Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment

In this paper, we report the results of a stated choice experiment, which was conducted to examine truck drivers?? route choice behavior. Of particular interest are the questions (i) what is the relative importance of road accessibility considerations via-a-vis traditional factors influencing route choice behavior, (ii) what are the influences of particular personal and situational variables on the evaluation of route attributes, (iii) how sensitive are truck drivers for possible pricing policies, and (iv) is there a difference in impact if environmental concerns are framed as a bonus or as a pricing instrument. The main findings indicate that road accessibility characteristics have a substantial impact on route preferences which is of the same order of magnitude as variation in travel times. This suggests that provision of adequate travel information in itself can be an effective instrument to prevent negative externalities of good transport associated with shortest routes. Furthermore, the results indicate that truck drivers/route planners when choosing a route are relatively sensitive to road pricing schemes and rather insensitive to environmental bonuses.     

Regimes in social–cultural events-driven activity sequences: Modelling approach and empirical application

In this study we propose and apply a Bayesian-network model to predict and analyse the factors that influence activity-travel sequences that are triggered by social–cultural events. The study is motivated by the intention to examine the wider context in which activity-travel decisions are made and to model such decisions under longitudinal time horizons. We assume that social events trigger a series of interrelated activities and corresponding trips. Data about events and related activities are collected using a month-diary and involving a large sample of households in the Eindhoven region, The Netherlands. A learning algorithm is applied to derive a Bayesian-network model from the event diary. The results show that indeed many travel choices are influenced by particular events, that these influences vary by socio-demographic variables and that the learned Bayesian-network model is able to represent these interdependencies among all these variables. We demonstrate how the model can be used to predict event-driven activity-travel sequences in a micro-simulation.     

An integrated model system and policy evaluation tool for maximizing mobility under environmental capacity constraints: A case study in Dalian City,China

This paper presents an integrated model system for mobility maximization based on a quantified specification of environmental capacity, and evaluates policy interaction and effectiveness by simulating a number of policy scenarios. The system is designed to specify the maximum level of car ownership and number of trips by private and public modes subject to an environmental capacity constraint defined as the frontier emission under maximum system efficiency. Four types of hypothetical policies (population change, urban sprawl, land-use pattern and network improvement) are designed and the effects of 13 policy scenarios are simulated using data of Dalian City, China. Results reveal that the integrated model system reacts sensitively to policy interventions. The urban sprawl reflected in a changing residential distribution from central to suburban areas is most instrumental from the perspective of pollution alleviation. If the goal is to simultaneously reduce emissions while accommodating mobility, two combinational policy scenarios outperform all others.     

Prevalence of alternative processing rules in the formation of daily travel satisfaction in the context multi-trip,multi-stage,multi-attribute travel experiences

Transportation - Requesting respondents to provide satisfaction ratings for multi-stage trips or daily travel experiences implies they have to value each stage, respectively trip, based on memory...     

Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Transportation - Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic...     

Turn taking behavior in dual earner households with children: a focus on escorting routines

Transportation - This article discusses results of a study on turn taking behavior in escorting children in dual-earner households. Using a multinomial logit model, the probability of different...     

Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.     

The prevalence of context-dependent adjustment of activity-travel patterns in energy conservation strategies: results from a mixture-amount stated adaptation experiment

People’s adaptive behaviour to increasing energy prices has been studied at length in transportation research. Prior research however has not addressed research questions concerning the contribution of travel-related changes in more encompassing energy conservation strategies. Moreover, context-dependency and choice under constraints has not been studies at any length. In this paper, we therefore report the results of a context-dependent elaboration of a mixture amount choice experiment to measure context-dependent responses to accumulative energy charges under budget constraints. Accounting for consumer heterogeneity in adaptive response behaviour, mixed logit analysis is used to analyse the extent and nature of adaptations of activity-travel behaviour and resource allocation in response to increasing energy costs. The results indicate that individuals are inclined to compensate for increased expenditures on energy due to price increases. Moreover, results show the existence of significant heterogeneity among respondents in terms of their adaptation strategies to various energy-saving choices.     

Modeling co-dependent choice of workplace,residence and commuting mode using an error component mixed logit model

Transportation - This paper develops an error component mixed logit model to analyze the multi-dimensional residential, work and transportation mode choice. It expanse previous studies based on...