated approach to modeling transportation, land use and air quality

The persistence of environmental problems in urban areas and the prospect of increasing congestion have precipitated a variety of new policies in the USA, with concomitant analytical and modeling requirements for transportation planning. This paper introduces the Sequenced Activity-Mobility Simulator (SAMS), a dynamic and integrated microsimulation forecasting system for transportation, land use and air quality, designed to overcome the deficiencies of conventional four-step travel demand forecasting systems. The proposed SAMS framework represents a departure from many of the conventional paradigms in travel demand forecasting. In particular, it aims at replicating the adaptative dynamics underlying transportation phenomena; explicitly incorporates the time-of-day dimension; represents human behavior based on the satisficing, as opposed to optimizing, principle; and endogenously forecasts socio-demographic, land use, vehicle fleet mix, and other variables that have traditionally been projected externally to be input into the forecasting process.     

What transportation modeling needs from a GIS: a conceptual framework

Both the geographic information system (GIS) and transportation modeling environments have seen continually developing analytic concepts and techniques. However, these developments have seldom resulted in the integration of GISs and transportation models. This paper explores the potential inherent in merging of these environments through a systematic investigation of the fundamental basis of integration. To do this, the traditional four step transportation modeling process is extended to include input and output steps. We then define functional components for GIS data handling — data management, manipulation, and analysis. The steps of modeling are matched against the list of GIS data handling functions within a matrix‐based framework. GIS functions that enhance a land‐use based urban transportation modeling process are then categorized. Conclusions are drawn and directions for future developments are discussed.     

Exploring transportation equity: Development and application of a transportation justice framework

Recent efforts to emphasize social equity in transportation are emerging as local, regional and national governments have set initiatives to identify, existing and potential, disproportionate impacts to low-income and minority populations, also referred to as transportation justice (TJ). Currently, there are suggested methods for identifying transportation justice areas; however, there is no streamlined method instituted across transportation agencies. Each jurisdiction identifies transportation justice (or environmental justice) areas based on their own methodology, typically based on either average regional thresholds, graduated thresholds, or a more unique in-house index methodology. This research explores and evaluates existing methods and develops a rigorous and comprehensive method called the Transportation Justice Threshold Index Framework (TJTIF) using Geographic Information Systems (GIS), as well as factors based on demographics, socio-economics, and transportation/land use. The framework is applied to a case study region in Pennsylvania reflective of the Marcellus Shale impact area, highlighting Sullivan County, PA. The methodology and the case study application serve as an example for how transportation agencies throughout the country can promote social sustainability and enhance transportation equity.