Empirical evaluation of public transport travel time variability

Although transit travel time variability is essential for understanding the deterioration of reliability, optimising transit schedule and route choice; it has not attracted enough attention from the literature. This paper proposes public transport-oriented definitions of travel time variability and explores the distributions of public transport travel time using the Transit Signal Priority data. First, definitions of public transport travel time variability are established by extending the common definitions of variability in the literature and by using route and services data of public transport vehicles. Second, the paper explores the distribution of public transport travel time. A new approach for analysing the distributions involving all transit vehicles as well as vehicles from a specific route is proposed. The Lognormal distribution is revealed as the descriptors for public transport travel time from the same route and service. The methods described in this study could be of interest for both traffic managers and transit operators for planning and managing the transit systems.

Establishing definitions and modeling public transport travel time variability

Public transport travel time variability (PTTV) is essential for understanding deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyses the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and researching the transit systems.

A design approach to the study of people's experiences with technologies in the context of public transport

Relevant to the study of people’s attitudes towards public transport use is the consideration to the role of technology as part of the travel experience. Technologies aim to enhance daily tasks but tend to change the way people interact with products and can be perceived as difficult to use. This is critical in the context of “public use” where products and services are to be used by the population at large: adults, children, elderly, people with disabilities, and tourists. From different perspectives, the topic of users and the use of technologies have been studied in the social sciences and human computer interaction fields; however, earlier approaches fail to address the ways in which experiential knowledge informs people’s interactions with products and technologies, and how such information could guide the design of future technologies. This paper describes a pilot study, part of a larger ongoing exploratory research that investigates people’s experiences with infrastructure, systems, and technologies in the context of public transport. The methodological approach included focus groups, field observations, and retrospective verbal reports. At this stage, the study found that four context led factors were the primary source of reference informing participants’ actions and interactions; they are: (i) context >> experience, (ii) context >> interface, (iii) context >> knowledge, (iv) context >> emotion.

Public transport travel time variability definitions and monitoring

Public Transport Travel Time Variability (PTTV) is essential for understanding the deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes the key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyzes the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach, using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and analyzing the transit systems.

Urban Informatics and Public Transport

The holistic urban experience we perceive when immersed in an urban context is at the heart of urban informatics. This experience encompasses all urban elements such as architecture, people, and culture. Urban informatics explores the possibilities and opportunities created by new technologies and information for enhancing the urban experience. Public transport is an essential urban experience. Everyday, urban dwellers takes public transport to commute and move between different parts of the city. Public transport serves people from all over the city and moves them through different places in the city, using different means of transportation. The nature of public transport—involving people, places, and technologies, makes it a fitting context for urban informatics interventions. There are three main aspects of the public transport experience that can readily benefit from urban informatics interventions the: pragmatic aspect, hedonistic aspect, and social aspect. From the pragmatic perspective, these interventions can help people to be more efficient and effective in taking public transport. Hedonistic-related interventions aim to bring enjoyment and fun to our mundane commute. Finally, urban informatics can strengthen the sense of community in a socially-passive context like public transport environments through adopting socially focused interventions.

Trends in Income and Price Elasticities of Transport Demand (1850-2010)

40 p.

Public transport

Public transport plays an essential role in enabling people from low income and other disadvantaged groups to access employment and services. It also contributes to the development of social networks and social capital, by helping people to visit friends and relatives and take part in community and other social activities. Public policy makers have begun to recognise that adequate public transport provision can play an important role in reducing social exclusion. [Taken from introductory paragraph.]

Public transport travel planning application

Here it is presented an application that plans out travel on public transports and that chooses the best ones, according to preference criteria provided by the user. These criteria are: the time spent on the travel, the price of the tickets and the quality of the transports. The application combines different means of transport. Algorithms and heuristics were developed to draw up transport plans and to choose the best ones. The best plans are determined using the multi-attributes decision techniques. The application uses a database that was developed in a Relational Database Management System. To draw the database at the conceptual and the applicational level, it was used one of the models based on the object, the Entity-Relationship Mode

ITEM : An Integrated Transport Economy Model - Price Elasticities of Freight Transport Demand in Canada : Three Canadian Markets and Four Commodities.

Rapport de recherche

Why doesn't investment in public transport reduce urban traffic congestion?

Includes bibliography

El desarrollo del transporte público urbano en América Latina y el mundo = The development of urban public transport in Latin America and the world

Incluye Bibliografía