Smartphone Foot Traffic Data Lets Cities Track Water Demand in Real Time Without Meters

Every time a city pumps, treats, and delivers a single litre of water, it consumes energy—and lots of it. Urban water supply and wastewater systems are now estimated to account for between 0.4 and 2.3 percent of global primary energy consumption, and when both direct and indirect emissions are counted, the water sector may contribute up to 10 percent of global greenhouse gas emissions. Yet despite the central role that water demand plays in both water security and energy planning, most utilities still rely on coarse, aggregated estimates of when and where people actually use water. The core problem is deceptively simple: nobody knows exactly how many people are in a given building at any given moment, so demand models default to static assumptions that bear little resemblance to the restless, daily churn of urban life. A new study published in Energy Reports offers an unexpected solution, and it comes from an unlikely source—the anonymous smartphone location data that Google already collects for its Popular Times feature.

The research, led by Milad Rajaei with Usman Safder, Sarah Cotterill, and Recep Kaan Dereli, presents a city-scale framework that simulates water demand in near real time by explicitly tracking how people move through a city. The work builds on SIMDEUM, a well-established stochastic end-use model originally developed in the Netherlands, which represents water consumption as a stream of random pulses—each toilet flush, shower, tap use, or dishwasher cycle—with timing, duration, and flow rate drawn from probability distributions derived from empirical observations of occupant behaviour. SIMDEUM has proven remarkably capable of reproducing realistic household demand patterns at high temporal resolution, and it has been extended to offices, hotels, nursing homes, and other non-residential buildings by dividing each building into functional rooms with their own appliances and users. But the model has always carried a fundamental weakness: it assumes occupancy is either static or averaged, which in dynamic urban environments can become the dominant source of error.

The importance of occupancy is not in doubt. Sensitivity analyses of stochastic residential demand models have found Spearman’s rank correlation coefficients between occupancy and both peak and average demand ranging from 0.99 to 0.995—an almost perfect relationship. Field experiments reinforce the point: researchers who installed flush counters on 119 toilets across seven university campus buildings demonstrated a strong, direct link between toilet water use and the number of people present. The COVID-19 pandemic made the consequences of ignoring this relationship vividly clear, as commuting collapsed, workplaces emptied, and hygiene practices intensified, producing higher residential demand alongside sharply reduced commercial consumption. Models grounded in static occupancy assumptions simply could not see these shifts coming.

The researchers’ insight was to recognise that the data needed to model dynamic occupancy already exists in aggregate form. Google Popular Times indicators describe how busy non-residential locations are at any given moment, derived from aggregated and anonymised smartphone location data, expressed on a relative scale from 0 to 100 compared with a location’s typical peak activity. The framework begins by collecting these signals at five-minute intervals through automated web scraping for every non-residential building in a study area. Where live data are available and pass quality checks, they are used directly; where they are not, the model descends through a careful hierarchy of fallbacks—historical average patterns for the same day of the week, then representative occupancy profiles derived from K-means clustering of tens of thousands of profiles collected nationwide, then literature-based profiles from U.S. Department of Energy reference buildings, adjusted with local correction factors for seasonal effects such as school terms and hotel occupancy statistics.

Converting relative busyness into absolute occupant numbers requires a further step: each Popular Times value is multiplied by the estimated capacity of the building, calculated by dividing floor area by occupancy load factors taken from building design and fire safety guidelines, and scaled to reflect normal operation rather than maximum permitted crowding. Data quality proved to be a genuine challenge. Across a one-month collection period in September 2024, 58 percent of live samples were classified as invalid under the study’s quality-control rules, which flagged suspicious sudden drops in occupancy that persisted briefly before abruptly returning to normal—patterns unlikely to represent real activity. Days with insufficient valid data were replaced wholesale with historical averages, while shorter gaps were filled by linear interpolation. The prevalence of anomalies, particularly at low-traffic locations, underscores that crowdsourced occupancy data is useful but demands rigorous preprocessing.

The most conceptually ambitious element of the framework is its treatment of residential occupancy, for which no direct crowdsourced signal exists. Rather than relying on census averages, the model infers where people are at home by tracking population movements. Drawing on two classic theories of human mobility—Zipf’s gravity model, which holds that movement likelihood rises with population and falls with distance, and Stouffer’s intervening opportunities model, which assumes people choose the nearest destination that satisfies their needs—the framework constructs a trip probability matrix at each time step. When non-residential occupancy rises, the corresponding number of people is drawn probabilistically from residential areas weighted by their populations and the distribution of nearby opportunities; when occupancy falls, people return to their original home areas. A tourist population, calibrated from national accommodation and tourism statistics, handles movements associated with hotels and nightlife, while a separate commuter population accounts for people travelling into the study area from outside.

These time-varying occupancy estimates then feed directly into a modified SIMDEUM model running at one-minute resolution in MATLAB. At each time step, the probability of a water-use event for each end-use is calculated from the occupancy, the per-person frequency of use, and a diurnal timing factor reflecting behavioural routines. A random draw is compared with this probability to decide whether an event occurs, and if so, its flow rate and duration determine the volume consumed, with each end-use temporarily locked during an event to prevent overlap. For occupancy-dependent end-uses such as toilet flushing and hand washing, the occupancy term drives the calculation; for scheduled activities such as office cleaning, occupancy is effectively set aside so that only frequency and timing matter.

Applied to Sligo, a coastal town of roughly 20,000 people in northwest Ireland, the framework simulated an entire month of city-scale demand. Residential consumption came out at approximately 129 litres per person per day—closely matching the metered benchmark of about 312 litres per household per day reported for Sligo—and the simulated end-use breakdown, with toilets accounting for 28 percent of consumption, showers 24 percent, and kitchen taps 21 percent, differed by no more than two percentage points from published values for Irish households. The temporal patterns behaved as one would expect: a pronounced morning peak between 6:00 and 9:00 a.m. on weekdays driven by showering and breakfast routines, a delayed peak on weekends, a midday dip as residents left for work or school, and an evening recovery as people returned home. Non-residential demand told equally coherent stories—restaurants showed sharp peaks aligned with mealtimes, food retail displayed the steadier profile of continuous cleaning and toilet use, and office buildings peaked at the start of the working day before collapsing after closure.

The study is candid about its limitations. The default SIMDEUM parameters derive from Dutch household data and may not transfer cleanly to Irish conditions—simulated restaurant water use of 6.2 litres per square metre per day diverged from the 2.48 litres reported by Irish Water for comparable commercial premises, a discrepancy the authors attribute primarily to uncalibrated appliance frequencies, durations, and flow rates. The conversion of relative busyness into absolute occupant counts also requires independent validation against footfall sensors or building occupancy systems. Nevertheless, the results demonstrate something genuinely significant: a scalable, transferable route to high-resolution water demand modelling that requires no dense smart metering infrastructure whatsoever. By resolving demand at the level of individual end-uses across entire cities, the framework opens the door to demand-responsive pumping schedules, energy-aware operation of distribution networks, and scenario testing for planners—capabilities that could meaningfully reduce the energy intensity and emissions of the urban water cycle, one flush at a time.

Subject of Research: A real-time, city-scale water demand modelling framework that integrates urban mobility data from Google Popular Times with a stochastic end-use water demand model.

Article Title: A framework for real-time water demand modelling at city scale based on urban mobility

Article References: Rajaei, M., Safder, U., Cotterill, S., & Dereli, R. K. (2026). A framework for real-time water demand modelling at city scale based on urban mobility. Energy Reports, 16, Article 109700. https://doi.org/10.1016/j.egyr.2026.109700

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109700

Keywords: water demand modelling, urban mobility, Google Popular Times, SIMDEUM, smart meters, occupancy, water-energy nexus, end-use model, stochastic simulation, urban water systems, Sligo Ireland, energy efficiency