KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods
Jingyuan Wang†, Chao Chen†, Junjie Wu‡∗, Zhang Xiong† † School of Computer Science and Engineering, Beihang University, Beijing China ‡ School of Economics and Management, Beihang University, Beijing China. {jywang, sxyccc, wujj, xiongz}@buaa.edu.cn, ∗corresponding author ABSTRACT Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly “bomb” we sleep with every day. In both academia and government, tremendous eorts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the prob- lem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features Figure 1: Tianjin port explosion on Aug. 12, 2015. a “duet” between DGT trajectory data and human mobility data 173 people killed and hundreds injured in the blast 1. In total 304 for risky zones identication. Moreover, DGeye innovatively takes buildings, 12,428 cars and 7,533 intermodal containers were seri- risky paerns as the keystones in DGT management, and builds ously damaged, and still more surrounding buildings were declared causality networks among them for pain points identication, at- as “structurally unsafe”. Local environments and air were also tribution and prediction. Experiments on both Beijing and Tianjin seriously polluted by exploded dangerous goods, which incurs im- cities demonstrate the eectiveness of DGeye. In particular, the measurable loss (see Fig. 1). is painful lesson brings urban safety report generated by DGeye driven the Beijing government to lay back to sight as top priority, and indicates the latent but deadly down gas pipelines for the famous Guijie food street. “bomb” we sleep with every day: dangerous goods like gas and haz- ardous chemicals tanks transported frequently through and around CCS CONCEPTS cities. •Information systems → Data mining; Geographic information In the literature, the problem of dangerous goods transportation systems; •Applied computing → Computers in other domains; (DGT) has aracts great aention, with the focuses on transporta- tion route planning [20] and risk analysis [34], both from an op- KEYWORDS erations and optimization view. ese studies, though providing Urban Safety, Intelligent Transportation, Paern Mining, Causal constructive managerial insights, usually lack of a micro view of Analysis DGT threatens from a big data perspective. Specically, for a prac- tical application purpose, we rst need to determine how to dene a dangerous-goods-aware risky zone in a quantitative manner so as to facilitate real-time general monitoring. Also, we should identify 1 INTRODUCTION the spatio-temporal paerns of DGT and gure out the intrinsic Nowadays countries and regions all over the world face the chal- mechanisms behind them for key monitoring and sustainable urban lenge of urbanization. e rapid agglomeration of population and in- planning. ese practical needs indeed motivate our study in this dustries not only creates monster megalopolises like Beijing, Tokyo paper, which aims to leverage heterogeneous big data for dealing and Seoul, but also exposes them to potentially catastrophic risks with DGT issues. Our study can also fall into the research category of various types. For instance, on August 12, 2015, a warehouse of urban computing [37], and enrich the dangerous-goods-related storing dangerous goods at the port area of Tianjin exploded, with studies in this area based on obtained rich DGT trajectory data. Our main research contributions are summarized as follows. Permission to make digital or hard copies of all or part of this work for personal or Firstly, we present and deploy a novel system called “City Eyes classroom use is granted without fee provided that copies are not made or distributed on Dangerous Goods” (DGeye) for real-world DGT risks man- for prot or commercial advantage and that copies bear this notice and the full citation agement (DGTRM). DGeye features a “duet” between DGT trajec- on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, tory data and human mobile-phone signaling data, and employs a to post on servers or to redistribute to lists, requires prior specic permission and/or a Mahalanobis-distance based measure for risky zones identication. fee. Request permissions from [email protected]. Secondly, DGeye innovatively takes risky paerns as the keystones KDD’ 17, August 13–17, 2017, Halifax, NS, Canada © 2017 ACM. 978-1-4503-4887-4/17/08...$15.00 DOI: hp://dx.doi.org/10.1145/3097983.3097985 1hps://en.wikipedia.org/wiki/2015 Tianjin explosions
1673 KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
Sect. 5.1 for details). e causal network building function gener- DGT DGT Patterns Data Weights Ranking ates a causal network using risky paerns as vertexes and DGT
Casual Analysis trajectories passed these paerns as directed edges (see Sect. 5.2 for Networks Reports Urban details). If the destination of DGT trajectories that have passed the Planners paern p is the paern p , we can say the dangerous goods threats City Map MapM Risky x y Partition Zones Data in px is caused by dangerous goods transportation requirement Patterns from py . erefore, we call the network as a causal network. Prediction Based on the causal networks, in the user application layer, we Patterns A Real Time CrowdCrowd Cell Phone Mining Application Emergency Data Weights develop two applications for dierent types of users. For urban Monitoring planners, the system generates paern importance rankings for *GZG6XUIKYYOTM 1TU]RKJMK3UJKROTM *GZG9U[XIKY ;YKX'VVROIGZOUTY @UTK2K\KR 6GZZKXT2K\KR risk causal analysis (see Sect. 6.1 for details). e ranking gives high Figure 2: Framework of DGeye. priority to the paerns that lead to many other paerns of high importance. According to the ranking list, urban planners can x the pain points gradually from high priority paerns to low priority in DGTRM, and is equipped with an ecient algorithm for max- ones. For the emergency monitoring application, the system gives imal paerns mining. A novel trajectory-driven causal network accurate state predictions for every paerns, which is of great help is then built upon these paerns for paern importance ranking to ocial emergency agencies in allocating emergency resources and risks aribution analysis. irdly, DGeye is capable of risks appropriately and proactively (see Sect. 6.2 for details). prediction by adopting an EM-enabled Bayesian network model upon the causal network of risky paerns. Comparative experi- 3 DATA SOURCES mental studies with various baselines demonstrate the excellent e data used in DGeye include: mobile phone signaling data, DGT predictive power of DGeye. Finally, DGeye has established itself as trajectories, and city maps. In what follows, we provide detailed a successful deployment in various real-world applications. For in- descriptions to the former two types. stance, as a look back to the Tianjin port explosion disaster, DGeye Mobile operators build base accurately captures the blast site as one zone inside the rst-rank Mobile Phone Signaling Data: stations all over a city to oer a “full coverage” signaling service to risky paern. More interestingly, DGeye discloses that the rst mobile phone users, and the service records between mobile phones ranking risky source, which causes 5% downtown areas of Beijing and base stations are called “mobile phone signaling data”. A record under risk, is the transportation of liqueed gas cylinders to an old contains
1674 KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
mn Slices 1 Slices 2 Slices 3 Slices n on the m-th day, we dene a DGT weight dij and a crowd weight mn c to measure the number of DGTs and the human population at 11 1n ij Day 1 R … … R zone zij , respectively. We also denote the two weights as dij and A pattern cij or dx and cy for concision when there is no ambiguity. of the time 21 2n slice set n DGT Weight: e DGeye system extracts DGT weight dij from Day 2 R … … R the DGT trajectories. For a DGT, when its location lt is reported … … … …… … … … at time t, we denote z(lt ) as the zone lt falls in, and process lt as M 1 Mn follows: If z(lt ) and z(lt−1) are spatially adjacent or the same, we Day M R … … R count a transporter for z(lt ); otherwise, we count a transporter for the time slice set n every zones on the shortest path from z(lt−1) to z(lt ). In this way, we re-sample a transporter if it stays at a zone for a long time. is Figure 3: An illustration of risky patterns mining. is reasonable because this kind of places are very likely to be the storage places of dangerous goods. At the end of a time slice, the Algorithm 1 Risky Paerns Mining from Rn number of passed DGTs of an urban zone is set as the DGT weight of that zone. 1: Let the paern candidate set L1 = {p˜|p˜ = {zij } ∧ supp(p˜,n) ≥ Crowd Weight: For each time slice, the system maintains a a threshold, ∀zij }. n binary user-zone matrix U, where a row vector corresponds to a 2: Let the paern set as P = L1. cell phone user and a column corresponds to a urban zone. e 3: Let the paern candidate size counter cnt = 2. element uxy = 1 indicates user x appears in zone y during the time 4: repeat slice, and 0 otherwise. At the end of a time slice, the crowd weight 5: Lcnt = ∅ of any zone y is calculated as 6: for paern candidates p˜cnt−1 ∈ Lcnt−1 do 7: ∈ { | ˜ } ∩ { | ∈ X uxy for zb zij zij is adjacent with pcnt−1 zij zij cy = . (1) 0 ∧ 0 ∈ ˜0} P p˜cnt−1 p˜cnt−1 P do x y uxy 8: if supp(p˜cnt−1 ∪ zx ,n) ≥ a threshold then In this way, if a user visits K zones in a time slice, we only count 9: p˜cnt = {p˜cnt−1 ∪ zx }, Lcnt = Lcnt ∪ p˜cnt n n n n the user 1/K times for each zone. 10: P = P ∪ p˜cnt , P = P − p˜cnt−1 11: end if 4.2 Risky Zones Detection 12: end for In the risky zones detection function, the system calculates a risk 13: end for score for each urban zone in a time slice. We say an urban zone is 14: cnt = cnt + 1 { | } ∩ { | ∈ 0 ∧ at risk because the zone contains a large population and too much 15: until zij zij is adjacent with p˜cnt−1 zij zij p˜cnt−1 0 ∈ ˜0} ∅ dangerous goods. Accordingly, using the product of the crowd p˜cnt−1 P = n weight and the DGT weight as the risk score is appropriate, since 16: return P we could get a high score only when both the two weights are large enough. ere is still an obstacle here — the two weights are not in the same order of magnitudes. For example, the population in We further dene a time slice set Rn = {R1n, R2n,..., RMn }, which Beijing is more than 20 millions, but the DGTs in our Beijing data set contains the n-th time slices of all M days. When in W matrices of is only 3,790. To deal with this, we adopt the Mahalanobis distance Rn, a set of adjacent zones X are all at risky state, we dene the for weight scaling. e Mahalanobis distance of two vectors a and daily support of X w.r.t. Rn as b in a vector set is dened as q supp(X,n) = W /M. (4) −1 > DM (a, b) = (a − b)Σ (a − b) , (2) Based on the concept of daily support supp(X,n), we give the n where Σ is the covariance matrix of the vectors set. e risk score formal denition of the paerns of the time slice set R as follows. Rn of zij is then dened as Denition 1 (Risky Paerns of ) A risky paern of the time slice set Rn is a set of zones that satises: 1) the zones are spatially > > RSij = DM (dij , 0) , 0 × DM (0,cij ) , 0 . (3) adjacent; 2) the daily support of the set for Rn is larger than a given If RS is greater than a threshold, we say zone z is at a risky state, threshold; 3) the set is not a proper subset of any other risky paerns ij ij n otherwise at a low-risk state. In practice, the threshold is set to the of R . 90% upper quantile of all the risk scores. Figure 3 gives an illustration of risky paerns. e DGeye system mines risky paerns of Rn using an Apriori-like algorithm [4], with 5 KNOWLEDGE MODELING the pseudo-codes given in Algorithm 1. e algorithm maintains a group of paern candidate sets {L1, L2,..., Lcnt−1, Lcnt ,...}, 5.1 Risky Patterns Mining where Lcnt contains all cnt-size zone sets that satisfy the conditions A risky paern refers to a set of adjacent urban zones that are at the 1) and 2) of Denition 1. e algorithm also maintains a risky risky state together frequently in a same time slice of a day. For the paern set Pn that is used as a return value of the algorithm. In lines mn I ×J n-th time slice on daym, we dene a risk matrix R ∈ R , where 1-2, the algorithm initializes L1 using all one-size paern candidates, mn n the element rij = 1 indicates the risky state of zij and 0 otherwise. and uses L1 to initialize the risky paern set P . In the 6-13 lines,
1675 KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
2 The Time p Pattern 1 Pattern 2 Pattern 3 1 p5 6 USER APPLICATIONS Slice n-1 1 1 1 6.1 Pattern Importance Ranking p 1 p 2 4 1 e application of the paern importance ranking is to oer a rank- 1 The Time Pattern 4 1 1 ing list of risky paerns based on the causal network for urban Slice n p p 3 1 6 safety management. e DGeye system ranks risky paerns fol- The causal network lowing the rule as: a paern that i) causes many paerns and/or p p p p p p 1 2 3 4 5 6 ii) causes important paerns should have a high importance pri- The Time Pattern 5 Pattern 6 p1 0 0 0 1 2 0 Slice n+1 p2 1 0 0 1 0 0 ority in the ranking list. To this end, we apply a Random Walk p 3 0 1 0 1 0 1 with Restart (RWR) model [32] to the causal network to generate p4 0 0 0 0 1 1 p5 0 0 0 0 0 0 ranking scores for risky paerns. Trajectory Pattern p 6 0 0 0 0 1 0 Assume there are K paerns in the causal network. We dene The adjacent matrix > a ranking score vector s = (s1,s2,...,sk ,... sK ) , where sk is the Figure 4: An illustration of causal networks building. score of paern pk . Given the weight wxy for the edge from px to py , we dene a causal transition probability from px to py as an out-degree normalized wxy , i.e., for every paern candidate p˜cnt−1 in Lcnt−1, we enumerate zx in w a zone set, in which all zones are adjacent to p˜ − to generate a xy cnt 1 gxy = . (5) ∪ { } PK new paern candidate via p˜cnt = p˜cnt−1 zx . In lines 8-11, if the k=1 wxk daily support of p˜cnt is larger than the preset threshold, say 0.8 in n e system iteratively updates the ranking score vector s using our DGeye system, we add p˜cnt to Lcnt and P . Because p˜cnt−1 is a transition matrix G composed of gxy . In the (t+1)-th iteration a proper subset of p˜cnt , according to the condition 3) of Denition n round, s is updated by 1, we remove p˜cnt−1 from P in line 10. In order to avoid redundant daily support computation, we in- s(t + 1) = α G · s(t) + (1 − α) q, (6) troduce a “F × F ” method [31] to lter z in line 7. By > k−1 k−1 x where q = (q1,q2,...,qk ,...,qK ) is a paern-size ratio vector. ˜0 − dening Pcnt−1 as a set of cnt 1-size paern candidates in which at is, the k-th element of q is the normalized size of the risky 0 − ˜0 all candidates p˜cnt−1 share cnt 2 zones with p˜cnt−1, i.e., P = paern px , i.e., { 0 | 0 ∈ ∧ | 0 ∩ | − } p˜cnt−1 p˜cnt−1 Lcnt−1 p˜cnt−1 p˜cnt−1 = cnt 2 , the algo- size(px ) 0 qx = P . (7) rithm is required to select zx from a paern candidate in P˜ . K ( ) k=1 size pk When there is no z could be used to increase size of the paern x Note that we also q to initialize s, i.e., let s(0) = q. candidates, the algorithm returns Pn as the risky paern set of It is easy to show that the above iterations will converge to the Rn in line 16. e DGeye system uses Algorithm 1 to mine risky following steady state when t → ∞ [40], paerns for all time slice sets Rn,n = 1, 2,..., N , and uses their SN n s∗ = (1 − α)q(I − αG)−1, (8) union P = n−1 P as the nal risky paern set of a city. which is nally adopted to rank the importance of risky paerns. 5.2 Causal Network Building e paern with a greater score has a higher importance priority. A Causal network building is another function in the knowledge list of ranked paerns is valuable to urban planners and dangerous modeling layer of the DGeye system. In this function, the system goods management department of a city. Based on the list, urban uses risky paerns as vertexes and DGT tracs among the paerns planners could undertake an operable plan to clear these risky as edges to build a weighted directed network. Assuming there paerns progressively from the high priority ones to the low ones. are K paerns in the risky paern set P, the system maintains an K×K adjacent matrix W ∈ R where the element wxy is the weight 6.2 Risk State Prediction for the edge directed from paerns px to py . When a DGT orderly Another application oered by the DGeye system is the risk state passes px and py , we add one to wxy . Note that passing a paern prediction. Urban emergency departments need to monitor the here means a DGT passes at least one zone in the paern. If a DGT states of risky paerns before all clearance. An accurate prediction passes many paerns in sequence, we add an directed edge to any of risky paerns’ states could also give a proactive guidance to pair of the paerns and count that DGT to all the edge weights. deploy limited urban emergency resources. To further illustrate the process of causal network building, we We use Bayesian inference for predictive modeling, which adopts go through a toy example in Figure 4. As shown in the gure, a the causal network built in Sect. 5.2 as the Bayesian network. As DGT orderly passes the paerns p1, p4, p5, the weights of the edges shown in Fig. 5, the paerns that have causal relations with the p1 → p4, p1 → p5 and p4 → p5 all should be increased by one. paern to be predicted can be categorized into two types, i.e., pat- In the causal network, an edge px → py with a weight greater terns with observable states in historical times slices, and paerns than one means that some DGTs passes px for py . In the other with unobservable states in the future. For example, suppose we words, the reason for px being risky is that there are some dan- want to predict the paern states at the time slice n. e states ≤n−1 gerous goods requirements in py . erefore, we can regard the of risky paerns mined from R are observable but the ones n dangerous goods risk in py as a cause of the dangerous goods risk mined from R are unobservable. For convenience, we denote the in px . at is why we call the network as a causal network. states of observable paerns as H = {h1,h2,...,hx ,...,hK1}, and
1676 KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
The pattern to States are unobservable be predicted 7 EXPERIMENTS AND APPLICATIONS
pe pf1 pf2 7.1 Experimental Setup Future We apply the DGeye system to two big cities of China: Beijing 3 4 History and Tianjin . Beijing is the capital of China with a 20 million p p p h1 h2 h3 population, and Tianjin is a municipality directed by the central States are observable government with a 15 million population. e urban safety of the Figure 5: An illustration of the predictive model. two cities is of the utmost importance undoubtedly. e data sets used in the experiments were collected from January 1 to March the states of unobservable paerns as F = {f , f ,..., f ,..., f }. 1 2 y K2 31 in 2015 for Beijing, and from January 1 to February 31 in 2015 e state of the paern to be predicted is denoted as e, e < F. for Tianjin. In the experiments, the system divides one day into 24 According to the Bayes’ theorem, the posterior probability of e time slices, i.e., one hour per slice, and divide the maps of the two conditioned on H and F is cities into 500m × 500m urban zones. e covered area of the two Pr(e)Pr(H, F |e) Pr(e |H, F ) = , (9) cities contain 80 × 160 zones, respectively. Pr(H, F ) which could be approximated using a na¨ıve Bayesian method as 7.2 Risky Zones Detection K1 K2 Y Y We here verify the risky zones detection function of the system. Pr(e |H, F ) ∝ Pr(e) Pr(hk |e) Pr(fk |e) k=1 k=1 Figures 6(a) and 6(b) show the spatial distributions of crowd weights (10) XK1 XK2 and DGT weights in Beijing at the 10:00 time slice on one day in ∝ ln(1 + Pr(e)) + ln(1 + Pr(hk |e)) + ln(1 + Pr(fk |e)). Jan. 2015, and Fig. 6(c) depicts the distribution of the risky zones in k=1 k=1 Beijing at the same time slice for comparison. e colors indicate However, since the paern states in F are unobservable, we cannot the weights and risky score of each zone — the redder, the higher. directly use (10) to calculate the posterior probability of e. Moreover, As shown in Figs. 6(a) and 6(b), the population of Beijing are the impact of edge weights of the causal network are not considered mostly distributed in the downtown area, but high DGT weight by the posterior probability in (10). To address these, we propose zones are mainly distributed on an outer beltway surrounding an Expectation-Maximization (EM) algorithm to estimate F and Beijing, i.e., the 5th ring road 5. As a result, it is interesting to see predict e through an iterative way. from g. 6(c) that many of high-score risky zones detected by the Using the causal network as a Bayesian network, the EM al- system are not overlapped with the high DGT weight zones, e.g., gorithm initializes fx and e using an edge-weighted na¨ıve Bayes the red areas inside the 2nd ring covering an entertainment district model as follows: of Beijing: Dongzhimen and Dongsi. is indeed illustrates why XK1 DGeye considers both human population and DGTs in a “duet” way. f (0) = argmax w ln(1 + Pr(f )) + w ln (1 + Pr(h |f )) , x fx x (hk ,fx ) k x Figures 7(a) to 7(c) exhibit the case of Tianjin at the same time fx ∈{0,1} k=1 (11) XK1 slice as Beijing. As shown in the gures, the population of Tianjin e ( ) = w ( + (e)) + w ( + (h |e)) , 0 argmax e ln 1 Pr (hk ,e ) ln 1 Pr k concentrates in two areas, the main urban area and the port area. e∈{0,1} k=1 e DGTs, however, are mainly distributed on beltways and ex- where w(x,y) is the weight of the edge from px to py in the causal pressways that connect the port area with the main urban area. As w w network, e and fx are the DGT trac inner the paerns corre- to risky zones in Fig. 7(c), again we can nd the inconsistency with sponding to e and fx . In the t-th round of the E-step, we use e(t −1) high DGT weight zones — the high score risky zones are mainly and F (t − 1) estimated in the last round as well as H to update fx (t) distributed over urban-rural fringe of the main urban area and the as follows: downtown of the port area. is is also the result of taking human fx (t ) = argmax wfx ln(1 + Pr(fx )) + w(e,fx ) ln(1 + Pr(e (t − 1) |fx )) fx ∈{0,1} population into consideration in DGeye. XK1 XK2 (12) Figure 8 shows the proportions of risky zones to all urban areas + w ( + (h |f )) + w ( + (f (t − ) |f )). (hk ,fx ) ln 1 Pr k x (fk ,fx ) ln 1 Pr k 1 x of Beijing and Tianjin for the 24 time slices. Note that all these k=1 k=1 values are averaged on all days in the data set. It is interesting In the M-step, we predict e using H and estimated F as to see that the temporal distributions of risky zone proportions XK1 for the two cities are similar to each other, which indeed coincide e (t ) = argmax w ln(1 + Pr(e)) + w ln(1 + Pr(h |e)) e (hk ,e ) k more with the rhythms of human activities rather than that of e∈{0,1} k=1 (13) DGTs. Nevertheless, the dierence does exist: the emergence of XK2 + + | . risky zones in the morning peak seems more severe for Beijing. w(fk ,e ) ln(1 Pr(fk (t ) e)) k=1 When the algorithm reaches a stable state, we use the nal e(t) as 7.3 Risky Pattern Mining the state prediction result. We let a paern state equal to 1 for the is subsection demonstrates the risky paern mining function of risky state, and 0 for the non-risky state. e prior probability and the DGeye system. Figure 9 gives temporal distributions of paern likelihoods are counted from the data set. Compared with tradi- amounts in Beijing and Tianjin, where dierent colors indicate tional Bayesian methods, our predictive model has two advantages: 3hps://en.wikipedia.org/wiki/Beijing i) it exploits the causal dependency among paerns; ii) it makes 4hps://en.wikipedia.org/wiki/Tianjin use of causal information in unobservable paerns. 5hps://en.wikipedia.org/wiki/5th Ring Road (Beijing)
1677 KDD 2017 Applied Data Science Paper KDD’17, August 13–17, 2017, Halifax, NS, Canada
Dongzhimen and Dongsi
(a) Crowd Weights in Beijing (b) DGT Weights in Beijing (c) Risky Zones in Beijing
The main urban areas
The port areas
(d) Crowd Weights in Tianjin (e) DGT Weight in Tianjin (f) Risky Zones in Tianjin Figure 6: Spatial distributions of crowd weights, DGT weights, and risky zones in Beijing and Tianjin.
120 20 Beijing Pattern Size: 1 2 3 4-5 6-11 >11 Tianjin 100
15 80
60 10 40 Pattern Number Pattern 5 20 Proportion of Risky Zones (%) 0 0 0:00 5:00 10:00 15:00 20:00 Time 0:00 5:00 10:00 15:00 20:00 Time Figure 7: Temporal distributions of risky-zone proportions. (a) Beijing paerns of dierent sizes. As can be seen, the temporal distributions Pattern Size: 1 2 3 4-5 6-11 >11 of paerns in Beijing and Tianjin are very dierent, which is in 60 sharp contrast to that of risky zones in Fig. 8. is well illustrates why we need risky paerns given risky zones already; that is, 40 paerns indicate relatively stable rules and zones depict instant phenomenon. Two another observations are worth noting. First, the temporal distribution of paerns has an obvious tide in Beijing, Number Pattern 20 but uctuates much more smoothly in Tianjin. Second, compared with Tianjin, Beijing has obviously more big-size paerns. 0 e reason for the above dierences lies in the diverse require- 0:00 5:00 10:00 15:00 20:00 Time ments of dangerous goods of the two cities. Dangerous goods in Beijing are consumed by citizens in daily life, such as gasoline (b) Tianjin requirements of gas stations and liqueed gas of restaurants. ere- Figure 8: Temporal distributions of risky patterns. fore, the temporal distribution of risky paerns has a similar rhythm with people’s daily life. Moreover, since dangerous goods must be delivered to gas stations and restaurants in downtown area of Bei- jing every day, DGTs have to drive through many high-population in Tianjin main urban area as well as the port area for the 10:00 zones in adjacent, which results in many big-size risky paerns in time slice. As shown in the gures, most of big-size paerns are in Beijing. Figure 10(a) is a risky paerns’ map of Beijing at the 10:00 the port area, and the main urban area is relatively safe. time slice. As can be seen, many big-size risky paerns are located e above dierences in paern distributions suggest dierent in the downtown area. DGT monitoring strategies for Beijing and Tianjin. e Beijing Unlike Beijing, dangerous goods requirement in Tianjin is driven government should pay more aention to DGTs in many areas by chemical materials import and export in the Tianjin port. ere- of the downtown in the middle of a day. Oppositely, the Tianjin fore, the correlation between paern amount and city life rhythm is government could only monitor some particular areas in the port very weak. Figures 10(b) and 10(c) show the maps of risky paerns area but for a whole day.
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(a) Downtown Area of Beijing (b) Main Urban Area of Tianjin (c) Port Area of Tianjin Figure 9: Distributions of risky patterns in Beijing and Tianjin.
oered by restaurants in Guijie is “hot pot” 8, which is a kind of interesting cuisine that cooks raw foods in a simmering metal pot 10:00 at the center of dining tables. A hot pot table usually equips with a mini gas stove that connects to a liquid gas cylinder, which forms an 10:00 enormous demand of gas cylinders transported by DGTs to Guijie every day. As shown in Fig. 11(a), the blue and green paerns cover 9:00 three main roads heading to the red paern from the north, east and west, respectively. is implies that DGTs transport liquid gas Jan. 17, 2016 cylinders from suburbs to the red paern through these three roads, and thus expose the zones in the blue and green paerns to the (a) e Beijing Showcase threaten of explosion. On January 17, 2016, a truck fully loaded with liquid gas cylinders was on re at the road covered by the August 12, 2015 green paern [2]. According to the causality modeled by the causal network, there are 150 risky zones that are directly caused by the red paern, which cover about 5% downtown areas of Beijing. 7:00 As shown in Fig. 11(b), the rst ranking paern in Tianjin is 8:00 located in the port area, covering a north-south road aside a wharf. e rst and second ranking paerns caused by the red paern cover an east-west road across residential areas of the port. Ob- viously, the purpose of DGTs driving through the green and blue 8:00 paerns is going to the wharf aside the red paern to load or un- load dangerous goods. Based on above analysis, we can discover an urban planning defect in the port area of Tianjin: the depots of Explosion site dangerous goods in the wharf are too close to residential areas, and the government should not let DGTs drive across residential areas. (b) e Tianjin Showcase is fatal defect actually has triggered an irreparable tragedy: the Figure 10: Applications of pattern importance ranking. Tianjin port blast of dangerous goods on August 12, 2015, which was happened right at the intersection of the roads covered by the 7.4 Importance Ranking red paern and the green/blue paerns! We here give two showcases of the paern importance ranking application of DGeye. Figure 11 shows the rst ranking paerns 7.5 Patten State Prediction (the red blocks in the maps) in Beijing and Tianjin, and the green and In this subsection, we evaluate the performance of the paen state blue blocks are the rst and second ranking paerns, respectively, prediction function in the DGeye system. e benchmarks include: that are caused by the red paerns. i) the Prior model (Pr), which only uses prior probability Pr(e) to As shown in Figure 11(a), the rst ranking paern in Beijing is predict paern states. Because the daily support threshold of risky located at the Dongzhimen and Dongsi district, which is a famous paerns is set to 0.8, the Prior model always predicts paerns at the entertainment district of Beijing 6. Especially, the Dongzhimen area risky state. ii) e Likelihood model (LL), which uses the likelihood 7 PK1 w Pr(h |e) to predict paern states. e likelihood model has an extremely well known food street, Guijie . A major cuisine k=1 (hj,e) j is used to evaluate the predictive eciency of observable paerns. 6hps://en.wikipedia.org/wiki/Dongzhimen 7hps://www.travelchinaguide.com/araction/beijing/guijie-street.htm 8hps://en.wikipedia.org/wiki/Hot pot
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Table 1: Prediction performance comparison. 8 RELATED WORKS Beijing Dangerous goods transportation becomes a very hot topic in haz- ardous materials management and intelligent transportation system EM NB Pr LL LR SVM ANN (ITS) areas. In order to control societal risks caused by dangerous Pr-risky 0.89 0.83 0.80 0.84 0.80 0.80 0.79 goods, some DGT monitoring systems, such as MITRA [26] and Re-risky 0.93 0.94 1.00 0.66 0.90 0.92 0.88 GOOD ROUTE [5], are deployed. Most of them focus on monitoring F1-risky 0.88 0.89 0.74 0.85 0.85 0.83 0.91 and collecting locations of DGTs only but omit the important hu- Pr-all 0.79 0.74 0.65 0.72 0.70 0.71 0.70 man activities for “duet playing”. In academia, ITS researchers focus Re-all 0.82 0.79 0.80 0.62 0.75 0.75 0.74 F1-all 0.80 0.76 0.72 0.67 0.73 0.73 0.72 on DGT route planning [20] and transportation systems designing such as rail way DGT [33]. In hazardous materials management, re- Tianjin searchers focus on DGT risk denition [9, 28] and analysis [12, 34]. EM NB Pr LL LR SVM ANN Most of these works study DGT from an operations and optimiza- Pr-risky 0.93 0.85 0.76 0.92 0.77 0.76 0.76 tion view, and have a basic assumption: if a plan is well designed Re-risky 0.82 0.87 1.00 0.43 1.00 1.00 1.00 and executed, DGT risks will be under control. In practice, however, F1-risky 0.87 0.86 0.86 0.59 0.87 0.86 0.86 many uncertainties could disturb the deployment of plans. Data Pr-all 0.89 0.78 0.58 0.78 0.59 0.58 0.58 driven approaches are becoming more desired to detect and analyze Re-all 0.82 0.79 0.76 0.54 0.77 0.76 0.76 risks of dangerous goods in real-world applications. F1-all 0.85 0.79 0.66 0.64 0.67 0.66 0.66 Spatial paern mining is a key function of DGeye. Traditional frequent paern mining algorithms, such as Apriori [4] and FP- iii) e Na¨ıve Bayes model (NB), which uses the initialization model growth [14], discover frequent paerns from a transaction set. of the EM algorithm in (11) to predict paern states. iv) e Logistic Many spatial clustering algorithms, such as CLARANS [23], DB- Regression model (LR), Support Vector Machine (SVM) model, and SCAN [11], and ST-DBSCAN [7], generate spatial paerns from a Articial Neural Networks (ANN) model. e three models use the spatial distance view [13]. e collocation [17] and spatio-temporal states of observable paerns as inputs, which are used to evaluate sequential paerns mining [18] algorithms detect frequent collo- the performance of classical models in our prediction scenarios. e cations and/or concurrences from spatio-temporal data sets. e data set of Beijing contains trajectories and mobile phone records of risky paern mining method in DGeye is a union of the above algo- 90 days, and that of Tianjin contains trajectories and mobile phone rithms, which mines frequent concurrences of spatial paerns for records of 60 days. We use data of the rst 2/3 days as training sets collocated dangerous goods and populations. erefore, we adopt and the remaining 1/3 days as test sets. a zone-paern two step mining scheme based on Mahalanobis e prediction results are listed in Table 1, where precision (Pre), distance and an Apriori-like algorithm. recall (Re) and F1 scores (F1) for the risky state and all states are used Another key function of DGeye is transportation causal analysis. as evaluation measures. As can be seen, in both Beijing and Tianjin In this area, most studies focus on analyzing causality between data sets, the proposed model (EM) achieves the best performances transportation and economic indicators from a macro view, such as compared with all baselines in terms of all measures except for using the Granger test to analyze causality between transportation the recall of the risky state — the prior model always predicts and GDP [6] or regional economic growth [19, 22] in dierent coun- paerns as risky and therefore its risky state recall is 100%. Another tries and regions [3, 8, 27] . In the micro level, Ref. [21] proposes an observation is the relatively poor performances of LR, SVM and outlier tree based causality discovery algorithm for spatio-temporal ANN. is might be ascribed to the scarcity of training samples; interactions in urban trac data, and Ref. [10] proposes a two-step that is, we can only sample the state of a paern one time in one framework for inferring the root cause of anomalies in urban trac day, and hence only have 60 and 40 training samples for every data. Few works have analyzed causality among paerns/events of paerns in Beijing and Tianjin, respectively. In this kind of scenario, dangerous goods transportation. Bayesian methods seem more eective than completely supervised Our study can also fall into the research category of urban com- classication models. puting [37]. In this area, research works related to our study in- To sum up, we have: i) the causality relations in the causal net- clude: data-driven urban analysis [16, 38], urban anomaly detec- work is very eective for paern state prediction; ii) the information tion [24, 25, 39], urban public security [29, 30], citizens behaviors of unobservable states exploited by the proposed EM algorithm prediction [15, 35, 36], and still many. To our best knowledge, our could improve the prediction performance; iii) the Bayesian method work is among the earliest studies in urban computing area that adopted by our model is very suitable to the prediction scenario try to snu out the threats from dangerous goods. with small training sets.
7.6 Policy Applications 9 CONCLUSIONS e Beijing showcase introduced in Sect. 7.4 has been reported In this paper, we present a novel system call DGeye for urban dan- to the Beijing government as a report of the system. e Beijing gerous goods management. DGeye features in leveraging both DGT government started a gas pipeline laying project in the Guijie food trajectory data and human activity data for timely risk monitor- street of the Dongzhimen and Dongsi district in September 2016. ing as well as proactive risk mitigation. Specically, DGeye rst As reported by the news [1], Beijing “Guijie” is about to bid farewell adopts Mahalalnobis distance for scaling and denes risky zones in to the gas-cylinder era in 2017. a quantitative manner for real-time monitoring. e keystone of
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DGeye , however, lies in risky paerns that reveal the rhythms of [17] Yan Huang, Shashi Shekhar, and Hui Xiong. 2004. Discovering colocation paerns the risks in a city and are mined by a carefully designed Apriori-like from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering 16, 12 (2004), 1472–1485. algorithm. A causal network is then built by taking paerns as [18] Yan Huang, Liqin Zhang, and Pusheng Zhang. 2008. A framework for mining vertexes and trajectories as directed edges for risk ranking, aribu- sequential paerns from spatio-temporal event data sets. IEEE Transactions on Knowledge and data engineering 20, 4 (2008), 433–448. tion and prediction, which makes DGeye an ideal decision support [19] Michael Iacono and David Levinson. 2016. Mutual causality in road network system for urban planning and emergency management. DGeye growth and economic development. Transport Policy 45 (2016), 209–217. has proven itself in successfully recognizing the hidden explosion [20] Bahar Y Kara and Vedat Verter. 2004. Designing a road network for hazardous materials transportation. Transportation Science 38, 2 (2004), 188–196. risks in Guijie food street of Beijing and in port area of Tianjin. In [21] Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing. 2011. Discovering particular, the report from DGeye has driven the Beijing govern- spatio-temporal causal interactions in trac data streams. In Proceedings of the ment to lay down a gas pipeline in Guijie and bid farewell to the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1010–1018. gas cylinder transportation history. [22] Kirsi Mukkala and Hannu Tervo. 2013. Air transportation and regional growth: which way does the causality run? Environment and Planning A 45, 6 (2013), 1508–1520. ACKNOWLEDGMENTS [23] Raymond T. Ng and Jiawei Han. 2002. CLARANS: A method for clustering objects Jingyuan Wang, Chao Chen and Zhang Xiong were supported in for spatial data mining. IEEE transactions on knowledge and data engineering 14, 5 (2002), 1003–1016. part by the National Natural Science Foundation of China (Grants [24] Bei Pan, Yu Zheng, David Wilkie, and Cyrus Shahabi. 2013. Crowd sensing of 61572059, 61202426, 61332018) and the State’s Key Project of Re- trac anomalies based on human mobility and social media. In Proceedings of search and Development Plan of China (Grant 2016YFC1000307). the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 344–353. Junjie Wu was supported in part by the National Natural Science [25] Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, and Yu Zheng. 2011. On min- Foundation of China (Grants 71531001, U1636210, 71490723) and ing anomalous paerns in road trac streams. In International Conference on Advanced Data Mining and Applications. 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