Volume and GHG emissions of long-distance travelling by Western Europeans

It is generally recognised that long distance travelling accounts for a significant part of the mileage of person travel. However, estimates have been hardly made. The paper estimates volume and GHG emissions of long-distance travel by Western Europeans. The analysis is predominantly based on data of the DATELINE project, the only EU-wide survey on long-distance travelling, conducted in 2001 and 2002. Some studies demonstrate that DATELINE suffers from serious underreporting of journeys. We analysed the causes for underreporting and developed expansion factors that correct for that. These gave the opportunity to estimate long-distance travel volumes and related GHG emissions in 2001/2002. Next an update to 2013 is made using statistics on the development of tourist travel and patronage of long-distance modes. Defining long distance ⩾100 km crow-fly, the estimates per capita in the Western European countries in 2013 are 7.5 journeys (defined as round-trips), 8600 km, and 1300 kg greenhouse gasses. The estimated total GHG emissions of long-distance travelling is 520 megaton. In the Netherlands and Flanders, countries where data on short-distance travelling were available, long-distance travelling accounts for 45% of the mileage and nearly 50% of the GHG emissions of all person transport. Long-distance travelling is growing and is expected to continue to grow, particularly by air. The GHG emissions are expected to grow as well, though to a smaller extent. Because short-distance travelling is stagnating, the shares of long distance travelling in both mileage and GHG emissions are likely to increase.     

Application of the support vector machine and heuristic k-shortest path algorithm to determine the most eco-friendly path with a travel time constraint

This study aims to determine an eco-friendly path that results in minimum CO₂ emissions while satisfying a specified budget for travel time. First, an aggregated CO₂ emission model for light-duty cars is developed in a link-based level using a support vector machine. Second, a heuristic k-shortest path algorithm is proposed to solve the constrained shortest path problem. Finally, the CO₂ emission model and the proposed eco-routing model are validated in a real-world network. Specifically, the benefit of the trade-off between CO₂ emission reduction and the travel time budget is discussed by carrying out sensitivity analysis on a network-wide scale. A greater spare time budget may enable the eco-routing to search for the most eco-friendly path with higher probability. Compared to the original routes selected by travelers, the eco-friendly routes can save an average of 11% of CO₂ emissions for the trip OD pairs with a straight distance between 6 km and 9 km when the travel time budget is set to 10% above the least travel time. The CO₂ emission can also be reduced to some degree for other OD pairs by using eco-routing. Furthermore, the impact of market penetration of eco-routing users is quantified on the potential benefit for the environment and travel-time saving.     

The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach

People’s daily decision to use car-sharing rather than other transport modes for conducting a specific activity has been investigated recently in assessing the market potential of car-sharing systems. Most studies have estimated transport mode choice models with an extended choice set using attributes such as average travel time and costs. However, car-sharing systems have some distinctive features: users have to reserve a car in advance and pay time-based costs for using the car. Therefore, the effects of activity-travel context and travel time uncertainty require further consideration in models that predict car-sharing demand. Moreover, the relationships between individual latent attitudes and the intention to use car-sharing have not yet been investigated in much detail. In contributing to the research on car-sharing, the present study is designed to examine the effects of activity-travel context and individual latent attitudes on short-term car-sharing decisions under travel time uncertainty. The effects of all these factors were simultaneously estimated using a hybrid choice modeling framework. The data used in this study was collected in the Netherlands, 2015 using a stated choice experiment. Hypothetical choice situations were designed to collect respondents’ intention to use a shared-car for their travel to work. A total of 791 respondents completed the experiment. The estimation results suggest that time constraints, lack of spontaneity and a larger variation in travel times have significant negative effects on people’s intention to use a shared-car. Furthermore, this intention is significantly associated with latent attitudes about pro-environmental preferences, the symbolic value of cars, and privacy-seeking.     

突发事件下路网运行时间可靠性研究

对突发事件下的路网运行时间可靠性进行分析,从路段通行能力与运行时间关系的角度,对交通流量与运行时间关系进行讨论。在研究运行时间的均值、标准差等特征值在突发事件下变化的基础上,利用机会约束模型,建立异常状态下路网容量分析的非线性规划模型。实例分析结果表明,突发事件出现时路网运行时间可靠性与其承担的交通流量有直接的关系。     

路网可靠度模型的研究与拓展

回顾了路网可靠度模型的研究进展：包括不同的可靠度指标、模型以及算法。在分析传统可靠度模型优缺点的基础上，提出将随机网络平衡模型用于路网可靠度分析。     

Car ownership and attitudes towards transport modes for shopping purposes in Singapore

The heightening of issues, such as sustainable development and environmental pollution have resulted in many governments pursuing transport policies which aim to promote the use of public transport modes, including walking, as well as discourage the use of the car for various activities, such as shopping, work, recreation, etc. However, little has been done on understanding shoppers' perceptions of transport modes for shopping purposes. Particularly, not much research has been done on examining the attitudes of car owners and non-car owners towards transport modes for shopping purposes. Using Singapore as a study area, this study has attempted to analyse car owners and non-car owners' perceptions of the different types of transport modes (i.e., car, taxi, bus, mass rapid transit and walk) in their shopping trips. The research found that each transport mode has its own unique set of attributes. In addition, car owners and non-car owners portray different attitudes towards the public transport modes and the car. This calls for different strategies for these two groups of shoppers in encouraging them to use the public transport modes and restrain the use of the car.     

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Household type and structure, time-use pattern, and trip-chaining behavior

In order to examine time allocation patterns within household-level trip-chaining, simultaneous doubly-censored Tobit models are applied to model time-use behavior within the context of household activity participation. Using the entire sample and a sub-sample of worker households from Tucson’s Household Travel Survey, two sets of models are developed to better understand the phenomena of trip-chaining behavior among five types of households: single non-worker households, single worker households, couple non-worker households, couple one-worker households, and couple two-worker households. Durations of out-of-home subsistence, maintenance, and discretionary activities within trip chains are examined. Factors found to be associated with trip-chaining behavior include intra-household interactions with the household types and their structure and household head attributes.