Civil infrastructure systems, such as roadways, pipelines and buildings, are among the largest investments in any society. Most of these infrastructure systems in the U.S. were built during World War II era and they have exceeded their design lifespans. Typically, infrastructure systems are located in close proximity, and are deployed underneath the urban roadway networks. When they breakdown, there is a need to cut through roadway pavements to access them - which in turn creates significant mobility implications to motorists. All of these indicate a tightly-coupled nexus of interdependencies amongst underground utility/infrastructure systems, roadways and motorist mobility. Most of the current approaches in managing these relationships and corresponding disruptions are reactive; resulting in longer times of impact, higher costs and significant mobility issues. There is an opportunity to rethink the current reactive approach and make it much more proactive and effective. The proposed research targets that through foundational works towards the development of; (1) predictive infrastructure maintenance operations that predict and coordinate major repairs amongst different infrastructure systems to minimize disruptions to a community; and (2) dynamic incentive/disincentive models provided to motorists to proactively manage and increase their mobility under a given maintenance related disruption. The proposed approach includes ethnographic studies around existing planned disruptions through maintenance, development of analytics for predictive maintenance and data-driven agent-based approach for modeling motorist behaviors under different incentives/disincentives and disruption situations, and development of an integrated analysis framework. If successful, the proposed research will provide the foundational work towards a first of a kind integrated predictive coordinated maintenance and mobility management framework. The research team will evaluate the framework through assessing how it reduces delays on the road network segments and travel time for standard origin destination pairs, and how it enables time and money savings for roadwork and utility companies through predictive and coordinated maintenance activities.
Motivating Vignette: Forbes Avenue between Morewood and South Craig Street at Pittsburgh, was shut down unexpectedly on February 28th because of a 20 inch water main break. This required a new pipeline to be installed and a major road construction. As a result, that portion of Forbes Avenue was closed for more than one month and reopened on April 2nd. The closure impacted about 19,000 drivers, who uses that section of the road on a daily basis, and the traffic got diverted onto much smaller streets, creating a major mobility challenge for all motorists. This vignette shows how tightly coupled our underground infrastructure systems, roadways and the mobility of the motorists are. Unfortunately, this situation is not uncommon and is occurring at higher frequencies with the aging infrastructure. A major problem with such situations is that in most of the cases that disruptions occur and are managed in a reactive manner resulting in longer times of impact, higher costs and significant mobility issues. There is an opportunity to rethink the current modus of operands and make it much more proactive and effective. The proposed research targets that by doing foundational works towards the development of; (1) predictive infrastructure maintenance operations that predict and coordinate major repairs amongst different infrastructure systems to minimize disruptions to a community; and (2) dynamic incentive/disincentive models provided to motorists to proactively manage and increase their mobility under a given maintenance related disruption. Introduction and Overview of the Project Civil infrastructure systems, such as roadways, pipelines and buildings, are among the largest investments in any society. Most of the infrastructure systems in the U.S. were built during World War II (Chou et al. 2007). They have met or are close to meeting their design lifespans, and are in need of rehabilitation to continue serving the needs of the community. Typically, such utilities are located in close proximity, and are deployed along the urban roadway networks. In order to provide new services or maintain the deteriorating infrastructure networks, utility companies have to cut pavements to access the utilities buried beneath (Jung 2012). Any disruptions due to utility breakdowns and maintenance activities have direct and significant mobility implications. Hence, there is a need to minimize road and utility maintenance through coordination of such activities. At the same time, incentives (or disincentives) can be used as a way to alleviate mobility issues whenever there is a maintenance activity. All of these suggest the existence of a tightly-coupled complex systems of systems between utility systems, transportation networks and the citizens of a city. Frequent cut and reinstatement of pavements have been shown to place physical stress on the pavement, leading to reduction of lifespans by as much as 7 to 12 years (Jensen et al. 2005). This has also resulted in uneven pavements, poor ride quality and increased accidents due to poor road safety (Khogali and Mohamed 1999). This is significant, especially given that 32% of America’s major roads are in less than mediocre condition (ASCE 2014). The cost of traveling on deficient pavement is estimated to be $67 billion a year, or $324 per motorist due to repairs and operating costs (ASCE 2016). Deficient pavements are observed more commonly in urban areas, with almost half of the interstate vehicle miles traveled are estimated to be over deficient pavements compared to only 15% on rural interstates (ASCE 2016). Knowing the existence of such issues, FHWA has published a manual on controlling and reducing the frequency of utility cuts (Wilde et al. 2002). This manual outlines various policies relating to Right-Of-Way, further classifying them according to incentive-based, fee-based and requirement-based policies. While such policies are very much needed, the effective usage of them still needs to be determined. In this proposed research, our goal is to do foundational work towards the development of novel approaches that optimize the coordination of utility/infrastructure and transportation maintenance and repair activities and that enable experimenting with different incentive/disincentive strategies to minimize the mobility impact of scheduled maintenance activities. We are proposing to do this in an integrated manner so as to be able to develop a framework that not only minimizes the mobility impact of maintenance activities, but also proactively manages them through incentives/disincentives that would be dynamically generated for motorists in a given context. Theproposed activities will pave the way to a unique framework that approaches mobility issues in an integrated endto- end manner and proposes strategies to proactively manage them. Background Research: Typical Utility Management Systems include a database of utilities, with extensions to Geographic Information Systems (GIS) for spatial visualizations and spatial data objects. A schema representing the structure of data objects in the database typically include details on types of utility conflicts, work times, responsible parties, etc. The typical goal of such UMS is to regulate and streamline the flow of utility information, and enable information access to relevant parties. Prior research in the area of coordination of maintenance of infrastructure systems can be categorized into two: (1) Identification of information needed and the corresponding representation models to support coordination of maintenance activities (e.g., Halfawy 2008 and 2010, Ganeshan et al. 2001); (2) Developing a decision-support system for utilities contractors, providers and municipal authorities to strategically combine maintenance activities to reduce mobility issues and corresponding time delays to the motorists (e.g., Chou et al. 2007; Arudi et al. 2000). At the same time, while there is a large amount of prior work on modeling travel behavior, there are not many studies on behavioral responses to major network disruptions, such as the Forbes Avenue case described above (Giuliano and Golob 1998). An agent-based approach has been demonstrated to be effective in modeling the travel behaviors under a network disruption, such as snowy and icy conditions on roadway networks during a snowstorm (Gonzalez 2015) While these prior research studies provide a good foundational work for our proposed research, they have not considered maintenance activities and possible dynamic incentives/disincentives that could be provided to the motorists based on their anticipated behaviors to the disruptions in an integrated manner. Hence, they did not approach maintenance, mobility issues, and motorist behavior as a nexus of interdependent systems that need to be managed as such. Research Approach We will approach the proposed foundational work from two complementary and integrated perspectives: (1) Modeling of maintenance needs of different infrastructure systems, and development of an optimization approach to manage those needs in a predictive manner; and (2) Modeling how motorists behave under different incentive/disincentives and developing a framework that dynamically generates incentives/disincentives to motorists under given roadway construction activities due to maintenance and repair of infrastructure systems. We would take advantage of the prior work in these discrete areas and identify new data sources to develop predictive maintenance capability and more accurate cost models associated with disruptions. The absence of metering or tolling systems is not an impediment. For example, it is possible to consider delays imposed on individuals as “prices” faced by individuals on account of the disruption. There are also new data sources, such as social media (e.g., twitter, Waze), that are being used to facilitate coordination in the face of disruptions. These methods combined with simulation models can be used determine such “prices” – akin to what is described in Gonzalez, 2015. City of Pittsburgh provides a great test bed to develop and test several proposed research activities since they are already working on ways to coordinate infrastructure maintenance activities to reduce mobility disruptions. We will work closely with the local utility companies (such a People’s Gas, America’s water, Alcosan, Duquesne light) together with the City of Pittsburgh to start modeling the maintenance needs and developing an optimization approach towards managing those needs. The PIs already have close relationships with some of these companies and the City of Pittsburgh to be able to implement that. At the same time, we will develop traffic models and a simulation platform to perform different incentive scenarios and their implications. We will formalize both maintenance optimization and incentive models in a combined manner so as to be able to have an integrated framework for proactively managing both infrastructure maintenance and mobility needs.
Quarter 1 - Ethnographic studies of infrastructure repair activities and corresponding mobility challenges. People’s Gas is planning to repair/replace 100 miles of their gas pipelines in the City of Pittsburgh and its metropolitan area throughout Summer and Fall 2016. Each segment of repair will be done at a different location – which provide the research team a great opportunity to identify and observe the mobility and traveler behavior at different contexts. An outcome of this effort is to develop an ontology of disruptive events caused by breakdown, scheduled repair or emergency repair activities. With respect to mobility, we are interested in the nature of the change to mobility caused by the event. For example, the closure of the segment of Forbes Avenue resulted in loss of access to that segment for all vehicles that wanted to use it as a thoroughfare. Limited access to buildings on Forbes Avenue was available to vehicles for pick up and drop off. Pedestrians and bicycles had access to the segment during the course of the repair. Quarter 2 - Analytics for predictive maintenance based on the data that is provided for gas pipelines. A key challenge here is predicting the likelihood of failure associated with various segments of a buried network infrastructure taking into account the correlations induced both by the network, joints that make up the physical structure of the pipes and the physics of fluid flow. - Associated with each segment of interest of the buried infrastructure, we will develop a mapping between buried infrastructure segments and above ground infrastructure or roadway segments. In many cases, it will be a 1-1 mapping (e.g., like the mapping between the 20” water mains and the Forbes Avenue segment), in others it may involve more than one road segment. Quarter 3 - Analytics for predictive maintenance based on the data that is provided for gas pipelines (continued). - Development and calibration of an agent-based model of mobility behavior under disruption. There are existing simulation models of traffic flow and our proposal is to model and develop estimates of delays under different vehicular and public transportation vehicular use assumptions in the presence of disruption. - The mobility model will be used to understand the impact – spatial and temporal scope -- of disruptions (from simple to more significant as per the ontology we will develop). A goal is to develop use cases of correlated disruptions. For example, the water mains break may cause a problem on a segment of Forbes Avenue and a Gas Leak could cause a lane restriction in a segment of Fifth Avenue. While the buried infrastructure disruption may be independent events from a causal standpoint, they induce significant changes to mobility since the two disruptions affect a common shared infrastructure. - Our primary measure of impact will be induced delay on road network segments with a focus on vehicular commuters. These delays will be used as “prices” faced by commuters in our modeling framework. In the future we will consider pedestrian and bicyclists among the users of the road infrastructure. Quarter 4 - Integrated analytics, optimization and proactive mobility management framework. This work will seek to integrate the predictive analytics associated with maintenance with a mobility management under disruption. - The mobility management model will enable policy interventions to be evaluated against the use cases of buried infrastructure induced disruptions derived from our ethnographic case studies. The objective function will be both delays on road segments in the presence of policy interventions as well as travel time for standard origin destination pairs (e,g, typical commutes such as into downtown in the morning). - Since metering and tolling have not been deployed in Pittsburgh, our approach will be to simulate these policy interventions – information and routing decision support tools to enable consumers to make local travel choices in the presence of delays induced by the disruption with the objective of balancing traffic load over the transportation network. - Testing and validation of integrated framework in retrospective cases to be developed based on the ethnographic case studies
We have a virtual deployment plan based on the real data and cases to be developed through ethnographic studies
Many of the expected accomplishments and metrics are described in Section 8. In this section, we provide a highlevel overview and summary. - Development of the first version of an integrated predictive coordinated infrastructure maintenance and mobility management framework. - Detailed case studies of mobility implications of network disruptions due to infrastructure maintenance activities. - Metrics: (1) Reduced mobility issues via virtual agent-based simulations/demonstrations by tracking metrics such as delay on road network segments and travel time for standard origin destination pairs. (2) Time and money savings to roadwork and utility companies through predictive and coordinated maintenance activities.
|firstname.lastname@example.org||Akinci, Burcu||CEE||PI||Faculty - Tenured|
|email@example.com||Garrett, James||CIT Deans Office||Other||Faculty - Tenured|
|firstname.lastname@example.org||Krishnan, Ramayya||Carnegie Mellon Heinz College||Other||Faculty - Tenured|
|email@example.com||Qian, Sean||Carnegie Mellon University||Co-PI||Faculty - Untenured, Tenure Track|
|firstname.lastname@example.org||Zhang, Zhuoran||Carnegie Mellon University||Other||Student - PhD|
|Data Management Plan||198_-_DMP_Proactive_management_of_mobili.pdf||Dec. 17, 2018, 8:02 a.m.|
|Progress Report||198_Progress_Report_2019-09-30||Oct. 1, 2019, 7:08 a.m.|
|Progress Report||198_Progress_Report_2020-03-30||March 31, 2020, 7:36 a.m.|
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