Ramp meters on trial: Evidence from the Twin Cities metering holiday

Ramp meters in the Twin Cities have been the subject of a recent test of their effectiveness, involving turning them off for eight weeks. This paper analyzes the results with and without ramp metering for several representative freeways during the afternoon peak period. Seven performance measures: mobility, equity, productivity, consumers’ surplus, accessibility, travel time variation and travel demand responses are compared. It is found that ramp meters are particularly helpful for long trips relative to short trips. Ramp metering, while generally beneficial to freeway segments, may not improve trip travel times (including ramp delays). The reduction in travel time variation comprises another benefit from ramp meters. Non-work trips and work trips respond differently to ramp meters. The results are mixed, suggesting a more refined ramp control algorithm, which explicitly considers ramp delay, is in order.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey

The main focus of travel behaviour research has been explaining differences in behaviour between individuals (interpersonal variability) with less emphasis given to the variability of behaviour within individuals (intrapersonal variability). The subject of this paper is the variability of transport modes used by individuals in their weekly travel. Our review shows that previous studies have not allowed the full use of different modes in weekly travel to be taken into account, have used categorical variables as simple indicators of modal variability and have only considered a limited set of explanatory indicators in seeking to explain modal variability. In our analysis we use National Travel Survey data for Great Britain. We analyse modal variability with continuous measures of modal variability (Herfindahl–Hirschman Index, the difference in mode share between the primary and secondary mode, the total number of modes used). Taking inspiration from Hägerstrand (1970), we conceive that modal variability is determined by different types of spatial mobility constraints and find that reduced modal variability is predicted for having mobility difficulties, being aged over 60, being non-white, working full-time, living in smaller settlement, lower household income, having regular access to a car, having no public transport pass/season ticket and not owning a bicycle. The findings can support a change in perspective in transport policy from encouraging people to replace the use of one mode with another to encouraging people to make a change to their relative use of different transport modes.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

Happy or liberal? Making sense of behavior in transport policy design

Appropriate microeconomic foundations of mobility are decisive for successful policy design in transportation and, in particular, for the challenge of climate change mitigation. Recent research suggests that behavior in transportation cannot be adequately represented by the standard approach of revealed preferences. Moreover, mobility choices are influenced by factors widely regarded as normatively irrelevant. Here we draw on insights from behavioral economics, psychology and welfare theory to examine how transport users make mobility decisions and when it is desirable to modify them through policy interventions. First, we explore systematically which preferences, heuristics and decision processes are relevant for mobility-specific behavior, such as mode choice. We highlight the influence of infrastructure on the formation of travel preferences. Second, we argue that the behavioral account of decision-making requires policy-makers to take a position on whether transport policies should be justified by appealing to preference satisfaction or to raising subjective well-being. This distinction matters because of the (i) influence of infrastructure on preference formation, (ii) health benefits from non-motorized mobility, (iii) negative impact of commuting on happiness and (iv) status-seeking behavior of individuals. The orthodox approach of only internalizing externalities is insufficient because it does not allow for the evaluation of these effects. Instead, our analysis suggests that transport demand modeling should consider behavioral effects explicitly.     

The effect of attitudes toward cars and public transportation on behavioral intention in commuting mode choice—A comparison across six Asian countries

This study investigated the contribution of psychological factors in explaining the choice of transportation mode in six Asian countries. Data were collected from 1118 respondents in Japan, Thailand, China, Vietnam, Indonesia, and the Philippines. The dependent variable was the intention to use one of three modes for work travel after getting a job: car, public transit, or other modes. The explanatory variables were three attitude factors taken from a previous study, including: 1/symbolic affective, reflecting affective motives of travel mode use; 2/instrumental, referring to functional attributes of travel modes; and 3/social orderliness which represents for environmental friendliness, safety, altruism, quietness et cetera. Several logit model estimates were made using the samples from the six countries separately and together. We obtained three main findings. First, attitude variables about the car were all significant determinants for the entire sample from Asian countries. Second, the social orderliness aspect of public transit was a common concern of respondents from developing countries in selecting this mode for work trips. Third, in countries in which the intent to use a car was not very high, attitude factors about the car were found to be significant determinants of the behavioral intention to commute by car but were less significant in countries in which the desire to use a car was high.     

Mobile ICTs and physical mobility: Review and research agenda

The question of the relationship between the spread of communication tools and the physical mobility of individuals is not new and arose with the arrival of the fixed telephone and, more recently, the development of the Internet and especially e-commerce. The extraordinary spread of individual, especially portable, communication tools like the mobile phone, has recently generated new interest in this topic in the fields of transportation economics, geography and sociology. This article discusses the main topics that have been explored, from the debate between complementarity and substitution to analyses in terms of interactions with the spatiotemporal organization of daily activities, the size and maintenance of social networks, and, finally, perception of travel and spaces. We then identify several issues that we think merit further exploration.     

Trade-offs between mobility and equity maximization under environmental capacity constraints: A case study of an integrated multi-objective model

This paper investigates the performance of a policy decision tool proposed for multi-objective decision under different policy interventions. This tool deals with the trade-off between mobility and equity maximization under environmental capacity constraints. Two system objectives, maximization of mobility and equity, are formulated in terms of the sum of total car ownership and number of trips, and the differences in accessibility between zones. Environmental capacities are based on production efficiency theory in which the frontier emission under maximum system efficiency is taken as environmental capacity. To examine the performance of the proposed model, three types of hypothetical policies (network improvement, population increase and urban sprawl) are formulated. Effects are simulated using data pertaining to Dalian City, China. Results show that the proposed model is capable of representing the trade-offs between mobility and equity based on different policy interventions. Compared with two extreme cases with the single objective of mobility maximization or equity maximization, the Pareto-optimal solutions provide more interesting practical options for decision makers. Taking the solution based on the maximum equity as an example, the policy of urban sprawl yields the most significant improvement in both emission and accessibility of the three scenarios.     

To fully automate or not? Investigating demands and willingness to pay for autonomous vehicles based on automation levels

We review the literature on the motivators of consumers’ purchasing decisions regarding autonomous vehicles (AVs), focusing on environmental awareness, vehicle types, concerns about accidents, and merits. The willingness to buy (WTB) and willingness to pay (WTP) for AVs have been extensively studied. However, it is imperative to broaden the outlook and consolidate the existing knowledge base. Although the WTB and WTP for AVs have been studied, automation-level preferences have been underresearched. Based on more than 150,000 observations, we construct a choice model and calculate the WTP for each level of automation. Our results show that there is a disparity between people's WTB and WTP according to the automation level. In particular, we find that the coefficients regarding FAVs’ benefits and accidents significantly exceed those related to environmental concerns. Such results indicate that practical benefits and concerns influence AV demand and WTP to a greater extent than environmental awareness. Our model results indicate a disparity between WTB and WTP for AVs that mostly derives from the different types of environmental concerns, concerns about accidents, and perceived benefits from these automobiles. Our results urge policies according to which governments and companies closely examine consumers who exhibit WTB-WTP disparities. Issues relating to demand patterns, WTP, and a suitable policy framework are discussed.     

Mobility, social exclusion and well-being: Exploring the links

Building on a growing research foundation, transport policy makers have begun to associate the ability to be mobile with having a role in the facilitation of social inclusion. However, the further connection to well-being is not as well understood. This paper explores the association between a person’s travel patterns, their risk of social exclusion and self-assessed well-being. Key influences on social exclusion are discussed, with trip making emerging as a significant influence. Trip making is not a significant direct influence on well-being but does exercise an indirect influence through the impact on risk of social exclusion. The modelling process enables a value for additional trips to be estimated, the value being about four times the values derived from conventional generated traffic approaches. Similar high values are found in separate metropolitan and regional case studies, confirming the significance of the results.