A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

TL;DR

This paper introduces a stigmergy-based method to analyze urban mobility data, identifying city hotspots, activity trends, and anomalies to support dynamic policy-making in sustainable transportation.

Contribution

It presents a novel bio-inspired stigmergy approach to aggregate and analyze urban mobility data for detecting hotspots and anomalies, aiding urban policy decisions.

Findings

Identified key city hotspots using taxi data in Manhattan.

Detected temporal activity patterns and anomalies.

Provided a measure for policy impact evaluation.

Abstract

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.