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.     

A data-driven travel mode share estimation framework based on mobile device location data

Transportation - Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger...     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

桩网复合地基工作性状有限元法分析

针对桩网复合地基工作性状与荷载传递机理进行了数值分析研究。计算结暴表明：桩网复合地基通过拉膜效应、应力集中与土拱效应，将软基承担的部分荷载转移至桩承担，从而有效减小软基表面的总沉降与不均匀沉降，达到加固软土地基的实际工程效果。此外，针对影响复合地基工作性状的填土高度、格栅拉伸刚度与桩模量三因素进行了相应分析，获得了与加固技术相关的有益结论。     

铺管船铺管作业锚泊系统分析

针对海管铺设作业中铺管船的锚泊系统分析,基于三维势流理论,采用ANSYS-AQWA软件,考虑风载荷、流载荷、波浪载荷等因素,建立了铺管船与锚泊系统时域耦合分析方法。在此基础上,针对西气东输香港支线的海管铺设作业,根据作业海区的海况条件,分析了铺管船运动响应与锚泊系统的载荷特性,完成了香港支线海管铺设作业的安全性评估,给出了不同海况下铺管船安全作业的包络曲线。最后,根据评估报告与指导意见,顺利完成了香港支线的海管铺设作业。     

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.

     

Methodological Options and Data Sources for the Development of Long-Distance Passenger Travel Demand Models: A Comprehensive Review

Since the passage of the Intermodal Surface Transportation Efficiency Act in 1991, a significant number of state highway agencies have started to develop and implement statewide travel demand models to meet policy and legislative development needs. Currently, however, a lack of up-to-date multimodal and inter-regional passenger travel data hampers analysts’ ability to conduct quantitative assessments of long-distance travel infrastructure investment needs, at both the national and statewide levels. Despite these data limitations, but also largely shaped by them, long-distance travel modelling has become an increasingly popular topic in recent years. This paper reviews several methodologies for multimodal inter-regional travel demand estimation, drawing examples from both state-specific modelling within the USA and from fully national models being developed and applied in other parts of the world, notably in Europe.     

Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes

This paper proposes a conceptual framework to model the travel mode searching and switching dynamics. The proposed approach is structurally different from existing mode choice models in the way that a non-homogeneous hidden Markov model (HMM) has been constructed and estimated to model the dynamic mode srching process. In the proposed model, each hidden state represents the latent modal preference of each traveler. The empirical application suggests that the states can be interpreted as car loving and carpool/transit loving, respectively. At each time period, transitions between the states are functions of time-varying covariates such as travel time and travel cost of the habitual modes. The level-of-service (LOS) changes are believed to have an enduring impact by shifting travelers to a different state. While longitudinal data is not readily available, the paper develops an easy-to-implement memory-recall survey to collect required process data for the empirical estimation. Bayesian estimation and Markov chain Monte Carlo method have been applied to implement full Bayesian inference. As demonstrated in the paper, the estimated HMM is reasonably sensitive to mode-specific LOS changes and can capture individual and system dynamics. Once applied with travel demand and/or traffic simulation models, the proposed model can describe time-dependent multimodal behavior responses to various planning/policy stimuli.     

Decision tree method for modeling travel mode switching in a dynamic behavioral process

As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers’ mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers’ switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.     

Deciding whether and how to improve statewide travel demand models based on transportation planning application needs

Many states in the USA have developed statewide travel demand models for transportation planning at the state level and along intercity corridors. Travel demand models at mega-region and provincial levels are also widely used in Europe and Asia. With modern transportation planning applications requiring enhanced model capabilities, many states are considering improving their four-step statewide demand models. This paper synthesizes representative statewide models developed with traditional four-step, advanced four-step, and integrated micro-simulation methods. The focus of this synthesis study is as much on model applications and data requirements as on modeling methods. An incremental model improvement approach toward advanced statewide models is recommended. Review findings also suggest model improvement activities should be justified by planning application needs. For statewide model improvement plans to be successful and financially sustainable, the return on model improvement investment needs to be demonstrated by timely applications that rely on improved model capabilities.