Generation of synthetic datasets for discrete choice analysis

Despite the widespread use of synthetic data in discrete choice analysis, little is known about how the methodology used to generate synthetic datasets influences the properties of parameter estimates and the validity of results based on these estimates. That is, there are two potential sources of biases when using synthetic discrete choice data: (1) bias due to the method used to generate the dataset; and, (2) bias due to parameter estimation. The primary objective of this study is to examine bias due to the underlying data generation method. This study compares three methods for generating synthetic datasets and uses design of experiments and analysis of variance methods to investigate the ability to recover estimates for “true” logsum parameters for nested logit models. The method that uses nested logit probabilities to generate the chosen alternative results in unbiased parameter estimates. The method that is based on Gumbel error component approximations reveals that while the error components themselves are unbiased, subtle empirical identification problems can arise when these error components are combined with synthetically generated utility functions. The method that is based on normal error component approximations reveals that all logsum coefficients are biased upwards; the bias dramatically increases for those nests that have a low choice frequency and is most pronounced for those nests with high correlations among alternatives. Based on the results of the analysis, several recommendations for the generation of synthetic datasets for discrete choice analyses are provided.     

Explaining public transport information use when a car is available: attitude theory empirically investigated

Despite recent investments in and growing availability of various public transport information services, levels of apparent non-use (of particular information services) across the population remain high. Policymakers and information service providers could benefit from a better understanding of factors affecting information use. The goal of this paper is to provide more insight into the (non-)use of public transport information by applying attitude theory. A postal survey was sent to a random sample of 10,000 households in Bristol and Manchester, UK. The response rate was 13%. Respondents were questioned about an uncertain journey they were going to make. Structural equation modelling has been used to investigate interdependencies among the factors studied. The results show that the desire to consult public transport information for an uncertain journey is affected by attitudes, subjective norms, and past behaviour. These social-psychological factors are in turn affected by constraints such as travel behaviour and trip context. Crucially in terms of addressing issues of non-use of information it is found that consulting information is influenced by propensity to consider using public transport rather than vice versa as has hitherto been implicitly assumed by many involved in the provision of transport and information services.     

A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions.     

Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand

Activity-based models for modeling individuals’ travel demand have come to a new era in addressing individuals’ and households’ travel behavior on a disaggregate level. Quantitative data are mainly used in this domain to enable a realistic representation of individual choices and a true assessment of the impact of different Travel Demand Management measures. However, qualitative approaches in data collection are believed to be able to capture aspects of individuals’ travel behavior that cannot be obtained using quantitative studies, such as detailed decision making process information. Therefore, qualitative methods may deepen the insight into human’s travel behavior from an agent-based perspective. This paper reports on the application of a qualitative semi-structured interview method, namely the Causal Network Elicitation Technique (CNET), for eliciting individuals’ thoughts regarding fun-shopping related travel decisions, i.e. timing, shopping location and transport mode choices. The CNET protocol encourages participants to think aloud about their considerations when making decisions. These different elicited aspects are linked with causal relationships and thus, individuals’ mental representations of the task at hand are recorded. This protocol is tested in the city centre of Hasselt in Belgium, using 26 young adults as respondents. Response data are used to apply the Association Rules, a fairly common technique in machine learning. Results highlight different interrelated contexts, instruments and values considered when planning a trip. These findings can give feedback to current AB models to raise their behavioral realism and to improve modeling accuracy.     

Temporal transferability of updated alternative-specific constants in disaggregate mode choice models

Methods of updating disaggregate discrete choice models have been proposed as a means of obtaining better transferability. However, the temporal transferability of models updated for better spatial transferability has rarely been analysed, and the factors affecting temporal transferability have not been determined. This paper deals with one updating method—the use of disaggregate data to update alternative-specific constants—and investigates the factors affecting the temporal transferability of the updated constants. In the analysis, repeated cross-section data collected in the Chukyo metropolitan area are divided, efficiently generating many application areas. The analysis showed that the factors can depend on regional characteristics and past travel behaviours (inertia), and are anti-symmetric and path-dependent of changes in the level of service.     

The <Emphasis Type="Italic">Santiago Panel</Emphasis>: measuring the effects of implementing Transantiago

Although panels offer significant advantages over cross sectional data especially in terms of evaluating the effects of significant policy changes, there are precious few examples of panels built around an important change to an urban transport system. For this reason we took the opportunity of the introduction of Transantiago, a radically new public transport system for Santiago de Chile in February 2007, to form a panel, the first wave of which was taken in December 2006. The final objective was to use this Santiago Panel to estimate mode choice models considering both inertia and policy effects. This document describes both the design and construction of the panel, and presents some results based on an analysis of its four waves; for example, we registered a high percentage of mode change (55.1%) attributable to the introduction of the new system. The panel can claim the highest response rate (or lowest attrition) reported in the literature (95% in the second wave, 92% in the third one, and 85% in the forth one). This hints at the possibility of developing sophisticated models to evaluate the effects of a system shock in the presence of inertia in decision making.     

Catching a ride on the information super-highway: toward an understanding of internet-based carpool formation and use

Fluctuating fuel prices, rising congestion, longer commutes, and related environmental and human health effects have combined to once more draw the interest of governments, commuters, and firms toward the concept of travel demand management (TDM). While TDM is not new, the proliferation of mobile telephony, fixed Internet, and associated applications has created fresh prospects for the implementation of commuter focused TDM strategies. One recent example is Carpool Zone, an on-line carpool-matching tool deployed and managed by the TDM group at Metrolinx, the regional transportation planning agency within Canada’s largest metropolitan region, the Greater Toronto and Hamilton Area. Using data provided by Metrolinx, this paper broadens current thinking on carpool formation and use. The main hypothesis guiding this work is that the carpool formation and use process is sensitive to personal and household characteristics, space, time, travel cost, and workplace TDM policies. Results from a logistic regression analysis suggest that geographical proximity to other users; workplace TDM policies; the scheduling of work; and commuter role preference increase the odds of successfully carpooling. Importantly, findings regarding the positive influence of workplace TDM policies suggest that Internet based TDM tools will likely require critically important investment in human capital at the back-end to ensure program participation.     

RANS方法的螺旋桨与舵附推力鳍相互干扰水动力性能

The Reynolds-averaged Navier-Stokes (RANS) method, along with the Fluent software package, was used to study the steady and unsteady interaction of propellers and rudders with additional thrust fins. The sliding mesh model was employed to simulate unsteady interactions between the blades, the rudder and the thrust fins. Based on the numerical results, the pressure distribution on the propeller and the efficiency of the fins were calculated as a function of the attack angle. The RANS results were compared with results calculated by the potential method. It was found that the results for the potential method and the RANS method have good consistency, but they yield maximum efficiencies for the fins, and thus corresponding attack angles, that are not identical.     

基于Simulink的带式浮桥动态仿真

A mathematical model of a ribbon pontoon bridge subjected to moving loads was formulated using the theory of simply supported beams. Two types of moving load models were used, the first a moving-constant-force model and the second a moving-mass model. Using both types of loads, the dynamic behavior of a ribbon pontoon bridge was simulated while subjected to a single moving load and then multiple moving loads. Modeling was done with the Simulink package in MATLAB software. Results indicated that the model is correct. The two types of moving load models made little difference to the response ranges when loads moved on the bridge, but made some difference to the response phases. When loads left, the amplitude of the dynamic responses induced by the moving-constant-force model load were larger than those induced by the moving-mass model. There was a great deal more difference when there were more loads.     

多Agent的船舶电力系统网络重构技术

Reconfigurability of the electrical network in a shipboard power system (SPS) after its failure is central to the restoration of power supply and improves survivability of an SPS. The navigational process creates a sequence of different operating conditions. The priority of some loads differs in changing operating conditions. After analyzing characteristics of typical SPS, a model was developed used a grade III switchboard and an environmental prioritizing agent (EPA) algorithm. This algorithm was chosen as it is logically and physically decentralized as well as multi-agent oriented. The EPA algorithm was used to decide on the dynamic load priority, then it selected the means to best meet the maximum power supply load. The simulation results showed that higher priority loads were the first to be restored. The system satisfied all necessary constraints, demonstrating the effectiveness and validity of the proposed method.