Abstract

The aim of this paper is to provide feedback on the question of socioeconomic benefits from rural road development and the impact of transport infrastructure on the poor, particularly the poorest and the bottom 20 percent of the population. This paper relies on impact evaluation methodologies, which are traditionally used in social sectors but less so in the transport sector. The study, including first surveys, was launched in 2003 under the Tocantins Sustainable Regional Development Project. The paper highlights the context that led to the project’s design, which included an impact evaluation of the works envisaged under the project. The paper also highlights some of the main challenges faced by this impact evaluation and how these challenges were addressed for the present study. It then provides details about the data collected during the surveys and the key relevant characteristics of the population targeted by the surveys. It discusses the possible estimation methods envisioned to undertake the study and provides the main results of the assessment based on these methods. The analysis shows that improved rural roads changed people’s transport modal choice. People used more public buses and individual motorized vehicles after the rural road improvements. The paper also finds that the project increased school attendance, particularly for girls. Although the evidence is relatively weak in statistical terms, it indicates that the project contributed to increasing agricultural jobs and household income in certain regions. Document type: Book

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

ualmente existem muitas fontes de dados oriundos de movimentação de pessoas e veículos. Deduzir o comportamento do tráfego a partir desses dados pode ser de grande utilidade para a gestão do tráfego. Entretanto, os métodos existentes podem não abordar todas as dimensões referentes ao comportamento do tráfego, em especial sob efeito de adversidades, como por exemplo - eventos climáticos. O objetivo dessa dissertação é apresentar uma combinação de métodos estatísticos e de análise espacial, para uso na compreensão dos padrões temporais e espaciais de variabilidade das velocidades, em condições normais e sob o impacto da precipitação. O método consiste na mineração de dados de micro trechos de deslocamento (microviagens), através da combinação de estatísticas descritivas, árvores de regressão CHAID (Chi square automatic interaction detection) e análises espaciais (Krigagem). Para a validação do método foram utilizados dados obtidos, durante março de 2014, de operação da frota de ônibus municipal da cidade de São Paulo, fornecidos pela São Paulo Transportes (SPTrans). Os dados de precipitação de chuva foram fornecidos pelo Instituto Nacional de Meteorologia (INMET). Os resultados demonstraram o impacto da precipitação sobre a variabilidade das velocidades por dia da semana e hora do dia. Adicionalmente, com base nos conjuntos de dados classificados via CHAID, foram produzidas representações da distribuição das velocidades sobre a superfície através uso da Krigagem. Observou-se uma consistente queda das velocidades no período vespertino, principalmente entre 16:00 e 19:00 horas, frequentemente agravados pela presença de precipitação. Foram selecionados casos de alta variabilidade, a partir da técnica CHAID, que foram analisados espacialmente. Os efeitos da queda da variabilidade foram observados geograficamente, demonstrando que a infraestrutura de transporte é um importante fator em sua caracterização. O estudo demonstra a viabilidade desta abordagem e a importância de incorporar a geografia no entendimento da variabilidade das velocidades em geral. Nowadays there is a wide number of movement data sources which can be useful in transportation management. However, the existing methods may not cover all traffic dimensions, especially under inclement weather. The objective of this work is presenting a combination of statistical and spatial methods which allows to understand space and time speed patterns, considering regular operation conditions in comparison with rainfall events. The method consists in sequential bus tracking data segments (micro travel) mining by the combination of descriptive statistics, CHAID (Chi square automatic interaction detection) regression trees and spatial analysis (Kriging). To validate the method, São Paulo bus fleet tracking data provided by SPTrans, and rainfall precipitation, provided by INMET was combined and processed. The results presented rainfall as a relevant factor of speed variability by day of week and time of the day. A consistent speed decrease in the afternoon period, mainly between 4:00 PM and 7:00 PM, was observed. Such effects are more intense in face of rainfall precipitation. The highest variability cases obtained by CHAID classification was selected and spatially analyzed showing the transportation infrastructure is an important variability component. Such results show the importance of a geographic approach over speed variability.