Although the improvement of well-being is often an implicitly-assumed goal of many, if not most, public policies, the study of subjective well-being (SWB) and travel has so far been confined to a relatively small segment of the travel behavior community. Accordingly, one main purpose of this paper is to introduce a larger share of the community to some fundamental SWB-related concepts and their application in transportation research, with the goal of attracting others to this rewarding area of study. At the same time, however, I also hope to offer some useful reflections to those already working in this field. After discussing some basic issues of terminology and measurement of SWB, I present from the literature four conceptual models relating travel and subjective well-being. Following one of those models, I review five ways in which travel can influence well-being. I conclude by examining some challenges associated with assessing the impacts of travel on well-being, as well as challenges associated with applying what we learn to policy.
In Brazil, the explosion of informal transport activity during the past decade has had profound effects on formal public transport
systems and is a source of great controversy in the urban transportation sector. A variety of policies have been proposed
to manage the growth of the sector. This study seeks to understand how proposed policies will impact the users of these systems.
A corridor in Rio de Janeiro with substantial informal activity was used as a case study. Measures of welfare changes in a
discrete choice framework were used to estimate proposed policies’ impacts on users. Eleven candidate policies were evaluated,
ranging from the eradication of the informal modes and investment in formal modes, to the legalization of the informal modes.
Benefits were compared with costs and the distribution of benefits across income classes was explored. Net benefits from some
policies were found to be substantial. Legalizing the informal sector was found to benefit users slightly but further investments
in the sector are probably inefficient. Users benefited most from improvements in formal mass transit modes, at roughly 100–200
dollars per commuter per year. Finally, policies to foster a competitive environment for the delivery of both informal and
formal services were shown to benefit users about 100 dollars per commuter per year. Together, the regulation of the informal
sector and investments in the formal sector serve to reinforce the movement towards competitive concessions for services and
help reduce the impacts of cartelization and costly in-road competition.
Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services. 相似文献
Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester. 相似文献
In this paper, annoyance ratings from traffic noise recorded on cobblestones, dense asphalt, and open asphalt rubber pavements are assessed with regard to car speeds and traffic densities. It was found that cobblestones pavements are the most annoying; also while open asphalt rubber pavement imposes less annoyance than dense asphalt it is not significantly different. Higher car speeds always lead to greater annoyance, as does higher traffic densities. LAeq and LAmax correlate well with annoyance, but loudness is the best predictor. Roughness and sharpness exhibit inconsistent interactions. 相似文献