Access to this full-text is provided by Springer Nature.

Content available from

**Scientific Reports**This content is subject to copyright. Terms and conditions apply.

1

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

www.nature.com/scientificreports

Revealing the day-to-day regularity

of urban congestion patterns with

3D speed maps

Clélia Lopez1, Ludovic Leclercq

1, Panchamy Krishnakumari2, Nicolas Chiabaut

1 &

Hans van Lint2

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We rst partition

link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion

pulse. We then gather days with similar patterns and use consensus clustering methods to produce a

unique global pattern that ts multiple days, uncovering the day-to-day regularity. We show that the

network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9

clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities.

By matching the current observation to historical consensual 3D speed maps, we design an ecient

real-time method that successfully predicts 84% trips travel times with an error margin below 25%.

The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of

link speed observations and as a result reveals a global and previously mostly hidden picture of trac

dynamics at the whole city scale, which may be more regular and predictable than expected.

Studying human mobility in large cities is critical for multiple applications from transportation engineering to

urban planning and economic forecasting. In recent years, the availability of new data sources, e.g. mobile-phone

records and global-positioning-system data, has generated new empirically driven insights on this topic. A central

question at large spatial and temporal scales is which (dynamic) components of human mobility can be consid-

ered as predictable and thus suitable for explanatory and predictively valid mathematical models, and which part

is unpredictable. Earlier studies of human trips shows that traveled distance can be described by random walks

and more precisely as Lévy-ights1. Latter studies partly amend this theory by recognizing some regularity fea-

tures in peoples’ trips. Individuals obviously frequently move between specic locations, such as home or work2.

Such patterns are also regular in time3,4 meaning that the most frequent locations are likely to be correlated with

daily hours and dates. Regularity can also come from decomposition by transportation modes5. Human mobility

can be studied at the microscopic level, i.e. through person trajectories, but also at the macroscopic level, for

example by estimating commuting ows between dierent regions (origins to destinations) or on the dierent

links of a transportation network6,7. Such collective mobility patterns can be explained for example by distances

between regions8,9, trip purposes10 and road attractiveness related to road types, e.g. freeways, or locations, e.g.

in major business districts11. Predicting commuting ows oen requires local data for calibration12 meaning that

results cannot easily be transferable to other regions or cities. Recent ndings13, however, show that a scale-free

approach corresponding to an extension of the radiation model can successfully be applied to commuting ow

estimation. is means that some regular patterns can be observed also at the macroscopic level.

In this paper, we aim to pursue the investigation of regularity in macroscopic mobility patterns not by focusing

on the commuting ow distributions; but on the resulting level of service of the transportation (road) network,

i.e. on congestion patterns. Along with commuting ows, congestion patterns vary both within days and from

day-to-day at large urban scales. It is common knowledge that some regularity happens as congestion is usually

observed during peak hours on the most critical links of the network. In contrast to commuting ows, congestion

patterns are more easily observed using real data as they only require speed information in the dierent network

links. Nowadays, such information is easily accessible through dierent sensing technologies that are massively

deployed in many cities. However, in large networks with speed data on hundreds (or thousands) of links over a

large number of time periods, studying regularity and identifying distinct network congestion patterns is not an

1Univ. Lyon, IFSTTAR, ENTPE, LICIT, Lyon, F-69675, France. 2Delft University of Technology, CITG, Delft, N-2600GA,

The Netherlands. Correspondence and requests for materials should be addressed to L.L. (email: ludovic.leclercq@

ifsttar.fr)

Received: 12 July 2017

Accepted: 6 October 2017

Published: xx xx xxxx

OPEN

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

2

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

easy task to undertake: the challenge is to see the forest (regular large-scale trac patterns) for the trees (many

local pockets of queuing and congestion spillback processes). Here, we propose a new concept to address this

challenge. We synthesize within days link speed data and simplify day-to-day comparisons, by means of so-called

spatio-temporal speed cluster maps. Such 3D speed maps consist of a joined partition of space (road network

links) and time (the dierent observations) into homogeneous clusters characterized by a constant mean speed.

More precisely, such a partitioning should fulll the following criteria: (i) all clusters should contain a single con-

nected graph component meaning that all links are reachable within a cluster, (ii) the internal speed variance for

all clusters should be minimized - the intra-cluster homogeneity criterion and (iii) the dierence in speed between

neighboring clusters should be maximized - the inter-cluster dissimilarity criterion.

Clustering is a common problem in dierent elds of engineering such as data mining14 or image segmen-

tation15. Two recent and signicant contributions in transportation for our work are (i) the application of the

k-means algorithm16 to partition urban networks by considering spatial locations of the road as new features in

the data and (ii) the denition of a similarity matrix between observations and the application of the Ncut algo-

rithm17. ese works result in 2D clusters, covering a spatial portion of transportation networks for a given time

period. To obtain a picture of the trac dynamics over dierent time periods, the algorithms are simply iterated

for each time period without connecting the 2D clusters. Note that usual clustering works in transportation also

include compactness as a requirement for clusters. is is because the main application is perimeter control. In

this paper we present an algorithm that directly unravels trac dynamics over both space and time. We favor

connectivity - requirement (i) - rather than compactness for clusters, which makes more sense in 3D. To this

end, we rst determine which clustering method is the most ecient to cluster all time-dependent link speed

observations into 3D speed maps, where we consider the intra-cluster homogeneity and inter-cluster dissimi-

larity criteria as well as the computational times to determine the optimal number of clusters. Second, we apply

consensus learning techniques18,19 to summarize multiple 3D speed maps from a training set of days, into a single

common pattern. Interestingly, such a meta-partitioning operation can be fullled with a very small number of

groups. is means that the day-to-day regularity of daily congestion patterns can be easily revealed based on

such a classication. Finally, we will show that using a single consensus pattern for each class of 3D congestion

maps is sucient to accurately estimate in real-time travel times in the city. is means that addressing congestion

patterns directly at the whole city scale for all time intervals reveals a meaningful and accurate global picture of

the city trac dynamics that can be used as an ecient alternative to classical methods that process much more

data at local and short-term scales.

Results

Our case study corresponds to most of the major street network of Amsterdam city excluding the freeways, see

Fig.1(a). Whereas the original mapping of the inner city network contains over 7512 links, it is coarsened in this

paper to 208 links and 214 nodes. Such an operation basically merges all successive links in the same direction

between two intersections into a single one and disregards the internal links in the original mapping for intersec-

tions, see the method section. Mean speed information is available every 10 min between 7am and 3 pm for all

208 links during 35 days. is information is derived from license plate recognition systems at dierent critical

points of the network. e methodology to derive link speed data from passing times, coarsen the network, and

reconstruct missing data has already been published20. It should be noticed that all the methods elaborated in this

paper can be applied to any set of time-dependent link speed data combined with the related connected graph

(contiguous time intervals for the same network link should be connected by an edge) whatever the initial sensing

method is.

Clustering results for individual days. So, the initial data for a particular day is an undirected graph in

which links are connected in space with their upstream and downstream neighbors following the road network,

and in time by their immediate neighbors, i.e. the previous and the next time intervals for a given link. Each link

is characterized by a spatial (x, y) position, a time and a speed value. Link directions are not considered during

the clustering process because changes in trac volume propagate forward while congestion propagates back-

ward and we want to capture both phenomena. To obtain the 3D speed map related to such data, we rst bench-

mark dierent clustering algorithms from the literature. We choose to oppose the most recent development in

clustering for transportation networks, i.e. the Ncut algorithm with snake similarity also referred to as S-Ncut17

(see supplementaryS1 for more details) with two simpler clustering algorithms, the k-means21 and DBSCAN22

algorithms, see the method section. e main dierence between these, is that S-Ncut uses network topology

when calculating the similarities between observations; whereas the two other methods simply use normalized

Euclidean distances (regardless of topology) to balance both space, time and speed values. Note we weigh speed

three times more heavily (α = 3) compared to space and time (vicinity) since our objective is to obtain clusters

with a narrow speed distribution, see supplementaryS2 for more rationales about the choice of α. e quality

of the clustering results is assessed for a given number of clusters n through two indicators that relate to the

intra-cluster homogeneity and the inter-cluster dissimilarity criteria respectively: the total within cluster variance

(TVn) and the connected cluster dissimilarity (CCDn).

TV n

ns

sCCD

nn xx

nn

1;

(1)

n

i

n

i

i

n

ii ni

n

ki

n

ik ik ik

i

n

ki

n

ik ik

1

1

2

211

11

δ

δ

=∑

∑=∑∑

−

∑∑

=

===+

==+

where ni is the number of links in cluster i,

xi

and si are respectively the mean and the standard deviation of link

speeds for cluster i,

ik

δ

is equal to 1 only if clusters i and k have a common border and s is the standard deviation

of link speeds for the whole network. Since we also impose that each cluster should contain a single connected

graph component, clustering results should be post-processed, see supplementaryS3. Note that S-Ncut results,

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

3

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

even though the method includes topological considerations to calculate similarity between observations, also

require post-processing, see supplementaryS1. Post-processing has very little impacts on TVn and CCDn values

for S-Ncut. It deteriorates TVn values and to a lesser extent also CCDn values for DBSCAN and k-means methods,

see supplementaryS3. is is not surprising as these two methods only account for proximity (distance between

links) and not for connectivity within a cluster. In the end, what is important to assess the quality of a method is

to compare TVn and CCDn values aer post-processing when we are sure that connectivity - requirement (i) - is

veried.

Clustering results aer post-processing are presented for a randomly selected day among the 35 available

in Fig.1(b–f). e evolutions of TVn in Fig.1(b) and CCDn in Fig.1(c) are comparable for all three methods,

although k-means can be identied as the best method to minimize TVn, and DBSCAN appears slightly more e-

cient in maximizing CCDn. DBSCAN also appears to provide more stable (i.e. monotonically decreasing) results

for increasing cluster numbers than the other two. However, the TVn and CCDn values are not suciently dier-

ent to provide conclusive evidence that one method is better than the other two. What can be concluded is that the

S-Ncut algorithm has much higher computational times than the other two, which disqualies the method since

clustering has to be repeated for multiple dierent days. Both k-means and DBCAN are over 20 times faster than

S-Ncut on the same computer, see Fig.1(f). Finally, Fig.1(b,c) highlight that improvements to TVn and CCDn

values tend to signicantly reduce when the number of cluster exceeds 9 to 10. is means that for this particular

day, the optimal number of clusters can be xed to 9. e resulting 3D speed map is presented in Fig.1(d). A 3D

video is also visible on the data repository website, see additional information. In Fig.1(e) a slice at time t = 9am

is shown to illustrate the clustering results in detail. Note that links from the same cluster may look not connected

because of the slicing but they are of course connected through time links and dierent time periods.

Figure2 now presents the clustering results for all 35 days. Figure2(a) shows that S-Ncut and k-means gen-

erally outperform the DBSCAN method with lower TVn values. e score on CCDn values is much less decisive.

However, when reducing the number of clusters to 9, and testing all methods with this same number of clusters,

k-means clearly outperforms the other methods over all 35 days. Interestingly, when comparing Fig.2(b) to

Fig.2(a), one can observe that for this relatively low number of clusters (9), using k-means results in TVn values

that are very close to the best results obtained with any of the other two methods for larger number of clusters.

Figure2(c) and (d) provide a direct comparison of the three methods with respect to minimizing TVn and maxi-

mizing CCDn for n = 9. e k-means method generates a distribution of TVn values for all days that is signicantly

Figure 1. Link speed 3D clustering for one particular day. (a) Sketch of Investigated network - Amsterdam

city (NL) - MapData @2017 Google (b,c) Evolution of the total variance (TVn) and the connected cluster

dissimilarity (CCDn) with respect to the number of cluster for dierent clustering methods (d) Resulting 3D

speed maps for 9 clusters (e) Slide of the 3D speed map for time period t = 9am (f) Computational times for

dierent clustering methods and a targeted number of clusters equal to 9. Graphs (b,c,f) show that the clustering

algorithms that do not consider the graph topology, i.e. the k-mean and the DBSCAN, blast the S-Ncut in terms

of computational times with analogous TVn and CCDn results. DBSCAN appears very stable when the number

of cluster exceeds 6. Selecting 9 clusters looks optimal for this dataset and network conguration.

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

4

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

better (lower) than both other methods. e distribution of CCDn with k-means is not the best (the highest), but

it is very close to what is obtained with the best methods for this indicator, i.e. the S-Ncut see Fig.2(d). Since

k-means is the most economical method in terms of computational cost, we can conclude that it must be favored

to obtain 3D speed maps in this case. Furthermore, the results provide evidence to x the optimal number of 3D

clusters to 9 for our case study.

Classication of multiple days to identify consensual congestion patterns. Now our objective

is to nd commonalities in the 35 daily congestion patterns, and, ideally, summarize these with a fewer number

of “consensual” patterns. To this end, we rst have to dene a common link network for all the 35 days, see sup-

plementaryS4. is is necessary because some links may have insucient observations on particular days to be

assigned with a signicant value. e procedure has 3 main steps as outlined in Fig.3(a). In step 1 we obtain 3D

speed maps related to each daily pattern, by running the k-means algorithm with 9 targeted clusters over all the

35 days of the dataset. Aer this, each observation, i.e. a couple composed by a link and a time period, is assigned

a cluster ID i. Each day k can then be synthesized into a single ordered vector of all observations πk, whose values

are the cluster ID. To compare two dierent days πk and πl and assess if their 3D speed maps have similar shapes,

we use the normalized mutual information (NMI) indicator. It has been designed to assess the proximity between

two clustering results18,23.

ππ

ππ

ππ

ππππ

ππ

==

+−

NMI

I

HH

HHH

HH

(, )

(, )

()()

() () (, )

()()

(2)

kl kl

kl

klkl

kl

where I(πk, πl) is the mutual information between πk and πl, which measures the mutual dependence between two

random variables18,

πH()

k

is the entropy of πk and

ππH(, )

kl

is the joint entropy of πk and πl. Calculating the NMI

for all day couples allows us to dene a similarity matrix. We can then classify the whole set of days using the Ncut

algorithm15, see step 2 in Fig.3(a). More specically, we apply a classical cross-validation approach by randomly

splitting our 35 days into a training set of 28 days and a validation set of 7 days and considering 12 replications in

total. e purpose of the validation set will be explained later. We test a partition of the 28 training days into 2 and

4 groups for all replications of the training set. It appears in all cases that 4 groups lead to better results, see sup-

plementaryS5. All four groups appear homogeneous with high mean NMI values inside a same group (usually

higher than 0.6) and low dierences between the maximum and the minimum NMI values (usually below 0.24).

When looking at the day labels (Monday, …) within the four groups, no clear pattern appears. The major

Figure 2. Clustering results for all 35 days. (a) Clustering eciency with respect to the number of clusters

(b) Clustering eciency for a number of clusters equal to 9 (c,d) TVn and CCDn values (respectively) for all

days and 9 clusters. (a) Shows that S-Ncut provides the best results compared to the other two methods when

the number of cluster is large (above 15). However, when the number of clusters is reduced to 9 (b), k-means

provides in general the lowest TVn values while leading to similar CCDn values than S-Ncut and DBSCAN. is

is conrmed by (c) and (d) that show TVn and CCDn distribution for all methods when the number of cluster is

9. More interestingly, by comparing (b) and (a), it appears that k-means with only 9 clusters usually lead to close

results compared to S-Ncut with a signicant higher number of clusters. So, we dene as 9 the optimal number

of clusters for all days.

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

5

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

conclusion at this stage is that the 28 days can be classied into only 4 groups, which exhibits close 3D speed map

shapes. We are now going to adjust the cluster shapes of the days belonging to the same group to obtain a unique

consensual shape that can be applied within the group.

e consensus clustering problem consists in identifying the most representative partition from a group of

partitions18,24. e best of K (BOK) algorithm19 can be used to determine the median partition m (the 3D speed

map shape of a single day in our case) that maximizes the total similarity TS with all the other days belonging to

the same group:

∑ππ=

=

TS NMI(,)

(3)

k

a

mk

1

where a is the number of days in the targeted group,

m

π

is the vector resulting from the initial clustering (3D speed

map) for the median partition, and

k

π

the same vector but for each other day of the same group. e median par-

tition can be further improved (increasing TS) by moving some of its elements from one cluster to another, i.e.

changing the cluster ID of some elements in the vector. To realize such an optimization, we apply the one element

move (OEM) algorithm19. It consists in randomly changing the label of one element of the vector and assess if

such a change improves the TS value. The algorithm stops when TS has not been improved for a while.

Determining the consensus shaping for all 4 groups corresponds to the nal step 3 of the data processing, see

Fig.3(a). Figure3(b) and (c) illustrate the dierence between the original cluster shape of a particular day and the

consensual shape resulting from the processing of all days in the same group. Figure3(d) and (e) respectively

show the variations of the TVn and CCDn values when comparing the consensus cluster shape with the original

one for all the training days and all replications. It appears that the TVn values signicantly deteriorate (increase

by more than 2%) for only 15% of the days while the CCDn are significantly worse (decrease by more than

−0.5 m/s) for only 20.8% of the days. Even for the days that see a signicant change in the clustering quality, the

nal values related to the consensual shape remain always acceptable. is means that the consensual shape is

relevant to describe in a unique and common manner the congestion patterns of the same group of days. Since

Figure 3. Classication of multiple days and congestion patterns identication for training sets. (a) e three

steps to obtain consensual 3D speed maps (b) Original clustering for a particular day (c) Consensus clustering

for the same day (d) Variation of TVn between the original and the consensual cluster shapes for all days and

all replications of the training set (e) Variation of CCDn between the original and the consensual cluster shapes

for all days and all replications of the training set (f) Distribution of the standard deviation of the mean cluster

speed within a group of days (one value per cluster ID, group and replication). (d) and (e) Show that in most

case switching from the original to the consensus shapes for a day has minor to acceptable impacts on the TVn

and CCDn values. is means that the consensus shapes can be considered as a good proxy for the clustering of

each day. (f) Shows that the consensus shape is also relevant to identify homogeneous regions in speed within a

group as the standard deviation of the mean cluster speed remains below 0.5 m/s for the vast majority of cases.

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

6

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

classication into 4 groups appears sucient, the conclusion is that the 3D speed maps of the 28 training days can

be synthesized into only 4 dierent consensual congestion patterns. For now, the groups and the consensual

shapes have been dened based on the initial cluster shapes without using the link speed information. e

remaining question is to assess whether the consensual shapes are also relevant to dene homogeneous regions in

speed for each group of days. Because the consensual shape is the same within a group, it is easy to calculate the

mean speed for each of the 9 cluster IDs and each day. Figure3(f) shows the distribution of the standard deviation

of such a mean cluster speed among all days belonging to the same group. Such a calculation has been performed

for all replications of the training set. It turns out that 37% of the standard deviation values are below 0.5 m/s and

the vast majority (85%) is below 1 m/s. is means that the mean cluster speeds are very close for the same cluster

ID among the days of the same group.

is is a major result because it implies that the consensual shape is also relevant to summarize the speed

prole observed in the network over time for a same group of days. For a given group, we can associate to each

consensual cluster ID the mean of the mean cluster speeds for each day and so, obtain a single 3D speed map that

denes the congestion pattern of this group. In other words, all days of the same group can be synthesized into no

more than 9 cluster shapes and 9 mean speed values. For our case study (the Amsterdam network), 4 consensual

3D speed maps look sucient to capture the functioning of the entire work network over the 35 days and to get a

full overview of the dynamic trac conditions within the major road network of the city. is is strong evidence

for a high degree of regularity and predictability of macroscopic trac conditions in this network.

Application to real-time travel time prediction. We are now going to take advantage of the above major

result to propose a fresh new look on a classical and popular problem in transportation systems, i.e. travel time

prediction. is problem has been extensively investigated in the transportation literature using both (simula-

tion) model-based and data-driven approaches as shown by recent review papers25,26. Model-based approaches

use network trac ow models in conjunction with data assimilation techniques such as recursive Bayesian

estimators to predict the trac state and the resulting travel times in networks27–29. Data-driven approaches use

general purpose parameterized mathematical models such as (generalized) linear regression30,31; kriging32; sup-

port vector regression33; random forest34; Bayesian networks35; articial neural networks, e.g. dynamic36,37 and

(increasingly oen) deep learning architectures38,39; and many other techniques to capture (learn) from data the

correlations between trac variables (speed, travel time) over space and time. When reviewing the literature,

there are many more approaches reported for estimation and prediction on freeway corridors, than for mixed

or urban networks, which we hypothesize is due to two reasons. First, until recently, insucient data sources

were available for such large-scale urban prediction models. Additionally, and more tentatively, the urban pre-

diction problem is a more complex problem to address than the freeway prediction problem because there are

many more degrees of freedom that govern the underlying local trac dynamics (e.g. intersection control, cross-

ing ows, high-frequency queuing also under free owing conditions, much more route alternatives, etc), and

thereby also the dynamics of speed and travel time. Recently, both model-based29,40 and more unied and sys-

temic data-driven approaches38,41–43 have been proposed that, at least in principle, can be used to predict trac

variables in large-scale urban networks. However, when applied to large-scale networks, both model-based and

data-driven approaches are indeed computationally complex, and methodologically cumbersome due to the high

number inputs and parameters that continuously need to be calibrated and validated from data.

As an alternative, we propose a very simple and systemic approach that uses the consensual congestion pat-

terns obtained in the previous section. First, let us dene a number of probe trips that we will use for investigating

the methods and the validation. Based on the network map, we dene 10 trips that cover most of the network

links, see Fig.4(a). A virtual probe vehicle is launched every 10 min over the time period between 8 am and 2

pm and its travel time is calculated based on the time-dependent link speed information of the studied day. is

denes for each day 370 probe trips characterized by the travel time that a vehicle would have experimented for

this trip and this departure time. Note that travel time calculations are made on the directed version of the road

network graph while the initial and consensual clustering were made without considering directions. First, we

are going to investigate if the mean speed values related to the 3D congestion maps can be considered as a good

proxy for the travel time calculation. For now, only the days included in the 12 dierent training sets are consid-

ered because their group label and thus their consensus clustering shapes are known. We dene three methods to

estimate the travel time depending all the options we have to dene congestion maps:

• M1: initial cluster shape of the day + link speeds equal to the mean speed value of all links in each initial

cluster and the same day

• M2: consensus cluster shape of the group + link speeds equal to the mean speed value of all links in each

consensus cluster and the same day

• M3: consensus cluster shape of the group + link speeds equal to the mean speed value for all links in each

consensus cluster over all days of the group.

Figure4(b) shows the distribution (box plot) of the travel time estimation errors for all probe trips, all training

days and the three methods. It appears that averaging the link speeds within each initial cluster (M1) obviously

introduces errors in the travel time estimation: (i) the mean and median errors are respectively equal to −2.0%

and −2.3%, and are thus close to 0 (ii) 50% of the probe trips (25th to 75th percentiles) have errors between

−13.7% and 8.6% and (iii) 80% of the probe trips (10th to 90th percentiles) have errors between −22.1% and

17.6%. Interestingly, most of the errors come from the averaging process within the cluster: when switching to

the consensus cluster shape (M2) or replacing mean cluster speeds of the day by the mean cluster speeds of the

group of days (M3) leads to error distributions that are very close to what is observed for (M1). In particular, for

M3, the mean and median error values are respectively −2.7% and −3.6%, 50% of the probe trips exhibit errors

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

7

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

between −15.7% and 9.4% and 80% of the probe trips have errors between −23.9% and 19.2%. ese results are

fundamental because they rst conrm from another perspective (here the travel time estimation) that consensus

congestion maps with mean speed in each cluster determined over a similar group of days are very relevant to

synthesize the trac congestion pulse at the city level. We see no discrepancy when switching from M2 to M3

meaning that all days of a group have similar speed behavior within each consensus cluster. As no discrepancy is

observed when switching from M1 to M2, the consensus shape appears to be a good proxy to partition all the days

of the same group. Together, these two results demonstrate that the consensus cluster decompositions are relevant

not only in terms of shape but also in terms of mean cluster speed values and provide a unique and systemic pic-

ture of what happen for all days belonging to a same group. Note that the same graph as Fig.4(b) but with absolute

travel time errors is presented in supplementaryS6.

e previous analysis provides the rationale for a simple, systemic and real-time travel time prediction method

for new days belonging to the validation sets. For a new day, M1 and M2 are no longer relevant because they

require the data of this particular day. However, M3 still holds as long as the new day can be assigned to an exist-

ing group obtained through historical analysis, i.e. over the training set. e only missing component is a method

to allocate in real-time the current observations of a new day to an existing group. Knowing the group, the prede-

termined consensus cluster shape and the related mean speed values for each cluster can be applied to predict the

future travel times. Here, we propose a simple method with very low computational times to match a new day

with an existing group. is method only requires the link speed information until the actual time t of the new

day. First, we reduce the consensual cluster shape of each historical group (4 in our case) to the period of time

between 7am and t. en, we apply all restricted consensual cluster shapes both on the new day data and on the

consensus map of the related group. Mean speed values for the same cluster i in the new day

xig,

and the consen-

sus

yig,

are compared. e optimal group index g* minimizes the Euclidean speed distance between the current

day and the group:

gnxyargmin 1()

(4)

g

i

n

ig ig

1

,,2⁎∑

=

−

=

Figure 4. Travel time estimation based on congestion patterns. (a) Map of the probe trips (b) Travel time

estimation errors for all probe trips and all training days considering the three estimation methods: M1, link

speed is the mean speed in the original cluster; M2, link speed is the mean speed in the consensus cluster; M3,

same as M2 but the mean speed is calculated over all days of the same group (c) Estimated vs. experimented

travel times for the 10 probe trips, one validation day and a departure time equal to 9am (d) Estimated vs.

experimented travel times for the 10 probe trips, all validation days and all departure times (e) Distribution of

the travel time estimation errors for the 10 probe trips, all validation days and all departure times. (b) Shows

that travel time errors are in most case relatively low. Averaging speed within each cluster has the highest

contribution to errors. Interestingly, using the consensus cluster shape (

M M12→

) and the average of all days

within a group (

M M23→

) have very impacts on errors. (c–e) Show that travel time predictions based on

assigning a new day to an historical group and using the consensus cluster shape and the mean cluster speed of

the group are very good for most probe trips.

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

8

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

Note that the number of clusters n within the restricted time period (7am-t) can be lower than 9 in particular

at the beginning of the day where all 3D patterns have not yet necessarily appeared. In practice, we can refresh

the assignment of the new day to a group every hour starting at 8am, and assess the travel time predictions on the

probe trips where a new virtual vehicle starts every 10 min. Figure4(c) shows the results for a particular validation

day and all trips starting at 9 am. It appears that, even if the reference time period to assign the day to a group

is short (here 7 am–9 am), the predicted travel times are close to the experimented one for all trips, i.e. all error

values but one fall between −20% and 20%. Note that the travel times are simply calculated using the link speed

values of the full day since we are not testing the application in real-time here. is means that we already know

all the link speed information for the validation days on the contrary to a real-time implementation where the

future is unknown. Figure4(d) shows exactly the same results but now for all validation days (7 days and 12 rep-

lications meaning 84 in total) and all departure times between 8am and 2 pm. Again, a large fraction of the travel

time predictions (72.1% of the total probe trips) exhibit errors between −20% and 20% and almost all (91.9%) fall

within an ±30% error margin. Despite its simplicity and its very low computational cost, the proposed method

leads to accurate travel time predictions for most trips. is is conrmed by Fig.4(e), which shows the cumu-

lative distribution of all prediction errors. e mean and median values are equal to −2.2% and −2.7%, 50% of

the probe trips experiments errors between −15.5% and 10.0% and 80% of the probe trips have errors between

−24.5% and 20.8%. e counterpart of Fig.4(e) with absolute travel time errors is provided in supplementaryS6.

Discussion

In this paper, we questioned the regularity of day-to-day mobility patterns at the macroscopic level. e global

analysis of Amsterdam link speed data over 35 days shows a high degree of regularity when comparing the daily

congestion patterns. In our case, four consensual 3D speed maps related to four groups of days are sucient to

describe the daily trac dynamics at the city scale. is is remarkable given the fact that these consensual 3D

speed maps are very parsimonious: for our case study, they consists of 9 clusters (collections of link and time ID)

only, each characterized by a single mean speed value. A key contribution here was to use consensus learning

methods to turn the cluster shapes of dierent days belonging to the same group into a single common pattern.

Note that if more days are available for the learning, it is possible to keep the same level of quality for the con-

sensual shape by increasing the number of groups. e NMI index permits to monitor the level of dissimilarity

within a group of days and determine if a group should be split or not. is paper has thus demonstrated that

consensual 3D speed maps are a new and very powerful tool to capture the congestion pulse in one shot at the

whole city scale. It should be noticed that some factors that have not been observed during our sample of 35 days

may inuence the regularity of congestion patterns. From our experience, we can mention adverse weather condi-

tions; exceptional (large cultural) events; or incidents as sources of major disruptions in the network. Over longer

time periods, during which such situations are observed multiple times, the number of groups will increase to

accommodate the resulting broader array of patterns, and most likely some regularity patterns with low frequency

of appearance will emerge. Only the consequences of very rare or specic events are fully unpredictable.

A second major nding in this paper is that these consensual 3D speed maps allow us to design a simple and

systemic method to predict travel times in an entire city. In this method rst prevailing link speed observations

are matched to an existing group of days. Subsequently, the consensual 3D speed map related to this group is used

to predict the travel time of any trip within the city. is method is real-time and practice ready as the matching

step is computationally lightweight. It corresponds to the selection of the best consensual 3D speed maps among

the existing group of days based on the comparison of the mean speed in each cluster. In our data set, we suc-

ceeded in making travel time predictions for more than 84% of the trips with an absolute error lower than 25%,

which is sucient for most potential practical applications like trac information provision, route guidance,

trac control and management, or optimizing good deliveries and solving vehicle routing problems.

e methodology presented in this paper to derive consensual 3D speed maps can be easily implemented in

the real eld. Link speed data at a granularity of say 1–10 minutes become more and more readily available thanks

to advances in estimation methods using classical data (induction loops, cameras) and new data sources based on

crowd-sourcing (mobile-phone records, GPS tracking). One clear direction for (methodological) improvement

relates to decreasing computational costs, particularly when determining the initial 3D speed map for a new day

on much larger networks in terms of number of links. Our aim was to make the case for 3D patterns as a new way

to identify large-scale regularity in trac networks and it turned out that with these 3D congestion patterns a new

approach to a notoriously dicult problem (predicting travel times in urban networks) is possible. Even though

optimizing the clustering and the post-treatment operations is very important for larger networks with (much)

more links and data, it should be noticed that (continuously) learning and updating the consensual patterns with

new daily patterns are o-line steps that can be performed over the night (determining the 3D congestion maps

for a new day) and over the weekend (updating the consensual patterns). e critical component for real-time

travel time estimation is the matching between the current observations and the historical data included in the

3D consensual congestion maps. With our method, this operation is so fast that it can already be applied in much

larger networks. In this paper, we do already hint at an important avenue to signicantly cut computational

costs for the original clustering operations. We constructed the 208 link graph of Amsterdam through coarsening

the original 7512 link OSM network, using a constrained version of contraction hierarchy44 as explained in20.

Network coarsening45 appears then as an ecient strategy to reduce the network size while preserving both

network topology and the underlying data patterns. Also this strategy deserves further in-depth analysis and

research.

Clearly, there are numerous other directions to further improve the methodologies behind the two contri-

butions oered here. ese relate for example to improve the underlying data processing methods, or to more

advanced clustering techniques and matching procedures. Nonetheless, we believe the main results stand and

touch upon a fundamental property of city trac dynamics, and that is, that these dynamics may be more regular

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

9

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

and predictable than expected. Consensual 3D speed maps enable us to extract the essence of large sets of detailed

data to reveal the global picture about trac dynamics in cities. We expect many applications of this concept not

only for trac monitoring and control but also for policy making and urban planning in general.

Methods

Initial dataset. In this study, link speed data are reconstructed from trip travel time observations. In

Amsterdam, 127 cameras are recording license plates at the critical points of the major street networks (excluding

freeways). is denes 314 single origin-destination (OD) pairs. For each OD pair the shortest path in distance is

determined using the OpenStreetMaps GIS database46. e nal network consists in all the links included in all

the shortest paths, i.e. 7512 links in total. We apply an algorithm that merges together successive links in the same

direction between two intersections. Internal links for intersections are also merged into a single node that only

reproduces the available turning movements. At the end, the network has 208 links and 214 nodes20. e nal step

is to calculate the link speed information for 10 min time intervals from the individual travel times between OD

pairs. We have a complete database of 35 days where we select the time period between 7am and 3 pm (morning

peak hour and lunch time). e mean number of individual travel time records per day is 171000. Each individual

travel time information provides both the departure and the arrival times. All travel time data that exceeds a given

threshold added to the current moving average for a given OD are considered as outliers and then disregarded

(7% in total). e remaining information are then matched to links assuming a constant travel speed. We used a

10 min time window for link speed data, meaning that all observations coming from vehicles that drive through a

link during the same 10 min period are averaged into a single link speed value. A complete description of the data

preparation can be found in20. Note that the data processing in this paper is not restricted to the data we used for

the Amsterdam network but can be applied to any network with link speed information.

Ncut algorithm. Ncut is a clustering algorithm based on a similarity matrix S(i, j) that denes the level of

similarity between two elements i and j of the dataset15. In this paper, we use two dierent metrics to dene the

similarity: the Snake similarity17 when determining the original clustering for each day and the NMI, eq.2, when

gathering days with similar patterns. More details about the Snake similarity are provided in supplementaryS1.

e dierent steps of the Ncut algorithm are:

1. Calculate the diagonal matrix D of the similarity matrix S

2. Calculate the normalized Laplacian matrix

=−

−−

LD DSD()

1/21/2

3. Calculate the eigenvalues of L and increasingly order the eigenvectors with respect to the eigenvalues

4. To obtain a partition in 2m clusters, select the 2nd to the

+−m(2 1)th

eigenvectors in the ordered list. e

splitting point here is equal to 0 meaning that we separate for each eigenvector the values >0 and ≤0. Each

observation is then codied into a set of m binary values > or ≤0 depending on the related values in the

eigenvectors. Each observation with the same codication falls into the same cluster.

5. When the targeted number of clusters is not a power of 2, take the closest higher value for 2m that then

apply a merge algorithm. Clusters with the closest similarities are iteratively merged two by two17.

k-means and DBSCAN. Before running the k-means or the DBSCAN we rst normalized each observation

i dened by the following vector

xytv(, ,,)

iiii

, where xi and yi are the geographical coordinates of the middle of a

link, ti denes the time period and vi the speed value. Normalization is performed based on the global minimal

and maximal values for all coordinates. Speed values are then overweighted by a factor 3 because this variable

should play a predominant role during the clustering process. For both algorithms, the distance between two

observations is assessed based on the Euclidean one. e details of k-means algorithm can be found in21. e only

parameter is the number of targeted clusters. e DBSCAN (Density-based spatial clustering of applications with

noise) has been proposed by Ester et al. in 199622. It is a density-based clustering algorithm that groups together

points that are close, i.e. within a circle of radius

ε

(0.005 in our case). ere is no targeted number of clusters but

a minimal number of points to dene a cluster (10 in our case). e algorithm stops when all points have been

labeled. To obtain a given number of clusters, clusters are nally merged using the same algorithm as for the

Ncut17. In practice both k-means and DBSCAN scripts have been retrieved from the MATLAB© File Exchange

website47,48.

Data availability. All the data related to this study and its documentation are accessible using the following

links: http://dittlab.tudel.nl:8080/3DPartitioning or https://doi.org/10.6084/m9.gshare.5198566.

References

1. Brocmann, D., Hufnagel, L. & Geisel, T. e scaling laws of human travel. Nature 439, 462–465 (2006).

2. GonzÃ¡lez, M., Hidalgo, C. & Barabási, A. Understanding individual mobility patterns. Nature 453, 779–782 (2008).

3. Song, C., oren, T., Wang, P. & Barabási, A. Modelling the scaling properties of human mobility. Nature Physics 6, 818–823 (2010).

4. Song, C., Qu, Z., Blumm, N. & Barabási, A. Limits of predictability in human mobility. Science 327, 1018–1021 (2010).

5. Zhao, ., Musolesi, M., Hui, P., ao, W. & Taroma, S. Explaining the power-law distribution of human mobility through

transportation modality decomposition. Scientic reports 5, srep09136 (2015).

6. Ortuzar, J. & Willumsen, L. Modelling transport. (Wilsey,Chichester) (1994).

7. Wilson, A. Land-use/transport interaction models:past and future. Journal of Transportation Economic Policy 32, 3–26 (1998).

8. Chouroun, J. A general framewor for the development of gravity-type trip distribution models. egional Science and Urban

Economics 5, 177–202 (1975).

9. Lenormand, M., Bassolas, A. & amasco, J. J. Systematic comparison of trip distribution laws and models. Journal of Transport

Geography 51, 158–169 (2016).

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

10

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

10. Peng, C., Jin, X., Wong, ., Shi, M. & Lio, P. Collective human mobility pattern from taxi trips in urban area. PLOS ONE 7, e34487

(2012).

11. Wang, P., Hunter, T., Bayen, A., Schechtner, . & GonzÃ¡lez, M. Understanding road usage patterns in urban areas. Scientic reports

2, srep011001 (2012).

12. Anas, A. Discrete choice theory, information theory and the multinomial logit and gravity models. Transportation esearch part B

17, 13–23 (1983).

13. Yang, Y., Herrera, C., Eagle, N. & González, M. Limits of predictability in commuting ows in the absence of data calibration.

Scientic eports 4, srep05662 (2014).

14. Alpaydin, E. Ntroduction to Machine Learning–3rd edition (MIT Press, 2014).

15. Shi, J. & Mali, J. Normalized cuts and image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(8),

888–905 (2000).

16. Ji, Y. & Geroliminis, N. On the spatial partitioning of urban transportation networs. Transportation esearch Part B 46(10),

1639–1656 (2012).

17. Saeedmanesh, M. & Geroliminis, N. Clustering of heterogeneous networs with directional ows based on “snae” similarities.

Transportation esearch Part B 91, 250–269 (2016).

18. Cover, T. M. & omas, J. A. Elements of Information eory 2nd Edition (Wiley Series in Telecommunications and Signal Processing)

(Wiley-Interscience, 2006).

19. Filov, V. & Siena, S. Integrating microarray data by consensus clustering. International Journal on Articial Intelligence Tools 13,

863–880, https://doi.org/10.1142/S0218213004001867 (2004).

20. Lopez, C., rishnaumari, P., Leclercq, L., Chiabaut, N. & van Lint, H. Spatio-temporal partitioning of the transportation networ

using travel time data. Transportation esearch ecords 14Â p. https://doi.org/10.3141/2623-11 (2017).

21. MacQueen, J. B. Some methods for classication and analysis of multivariate observations. In Cam, L. M. L. & Neyman, J. (eds) Proc.

of the h Bereley Symposium on Mathematical Statistics and Probability, vol. 1, 281–297 (University of California Press, 1967).

22. Ester, M., riegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise.

In Proceedings of the Second International Conference on nowledge Discovery and Data Mining, DD'96, 226–231 (AAAI Press,

1996).

23. Yang, F., Li, T., Zhou, Q. & Xiao, H. Cluster ensemble selection with constraints. Neurocomputing 235, 59–70, https://doi.

org/10.1016/j.neucom.2017.01.001 (2017).

24. Ozay, M. Semi-supervised segmentation fusion of multi-spectral and aerial images. In 2014 22nd International Conference on

Pattern ecognition https://doi.org/10.1109/icpr.2014.659 (IEEE 2014).

25. Vlahogianni, E. I., arlais, M. G. & Golias, J. C. Short-term trac forecasting: Where we are and where we’re going. Transportation

esearch Part C: Emerging Technologies 43, 3–19, https://doi.org/10.1016/j.trc.2014.01.005. Special Issue on Short-term Trac Flow

Forecasting (2014).

26. Mori, U., Mendiburu, A., Álvarez, M. & Lozano, J. A. A review of travel time estimation and forecasting for advanced traveller

information systems. Transportmetrica A: Transport Science 11, 119–157, https://doi.org/10.1080/23249935.2014.932469 (2015).

27. Wang, Y., Papageorgiou, M. & Messmer, A. enaissance - a unied macroscopic model-based approach to real-time freeway

networ trac surveillance. Transportation esearch Part C: Emerging Technologies 14, 190–212 (2006).

28. umar, B. A., Vanajashi, L. & Subramanian, S. C. Bus travel time prediction using a time-space discretization approach.

Transportation esearch Part C: Emerging Technologies 79, 308–332 (2017).

29. Nantes, A., Ngoduy, D., Bhasar, A., Misa, M. & Chung, E. eal-time trac state estimation in urban corridors from heterogeneous

data. Transportation esearch Part C: Emerging Technologies 66, 99–118, https://doi.org/10.1016/j.trc.2015.07.005 (2016).

30. Zhang, X. & ice, J. A. Short-term travel time prediction. Transportation esearch Part C: Emerging Technologies 11, 187–210 (2003).

31. ice, J. & Zwet, Ev A simple and effective method for predicting travel times on freeways. IEEE Transactions on Intelligent

Transportation Systems 5, 200–207 (2004).

32. Biswas, S., Charaborty, S., Chandra, S. & Ghosh, I. riging-based approach for estimation of vehicular speed and passenger car

units on an urban arterial. Journal of Transportation Engineering 143 https://doi.org/10.1061/JTEPBS.0000031 (2017).

33. Xu, Y., Chen, H., ong, Q.-J., Zhai, X. & Liu, Y. Urban trac ow prediction: A spatio-temporal variable selection-based approach.

Journal of Advanced Transportation 50, 489–506, https://doi.org/10.1002/atr.1356 (2016).

34. Bahuleyan, H. & Vanajashi, L. D. Arterial path-level travel-time estimation using machine-learning techniques. Journal of

Computing in Civil Eng ineering 31 (2017).

35. Huang, W., Song, G., Hong, H. & Xie, . Deep architecture for trac ow prediction: Deep belief networs with multitas learning.

IEEE Transactions on Intelligent Transportation Systems 15, 2191–2201, https://doi.org/10.1109/tits.2014.2311123 (2014).

36. Wang, J., Tsapais, I. & Zhong, C. A space-time delay neural networ model for travel time prediction. Engineering Applications of

Articial Intelligence 52, 145–160 (2016).

37. Van Lint, J. W. C. Online learning solutions for freeway travel time prediction. IEEE Transactions on Intelligent Transportation

Systems 9, 38–47 (2008).

38. Lv, Y., Duan, Y., ang, W., Li, Z. & Wang, F. Y. Trac ow prediction with big data: A deep learning approach. IEEE Transactions on

Intelligent Transportation Systems 16, 865–873 (2015).

39. Yang, H., Dillon, T. S. & Chen, Y. P. Optimized structure of the trac ow forecasting model with a deep learning approach. IEEE

Transactions on Neural Networs and Learning Systems (2016).

40. Bhasar, A., Tsubota, T., ieu, L. M. & Chung, E. Urban trac state estimation: Fusing point and zone based data. Transportation

esearch Part C: Emerging Technologies 48, 120–142 (2014).

41. Li, L. et al. obust causal dependence mining in big data networ and its application to trac ow predictions. Transportation

esearch Part C: Emerging Technologies 58B, 292–307 (2015).

42. Laharotte, P.-A., Billot, ., El-Faouzi, N.-E. & aha, H. A. Networ-wide trac state prediction using bluetooth data. In TB 94th

Annual Meeting Compendium of Papers, 15-3022 (Transportation esearch Board, 2015).

43. Fusco, G., Colombaroni, C. & Isaeno, N. Short-term speed predictions exploiting big data on large urban road networs.

Transportation esearch Part C: Emerging Technologies 73, 183–201, https://doi.org/10.1016/j.trc.2016.10.019 (2016).

44. Geisberger, ., Sanders, P., Schultes, D. & Delling, D. Contraction hierarchies: Faster and simpler hierarchical routing in road

networs. In McGeoch, C. C. (ed.) Experimental Algorithms: 7th International Worshop, WEA 2008 Provincetown, MA, USA, May

30-June 1, 2008 Proceedings, 319–333 (Springer, Berlin, Heidelberg, 2008).

45. Chevalier, C. & Safro, I. Comparison of coarsening schemes for multilevel graph partitioning. In Lecture Notes in Computer Science,

191–205 (Springer Berlin Heidelberg, 2009).

46. OpenStreetMap contributors. Planet dump retrieved from, https://planet.osm.org. https://www.openstreetmap.org (2017).

47. Mo C. means algorithm retrieved on 2016-12-10 from, https://fr.mathwors.com/matlabcentral/leexchange/24616-means-

clustering (2016).

48. Yarpiz. DBSCAN Clustering Algorithm retrieved on 2016-12-10 from, https://fr.mathwors.com/matlabcentral/leexchange/52905-

dbscan-clustering-algorithm (2016).

Content courtesy of Springer Nature, terms of use apply. Rights reserved

www.nature.com/scientificreports/

11

Scientific RepoRts | 7: 14029 | DOI:10.1038/s41598-017-14237-8

Acknowledgements

is study has received funding from the European Research Council (ERC) under the European Union’ Horizon

2020 research and innovation program (grant agreement 646592–MAGnUM project); and the Horizon 2020

SETA project (grant agreement No 688082). Map data copyrighted OpenStreetMap contributors and available

from https://www.openstreetmap.org.

Author Contributions

L.L., N.C. and H.V.L. designed the research; C.L. and P.K. performed the research in collaboration with L.L.,

H.V.L. and N.C.; L.L. wrote the paper with support of H.V.L. All authors participated in analyzing the results and

reviewed the manuscript.

Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-14237-8.

Competing Interests: e authors declare that they have no competing interests.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and

institutional aliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International

License, which permits use, sharing, adaptation, distribution and reproduction in any medium or

format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-

ative Commons license, and indicate if changes were made. e images or other third party material in this

article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the

material. If material is not included in the article’s Creative Commons license and your intended use is not per-

mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the

copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© e Author(s) 2017

Content courtesy of Springer Nature, terms of use apply. Rights reserved

1.

2.

3.

4.

5.

6.

Terms and Conditions

Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).

Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-

scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By

accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these

purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.

These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal

subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription

(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will

apply.

We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within

ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not

otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as

detailed in the Privacy Policy.

While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may

not:

use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access

control;

use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is

otherwise unlawful;

falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in

writing;

use bots or other automated methods to access the content or redirect messages

override any security feature or exclusionary protocol; or

share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal

content.

In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,

royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal

content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any

other, institutional repository.

These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or

content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature

may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.

To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied

with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,

including merchantability or fitness for any particular purpose.

Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed

from third parties.

If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not

expressly permitted by these Terms, please contact Springer Nature at

onlineservice@springernature.com

Content uploaded by Clélia Lopez

Author content

All content in this area was uploaded by Clélia Lopez on Oct 25, 2017

Content may be subject to copyright.