Analysis of Place and Traffic from a Large Volume of GPS Data

Transcription

1 Analysis of Place and Traffic from a Large Volume of GPS Data Seulki Lee Department of Computer Science The University of North Carolina at Chapel Hill 1. Introduction There are three things that matter in property: location, location, location. Lord Harold Samuel Locations and whereabouts of people always have been the most sought-after knowledge in our history. Especially, the identification of popular places and roads has been an endless source of interest for various areas from public city planning to commercial development. One of the best ways to analyze popular places and road traffic is to utilize GPS system which can track each individual s moving route in real-time. Although this approach is the easiest, it has some practical issues like privacy and GPS deployment problem for every individual. Alternatively, we can install GPS system to taxis and record their movements since a person or group of people less than five usually take a taxi for their movement. GPS data from a taxi can represent a personal moving route in contrary to public transportation such as bus or subway which conveys a large number of people at the same time. Such transportation systems cannot track an individual s moving route since they only travel back and forth between fixed stations. In order to investigate the usefulness and potential of this approach, we analyze a large amount of taxi GPS data. Especially, we focus on the case of Porto, the city of Portugal. The city of Porto conducted an interesting experiment to equip 442 taxis running in the city with GPS system and recorded their GPS coordinates for one year. Basically, popular places and road traffic are main focus in this work since they are the most interesting result people want to know as mentioned at the very beginning. In this report, we first introduce the source data set in the section 2 and present the analysis results in the section 3 and 4. Then, the section 5 describes the methodology of how we analyzed the data and interpret it to meaningful information. Finally, the section 6 makes a conclusion. 2. Data Set Source Data Set Source data set is from the University of Porto, Portugal[1]. It is accurate data set describing complete year (from 01/07/2013 to 30/06/2014) of the (busy) trajectories performed by all the 442 taxis running in the city of Porto, in Portugal. Each data sample corresponds to one completed trip. It contains a unique identifier for each trip, a unique identifier for the taxi driver and a list of GPS coordinates. Each pair of coordinates is identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip's destination while the first one represents its start. The total number of data sample is about 1.5 million travels and each travel consists of average of 50 GPS coordinates. Unfortunately, the data set does not provide any time information. An example of GPS data for one trip is shown in the following. [ , ],[ , ],[ , ],[ , ] The first and the last coordinates indicate the point of departure and destination respectively. The middle coordinates between them indicate the route that a taxi takes between the point of departure and destination.

2 2.2. Place and Road Data Unfortunately, a GPS coordinate itself in the form of [LONGITUDE, LATITUDE] does not provide any information about place and road. In order to obtain place and road information, they need to be fed to Google Place APIs[2] which translate a GPS coordinate into meaningful place information. An output data(json) from Google Place APIs looks like the following. Figure 1: Translation from a GPS coordinate to place information is shown here. Each place information consists of location, icon, id, name, opening hours and so on. Not all information is necessary for our work and the information used in this work is highlighted with blue. The information necessary for our analysis is colored with blue. Since Google Place APIs returns several number of place information(ranging from one to few hundreds) per GPS coordinate, an actual point of departure and destination should be chosen carefully from the given list of places with some insights before start analyzing. After processing raw JSON data with few more steps, the final results can be obtained. The detailed analysis process and methodology is described in section Analysis Result of Popular Places First, the final analysis results are presented in the section 3 and 4, and then the methodology about how the results were obtained is provided in the section Points of Departure and Destination The most popular places that people depart and visit are clarified from total 48,583 places. The top 100 popular points of departure in the city of Porto are shown in figure 2 and the 100 most popular destinations are shown in figure 3. We can see that the points of departure are located in some concentrated area of the downtown of Porto while the destinations are divided into two large areas (1) Francisco Sá Carneiro Airport and (2) downtown of Porto. Let s look at them in more detail. Table 1 lists the top 20 popular points of departure and destination. The number one departure point is the taxi stop located in front of Campanha train station and other popular points are also taxi stops, airport and train stations except a hotel ranked at 19th and a café named Costa Coffee with rank 20th. The café Costa Coffee is the only café in the list since it is located near Square of Lisbon which has a large floating population. It is a reasonable result since a large number of taxis are usually waiting passengers at a taxi stop and starting trip from there. Interestingly, the number one destination is a car repair shop. It seems unreasonable at first, but it turns out that near the repair shop, there is also a huge taxi stop. Actually most of top 20 destinations are taxi stops with some exception of train station, finance center and a hotel.

3 Figure 2: Top 100 popular points of departure in downtown of Porto. The number in the circle indicates the rank of the place. We can see that popular places are concentrated in some areas. Figure 3: 100 most popular destinations around the city of Porto with a large map(left) and in downtown of Porto with smaller map(right). The red circle in the left map indicates Francisco Sá Carneiro Airport and blue box indicates downtown of Porto. The number also indicates the rank of the place.

4 Table 1: Top 20 popular points of departure and destination are listed here. The most popular places are taxi stops Bar, Café and Restaurant The analysis result that most of the popular places are taxi stops is acceptable but not interesting. The purpose of analyzing a large volume of taxi GPS is more than just showing that the most popular destinations are taxi stops and airport. So another analysis was conducted to investigate where is the most popular eateries including bar, café and restaurant. Figure 4 shows the top 100 popular bars, cafés and restaurants in downtown of Porto. Red circles show bars, blue circles show cafés and green circles show restaurants. Similar to the top 100 list of general destinations as shown in figure 3, the majority of eateries are concentrated on some arears. Table 2 shows the top 20 lists of popular eateries. One interesting observation is that hotels named Hotel HF such as Hotel HF Fénix Porto, Hotel HF Ipanema Park and Hotel HF Ipanema Porto are ranked at high positions for all three categories. In this case, only for eateries are searched but any other type of place such as hospital, church or bank can be also analyzed. It is possible because we can extract information about the type of place from GPS coordinates by using Google Place APIs. For example, some large residential areas in the city of Porto can be searched by extracting the places with type of neighborhood and sorting them with popularity. It is a valid analysis since we can assume that most passengers take taxis in front of their home to arrive their destinations. Plus, more in-depth analysis such as in which area people take taxis more than other area can be also provided. Another example would be to identify the moving route from one specific type of place to another type of place. For example, the question such as which bars are popular ones where people visit after finishing their dinner? can be answered Popular Routes The results presented in the previous subsections only provide the analysis about the points of departure and destination separately. Although they provide meaningful information about popularity in terms of individual places, popular route analysis(a pair of departure point and destination) between two points of places should be also analyzed. Figure 5 shows the top 10 popular routes around the city of Porto. As same as figure 3, there are two maps in figure 5. One is a larger map including both Francisco Sá Carneiro Airport and downtown of Porto and the other one is enlarged version for downtown of Porto. Table 3 shows the top 20 popular routes and they can be grouped into several routes that connect some popular arears such as a huge roundabout named Rotunda da Boavista, Campanhã train station and Francisco Sá Carneiro Airport. Since figure 5 and table 3 just show popular routes sorted with traffic volume, we need to look at road traffic with in-depth analysis such as road speed and single taxi travel. We take a look at them in more detail in the next section.

5 Figure 4: 20 most popular bar, café and restaurant. Red circles are bars, blue circles are cafés and green circles are restaurants. Table 2: 20 most popular bars, cafés and restaurants. 4. Analysis Result of Road Traffic For road traffic analysis, total 11,417 road names are obtained from the raw GPS data and they are analyzed to understand the traffic condition of the city of Porto Road Traffic and Speed The middle GPS coordinates positioned between the first one(departure) and the last one(destination) are all about road information. The volume of road GPS data in a single taxi travel is much larger than place information since a single taxi trip has average of 50

6 Figure 5: Top 10 popular travel routes(moving route between departure and destination) near Francisco Sá Carneiro Airport and in downtown of Porto. A is a point of departure and B is a destination. Only 10 routes are depicted here since Google Maps only allows maximum 10 routes for one map. Table 3: Top 20 popular routes(departure and destination). number of GPS coordinates and most of them except two(departure and destination) are road information. By analyzing this huge volume of data between departure and destination, more detail information such as travel range, speed of the road can be provided. The traffic condition in terms of speed in downtown of Porto is identified in figure 5. We can see that most of the roads are red and the speeds of roads are increasing as they are staying away from the center of the city. The top 20 heaviest traffic roads and top 20 slowest roads are also separately listed in table 4. The top 20 slowest roads are selected from the top 100 heaviest traffic roads since a slow road with small traffic is less important in terms of traffic planning Single Taxi Travel So far, the systemic analysis has been done for places and roads. However, a single trip analysis is also useful. In this section, we look at the analysis of single taxi travel. Total 1.5 million taxi travels were analyzed independently to obtain travel range, time and speed

7 Figure 5: The speed of 300 popular roads. Red line: 0~15m/h, orange line: 15~30m/h, yellow line: 30~45m/h, green line: above 45m/h. Table 4: Top 20 heavy traffic road(a) and top 20 slowest speed road among top 100 heavy traffic road(b). for each taxi travel. This is possible because travel range can be calculated between two GPS coordinates and each GPS coordinate is recorded for every 15 seconds. A distribution graphs of travel range, travel time and travel speed per a single taxi travel and their averages are shown in figure 6. For average, a taxi moves 4.19 miles for seconds with the speed of 18.16m/h. This is a reasonable result since area size of Porto is mi² and most of the roads are small alleys. 5. Methodology This section provides the detailed description of how the analysis results presented in section 3 and 4 are produced Data filtering

8 Figure 6: Distribution graphs of travel range, time and speed of each taxi trip are shown in (a), (b) and (c). Average, max, min value of travel range, time, speed are shown in table (d). Basically a taxi in the city of Porto can travel to anywhere either inside or outside of the city. Some taxis travel to the outside of Porto and some of them cross the border of Portugal to bring their passengers to Spain. That is too large area for our analysis. Therefore the size of area that we want to analyze is specified and the GPS coordinates only limited to that area are first selected from raw data. In order to do it, the city hall of Porto is selected as a central point and a square of size 20 mi² is chosen for target area. (Step 1) Any taxi travel that has GPS coordinate outside of the square is removed. Step one not only limits the area to be analyzed but also filters out wrong GPS data. For example, GPS coordinates that indicate the Atlantic Ocean or North America are removed automatically with step one. After extracting the travels only in the area of Porto, some incomplete travels should be also filtered out. First, we assume that a taxi trip less than one minute is incomplete or meaningless and filter out them. Since a GPS coordinate is recorded for every 15 seconds, taxi travels only with more than or equal to 4 GPS coordinates are survived. (Step 2) Any taxi travel that moves less than one minute is removed. Next, only the travel distance less than 20 miles are included. Even though the square of size 20 mi² filters out most travels more than 20 miles, there still remain some taxis moving more than 20 miles. Plus, the travel range below one mile is also removed since we assume that the travel data less than one mile is incomplete or meaningless. The travel distance between two coordinates can be calculated by using the approximate formula II as in the following. DISTANCE 69.1 ( LAT LAT ) LAT 2 1 DISTANCE 69.1 ( LONG LONG ) cos( LAT / 57.3) LONG DISTANCE ( DISTANCE ) ( DISTANCE ) 2 2 LAT LONG (1)

9 Figure 7: (a) A taxi travel with 5 GPS coordinates. The blue dots indicate each GPS coordinate. The blue lines indicate the accumulated distances between every adjacent pair of coordinates while orange line shows the distance between the point of departure and destination. The travel range summed up with four small distances is more accurate than the travel range obtained from only departure and destination point. (b) The place Livraria Lello gets the highest weight since it is closest from the center among the three places in the circle with radius 100 meter. After formula (1) is used for every adjacent pair of coordinates, the distance for each pair is calculated. Total travel range is then obtained by summing up all distances. For example, if a taxi travel has 5 GPS coordinates, 4 distances are calculated as shown in figure 7-(a). So, step three is done. (Step 3) Any taxi travel that moves less than one mile or more than 20 mile is removed. Now, we have only valid taxi travel GPS data for the city of Porto. However, they are still GPS coordinates which cannot provide any meaningful information about places and roads Google Place APIs In order to translate a GPS coordinate into place and road information, Google Place APIs is used. Especially, we extract place information instead of address since address itself does not provide any additional place information. By transferring a GPS coordinate to Google Place APIs, the useful information about place in the form of JSON is obtained as mentioned in section 2. However, the problem of Google Place APIs is that Google only permits 150,000 queries per day, which means our data with size of 1.5million travel with average of 50 GPS coordinates per each travel cannot be translated in a short period of time. In order to accelerate the speed of translation, we used two Google accounts and total 300,000 GPS coordinates have been translated per day for two weeks. The first account was used to translate GPS coordinates to road information and the second one was used to translate GPS coordinate to place information. Another problem is that one GPS coordinate translation returns a large number of place information around the coordinate since the accuracy of GPS is not reliable and Google also cannot match a coordinate to an exact place. Therefore, we need to estimate which place is the exact destination of the passenger from this list. This process is explained in the next session. (Step 4) Each GPS coordinate is translated to a list of place information by using Google Place APIs. This is our base data for place analysis. We are finally ready to start analysis Place Analysis As same as other GPS coordinates, a destination coordinate has a large number of place information around it that are obtained from Google Place APIs. Thus, the right estimation of final destination for a passenger from the place list is crucial part for our analysis. In order to conjecture the final destination correctly, we have to make several assumptions.

10 First, we assume that a passenger would not walk more than 200 meters after taking off the taxi. The distance of 200 meters is based on the average distance between two intersections in downtown of Porto. This assumption seems reasonable since people usually tend to get off the taxi at the nearest intersection of their destinations if they cannot get off at their exact destinations. (Step 5-1) Places only within 100 meters from the destination GPS coordinate are extracted from the place list. The second assumption is that a place is more likely to be a final destination if the place is closer to the destination coordinate. For example, if a bar is 80 meters far from the destination coordinate and a restaurant is 20 meters apart from it, we can assume that the probability that the passenger s final destination would be the restaurant is higher than the bar since the passenger would prefer the taxi to stop as close as her final destination. (Step 5-2) Every distance between places in the list and the destination coordinates is calculated by using formulation (1). Based on these two assumptions, every place around radius 100 meters centered at the destination coordinate is searched. Then, each place is given a weight which is inversely proportional to the distance from the destination coordinate. For example above, the bar will get 1/80 weight and the restaurant will have 1/20 weight. Based on this weight, we can conjecture the place with the highest probability that a passenger s real destinations is there as shown in figure 7-(b). (Step 5-3) Calculate weights for each place in the list and select the place with the highest weight as the final destination. The weights of destination places are summed up to produce popularity for a place. By using this popularity, place analysis presented in section 3 can be done with any type of place such as bar, café or restaurant. (Step 5-4) Do place analysis! Same process from step 5-1 to 5-3 should be done for the points of departure to guess the real departure place from the place list Traffic Analysis In order to obtain road information, all the GPS coordinates between the departure and destination should be translated to road names. Unlike finding a passenger s final destination, the list of road names a taxi takes can be easily obtained. By specifying query type as route, only route information can be obtained when GPS coordinates are fed to Google Place APIs. As a result, the estimation process which is done for selecting the final destination from a large number of candidate places is unnecessary here. However, there is a problem that the volume of GPS coordinates for road information is enormous comparing to departure and destination information. That is because GPS coordinates are recorded for every 15 seconds and every coordinates except departure and destination should be translated to road information by using Google Place APIs. 75million queries(1.5million 50 coordinates) cannot be translated in a short time since Google lets public users use only 150,000 APIs per day. Therefore, we decided to sample 2% of the data for our road analysis. It is around 30,000 travels with 1.5million GPS coordinates. (Step 5-5) Translate each GPS coordinate of step 3 to road information by using Google Place APIs with type route. After obtaining road information, the crowded road names are obtained by sorting them with the number of taxis. Plus, the travel distance, time and speed also can be calculated since we know the coordinates are recorded for every 15 seconds. Travel distance, time and speed for each taxi travel are also easily obtained with the same way. (Step 5-6) Do road analysis! 6. Conclusion A large volume of GPS data from taxis can be utilized for analysis of floating population and road traffic. In this work, total 1.5 million taxi travels in the city of Porto were analyzed to prove that this assumption is valid. In detail, one taxi travel consists of average 50 GPS coordinates and each coordinate was translated into information about places and roads by using Google Place APIs.

11 The translated data was first analyzed for identifying the popular places. Not only the entire list of popular places but also the lists with specific types of popular places such as bar, café or restaurant were analyzed. Most of the popular places were taxi stops, airport and train stations with some exceptional cases of café and hotel. Most popular routes between two places were also provided to understand the actual moving routes between two points. For the road traffic analysis, most crowded roads were identified by matching the middle GPS coordinates to names of road. The total travel time for each road and the travel speed were also obtained from the fact that GPS coordinates were recorded for every 15 seconds. Average travel range and speed for each taxi travel in terms of individual moving route were also identified. All the analysis in this work are visually presented on maps(google Maps) in order to help readers understand the results more efficiently. Combined with the visual representations on maps, the detail analysis in this work shows that it can provide reasonable analysis to make important decisions such as city planning or business strategy for the future. 7. Reference [1] Taxi Service Trajectory (TST) Prediction Challenge [2] Google Place APIs.