Abstract

In most large cities in developing countries, buses continue to be the public transport option of choice, carrying a large share of urban travelers. However, transit bus companies in these countries are often cash-strapped. In many cases, the operating cost per bus kilometer exceeds revenues and bus fares are often kept low irrespective of the cost of providing service. Many cities are dominated by old and fuel-intensive buses with high operating costs. Transit systems are also often plagued by overcrowded and undependable service, congested roadways and chaotic operating environments. Across the board, city officials in developing countries are under strong pressure to improve the efficiency and enhance the attractiveness of bus transportation. Fuel makes up a relatively large fraction of total bus operating costs, especially when labor costs are low, as in many developing countries. Fuel costs can be reduced by improving the driving style of bus drivers and through sound maintenance practices. A safe and economical driving style can reduce variable costs, decrease down time due to repair work and maintenance, mitigate negative environmental impacts and improve road safety. This guidance note1 provides detailed and practical recommendations on how city bus operations managers and their technical staff can plan and implement such enhancements to their fleets through Operations and Maintenance (O and M) practices without significant capital investments. By implementing such recommendations, municipal officials and bus operators can increase the efficiency and fuel economy of their bus systems and reduce their cities' energy consumption, congestion and pollution. This guidance note provides detailed and practical recommendations on how city bus operations managers and their technical staff can plan and implement such enhancements to their fleets through O and M practices without significant capital investments.

Abstract

Vietnam's transport sector plays an important role in its socioeconomic development. Passenger transport helps meets daily needs and contributes significantly to tourist service development while freight transport helps meet the country's demand for delivery of raw materials and of semi-finished products to production facilities and for transport of finished products to consumers. With the overall growth in transport in the country, each of the main forms of transport (road, railway, aviation, inland waterways and maritime) are currently increasing the total fuel use for transport and as a result overall greenhouse gas emissions. In addition to the mix of transport modes used, the major factors affecting the impact of this growth on current and future greenhouse gases from each mode of transport are the quality and quantity of the vehicles and supporting infrastructure and the type of fuels used and their efficiency. The following sections evaluate the infrastructure and fuel use of each of the modes of transport (sections 1.1-1.5), the current and projected greenhouse gas emissions from the sector and the reasons for this (section 2), and typologies of interventions that can reduce the emissions from the sector (section 3).

Abstract

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2019-08-29T11:47:04Z No. of bitstreams: 1 TARCISO BRAZ DE OLIVEIRA FILHO - DISSERTAÇÃO (PPGCC) 2019.pdf: 1872963 bytes, checksum: 87ed3cf8f4914f2822f9c5f2715ad487 (MD5) Made available in DSpace on 2019-08-29T11:47:04Z (GMT). No. of bitstreams: 1 TARCISO BRAZ DE OLIVEIRA FILHO - DISSERTAÇÃO (PPGCC) 2019.pdf: 1872963 bytes, checksum: 87ed3cf8f4914f2822f9c5f2715ad487 (MD5) Previous issue date: 2019-02-27 Como resultado do recente e rápido crescimento da população urbana, a mobilidade tem emergido como um dos problemas urbanos mais complexos e de rápida evolução no século XXI. Com o advento da Internet das Coisas, gigabytes de dados são gerados diariamente por Sistemas de Transporte Público ao redor do mundo, incluindo registros de GPS e velocidade dos ônibus, além de registros de embarque de passageiros. A despeito desses dados possuírem o potencial de auxiliar na melhoria da mobilidade, a enorme quantidade, dinamicidade e diversidade de dados produzidos por diferentes sistemas com diferentes objetivos e restrições, impõe dificuldades para a integração e análise do mesmo com o fim de ajudar os usuários, operadores e administradores do sistema. Esse estudo aborda esse problema, mais especificamente o de utilizar dados de programação dos ônibus, dados brutos de GPS e dados de cartão de embarque para reconstruir viagens de ônibus a nível de passageiro. São utilizados dados do sistema de ônibus de Curitiba no Brasil para conceber um processo de análise que combine e estenda heurísticas consolidadas encontradas na literatura. Experimentos demonstram a utilidade da solução proposta em dois cenários de aplicações: a) a estimação de uma Matriz de Origem-Destino para usuários de Transporte Público, que foi validada através de uma comparação com uma Pesquisa Origem-Destino realizada recentemente na cidade; e b) uma análise da (in)eficiência da escolha de itinerário do passageiro, realizada contrastando o itinerário escolhido estimado (extraído da reconstrução da viagem) com o conjunto de itinerários disponíveis e viáveis no momento do embarque. As a result of the recent and fast rise in urban population, mobility has emerged as one of the most problematic and fast-evolving urban problems of the 21st century. With the advent of the Internet of Things, gigabytes of data are generated every day by Public Transportation Systems around the world, including bus GPS/speed records, and passenger boarding registries. Although this data has the potential to help improve mobility, the vast amount, dynamicity and diversity of data produced by different systems with different goals and constraints poses difficulties to integrate and analyze it and help the system’s users, operators and administrators. This study addresses this problem, more specifically the one of using bus schedule data, raw GPS and smart card records to reconstruct trips at passenger-level. We use data from the Curitiba bus system in Brazil to devise an analysis pipeline that combines and extends consolidated heuristics found in literature. Experiments demonstrate the utility of the proposed solution in two applications scenarios: a) the estimation of an Origin-Destination Matrix for Public Transport users, which was validated by a comparison to a recent Origin-Destination Survey performed in the city; and b) an analysis of the (in)efficiency of passenger itinerary choice, conducted by contrasting the estimated itinerary choice (extracted from trip reconstruction) to the set of available and feasible itineraries at the time of boarding.