From Waze Reports to Resilience Measurement: Mega-events and Transportation System Response

Mega-events such as festivals or marathon runs can dramatically impact the dynamic of an urban area. Each time a city hosts such events, the urban infrastructure is exposed to perturbation. The transportation systems, due to... [ view full abstract ]

Mega-events such as festivals or marathon runs can dramatically impact the dynamic of an urban area. Each time a city hosts such events, the urban infrastructure is exposed to perturbation. The transportation systems, due to their critical role in safety, mobility, and quality of life have a high degree of importance when they respond to perturbation. In this context, resilience has been increasingly used to evaluate the performance of urban infrastructure and is reflected as the ability of a system to recover from an external disturbance. Generally, the performance/resilience of transportation systems is assessed through simulation and scenario-based approaches, which do not directly account for real-world and contextual data from a system. This is mainly due to expensive and time-consuming procedures for contextual data acquisition. This study aims to address this gap by proposing a novel approach for assessment of transportation system resilience using people-centric information, retrieved from Waze “community-driven GPS navigation app” platform. To this end, we have analyzed Waze traffic data at the time of mega-events occurrences such as festivals, marathon runs or major construction works to move towards a more realistic performance assessment. Waze platform, in addition to mapping the traffic events on a live-map, provides a map of mega-events with the event description, timespan of the event and, the affected road sections. We aimed to measure the network performance in terms of recovery time, speed and congestion deviation from the normal state. This information can be used for urban decision makers to set policies and strategies for similar events with a more realistic insight into the implications of events. We have used the congestion in the affected region as the performance indicator and compared the values in the disturbed and undisturbed circumstances. Integrating the people-centric information for evaluation of the performance can improve the existing evaluation approaches from two perspectives: (1) the people-centric data provide contextual and realistic insight into the system performance while most of the existing frameworks mainly rely on simulation-based scenarios and limited field data for calibration; (2) the ease of access to the data and large spatiotemporal coverage can cut down the cumbersome procedures of collecting the field data and improve the granularity of the information. Using the proposed approach, the resilience is quantified as the change in the performance for selected road sections in New York, NY at the time of perturbing events (i.e. Women’s March on January 20^th). We looked at the local road network performance in the affected region in each event and collected the traffic reports corresponding to the road sections for evaluation purposes. The findings demonstrate the potential of Waze to analyze the transportation system response to real-world events. We are moving towards developing a framework that integrates the Participatory Sensing Network (PSN) data and conventional models of resilience to quantify the system response to help with decision making in smart cities.