Integrating Traffic Data Stream with Public Route Planning Algorithm
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Integrating Traffic Data Stream with Public Route Planning Algorithm and Personalizing it for the End Users Soham Sinha Sankalp Prabhakar University of Alberta University of Alberta Edmonton, AB, Canada Edmonton, AB, Canada [email protected] [email protected] ABSTRACT gestions are often not accurate. We want to mitigate these Route suggestions for public transportation has been inte- two limitations to improve the routing suggestions. grated to different mapping services such as Google Maps or Bing Maps. However, those suggestions are often found to Real-time and historical traffic conditions of roads have al- be unreliable for the end-users, mainly because of two rea- ready been well studied in the literature [14, 22, 6, 20, 31, sons. Firstly, these route-finding techniques do not consider 21, 19, 24]. Some industry-standard applications [18, 3] traffic conditions on the roads. Secondly, routes are gener- show real-time and historical traffic conditions in their soft- alized for every user, completely ignoring their commuting ware systems as well. However, the traffic information both patterns. real-time and predicted, has only been used for private car routing suggestions. This has been largely ignored in public We propose an architecture with a modified routing algo- routing in all the applications that we know of. This makes rithm that aims to solve both the problems. Our system public routing suggestions unreliable. incorporates both real-time and historical traffic-data into public-transit routing framework. We also personalize tran- The improvement of route suggestions in public transit is sit routes for users by analyzing their commuting behavior. important because it can increase the reliability in public By experiments, we show that our system significantly im- transport among people. Reliance on public transportation proves the quality of route-suggestions compared to state- translates into many benefits as described earlier. As the of-the-art techniques with minimal overhead (as low as 20 current routing suggestions for public transits do not con- ms for a 3 km long route). sider delays due to traffic, one may easily get delayed reach- ing his/her destination taking the public buses. This is re- ally inconvenient for the end-users. Another common issue 1. INTRODUCTION is that the state-of-the-art applications do not take into ac- Public transportation is one of the important means of com- count the different walking-speeds for various users; rather, munications around the globe. Wider adoption of public they just provide a generalized result for every user. To the buses, trains etc. is encouraged by the governments for a best of our knowledge, we are the first to propose a system number of reasons. Increasing use of public transport can that can integrate both real-time and historical traffic feed help to alleviate environmental problems, for example by ef- along with the end users’ commuting pattern for routing ficient usage of fuels. There are efforts in the research com- suggestions in a public transportation system. munity as well to promote public transit. As part of that on- To briefly put, our contributions are: going effort, a standard format is designed to represent the public transportation, its schedule and related information. 1. Analyzing real-time and historical traffic data and in- This standard is named General Transit Feed Specification tegrating traffic-speed into the public routing algo- (GTFS) [2]. GTFS-formatted data has enabled software rithm. systems to manipulate public transit data efficiently. The standardization of public transportation data in the form of 2. Analyzing the end users’ commuting pattern and de- GTFS feed has opened up numerous possibilities of visual- riving users’ average walking-speed to provide person- izations, optimizations and improvements related to public alized suggestions of public-transit routes. transit [8]. Many companies which provide map-related ser- vices, have adopted GTFS feed into their system [18, 23, 3, The paper is organized as follows. In the next section, we 5]. Most of these companies give public routing suggestions describe the relevant works and previous research done re- through various interfaces (online, mobile application etc.), lated to this problem. In Section 3, we formally describe mostly following Dijkstra’s Algorithm for shortest path [11]. our problem. We demonstrate the architectural overview Our work revolves around public routing suggestions and its of our solution in Section 4. Some of the implementation two main limitations. Firstly, the current implementations details are mentioned in Section 5. Section 6 demonstrates of public routing suggestions totally ignore the traffic con- our implementation for future traffic prediction and how it ditions of roads. Secondly, the suggestions are generalized integrates with our system. In Section 7, we present the and same for every users. They don’t account for users’ com- conducted experiments and compare with the existing tech- muting behavior in terms of their walking or cycling speeds. niques. Finally, we conclude and discuss future works in the Because of these two drawbacks, public transit routing sug- last section. 2. RELATED WORK 3. PROBLEM DEFINITION GTFS [2] was standardized around 2009 by the commu- In this section we formally describe the problems in pub- nity of public transit agencies, developers with primary help lic route suggestions. The problem can be divided into two from Google. Since then, many applications [18, 23, 3, 5] distinct parts: 1) Incorporating traffic feed in public route have integrated it to their systems to suggest public routes. suggestions, 2) Incorporating end user’s commuting behav- Google Maps [18], being the frontier of all these applica- ior. tions, suggests public transportation routes to its web-based and mobile-based users. Google Maps and other applica- 3.1 Incorporating Traffic Feed tions collect data in the form of GTFS from various agencies Public transit routing suggestions consist of several trips in around the globe and integrates it to their mapping system. buses, trains or metro railways. The routing suggestions However, they just work on the agency-provided data to give are based on the schedules provided by the public trans- routing suggestions from a source location to a destination. portation agencies in form of GTFS. These agencies make They do not consider any probable delay due to traffic. a generalized schedule, ignoring the probable traffic on the roads. Even if they care for the probable road-traffic, they On the other hand, the recent development of mobile lo- may not be accurate. As the traffic-information on the roads cation tracking systems have helped various software sys- is mainly ignored, the delay due traffic is also not accounted tems to keep track of real-time traffic information on their for in the routing suggestions. This makes the suggestions servers. Google keeps track of the real-time traffic levels [15] imprecise. Our challenge is to incorporate the available traf- by monitoring the users’ mobile devices [16] and also using fic information of roads into the routing suggestions, so that other services such as Waze [29]. Although this traffic infor- we can make them more realistic. mation is utilized to suggest optimized routing for private cars, it has not been incorporated in the domain of public 3.2 Incorporating End Users’ Commuting Be- transportation. Part of our work aims to mitigate this chal- lenge. Apart from real-time traffic information, there are havior previous research works [24, 31, 26, 10] which try to exploit One important aspect of public route suggestions is that the historical traffic data to predict future traffic-conditions. they consist of steps which involve walking. For instance, These are complementary to our work because a higher ef- a user must walk to a nearby bus-stop to begin his/her ficiency in traffic prediction will only improve our routing journey. In all the current state-of-the-art applications, the suggestions. walking-speed is assumed to be same for every user. In real- life, this is certainly not the case. For example, a fairly old There has been research on real-time tracking of public trans- lady is most likely to have a different average walking speed portation. Thiagarajan et. al. has shown how crowd can be than a young gentleman. Existing applications ignore this utilized to track public buses, underground metro rails etc fact, and it leads to poor suggestions. Our challenge is to [27]. Bast et. al has recently shown how the real-time move- understand the commuting patterns of end users and utilize ment of public transportation system can be integrated and it in route-suggestion. Now-a-days, different kinds of sen- visualized in maps [7]. TransitApp [5] also shows real-time sors are pretty common in mobile phones. We can use the bus status in their mobile application. However, none of data related to user-movement recorded by the sensors and these approaches try to suggest routes considering the delay integrate it into the public routing algorithm. caused by traffic. In our opinion, the reason for not ad- dressing this problem is the high time-complexity to process The above problems in public transport are the motivations the large amount of real-time public transit data. It is also for our work. We try to resolve both the challenges and cost-expensive. For example, Edmonton Transit System (a present a unified solution for public route suggestions. public bus agency for the city of Edmonton) [13] recently planned to deploy GPS devices and other advance features 4. ARCHITECTURE on their buses by expending a hefty 13.9 million Canadian We provide an overview of our architecture in this section. Dollars. Compared to these expensive solutions, our pro- We have divided our system into certain interrelated generic posed system has low overhead in terms of time-complexity components.