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.     

Simultaneous internalization of traffic congestion and noise exposure costs

This study elaborates on the interrelation of external effects, in particular road traffic congestion and noise. An agent-based simulation framework is used to compute and internalize user-specific external congestion effects and noise exposures. The resulting user equilibrium corresponds to an approximation of the system optimum. For traffic congestion and noise, single objective optimization is compared with multiple objective optimization. The simulation-based optimization approach is applied to the real-world case study of the Greater Berlin area. The results reveal a negative correlation between congestion and noise. Nevertheless, the multiple objective optimization yields a simultaneous reduction in congestion and noise. During peak times, congestion is the more relevant external effect, whereas, during the evening, night and morning, noise is the more relevant externality. Thus, a key element for policy making is to follow a dynamic approach, i.e. to temporally change the incentives. During off-peak times, noise should be reduced by concentrating traffic flows along main roads, i.e. inner-city motorways. In contrast, during peak times, congestion is reduced by shifting transport users from the inner-city motorway to smaller roads which, however, may have an effect on other externalities.     

Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset

Internet is capturing more and more of our time each day, and the increasing levels of engagement are mainly due to the use of social media. Time spent on social media is observed in the American Time Use Survey and recorded as leisure time on Personal Computer (PC). In this paper, we extend the traditional analysis of leisure activity participation by including leisure activities that require the use of a PC. We study the substitution effects with both in-home and out-of-home leisure activities and the time budget allocated to each of them. The modeling framework that includes both discrete alternatives and continuous decision variables allow for full correlation across the utility of the alternatives that are all of leisure type and the regressions that model the time allocated to each activity. Results show that there is little substitution effect between leisure with PC and the relative time spent on it, with in-home and out-of-home leisure episodes. Households with more children and full-time workers are more likely to engage in in-home and PC related leisure activities (especially during weekends). Increments in the travel time of social trips result in significant reductions in leisure time during weekdays.     

Factors associated with home-based e-working and e-shopping in Nanjing,China

The widespread adoption of information and communication technology has facilitated frequent e-activities in people’s daily life. From the perspective of individual’s time use on e-working and e-shopping at home, this paper aims to enhance our understanding of the function of home beyond a living space for family life. Using a household survey of 608 full-time paid employees who conducted e-activities at home in Nanjing, China, we investigated the characteristics and patterns of home-based e-working and e-shopping. Only 7.9% of the respondents neither e-shopped nor e-worked at home. We find that the socio-demographic context, Internet use habits, attitudes towards e-working/e-shopping, and geographical accessibility have influenced the patterns of home-based e-working and e-shopping. The results indicate that the rich e-activities taking place at home have changed the time use at home and reinforced the function of home as a multifunctional hub.     

Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey

According to US Census Bureau, the number of individuals in the age group above 65 years is expected to increase by more than 100% from the year 2000 to 2030. It is anticipated that increasing elderly population will put unforeseen demands on the transportation infrastructure due to the atypical mobility and travel needs of the elderly. Consequently, transportation professionals have attempted to understand the travel behavior of the elderly including the trip frequency, trip distance and mode choice decisions. Majority of the research on elderly travel behavior have focused on the mobility outcomes with limited research into understanding the tradeoffs made by this population segment in terms of their in-home and out-of-home activity engagement choices. The goal of the current research is to contribute to this line of inquiry by simultaneously exploring the daily activity engagement choices of the elderly Americans including their in-home and out-of-home activity participation (what activities to pursue) and time alloocation (duration of each activity) decisions while accounting for the temporal constraints. Further, the study attempts to explore the relationship between physical and subjective well-being and daily activity engagement decisions of the elderly; where subjective well-being is derived from reported needs satisfaction with life and different domains of it. To this end, data from the Disabilities and Use of Time survey of Panel Study of Income Dynamics was used to estimate a panel version of MDCEV model. In addition to person- and household-level demographic variables, activity participation and time use choices of elderly were found to vary across different levels of reported physical and subjective well-being measures. The model estimation results were plausible and provide interesting insights into the activity engagement choices of the elderly with implications for transportation policy development. Among other socio-demographic variables, living arrangements (living with family versus in elderly homes) were found to have significant influence on how people participate into different in-home versus out-of-home activities. For example, elderly living in the elderly home were found to participate more into out-of-home activities compared to people living with families. Elderly with disabilities were found to compensate lower participation into out-of-home activities with more participation into in-home activities. Considerable heterogeneity was observed in time engagement behavior of the elderly across reported levels of satisfaction with finance, job and cognitive needs. For example, elderly expressing high satisfaction with job was found to spend less time in in-home social activities. Elderly reporting higher satisfaction with finance were found to spend more time into OH social and shopping activities.     

Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam,Tanzania

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

沉管隧道穿越江河海湾的优越性

文章从工程的造价、质量、工期、安全、生态环境及社会效益等角度,通过对沉管隧道与桥梁、盾构隧道渡越方案的分析、对比和论证,着重指出采用沉管隧道方案穿越江河海湾的诸多优越性.     

Modeling household interactions in daily in-home and out-of-home maintenance activity participation

The activity travel patterns of individuals in a household are inter-related, and the realistic modeling of activity-travel behavior requires that these interdependencies be explicitly accommodated. This paper examines household interactions impacting weekday in-home and out-of-home maintenance activity generation in active, nuclear family, households. The in-home maintenance activity generation is modeled by examining the duration invested by the male and female household heads in household chores using a seemingly unrelated regression modeling system. The out-of-home maintenance activity generation is modeled in terms of the decision of the household to undertake shopping, allocation of the task to one or both household heads, and the duration of shopping for the person(s) allocated the responsibility. A joint mixed-logit hazard-duration model structure is developed and applied to the modeling of out-of-home maintenance activity generation. The results indicate that traditional gender roles continue to exist and, in particular, non-working women are more likely to share a large burden of the household maintenance tasks. The model for out-of-home maintenance activity generation indicates that joint activity participation in the case of shopping is motivated by resource (automobiles) constraints. Finally, women who have a higher propensity to shop are also found to be inherently more efficient shoppers.     

公路隧道洞口段支护结构加强设计优化研究

结合目前公路隧道洞口段支护结构加强设计存在的问题,文章从洞口段与洞身浅埋段的围岩特性、荷载作用特点入手,基于超前管棚、初期支护、二次衬砌的作用机理,分析了支护结构在不同围岩条件下的作用特点,给出了洞口加强段和洞身浅埋段衬砌设计的合理化建议。主要得到以下结论:(1)洞口段围岩破碎,自稳能力差,超前支护为围岩早期稳定提供条件,初期支护为主要承载结构,二次衬砌承担部分荷载并提供一定的安全储备;由于洞口段衬砌上覆荷载较洞身浅埋段小得多,不宜加强二次衬砌,因此洞口加强段衬砌设计应以加强超前支护与初期支护为主。(2)洞身浅埋段围岩较完整,有一定的自稳能力,二次衬砌为主要承载结构,初期支护主要发挥与围岩粘附的协同作用;洞身浅埋段衬砌设计应以加强二次衬砌为主,并要控制施作时机。