The context of this study is the increasing need for public transport as issues over high private vehicle usage are becoming increasingly obvious. Public transport services need to compete with private transport to improve patronage, and issues with reliability need to be addressed. Bus bunching affects reliability through disruptions to the scheduled headways. The purpose of this study was to collect and analyse data to compare how travel time and dwell time vary, to explore the variation of key variables, and to better understand the sources of these variations. The Orbiter bus service in Christchurch was used as a case study, as it is particularly vulnerable to bus bunching. The dwell time was found to be more variable than travel time. It appeared the Canterbury earthquake had significantly reduced the average speeds for the Orbiter service. In 1964, Newell and Potts described a basic bus bunching theory, which was used as the basis for an Excel bus bunching model. This model allows input variables to vary stochastically. Random values were generated from four specified distributions derived from manually collected data, allowing variance across all bus platforms and buses. However the complexity resulted in stability and difficulty in achieving convergence, so the model was run in single Monte Carlo simulations. The outputs were realistic and showed a higher degree of bunching behaviour than previous models. The model demonstrated bunching phenomena that had not been observed in previous models, including spontaneously un-pairing, overtaking of buses delayed at platforms, and odd-numbered bunches of three buses. Furthermore, the study identified areas of further research for data collection and model development.
The devastating magnitude M6.3 earthquake, that struck the city of Christchurch at 12:51pm on Tuesday 22 February 2011, caused widespread damage to the lifeline systems. Following the event, the Natural Hazard Research Platform (NHRP) of New Zealand funded a short-term project “Recovery of Lifelines” aiming to: 1) coordinate the provision of information to meet lifeline short-term needs; and to 2) facilitate the accessibility to lifelines of best practice engineering details, along with hazards and vulnerability information already available from the local and international scientific community. This paper aims to briefly summarise the management of the recovery process for the most affected lifelines systems, including the electric system, the road, gas, and the water and wastewater networks. Further than this, the paper intends to discuss successes and issues encountered by the “Recovery of Lifelines” NHRP project in supporting lifelines utilities.
The 22nd February 2011, Mw 6.3 Christchurch earthquake in New Zealand caused major damage to critical infrastructure, including the healthcare system. The Natural Hazard Platform of NZ funded a short-term project called “Hospital Functions and Services” to support the Canterbury District Health Board’s (CDHB) efforts in capturing standardized data that describe the effects of the earthquake on the Canterbury region’s main hospital system. The project utilised a survey tool originally developed by researchers at Johns Hopkins University (JHU) to assess the loss of function of hospitals in the Maule and Bío-Bío regions following the 27th February 2010, Mw 8.8 Maule earthquake in Chile. This paper describes the application of the JHU tool for surveying the impact of Christchurch earthquake on the CDHB Hospital System, including the system’s residual capacity to deliver emergency response and health care. A short summary of the impact of the Christchurch earthquake on other CDHB public and private hospitals is also provided. This study demonstrates that, as was observed in other earthquakes around the world, the effects of damage to non-structural building components, equipment, utility lifelines, and transportation were far more disruptive than the minor structural damage observed in buildings (FEMA 2007). Earthquake related complications with re-supply and other organizational aspects also impacted the emergency response and the healthcare facilities’ residual capacity to deliver services in the short and long terms.