Safe Route Recommender : An efficient way to find safeand short path between source and destination

Pratik BhangaleComputer Science, UMBCEmail: pratikb1@umbc.edu

Vishal RathodComputer Science, UMBCEmail: vishalr1@umbc.edu

Mayur PateComputer Science, UMBCEmail: mayurp1@umbc.edu

Abstract—Over the last decade , the use of maps hasincreased dramatically. Every car or traveling individualuse Google maps for navigating to their desired destination.Although the routes provided by Google maps helps usersto figure which is the shortest or traffic free path, buthere safety is not taken into consideration. In this paper,we propose and develop a mobile application that utilizescrime data to recommend the safe and shortest routebetween source and destination. We model the calculationof the safeness of the route based on 3 different data setsa) Crime records b) Accident cases c) Police station andhealth care services. Specifically, Using data of Baltimorecity we develop a safeness model for street urban networkwhich allows us to give safe factor to every street. Themodel uses hadoop and spark scripts for big data process-ing. Furthermore the paper also discusses caching systemused to provide real time response to the mobile app andalso identifies what could be done to improve such systems.

Keywords—Navigation, Big data, Android, NoSql,Hadoop, Spark, Web service, Google Maps, REST API.

I. INTRODUCTIONToday, 54 per cent of the worlds population lives

in urban areas, a proportion that is expected toincrease to 66 per cent by 2050. Projections showthat urbanization combined with the overall growthof the worlds population could add another 2.5billion people to urban populations by 2050 [1]says un.org. One of the main reasons for theselevels of urbanization is the long-lived thought ofcities as the paramount instrument for innovationand wealth creation. Due to increasing populationdensity in cities, they have become the main sourceof crimes, diseases and pollution, which is signif-icantly deteriorating the quality of life in cities.We have concentrated on the data of Baltimorecity. According to the FBI (Uniform crime report)Baltimore, Maryland is one of the high crime rated

city[2]. Many government authorities are maintain-ing a lot of data about the crime and accidents. Theopen government and data initiative from PresidentObamas administration [3] has further led manylocal authorities to systematically collect, organizeand publicize such datasets. In this paper, we focuson finding a solution for one of the major aforemen-tioned problems present in almost every megacitytoday: crimes and public safety. We develop amobile application and big data system aiming toidentify safe and short paths for people to take whenthey navigate through the city. For this purpose weanalyze both crime and accident cases present ona particular path. Major challenge in this projectis to approximate between safe and shortest path.So ideally, prioritizing the safety of dwellers, onewould like to provide the user with the safest pathfrom origin x to destination y. We have developed amobile application where user will enter the sourceand destination and user will be able to see the mul-tiple paths according to their safe scores on the mapinterface in app. The paper is structured as follows:Section 2 provides a motivation for developing thissafe route recommender service. Section 3 gives anoverview of related works done till date and howour idea is unique for commuters. Section 4 presentsdetails about who will be actual users of this service,and then approach and implementation details ofthis services, we discuss the algorithmic approachand big data computations to get the intended resultsand how system can be scaled to handle largeamounts of requests. The paper concludes with asummary and results in Section 10 and onward.

II. MOTIVATION

The way we navigate in cities has been revo-lutionized in the last few years by the advent of

GPS mapping programs. Most GPS mapping appsare designed to get you where you want to goquickly just by entering your start and end locationand these will give you the shortest route fromA to B. But consider if a tourist or a travelleris new two a certain place and he doesn’t awarethat how secure is that place for travelling throughvehicles or as a pedestrian. If considered places likeDetroit, where a violent crime rate of 1,988.63 per100,000 people, similarly for Memphis its 1,740.51,Oakland 1,685.39 and for Baltimore its 1,338.54,etc. Some of the most prominent crimes in thecity are aggravated assault, assault with a deadlyweapon, rape, armed robbery, murder and non-negligent manslaughter. Along with such recordsthere are also places where its too unsecure forvehicular travelling due to road conditions andstructures which caused tremendous increases inaccidental cases in last few years. These all caseswhich makes a reasonable requirement of alternativepaths those are not only short by distance but alsosecure for travelling or walking.

So tackle the everyday problem of public i.e.google map helps to find the shortest route withtraffic information etc but it sometimes shows theshortest path that involves a deserted route; whichis not safe. Thus the motivation chosen is an aptone to develop a service which is useful for everyindividual today for whom the safest routes to adestination, which is also an important factor. Thisservice plays vital role for those who unawareof a particular area/city. Also this service can beembedded with apps widely used like Ways, GoogleMaps. The project deals with a real world hugeopenly available data and addresses a social problemof making the travel of people more convenient andsafe. The designed algorithms of this project alsosupports those commuters are often concerned withthe quickest path to a destination by recommendingthe trade of between short and safe path. The goalof this work is to automatically suggest routes thatare not only short but also secure.

III. RELATED WORKS

There are few developments on the similar linesof the system we have built, some of which areenlisted below:

• The Shortest Path to Happiness:Recommending Beautiful, Quiet, and HappyRoutes in the City. The Yahoo researchershave developed the algorithm which allowsuser to take the Scenic Route through anew Mapping Software which gives ’BeautyScores of different routes between sourceand destination. The goal of this work is toautomatically suggest routes that are not onlyshort but also emotionally pleasant, they say.They then crowd sourced opinions about thebeauty of each location using a website calledUrbanGems.org.

• Safe Route - San Francisco 2016 TechCrunchDisrupt Hackathon. The one of runners-upteam built an application SafeRoute, a hackdesigned to give users a safer path to theirdestination. As its name implies, Safe Routeis designed to give users a safer path to theirdestination. The app utilizes Google Maps,Google Places, and Crimemapping.com APIsto help determine the best way to get frompoint A to point B, even if it means makingthe route a bit longer in the process.

• Safe Navigation in Urban Environments: Thisis a application that utilizes crime data to pro-vide safe urban navigation. Specifically, usingcrime data from Chicago and Philadelphia, its adeveloped risk model for their street urban net-work which allows us to estimate the relativeprobability of a crime on any road segment.

IV. SERVICE USERS

The service is useful for every individual todaywho travels with the help of mobile maps.Its veryuseful those who is new at a particular area/city.All the everyday commuters such as taxi drivers,pedestrian and tourists are the users to whom thisservice will provide the safest and shortest route.Apart from this it might help government to employmore security protocols on the routes those aremarked unsafe by this service.

V. DATASETS AND FORMATTING

We collected crime and accident data sets fromdata.gov website. Data.gov is the official website of

US Governments for open data. We collected datasets for various cities like New York, San Franciscoand Baltimore. For implementation purposes, wedecided to stick with only one city. We choose Bal-timore city and used all its crime and accident dataduring implementation of this project. We collectedcrime data which had 27000 crimes over the last 2years. We have also collected accident data whichhas more than 5000 records for last 2 years alonein Baltimore. We collected all this data in well-structured CSV files. We also need to collect datafor police and health care service in Baltimore city.We collected this data using Google Places API. Weimplemented this API and dumped all data in CSVfile which contains almost 1000 entries for policeand healthcare services.

Though all CSV files were well-structured, wehad to change the structure of CSV files to makespark computations faster. These files stored latitudeand longitude in single string format. For example,(12.33223 -12.2323). We needed these strings tobe separated into different columns. So, we wrotea python script which reads input CSV file andconverts it to custom structure specified by user.We performed this cleaning operation for both crimeand accident records.

VI. APPROACH

Here’s how we planned it to build :An aim it to build a product which will allow

an user to enter a current or start location, alongwith a desired destination and then by algorithmicprocessing it will select and suggest the route thatranks highest on a ”safe score” attributed to eachroute by the software. Safe route recommender con-sists of three main parts: one that is responsible forcomputing the safe score of routes through big datacomputations, another part is find trade of betweenshort and safe route using several algorithms andlastly to represents the different routes accordingto the results computed and recommend the routeneatly on friendly user interface.

The algorithm techniques to generate the safescore of every path between source and destinationconsumes the input information as set of datasetsmentioned above. These data sets have been col-lected from the authenticated government sources.

And processing these information it will help to de-termine the safeness of the routes. Additionally, wehave used Google API’s to get the different routesbetween source and destination. This API providesmeta data about the paths as a response so it againhelps to get the distances between two paths. Soprocessing the API results by different algorithms,its possible to generate the trade off between shortand safest path which ultimately helps commutersto have short and safest and wonderful travel. Theservice which we built that locates the user andallows to find him/her the efficient path between thecurrent and destination location. We are using thebig data computation technologies such as Hadoop,Spark for the algorithmic computations. To workout whether the routes chosen by the algorithm arereally safe and short, we have considered the dataof a particular location and verifying the results.We are showing the final results on the mobileapplication with user friendly map interface. Wehave also build a software technique which allows tohandle large number of requests and those requestsfor which currently safe factor is not present in thesystem. Overall systems aims to provide the betteruser experience and recommend the safe path byprocessing authenticated data.

VII. WEB SERVICE ARCHITECTURE

We have designed our system using three tiersoftware architecture. We have developed anandroid application which is a presentation layer ofour system. Our logic tier is responsible for requesthandling, algorithmic computations and responsegeneration. Finally, data tier is divided into twomain parts. First part consists of traditional NoSQLdatabase which processes and returns all data inreal time. Second part consists of Big data storedin Hadoop clusters. This data is used for safescore generation. Details of Safe score generationand data processing will be explained later in thispaper.Our system provides an elegant interface tothe users of this application. User provides a sourceand destination location as an input from androidapplication. Then our android application makes aservice call to application server by using RESTprotocol. This call is uses HTTP GET method forsending request parameter to server in form ofJSON string. Using these input parameters, our

web service calls Googles maps API to fetch allpossible routes between source and destination.

Fig. 1. System Architecture

Once we have all routes, we fetch safe score foreach route from MongoDB and then we apply ourapproximation technique to suggest best possibleroutes for travel. Our service returns these paths inencoded polyline string in JSON format to androidapplication. Then android application decodes theseencoded polyline strings and convert each route toactual location co-ordinates for plotting them onmap.

As mentioned previously, we have divided datalayer into two main parts. First part consists ofMongoDB - NoSQL database which we have in-cluded to process all real-time requests. Currently,MongoDB layer only stores route information andcount for crimes and accidents happened on thatroute. Second part consists of Hadoop cluster andSpark. This layer stores crime, accident, healthcareand police services data. This is nothing but BigData in our system. We are processing this bigdata using Spark scripts which are responsible forgenerating counts for every route that comes asrequest. We have designed caching approach bywhich both these layers communicate with eachother. This caching approach will be explained laterin this paper.

VIII. TECHNOLOGIESWe have used various technologies to develop

this system. We will be listing all technologies per

logical tiers.

A. Front End

We have developed an Android application as afront end. This application has used technologiessuch as Java8, Google maps API, Google Volleyand material design etc. Google maps and VolleyAPI has been used for plotting routes on mapsand providing auto complete functionality for usersinput of source and destination locations.

B. Back End

We have developed our web service using Py-lons Python web framework for developed ser-vice oriented applications. Pylons application aredeveloped in Python language and runs with autility called as Paster. We have also used pymongodatabase driver for communicating with MongoDBfrom pylons application. Additionally, we have useduseful python libraries such as Google Maps serverside API and hashlib. As mentioned previously, wehave used NoSQL database MongoDB 2.4.9 for thedevelopment of this project. We have used inbuiltutility in MongoDB called as mongo client fordevelopment and debugging of our application.

C. Big Data

We have used Hadoop clusters to store data fromvarious source such as crime and accident. We haveused Hadoop 2.7 for development of this applica-tion. We stored structured data in csv file formatin HDFS using PUT command provided in hadoopbinaries. We have used pyspark on top of HDFS tofetch and process data from HDFS clusters.

D. Production Environment

We have deployed this application on publiccloud using Amazon web services. We have usedEC2 micro instance to host our application. We havealso deployed our android application on Googleplay store and its publicly available in all countriesin the world. This application currently runs on an-droid mobiles and tablets. We have also configuredsingle node cluster running Hadoop and spark jobs.

IX. IMPLEMENTATION DETAILS

We have used modular approach for developmentof this project.

Fig. 2. User enters source and destination

In following points, we will be discussing de-velopment of every module in detail in terms oftechnical implementation and challenges.

A. User Interface

The project is mainly focused on map navigationso most handy interface was an mobile app. Wehave developed an android app - SafeT, for showingthe safe routes to user. Here user enter source anddestination. Figure 2 gives the screen shot of theuser input screen.

Fig. 3. Google places suggestions

We have integrated Google Place API to provideuser with real places suggestion . This is show infigure 3. When the user enter source and destinationand requests to see the route. The request is sent tothe web server. The app then receives Json responseof the routes which are in the form of polyline stringand their respective safe scores.

We use Google Volley API for communicatingwith the server.The detailed format of response isexplained in later section in paper. Polyline encod-ing is a lossy compression algorithm that allows youto store a series of coordinates as a single string [4].Point coordinates are encoded using signed values.The app then decodes this polyline string to get aseries of latlong points and plots them on the Googlemaps. The app uses google maps v2 API for this.According to safe score the app displays the routesas follows : Safest - green , Orange - medium safe, Red- unsafe the output is shown in figure 4.

Fig. 4. Google places suggestions

B. Web Service Development

Android application passes the source and desti-nation input in simple JSON string to web service.This web service reads these request parametersfrom URL and then makes a call to google mapsserver side API. Google provides a key based au-thentication mechanism for performing any typeof API calls. Once the authentication has beendone, google returns client stub authenticated tokenwhich can be used to make further calls for limitedsession. Once session expires or web service losesrequest instance, one must reauthenticate for makingfuture API calls to google maps. After successfulauthentication, our web service passes source, desti-nation and authenticated token to Google DirectionsAPI which in turn returns the huge JSON routearray as output. Each entry of this array containsroute information, polyline string and distance. Ourweb service extracts polyline string and distancefor each route and stores this in temporary datastructure. As explained in last point, polylines areencoded strings which stores series of latitude andlongitude in smaller length string. Our web servicefurther convert these polylines into secure hash of

32 bits using RSA md5 encryption technique. Wehave converted these polylines to hash keys as wemust uniquely identify each route in our system.We could have used polyline strings as a uniqueidentifier, but performing long length and stringintensive queries in not recommended approach inMongoDB. So, we converted these polylines intomanageable hash keys and stored them as primarykey for each route in our database.

Our web service then makes a search in databasewith hash keys for every path to fetch crime count,accident count and healthcare services. These countsare generated from processing of crime and hospitaldata in Spark and then updated to MongoDB. Howthese counts are generated and for which routes itsgenerated will be explained in further sub points.For simplicity, we will be assuming that we getsome count value for crime and accident for everyroute searched with unique hash keys. Web servicethen applies weighted average algorithm on themto generate safe score levels for every path. Thisalgorithm is explained below in detail. These algo-rithm considers both safety and shortness factor andmakes a best possible sequence of routes for travel.Here challenge was to balance out both factorsand make best choices. Details of this algorithm asfollows:

C. Safe Score Generation Algorithmic Techniques

1) Weighted Average Algorithm: We have cho-sen a weighted average technique cause its moreaccurate measurement of scores or investments thatare of relative importance to each other. It is verysimilar to an ordinary arithmetic mean (the mostcommon type of average), except that instead ofeach of the data points contributing equally to thefinal average, some data points contribute more thanothers. So it can also be considered as priority basedaverage algorithm. In case of recommending saferoute, let see how this technique helps in determin-ing the safe route among different routes betweentwo locations.As mentioned in data sets section andshown in below figure we are using three types ofdata which are treated as a factor while computingthe weighted average. On execution of sparks jobsthe generated counts are multiplied by the decidedfactor or weight and after multiplication all threetypes of scores are added together which decides

the safe score of a path. The priorities or weightageof these factors are decided in percentages. Whiletesting we have given the 50% weight to crimeevents, 30% weight to accidents and 20% to numberof police and health services.

Fig. 5. Weighted Average Technique

These weightage decided in a such way that,crime events happens intentionally than accidentsso giving higher priorities to those and safetyservices also plays major role in deciding thesafeness of routes. Multiplied scores of crime andaccidents are added as they contribute to unsafenessof the route and police and health services scoreare subtracted in weighted average calculationand the result is divided by the number of factorswhich is 3 gives the final result as a safe scoreof a single path. This way every path is given asa input to weightage average algorithm and safescore of every path is calculated individually whichis processed in further algorithm. Web service thenpasses these generated safe scores and distance foreach route to our custom approximation algorithm.This algorithm considers both safety and shortnessfactor and makes a best possible sequence of routesfor travel. Here challenge was to balance out bothfactors and make best choices. Details of thisalgorithm as follows:

2) Approximation Algorithm: Since both thelength and the risk of the path are equally importantbut cannot be combined into a single objective, theapplication’s approach the urban-navigation prob-lem as a biobjective shortest path problem andresults in recommending a path that provide trade-offs between distance and safety. This algorithms is

responsible for finding the trade of between two dif-ferent factors as short and safe route. The outcomeof this algorithm helps to suggest a path to the useramong different paths suggested by Google API.The mathematical definition of approximation algo-rithms is the algorithms used to find approximatesolutions to optimization problems and these areusually considered as NP-hard problems i.e. theseproblem have several different efficient solutions.

For Safe Route Recommendation, to find theapproximation between safe and short, we want theone provable solution that will provide the convinc-ing quality result. While developing the algorithm,we came across three different cases as follows:

1) The generated safe score of a path is muchless but the distance of a path is much morethat two paths recommended by Google ser-vice

2) The generated safe score of a path is higheri.e. path is more unsafe but the distance isvery short compared to other paths

3) The resulted safe score of a path and distanceboth are moderate compared to other pathsbetween same source and destination

So to build a solution which will provide boththe short and safe journey, we came up with ourown algorithmic approach. As the main aim of thisproject is to recommend the safest route, we gavethe high priority to safe score and then consideredthe short distance whenever possible. Hence, goingforward with this approach, whenever we cameacross the case 2 mentioned above, we eliminatedthe same choice in first iteration cause it completelycontradicts the main goal of project. We never wantto recommend the unsafe path to user even if he/sheneeds to compromise in the distance of a journey. Soat situation we only left with two cases i.e. 1st and3rd mentioned above. We came up with solution thatif the safe score of two paths are of ratio 2:1, thenwill consider the path which is more safer amongthem for eg. if safe score of route A is 20 andsafe score of route B is 43 then as the safe scoredifference is more than the double hence we willrecommend the route A to the user even if there arenoticeable differences in their distances.

Fig. 6. Approximation Algorithm Technique

On the other hand if safe score difference betweenpaths is less than the double, then will recommendthe path which is more shorter and take less timefor travel.

Execution of approximation algorithm generatesa sequence of routes such that safest and shortestwill be first, then second and so on. Finally, ourweb service makes JSON array of these polylinesequences and returns it to android application.

D. Path Caching

As mentioned in point B, we fetch crime, acci-dent and healthcare services count from MongoDB.But generation of these counts involves big dataprocessing of crime and accident data and cachingmechanism. Our application works in real time andneeds to generate output in few seconds. Uponrequest from user, it is impossible to generate countsfor crime and accident by iterating over big data.Moreover, it is also impossible to preload crime andaccident counts for every possible source and des-tination locations in world. So, we have decided touse MongoDB which serves all application queriesin real time and caches paths for which counts aremissing. We call table as Path Cache. When periodicspark job runs, it fetches entries from Path Cachetable and generates count for each path by iteratingover crime and accident data. Then this job updatesthe MongoDB Paths table with updated counts. Italso deletes the entries in path cache table.

Fig. 7. Caching Approach

If crime count or accident count is not present,then we show routes based on shortest distanceonly. After spark job execution, all future requestsfor that source and destination will be served fromMongoDB with updated crime and accident counts.In this way, this system will generate more scoresas it receives more requests.

E. Count GenerationAbove points mentioned that we are generating

safe score from crime, accident and healthcare ser-vices count. These counts are generated by iteratingover big data. Below point will explain this processin detail. Currently, we have dumped crime, accidentand healthcare services data in HDFS. This datais well structured and stored in CSV file format.Additionally, we have designed three spark jobsfor each source of data which are responsible forcounting the total number of crimes, accidents orservices on that path.

As explained in last point, spark job iterates overpath cache collection to read polyline of every route.Then our spark job coverts these polylines intoactual latitudes and longitudes. We have used poly-line python library which decodes these polylinesto convert them to actual co-ordinates. Our datafiles have latitude and longitude for every crimeand accident incidence. So, our job then loads theseseries of latitude and longitude from data files inSpark RDD object. Then job iterates over eachentry in RDD object and tries to match with inputroute coordinates. Here, the real challenge was to

figure out the factor for matching. We cannot simplysearch the exact co-ordinates of crime records withroute co-ordinates.

Fig. 8. Hadoop Architecture

Because practically, possibility of crime happen-ing near the road is greater than happening onactual road. Therefore, exact match would not haveworked. So, we decided that we will search around200 meters of given path. So, for searching within200 meters, we calculated the difference betweencrime location and current path coordinate. Forcalculating this difference, we have used geopypython library. This library calculates the differencebetween two methods using great circle method.The great-circle distance is the shortest distancebetween two points on the surface of a sphere, mea-sured along the surface of the sphere (as opposedto a straight line through the sphere’s interior).Once we have the distance, we compare with 200meters and if its less than that then we count thatrecord as crime happened on that path. In this way,we calculate crime and accident counts for everypath and updates them to MongoDB upon finishingcalculation.

X. SOURCE CODE AND PLAYSTORE LINKS

We have used GitHub to host our source code.This source code is currently availably publicly.Following are the links to source code:

https://github.com/Pratikgit/SafeRoute backendhttps://github.com/Pratikgit/SafeRouteApp

We have also deployed our android application onGoogle Play Store. Here is the link for downloadingthe application.

https://play.google.com/store/apps/details?id=edu.umbc.smartroute&hl=en

XI. RESULT AND ANALYSIS

In this section, we present an experimental assess-ment of our proposed methods. Our main objectiveis to evaluate the practical utility of our algorithms.We focus on two aspects: (i) the representativenessof the small set of paths that our algorithms returnand (ii) their Back end processing using Spark jobs.We build our evaluation benchmark by samplingco-ordinated paths of 4 localities in Baltimore forwhich we have collected the required data. On pro-viding any source and destination of these areas asa input to mobile application generates the effectiveresult which we have verified with the safe scoregenerated at the back end system. These resultsshows provable path recommendation before and af-ter applying the safe route computations. Followingare the few sample results generated at back endwhile processing the inputed co-ordinates:

Fig. 9. Web Service Request

Fig. 10. Web Service Response

XII. FUTURE WORK

While experimenting we dealt with sampleamount of data which is comparatively small inamount hence, we plan to scale the system andfind and prepare scores for most popular cities likeNew York, Los Angeles etc. and feeding them intothe system so that safe score will be generated forthose routes. Additionally, we plan to enhance userexperience, application’s processing speed and buildsimilar application for IOS users.

XIII. CONCLUSION

Created a mobile application that recommendsdifferent routes between source and destination ac-cording to their safety score, which is devised fromalgorithmic computation on authentic big data byusing Hadoop Spark cluster processing. The factorsderiving safety score of different routes are Crimi-nal, Accident cases as well as number of Police andHealthcare services.

XIV. ACKNOWLEDGMENT

We would like to thank Dr. Milton Halem for hisdirection and encouragement over the course of thisproject and his teaching assistant Yin Huang for hislectures on Hadoop, Spark big data computation andcontinued support during the project.

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