Highway Capacity Manual改訂の動向--テイラ-教授の講演より

http://www1.caminos.upm.es/music1/files/04_seminarios/1_Sem/05/descargas/5_Transport_Exclusion_Karen%20Lucas.pdf

Transport and social exclusion: Where are we now?

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

Scientific interest in the cognitive underpinnings of religious belief has grown in recent years. However, to date, little experimental research has focused on the cognitive processes that may promote religious disbelief. The present studies apply a dual-process model of cognitive processing to this problem, testing the hypothesis that analytic processing promotes religious disbelief. Individual differences in the tendency to analytically override initially flawed intuitions in reasoning were associated with increased religious disbelief. Four additional experiments provided evidence of causation, as subtle manipulations known to trigger analytic processing also encouraged religious disbelief. Combined, these studies indicate that analytic processing is one factor (presumably among several) that promotes religious disbelief. Although these findings do not speak directly to conversations about the inherent rationality, value, or truth of religious beliefs, they illuminate one cognitive factor that may influence such discussions.

https://science.sciencemag.org/content/sci/336/6080/493.full.pdf

Analytic thinking promotes religious disbelief.

Using Lorenz curves to assess public transport equity

Causes of Youth Licensing Decline: A Synthesis of Evidence

Equity has been a major concern of transit provision and is required by legislation in many countries. Several approaches measure equity in transit supply however none produce a simple system-wide measure of equity performance. A new approach is presented using Lorenz curves to measures the relative supply of transit to the population. Gini coefficients provide a single measure of overall equity using this method. A system-wide assessment of overall transit supply to the population in Melbourne, Australia shows that 70% of the population shares only 20% of the supply (Gini coefficient = .67). When employment is also taken into account, the situation is not much different; 70% of jobs and population share less than 25% of service (G = .62). In order to gain some understanding of vertical equity (supply for specific underprivileged groups), Lorenz curves were calculated for households with no car, low-income households, youth and the aged. Most groups experienced similar inequitable distributions of transit with Gini coefficients close to the overall average. In contrast, transit services were more equitable for households with no car (G = .50) which suggests better vertical equity for no-car households. Overall it is unclear how “fair” these distributions are compared to equity in other cities since this is the first time this method has been undertaken. Projects using similar approaches should provide a good basis for establishing comparative equity between cities.

Alexa Delbosc

Papers

Using Lorenz curves to assess public transport equity

Causes of Youth Licensing Decline: A Synthesis of Evidence

Using Lorenz curves to assess public transport equity

The spatial context of transport disadvantage, social exclusion and well-being

Exploring the relative influences of transport disadvantage and social exclusion on well-being