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SENSEABLE CITY LAB

Research Article

Drive-by Environmental Sensing Strategy
to Reach Optimal and Continuous
Spatio-Temporal Coverage Using Local
Transit Network

Transportation Research Record
1–15! The Author(s) 2024
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DOI: 10.1177/03611981241247051
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Mayar Ariss1,2 , An Wang1 , Sadegh Sabouri1, Fabio Duarte1, andCarlo Ratti1

Abstract
Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key datadriven insights to advise sustainable policies and city design. However, given the high variability of the environmental data,
achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as
Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an
inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed
sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a ‘‘drive-by’’ sensing solution for a structured network ofvehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which
are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coveragefor different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, ourproposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatiotemporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on
the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.
Keywords
sustainability and resilience, transportation and sustainability, air quality and greenhouse gas mitigation, air quality modeling,
emissions and air quality management

Rapid urbanization across the globe has led to several
environmental risk factors in cities, including air pollution, greenhouse gas emissions, noise, and urban heat.
Such factors result in various adverse public health outcomes and should duly be regulated. Strong evidence
demonstrates that air pollutants, such as particulate matter, carbon monoxide, and nitrogen oxides, can cause
acute and chronic respiratory, reproductive, and cardiovascular diseases (1, 2). Beyond its effect on auditory
health, noise is also commonly found to cause adverse
health effects (3, 4). Urban heat, a phenomenon gaining
increased attention in the context of climate change, isemerging as a significant public health concern, with amuch larger geographic scope. Monitoring such environmental features as air pollution, greenhouse gas

emissions, noise, and heat gives cities key data-driven
insights to advise sustainable policies and city design.
However, given the high variability of the environmental
data, achieving good spatio-temporal resolution and coverage remains a major challenge. To capture the environmental features of streets, sensors have recently been
mounted on vehicles; this allows for the collection ofnear real-time data over a given geographical area (5, 6).
1

Senseable City Lab, Department of Urban Studies and Planning, MIT,
Cambridge, MA
2
Department of Civil and Environmental Engineering, Imperial College,
London, UK
Corresponding Author:
Mayar Ariss, mayar313@mit.edu

2This drive-by sensing approach is cost-effective, as itenables the collection of data, for example, on air quality, noise, temperature, and road quality, using alreadydeployed vehicle fleets. It is also highly scalable, withadditional sensors being easily deployable, depending on
the desired level of coverage (7).
Previous research has already been focused on optimizing the number of vehicles needed in drive-by sensing
strategies. O’Keeffe et al. (8) explored the use of sensors
on taxis to monitor urban environments and found that
only 10 taxis cover up to one-third of Manhattan’s street
network at least once a day. O’Keeffe et al. (8) also showthat these findings are consistent across different cities,
given the taxi fleet over a given road network. Similarly,
Anjomshoaa et al. (9) highlighted the advantages of thisdrive-by sensing approach, such as lower deployment
and maintenance costs, compared with stationary sensor
networks. They investigated the effect of street network
topology on the quality of data captured through driveby sensing. Both these studies were investigations of theoretical problems. Practically, there are important spatial
biases and temporal limitations. Spatially, as acknowledged by the study authors, taxi trips are concentrated incommercial and tourist areas, leaving large swaths of cities uncovered (symptomatically, the model focuses on
Manhattan, rather than New York’s five boroughs)—
where socioeconomically deprived populations often
reside. Temporally, in these studies, it is not consideredthat environmental and urban features require different
resolutions—for instance, air pollution varies in a matterof seconds, whereas road condition is relatively stable.
This paper presents a practical approach: a drive-by
sensing strategy is proposed, based on structured mobility networks (buses and tramways), where vehicles’
paths and timetables are pre-established, allowing for theassignment of sensors to specific bus and tramway vehicles. We acknowledge that taxis’ unstructured networkand random motion encompass street segments not covered by structured mobility networks. However, besides
the spatial bias and temporal resolution limitation ofusing taxis, the highest concentration of air and noise
pollution (for instance) in a city most probably occurs inareas with high traffic density, where buses and tramways usually operate. In the interest of public health, it isimportant to monitor these segments continuously. By
integrating structured mobility in the sensing network,
popular segments are continuously sensed at regularspatio-temporal intervals.
To test the robustness of our model, we focus on oneof the environmental aspects that varies the most in space
and time, air quality, while including other aspects lesssensitive to spatio-temporal variation. We begin by defining the sampling constraints for several parameters (particulate matter [PM], O3, NO2, etc.) according to past

Transportation Research Record 00(0)research. While sampling at regular intervals allows forcontinuous monitoring in time, the aim in this study is tomonitor in both time and space by introducing the sampling frequency as the number of samples obtained along
specific locations throughout the network in the study
period. We then compute the spatial distribution andschedules of all the public fleets of Amsterdam to eliminate redundancy (near-simultaneous spatial and temporal readings) by discarding overlapping lines as hosts
for sensors. This reduces the manufacturing and computational cost of the sensing process. Further analysis isconducted by varying the required number of sensors
along the structured network against the sampling frequency, which is the inverse of the location-specific sampling interval. Lastly, we explore further optimization byconsidering the spatio-temporal sampling requirements
for each environmental parameter. The importance of
this study lies in its optimized deployment strategy forsensors on structured transportation, offering improved
monitoring of pollution-sensitive areas.

Literature Review
In this section, we describe previous findings for measuring (arguably) all the parameters that have been captured
by sensors to monitor the live environmental features inurban settings. These parameters are divided into anthropogenic emissions (which consist of air quality parameters and greenhouse gases), tree health, road quality,and noise sensing. Undoubtedly, some of these findings
should be referred to as a requirement for efficient monitoring, while the rest serve as a guideline.

Anthropogenic Emissions
Air Quality Parameters. A vast body of literature highlights
the significant risks posed by PM to public health, especially among vulnerable populations in densely populated
cities (10). The air quality guidelines (AQGs) establishedby the World Health Organization (WHO) set various pollutant measurement averaging times, ranging from 1 year
to shorter durations, such as 1 h for nitrogen dioxide(NO2) or 10 min for sulfur dioxide (SO2). These averaging
times (Table 1) are used to assess pollutant concentrationlevels over specific periods, including shorter- and longerterm exposures throughout the year.
To determine compliance with the AQGs listed inTable 1, pollutant measurements need to be made at regular intervals (e.g., every hour) and averaged over a given
time frame (e.g., 24 h) to determine the pollutant concentration. Although this provides a general approach tounderstanding air pollution, it would be more insightful
to consider spatial variations within the city. Drive-by

Ariss et al

3

Table 1. Short- and Long-term Air Quality Guidelines for
Particulate Matter, Ozone, Nitrogen Dioxide, Sulfur Dioxide, andCarbon Monoxide (11)Averaging time
PM10 (mg/m3)
Annual24 ha
PM10 (mg/m3)
Annual24 ha
O3 (mg/m3)
Peak seasonb
24 ha
NO2 (mg/m3)
Annual24 ha
1h
SO2 (mg/m3)
24 ha
10 minCO (mg/m3)
24 h
8 ha
1h
15 minAir quality guideline
5
15
15
45
60
100
10
25
200
40
500
4
10
35
100

a

99th percentile (i.e., 3–4 exceedance days per year).
Average of daily maximum 8-h mean ozone concentration in the six
consecutive months with the highest 6-month running-average ozone
concentration.

bsensing becomes relevant here, as it offers broader spatial
coverage and finer spatial resolution than fixed stations.
Indeed, street levels of air pollutants can undergo significant changes within a few hours, along with atmospheric transport and dilution processes affected byvarious meteorological factors (12). Sampling with ahigh time resolution provides a more comprehensive
understanding of the temporal patterns of pollution concentrations in specific road segments, which can also
help to identify specific pollution sources (13). However,high spatio-temporal resolution mapping implies a muchhigher computational demand and storage costs, owing
to much larger datasets.
Between PM2.5, PM10, O3, and NO2, the smallest averaging time set by the WHO is 1 h. Therefore, for better
accuracy, it is necessary to take enough measurements at
equal intervals to be averaged over a 1-h period. For SO2,
the minimum averaging time is 10 min; for CO, it is
15 min. However, it will later be shown that the frequencyof passage for a public vehicle is generally greater, at least
during non-peak periods. Averaging is unfortunately notpossible and there can only be about one measurement tobe recorded at a given location for both SO2 and CO.
Therefore, to acquire sufficient measurements, the sampling interval at one given location may not exceed theinterval of passage of a vehicle at that same location.

Greenhouse Gases. Monitoring greenhouse gases (GHGs)
in a city is important to identify pollution hotspots anddevelop effective strategies to mitigate climate change.
However, GHGs, such as carbon dioxide (CO2) andmethane (CH4), are not typically regulated when applying air quality standards based on short-term exposure or
specific concentration limits. In a study, the urban monitoring of CO2 and CH4 levels using mobile sensors along
94 km of public transit light rail in Salt Lake County,
Utah, provided averaged concentrations during 4-h time
windows (14). Indeed, CO2 and CH4 levels are often
monitored based on long-term exposure and assessed in
the context of climate change and global warming rather
than immediate health effects.
As it is most insightful to derive a long-term trend for
GHGs with high spatial resolution mapping, it is, therefore, enough for this study to consider one measurement
per location per hour.
Mobile Monitoring from Previous Studies. Table 2 lists previous mobile sampling parameters, along with their sampling intervals and study areas. The column headed‘‘Sensor sampling interval’’ gives the sensors’ inherent
sampling intervals along the taken paths in time, but notin space. Therefore, the studies referenced in Table 2 arelimited as they do not provide information about the
sampling frequency of specific points in the network.
Also, it is important to consider that previous researchis built on different network topologies, traffic conditions, sampling methods, and sensor types, and that the
spatial resolution changes drastically depending on these
conditions. Consequently, although no conclusions
should yet be drawn as to the ideal sensor sampling interval for our study, we can infer that it should be of theorder of seconds for all anthropogenic pollutants.

Monitoring Tree Health
Another application for monitoring urban environments
is mapping the health of trees using infrared thermography, which is a non-contact, non-destructive, and noninvasive technique that enables the detection of radiatedheat energy emitted by objects and bodies within theinfrared range of the electromagnetic spectrum, typicallyspanning wavelengths between 0.8 and 14 mm (19). The
conversion of infrared energy into visible imaging is
made possible through infrared cameras that produce
false-color images.
Sap circulation affects temperature variation along atree trunk, indicating the health and vitality of functional
tissue (20). Considering that the flow of sap changes
between seasons, it is reasonable to set a sampling frequency of one reading per season (every 4 months).
However, individual analyses are necessary for a precise

4Transportation Research Record 00(0)Table 2. Mobile Sampling Resolution and Study Area for Different Air Quality Indicators
Sensor sampling interval (s)
PM2.5
1
3
5
6
PM10
5
6
O3
2
6
20–60
NO2
1
10
SO2
1
10
CO
1
10
CH4
1
CO2
1
4

Study area

Reference

Light Rail, Salt Lake County, UT (94 km)
Mannerheimintie, Helsinki, Finland (3.8 km)
Tram, Karlsruhe, Germany (65 km)
Public Transport, Zürich, Switzerland (91.88 km2)
MiniMetro, Perugia, Italy (3 km)

Mitchell et al. (14)Pirjola et al. (15)Hagemann et al. (5)Hasenfratz et al. (16)Castellini et al. (17)Public Transport, Zürich, Switzerland (91.88 km2)
Tram, Karlsruhe, Germany (65 km)
MiniMetro, Perugia, Italy (3 km)

Hasenfratz et al. (16)Hagemann et al. (5)Castellini et al. (17)Light Rail, Salt Lake County, UT (94 km)
Tram, Karlsruhe, Germany (65 km)
Public Transport, Zürich, Switzerland (91.88 km2)

Mitchell et al. (14)Hagemann et al. (5)Hasenfratz et al. (16)Light Rail, Salt Lake County, UT (94 km)
Tram, Karlsruhe, Germany (65 km)
Mannerheimintie, Helsinki, Finland (3.8 km)
Public Transport, Zürich, Switzerland (91.88 km2)

Mitchell et al. (14)Hagemann et al. (5)Pirjola et al. (15)Hasenfratz et al. (16)South, West, and Turku Harbour, Helsinki, Finland
Island of Montreal, Canada (472.55 km2)

Pirjola et al. (15)
Levy et al. (18)Tram, Karlsruhe, Germany (65 km)
Public Transport, Zürich, Switzerland (91.88 km2)
Island of Montreal, Canada (472.55 km2)

Hagemann et al. (5)Hasenfratz et al. (16)
Levy et al. (18)Light Rail, Salt Lake County, UT (94 km)

Mitchell et al. (14)Tram, Karlsruhe, Germany (65 km)
Light Rail, Salt Lake County, UT (94 km)

Hagemann et al. (5)Mitchell et al. (14)health assessment, as different trees with the same pathology may exhibit distinct thermal patterns, owing to varying species, water availability, and temperature gradients.

Road Quality Sensing
Poor road conditions not only lead to vehicle damage but
also pose hazards to drivers and pedestrians. Maintaining
well-functioning roads is a challenging task, owing tooccasional adverse weather conditions, unexpected trafficloads, and natural deterioration that occurs within relatively short timeframes (weeks to months). Given the limited financial resources of municipalities, it becomes
crucial to identify and prioritize roads in need of repair.
Solving the problem of road surface monitoring fundamentally requires mobility and cannot be effectively
addressed by deploying stationary sensors on roads.
Typically, three-axis acceleration sensors and Global
Positioning System (GPS) devices are installed on in-car
embedded computers, leveraging the inherent mobility ofvehicles to traverse and monitor the roads (7).
Fortunately, since road deterioration occurs overextended periods, sampling the road surface daily, oreven less frequently, is sufficient (21). More specifically,road sensing can be activated after sudden changes intemperature and after extreme events (i.e., freeze–thaw
cycles or heavy rain), when roads are more likely to deteriorate. The proposed sampling frequency is, therefore,
one measurement per location per month.

Noise Sensing
Noise pollution poses a critical challenge in urban areas
by significantly affecting human behavior, overall wellbeing, productivity, and long-term health. As noise andair pollution always co-localize in a city, past research
outlined the grave consequences of long-term noise andair pollution co-exposure on human health (1, 4, 22).
Previous research also demonstrated that capturing noisedata throughout a city is possible using crowd sensing.
For instance, Maisonneuve et al. (23) developed a live
participatory noise sensing solution in Paris, France,with a 1-s sensor sampling interval using mobile phones.
Parvan et al. (24) used a 30-s sensor sampling interval.
All cases show that a high spatial and temporal granularity of data points is necessary for capturing the short-Ariss et al
and long-term variations of noise (7). However, thesepast applications are limited in nature, as a single pointin space is not being sensed at repeated time intervals.
To better monitor the long-term co-exposure of noiseand pollution on a city’s streets, consistency in time forthe noise data is needed, which we aim to provide here.
One advantage of our approach, in which both samplinginterval and frequency are considered, is that it allows
for near-continuous noise monitoring in time and space,storing long-term data that can help identify and localize
the highest co-exposures through street segments. Owing
to the potential risks of noise and air pollution co-exposure, the sampling frequency must be the same as for air
quality indicators, and may not exceed the frequency of
passage of a vehicle at one specific location.

Methods
We introduce an initiative that enables regular, real-timemonitoring of the urban environment. This project utilizes City Scanner sensors (7), which are low-cost opensource sensors developed by MIT Senseable City Lab.
The sensors are intended to be installed on bus and tramway vehicles, which traverse Amsterdam at regular intervals. Amsterdam’s tramway network consists of 15 lines,
which operate between 6 and 12:30 a.m., at intervals of
between 5 min and 10 min for all lines. The bus network
has 43 lines (32 regular lines and 11 night lines), withintervals mostly ranging between 8 min and 15 min during the day, and between 10 min and 35 min at night.
So far, only a handful of mobile urban observation
networks integrated with public transit systems have been
deployed, which can generate highly detailed air pollution maps. However, in all of these cases, mobile sensing
trades off temporal resolution against spatial resolution.
In our model, every location in space throughout the network is sensed at regular and short time intervals.
To conduct the optimization study for the structured
network in Amsterdam, comprehensive data for bus andtramway transportation networks were gathered from several reliable sources. To ensure accuracy, the primarysources of data were the General Transit Feed
Specification (GTFS) schedule, which provides standardized transit data, and the City of Amsterdam’s official
administration, known as Gemeente Amsterdam.
Additionally, data from Gemeente Vervoerbedrijf (GVB),
the municipal public transport operator responsible for
operating the bus and tram services in Amsterdam, were
utilized.

Sensor Sampling Resolution
To determine the optimal sampling frequency, we must
consider the highest sampling frequency, ftraffic , related to5
the traffic flow dynamics—that is, the time it takes forvehicles to pass a fixed point in space. Once the highest
frequency is determined, we can apply the Nyquist–
Shannon theorem to determine the minimum sampling
frequency required. According to this theorem, the sampling frequency should be at least twice the highest sampling frequency, ftraffic (25). For example, if the highest
sampling frequency for PM10 is 1 Hz (i.e., pollution variations occurring at a frequency of once per second), thesampling frequency should be at least 2 3 1 Hz = 2 Hz.
In other words, we would need to collect pollution samples at a frequency of at least two samples per second toaccurately represent spatio-temporal variations. This theorem applies simultaneously in space and time (7).
Considering the highest possible sampling frequency,
ftraffic , it is, therefore, necessary to set the desired sampling frequency as Fs = 2 3 ftraffic to accurately capture
the highest frequency ftraffic of the data being sampled.
Speed variations of the vehicle can introduce Doppler
shifts, leading to frequency shifts of the observed signals.
Fortunately, typical vehicle speeds are much lower than
the speed of light, which means the resulting frequency
shifts are generally insignificant. Considering these limitations, the sensors employed for our study typicallysample the environment at 1-s intervals (6). Therefore, toreduce the volume of data being processed, the sensors
are set to sample the environment at intervals ranging
from 5–10 s for both bus and tramway networks.

Structured Fleet Optimization
Structured modes of transportation refer to public vehicles following fixed trajectories and schedules. They also
generally correspond to streets with the highest traffic
density and are thus most prone to public health hazards.
We use GTFS data (26) to construct maps, outlining the
tramway lines, daytime bus lines, and night-time bus lines(Figure 1). Each color corresponds to one specific line.
Overlaps are not yet visible.
Similarity Criterion Approach. To determine which fixedpublic vehicles will be hosting sensors, the spatial similarity approach considers routing similarity, knowing thatschedules are similar throughout the network (mostly
between 8 min and 15 min). Considering spatio-temporal
granularity constraints, the line with the highest roadcoverage is the first selected to host sensors. Road coverage is defined by a similarity criterion, s, which is set
ahead of the analysis. For example, setting s = 0:7 would
mean that there is at least 70% similarity in the trajectory of two or more lines. The remaining 30% road segments are classified as under-sampled segments, whose
sampling frequency can be improved by the unstructured

6Transportation Research Record 00(0)Figure 1. Amsterdam’s (a) daytime bus network; (b) night-time bus network; and (c) day-only tramway network.
Note: Figures are generated using Folium v0.14.0 (https://pypi.org/project/folium/) for Python v3.8.2.

sensing network. The similarity criterion (described in
Figure 2) is simply calculated as
straj, k =sharedtraj, ktraj, kð1Þwhere, for increment k, sharedtraj, k (m) corresponds tothe trajectory length shared with the longest trajectory,
maxtraj, k (m), and traj, k(m) corresponds to the total
length of the considered trajectory.
To illustrate the nested loop for the similarity
approach, consider the theoretical network in Figure 3.
The initial iteration identifies the line covering the longest distance (bus line no. 2) and selects it as the first line
to host sensors. Bus line no. 1 shares around 80% of its
itinerary with bus line no. 2 (sbus1, k .0:7). Therefore, busline no. 1 is discarded for hosting the sensors, and thematrix of available trajectories is appended.
Then, bus line no. 3 shares around 45% of its itinerarywith bus line no. 2 (sbus3, k \0:7). Therefore, bus line no.
3 remains open to receiving sensors, and the matrix oftrajectories is not appended. If, during the next iterations,at least one other route is identified with sother, k + 1 .0:7,
these other routes will be discarded, and bus line no. 3
will be added to the modes hosting sensors.
Path Discretization and Storage of Overlaps. The similarities
in the network are quantified by storing all overlaps
between lines. The stored overlaps correspond to sharedtrajectories. A trajectory is defined as shared whenevertwo or more different lines run along the same street segment. To identify shared trajectories, the GeoJSON data
for all bus and tramway lines are imported into Python
and converted to Shapely objects. These objects are thendiscretized into a succession of equally spaced nodes
using linear interpolation, regardless of the total line
length, as shown in Figure 4.
Another nested for-loop iterates through all nodes forboth trajectories and checks whenever the Cartesian distance between two nodes from different trajectories issmaller than a given street width (set as 12 m). WheneverAriss et al

7

Figure 3. Theoretical bus network.

Figure 2. Similarity approach flowchart.

that condition holds true, the concerned nodes are stored,
defining all shared trajectories (visualized in Figure 7).
Shared trajectories belong to different lines describing
the same paths. The next step eliminates redundancy byclustering all similar shared trajectories into one.
Clustering with Symmetrized Segment-Path Distance. Further
data cleaning is needed to plot a smooth figure showing
the unique shared trajectories (Figure 5). To aggregate
similarities from large volumes of raw trajectory data, anadvanced approximation method to cluster portions oftrajectories with similar shape and length is presented
using a shape-based distance first introduced by Besse
et al. (27), the symmetrized segment-path distance (SSPD).
The SSPD is time-insensitive and evaluates both theshape and physical distance between two trajectory
objects. We apply this method here because it allows forthe comparison of trajectories of different lengths. It also
eliminates the need for any additional parameters.
Therefore, it can be applied to any set of trajectories,
regardless of their geographical origin, resulting inhomogeneous clusters. The SSPD is defined by Besse
et al. (27) as

Figure 4. Discretization close-up for Amsterdam’s bus lines 15
and 18.

8Transportation Research Record 00(0)Figure 5. Daytime bus network (a) before and (b) after symmetrized segment-path distance (SSPD). Night-time bus network(c) before and (d) after SSPD.
Note: Figures are generated using Folium v0.14.0 (https://pypi.org/project/folium/) for Python v3.8.2.

DSSPD ðT 1 , T 2 Þ =DSPD ðT 1 , T 2 Þ + DSPD ðT 2 , T 1 Þ2ð2Þ

where DSPD defines the asymmetric segment-path distance (SPD) from trajectory T 1 to trajectory T 2 and viceversa, meaning that it only considers the similarity in one
direction (from T 1 to T 2 or from T 2 to T 1 ). The SPD (seeFigure 6) is defined as the mean of all distances frompoints composing T 1 to T 2 (or T 2 to T 1 ):Ariss et al

9Figure 6. Segment-path distance (SPD) distance: (a) from point p1i1 to trajectory T 1 and (b) from trajectory T 1 to trajectory T 2 (28).

DSPD ðT 1 , T 2 Þ = n11n1Pi1 = 1!
"
Dpt p1i1 , T 2

ð3Þ

where, considering the minimum distances between point
p and segments s that compose T ,!

"Dpt p1i1 , T 2 =mini2 2½0, ..., n2 #1%!

DPS P1i1 , s2i2"

ð4Þ

Therefore, while the SPD considers similarity unidirectionally, SSPD captures the full similarity by taking themean of these distances. The final plots after applying the
SSPD clustering method are shown in Figure 7.
Once the similarity between all lines for both buses
and tramways has been computed, it is possible to run
an iteration to identify potential lines that could be discarded as sensor hosts as a function of total overlapping.
Figure 8 shows the variation in the number of lines to bediscarded from hosting sensors when the function of thesimilarity percentage ranges between 50% and 100%.
For instance, setting a minimum similarity tolerance of
60% leads to discarding four tramway lines and eight
daytime bus lines.
Because there is very little similarity in the bus network during the night (12:30 to 7 a.m.), we do not plot
the variation of discarded night-time bus lines withrespect to similarity percentage. Since structured networks correspond to the most polluted streets, we

decided to discard only lines that shared 100% of theirtrajectory with other lines (bus line no. 37).

Results and Discussion
As discussed previously, the AQGs for SO2 and CO
showed that the minimum averaging times should be 10
and 15 min, respectively. Therefore, we established thatthe sampling frequency should be the same as the frequency of passage for tram and bus vehicles, whichmostly varies between 6 and 15 min for a given point inspace. The number of required sensor-equipped tram
and bus vehicles is plotted against the location-specificsampling interval in Figure 9, a and b, and the sampling
frequency in Figure 9, c and d, using the available timetables for trams and buses for four different scenarios:
morning peak (7 to 9 a.m.), day (9 a.m. to 4 p.m.), afternoon peak (4 to 6 p.m.), and night (6 p.m. to 12:30 a.m.).
There is little daily variation in the number of sensors
for the tramway plots; tramway lines are not delayed bytraffic as they operate on their own road segments. For
buses, however, the morning, noon, and afternoon scenarios are similar except for the night scenario. As buses
share the road with cars, traffic affects the total trip
duration; therefore, more buses need to be dispatched torespect the frequency of passage. The more buses are dispatched, the more scanners are required, which is why

10Transportation Research Record 00(0)Figure 7. Amsterdam’s overlapping bus line segments: (a) daytime; (b) night-time; and (c) day-only tramway network.
Note: Figures are generated using Folium v0.14.0 (https://pypi.org/project/folium/) for Python v3.8.2.

the network requires fewer sensor-equipped buses tocover Amsterdam in the evening.
To satisfy WHO’s 10-min averaging time constraints
for SO2 and CO, the number of sensors required wouldbe 55 for trams and 87 for buses (Figure 9). This is for alocation-specific sampling interval of 10 min (frequency,
1:67 3 10#3 Hz). A total of 142 sensors would therefore
be needed to continuously cover Amsterdam’s structured
transportation network, considering the most conservative scenario. All curves converge to a fixed value at sampling frequencies smaller than 3:47 3 10#4 Hz for trams
and 2:98 3 10#4 Hz for buses. Equivalently, they converge for location-specific sampling intervals longer than
48 min for trams and longer than 56 min for buses. For
all parameters requiring sampling more often thanhourly, the minimum deployable number of sensors isfound at the plateaus for both curves (15 sensors fortrams plus 28 sensors for buses makes 43 in total).
If only PM2.5, PM10, O3, and NO2 were of interest forthe short-term analysis, a 1-h averaging time could imply

one measurement every 30 min at one specific location(i.e., two in total to be averaged over a 1-h period). This
would also comply with WHO’s guidelines on SO2 andCO emissions but only for the 1-h averaging case. The
total number of scanners would be drastically reduced toaround 64 scanners (24 scanners for the tramway network and 40 scanners for the bus network during theday).
Finally, we can determine the number of required sensors to effectively monitor different parameters in both
space and time. Table 3 shows our recommendations fordifferent types of parameter. Overall, the highest numberof sensors is needed for air quality and noise sensing
parameters. In contrast, GHGs, tree health, and roadquality-related parameters require the fewest sensors,namely 43.
Optimizing the sensors’ distribution for structured
modes of transportation is straightforward, assumingthat the existing network is properly designed in space
and that the frequencies of activity for buses and trams

Ariss et al

11

Figure 8. Variation of discarded (a) tram and (b) bus lines versus similarity percentage (day only).
Note: Figures are generated using Matplotlib v3.6 (https://matplotlib.org/) for Python v3.8.2.

Figure 9. Sensor-equipped (a) tram and (b) bus vehicles versus location-specific sampling interval (daytime). Sensor-equipped (c) tramand (d) bus vehicles versus sampling frequency, continuous coverage scenario.
Note: Figures are generated using MATLAB (R2023).

12Transportation Research Record 00(0)Table 3. Study’s Sampling Intervals for all Parameters: Continuous Coverage Scenario
Type
Anthropogenic emissions
Air quality

Greenhouse gases
Tree healthRoad quality
Noise sensing

Parameters

Sensing interval

PM2.5 (mg/m3)
PM10 (mg/m3)
O3 (mg/m3)
NO2 (mg/m3)
SO2 (mg/m3)
CO (mg/m3)
CH4 (mg/m3)
CO2 (mg/m3)
Infrared thermography (mm)Jerk (m/s3)
Noise (dB)

10–30 min10–30 min10–30 min10–30 min10–30 min10–30 min
1–4 h
1–4 h
1–4 month
1 month (+ after severe weather episodes)
10–30 minfall within the temporal requirements to satisfy accuracy
constraints.
But how much does the sampling frequency (andtherefore the number of sensors) affect the coverage ofthe transit network? Figure 10a shows the variation of
the total continuously covered distance against thelocation-specific sampling interval for morning, day,afternoon, and night scenarios. In total, the bus andtramway networks cover a length of approximately
360 km. The curves display a logarithmic growth pattern.
The reach increases exponentially at the beginning but at
a decreasing rate, indicating that the initial increase inreach is relatively rapid but gradually slows down. The
presence of a plateau at a sampling interval of approximately 30 min suggests that, beyond this point, the reachdoes not significantly increase. The curves level off andreach a point of saturation, indicating that the network
becomes fully and continuously covered for larger
location-specific sampling intervals exceeding 30 min.
The curves display an inflection at a 15-min locationspecific sampling interval threshold, meaning that coverage no longer increases rapidly. Therefore, for smaller
sampling interval times, sampling has the most effect onreaching broader coverage or achieving a certain level of
economic effectiveness.
Although we take Amsterdam as a case study, thepower of this research lies in its applicability to other cities. Public transit networks and scheduling are the only
required inputs to compute the optimal number of sensors for any given urban area, making this method also
adaptable to data-scarce cities in developing countries.
Therefore, the presented approach offers good scalability, provided that the spatial and temporal data are complete, regularly updated, and of acceptable quality.
There is also a potential for a hybrid solution integrating both structured and unstructured networks to coverless popular streets using sensor-equipped taxis. O’Keeffe
et al. (8) illustrated the extensive spatial reach of taxi

No. of sensors
64–14264–14264–14264–14264–14264–14243434343
64–142fleets, revealing that only a small number of taxis are sufficient to cover most of a given city spatially, as is consistently observed across various cities considered in theirresearch. However, this also presents certain challenges,
given the inherent unpredictability of taxis. Unlike public
transit, taxis paths vary, based on customer demands,
traffic conditions, and driver choices. This inconsistency
in routes and schedules can pose difficulties in ensuring
data consistency and optimizing sensing coverage. To
mitigate these challenges, a real-time tracking and analysis system could be implemented for the unstructured
network. This system would identify streets already covered by the structured network, which is sensed at a very
high frequency, and sensors mounted on vehicles withrandom motion (e.g., taxis) could automatically switch
off when passing by the structured network to avoid capturing redundant data. A major advantage would thenbe energy usage optimization. Because streets covered by
the structured network are usually the most popular, theenergy life of sensors fitted to taxis would increase significantly. A hybrid structured–unstructured sensing model
would, therefore, increase the spatio-temporal reach byencouraging the sensing of remote streets.

Conclusion
In this study, we focused on the practical implementationof a sensing network to monitor the live environmental
features of streets in Amsterdam. The drive-by sensing
approach, utilizing sensors mounted on public transport
vehicles, offers a cost-effective and scalable solution forcollecting real-time data on air quality, noise, temperature, and road quality.
The research builds on previous studies that have
explored the use of sensors on urban vehicles for environmental monitoring. It extends the scope by incorporating both structured mobility (buses and tramways) andpotentially random mobility (taxis) in the sensing

Ariss et al

13

Figure 10. Distance sensed for bus and tramway networks (daytime) continuously in space and time as a function of (a) location-specificsampling interval and (b) sampling frequency.
Note: Figures are generated using MATLAB (R2023).

network. By integrating structured mobility, the aim is
to target areas with high traffic density, where buses andtramways operate, as these areas are likely to have ahigher concentration of pollution.
We began the optimization process by defining spatial
and temporal monitoring constraints, looking at past
research and current regulations set by WHO. We thenidentified potential lines in the network for sensor
deployment. Redundancy was eliminated by discarding
overlapping lines to reduce manufacturing and computational costs. We conclude that the required number of
sensors varies from 43 sensors for sensing GHGs, tree
health, and road quality to between 64 and 142 for themost demanding parameters (PM, O3, NO2, etc.), considering the varying sampling frequency for buses andtrams.
Overall, this study provides insights into the designand optimization of a sensing network for monitoring
environmental parameters in an urban setting. By leveraging existing public fleets and incorporating structured
and random mobility, this approach offers a practicaland scalable solution for real-time environmental datacollection.
Real-time data provide critical insights, which canrevolutionize urban planning and policymaking. With
precise monitoring of environmental risks, such as air
pollution, noise, and temperature fluctuations, Gemeente
Amsterdam can more accurately track progress toward
its environmental targets. The data serve as a reliablebasis for evidence-based and better-informed policymaking, enabling city planners and policymakers toformulate informed decisions based on current, accurate,
data rather than on anecdotal evidence or outdated studies. By using the data as a benchmark, Gemeente
Amsterdam can set clear and measurable goals for environmental improvement, and regularly review progress.
Moreover, understanding the spatial distribution ofthese public health risks enables the identification of disproportionately affected communities. This insight supports targeted interventions, ensuring that the mostvulnerable populations receive necessary assistance andresources. For example, neighborhoods identified as particularly affected could receive increased funding forgreen spaces or air quality improvement initiatives. Suchtargeted measures not only optimize resource allocation,
but also promote social equity.
Additionally, making the data publicly accessible in auser-friendly format can heighten community awarenessof environmental issues and encourage active participation in sustainable practices. This approach could also
foster collaborations between the city, local communities,
and researchers, creating a more integrated approach toaddressing environmental challenges. Community feedback and involvement can further refine city policies andinterventions, leading to a more dynamic and responsive
urban sustainability strategy.
Finally, this study is subject to some limitations.
While the model presented in this study is practicallyapplicable to Amsterdam, we were not able to control
for some factors that can affect the suggested numbers
for the sensor deployment, that is, the actual number oftransit vehicles dispatched per line and per day, possible

14
switches of service bus lines, and dispatching wait times,
especially at terminals. In our future endeavors, we will
focus on the hybrid structured–unstructured sensing
model, using probabilistic modeling applied to typicalassignment algorithms, and addressing the aforementioned limitations.
Acknowledgments
The authors thank Volkswagen Group America, FAE
Technology, Samoo Architects & Engineers, GoAigua, DARGroup, RATP, Anas S.p.A., ENEL Foundation, Dover
Corporation, AMS Institute, KTH Royal Institute ofTechnology, and all the members of the MIT Senseable CityLab Consortium for supporting this research.

Author Contributions
The authors confirm contribution to the paper as follows: study
conception and design: F. Duarte, C. Ratti; data collection: M.
Ariss, F. Duarte; analysis and interpretation of results: M.
Ariss, A. Wang, S. Sabouri; draft manuscript preparation: M.
Ariss, A. Wang, S. Sabouri, F. Duarte, C. Ratti. All authors
reviewed the results and approved the final version of themanuscript.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest withrespect to the research, authorship, and/or publication of thisarticle.

Funding
The author(s) received no financial support for the research,authorship, and/or publication of this article.

ORCID iDs
Mayar Ariss
https://orcid.org/0000-0001-6570-3469
An Wang
https://orcid.org/0000-0002-1874-2702
https://orcid.org/0000-0003-2026-5631
Carlo Ratti

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