Abstract
Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.
Highlights
The urban mobility pattern is of interest to a wide range of stakeholders such as policymakers, businesses, and local transportation authorities as it can provide a detailed and in-depth understanding of day-to-day vehicle movements, which informs decision making at multiple levels
We aim to address the following two research questions: (1) how resilient is the deployed traffic-monitoring system in terms of its overall observability? and (2) given a series of random sensor failures, how can we plan for a recovery strategy to achieve a relatively higher resilience? To tackle the research questions, we used one week of traffic-monitoring data to reveal the vehicle mobility patterns in Cambridge, UK, and explored the resilience of the deployed sensor system in terms of its observability using a simulation-based approach
In order to better understand this concept in transportation systems and their associated smart sensor networks, it is important to understand the spatial–temporal mobility patterns of the city in advance
Summary
The urban mobility pattern is of interest to a wide range of stakeholders such as policymakers, businesses, and local transportation authorities as it can provide a detailed and in-depth understanding of day-to-day vehicle movements, which informs decision making at multiple levels. In this vein, various types of smart sensors have been deployed all over the world in cities for traffic-monitoring purposes [1]. With an increasing trend of using smart sensors in many cities, especially traffic-monitoring sensors in urban mobility projects, one particular issue is attracting increasing attention from practitioners, that is, “what would happen when sensors fail?” [3] Failures are not uncommon in traffic-monitoring sensors. There is value in exploring its resilient observability and the corresponding spatial–temporal features
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