Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate t...Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making.However,real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research,we demonstrate how we obtain historical time-series and real-time data from sensors oper-ated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne,UK.In the study,we assess the impact of rainfall on traffic volume and travel time,and the cascading impacts during a storm event in Newcastle during early October 2021.We also estimate the economic cost of the storm,with regards to transport disruption,as the cost of travel,using the“value of time”based on Department for Transport guidelines(2021).Using spatial-temporal analysis,we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm.We identified increases in travel times of up to 600%and decreases in traffic volume of up to 100%when compared to historical data.Further,we assessed cascading impacts at important traffic locations and their broader implications for city areas.We estimated that the storm’s economic impact on one sensor location increased by up to 370%of the reference value.By analysing historical and real-time data,we detected and explained patterns in the data that would have remained uncovered if they had been examined individually.The combination of different data sources,such as traffic and weather,helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.展开更多
OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attri...OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.展开更多
Partnerships have become a comer stone of contemporary research that recognizes working across disciplines and co-production with intended users as essential to enabling sustainable resilience-building.Furthermore,res...Partnerships have become a comer stone of contemporary research that recognizes working across disciplines and co-production with intended users as essential to enabling sustainable resilience-building.Furthermore,research that addresses sustainable development challenges brings an urgent need to reflect on the ways that partnerships are supported,and for the disaster risk management and resilience communities,efforts to support realization of the wider 2030 Agenda for sustainable development bring particular pressures.In November 2019,the UK Disasters Research Group(DRG)brought together a number of key stakeholders focused on disaster risk,resilience,and sustainability research relevant to Official DevelopmeAssistance to consider how fit for purpose existing partnership models are for the pace of change required to deliver the priorities of the wider 2030 Agenda.Participants were invited to discuss how research partnerships across three levels(individual and project-based;national and institutional;and international)could be improved based on elements that facilitate robust partnerships and learning from aspects that hinder them.From the discussions,participanls emphasized the importance of effective communication mechanisms in building partnerships,co-designing projects,and establishing shared objectives.Enhanced approaches to addressing equitable partnerships and funding more substantive timelines will be key to responding to the challenges of the 2030 Agenda.展开更多
基金supported by the United Kingdom’s Engineering and Physical Sciences Research Council(EPSRC)under grant number EP/S023577/1,and Ordnance Survey of Great Britain.
文摘Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making.However,real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research,we demonstrate how we obtain historical time-series and real-time data from sensors oper-ated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne,UK.In the study,we assess the impact of rainfall on traffic volume and travel time,and the cascading impacts during a storm event in Newcastle during early October 2021.We also estimate the economic cost of the storm,with regards to transport disruption,as the cost of travel,using the“value of time”based on Department for Transport guidelines(2021).Using spatial-temporal analysis,we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm.We identified increases in travel times of up to 600%and decreases in traffic volume of up to 100%when compared to historical data.Further,we assessed cascading impacts at important traffic locations and their broader implications for city areas.We estimated that the storm’s economic impact on one sensor location increased by up to 370%of the reference value.By analysing historical and real-time data,we detected and explained patterns in the data that would have remained uncovered if they had been examined individually.The combination of different data sources,such as traffic and weather,helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.
基金This research was supported financially by EU FP7 Marie Curie Initial Training Network MULTI-POS(Multi-technology Positioning Professionals)[grant number 316528].
文摘OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.
文摘Partnerships have become a comer stone of contemporary research that recognizes working across disciplines and co-production with intended users as essential to enabling sustainable resilience-building.Furthermore,research that addresses sustainable development challenges brings an urgent need to reflect on the ways that partnerships are supported,and for the disaster risk management and resilience communities,efforts to support realization of the wider 2030 Agenda for sustainable development bring particular pressures.In November 2019,the UK Disasters Research Group(DRG)brought together a number of key stakeholders focused on disaster risk,resilience,and sustainability research relevant to Official DevelopmeAssistance to consider how fit for purpose existing partnership models are for the pace of change required to deliver the priorities of the wider 2030 Agenda.Participants were invited to discuss how research partnerships across three levels(individual and project-based;national and institutional;and international)could be improved based on elements that facilitate robust partnerships and learning from aspects that hinder them.From the discussions,participanls emphasized the importance of effective communication mechanisms in building partnerships,co-designing projects,and establishing shared objectives.Enhanced approaches to addressing equitable partnerships and funding more substantive timelines will be key to responding to the challenges of the 2030 Agenda.
