Traffic information extracted from Bluetooth MAC scanner system

Transcription

1 Traffic information extracted from Bluetooth MAC scanner system *Porntep Puangprakhon 1) Thanat Nok-Iangthong 2) and Sorawit Narupiti 3) 1, 2) Department of Civil Engineering, Mahanakorn University of Technology, Bangkok, Thailand 3) Department of Civil Engineering, Chulalongkorn University, Bangkok, Thailand 1) ABSTRACT Bluetooth MAC Scanner (BMS) is the new emerging technology for traffic data collection. The concept of BMS in traffic data collection is simple. It scans and records MAC-ID and discovered time of the discoverable Bluetooth devices (BT) within its communication zone and feeds to data collection center. In this study, totally 40 BMSs have been placed within the police box at the intersections on urban roadway in Bangkok CBD for gathering the Bluetooth signal within its communication zone. The traffic conditions among two BMS locations which generally represented by travel time of vehicles can be directly extracted by matching the Bluetooth MAC-ID gathered from those stations. However, raw data from BMS system is generally contained noise and outliers from various causes. For that reason, the robust methodology for developing accurate segment travel time is crucial task. This article demonstrates the BMS data collection system and proposes frameworks for constructing segment travel time information from BMS system. 1. INTRODUCTION It is well recognized that travel time plays an important role in modern traffic information system particularly in the Advanced Travelers Information System (ATIS) (Yu et al. 2008), which has potential to provide crucial information to both road users and traffic operators and also leads to increase reliability of the overall road networks. The informed travelers can use travel time information in their pre-trip decision or rerouting their ongoing trips to avoid congestion. On the viewpoints of traffic operators, travel time information can be used as indicator for evaluating the efficiency of their traffic operational plans and also network performance. There are various approaches to collect traffic data from roadway network to form- 1,2) Lecturer 3) Associate Professor

2 up travel time information. In the past decades, research on traffic states and travel times study have been focused on data collected from traditional inductive loop detectors and probe vehicles system. For instance, using data from single loop detector (Wei et al., 2012; Chen et al., 2011; Coric et al., 2011; Jin et al., 2010; Dailey, 1999), dual loop system (Soriguera and Robuste, 2011; Rakha and Zhang, 2005), taxi probes (Herring et al., 2010; Wei et al., 2007), bus probes (Pu and Lin, 2009; Uno et al., 2009; Bejan et al., 2010; Vanajakshi et al., 2009), or test vehicles (Puangprakhon and Narupiti, 2015 and 2017; Jiang et al., 2009; Billings and Yang, 2006). Although the abovementioned systems are regarded as efficient approaches in traffic data collection, the requirement of mathematical and theoretical assumptions in converting data from loop detectors to segment travel time and the low penetration rates of probe vehicle in reality are the main drawbacks of those systems, respectively. With the advancement of technology, Bluetooth scanners are being considered as one of the promising techniques for transport and travel time data collection (Khoei, 2013). Recently, various studies on traffic information have been conducted by using Bluetooth scanners as the data collection devices, for instance, Wang et al. (2011) showed the promising results in travel time estimation while using BMS system compared to the travel time recorded from Automatic License Plate Recognition (ALPR) devices on both freeway and urban road. Bhaskar et al. (2013) have tested the BMS system on arterial roadway and showed the potential of BMS in providing urban traffic conditions. Despite, BMS system is considered as cost effective and efficient approach for gathering traffic data, the raw data from BMS system is generally contained noise and outliers from various causes. For that reason, the robust methodology for extracting noises and developing accurate segment travel time is crucial task. This article demonstrates the BMS data collection system and proposes frameworks for constructing segment travel time information from BMS system. This paper begins with details of BMS system and deployment locations, followed by definition of segment travel times and concepts of travel time estimation from Bluetooth data, framework for data cleansing and travel time estimation result. The results and main conclusions of the study are discussed last. 2. BLUETOOTH SCANNER SYSTEM 2.1 Bluetooth MAC Scanners (BMS) In this study, the Bluetooth MAC (Media Access Control) Scanners with the capability to scan and record Bluetooth MAC addresses from Bluetooth devices (each electronics device has their own unique identifier number) within the communication zone have been used as traffic data collection devices. Each of them comprises 5 main components which are (1) main board (RASBERRY PI 2 Model 2) which can run the Linux operating system to process and store the Mac address data, (2) Bluetooth adapter (Parini-UD 100), (3) antenna (TP link (9dBi), (4) router (TP-Link 3020 with 3g aircard) as the data transmission unit, the Bluetooth data which already store at the scanner will be send back directly to the server via aircard and (5) power supply. The assembly of BMS unit is illustrated in Fig. 1.

3 Totally of 40 BMSs have been placed within the police box near the intersections on urban road in Bangkok CBD for the purpose of steady and consistence power supply during day and nighttime, and also to detect the Bluetooth signal form electronics devices within its communication zone which is approximately 100 meters from the location of BMS (as recommended for traffic applications by Bhaskar and Chung, 2013). The installation locations of 40 BMSs are depicted in Fig. 2. Fig.1 Bluetooth MAC scanner and installation place Fig. 2 Locations of Bluetooth MAC scanners in Bangkok CBD

4 2.2 Data Collection System In this study, the inquiry cycle for each BMS has been fixed at 1 second. Data collected and stored by BMSs is saves in a table format as illustrated in Table 1; the first column represents the record number, second column is MAC-ID of Bluetooth device (known as vehicle ID) which comprises a sequence of twelve hexadecimal digits (six groups of two hexadecimal digits), third column is the detected date and time of BT devices, and forth column is the number of BMS that represents the location BMS on road network (as depicted in Figure 2). This data is available in the database for each BMS installed on road network. Table 1. Raw data from BMS Number BT MAC-ID Detected Time BT Scanners ID 1 AC:7A:4D:A3:E4:XX 4/2/2016 5:04: AC:7A:4D:A3:E4:XX 4/2/2016 5:04: AC:7A:4D:A3:E4:XX 4/2/2016 5:04: :D4:BD:D8:71:XX 4/2/2016 5:04: :D4:BD:D8:71:XX 4/2/2016 5:04: Remarks: In column 2, the last two digits are blinded due to privacy concerns 2.3 Data from Bluetooth Scanners The total MAC-ID and Unique MAC-ID which can be detected from 40 BMSs installed at intersections on urban roadway in Bangkok CBD during February 4-5, 2016 is presented in Table 2. As can be seen, at each BMS location there were plenty of total recorded of MAC data, these data can be grouped by the same ID to find the unique MAC (or number of vehicle). However, it can be noticed that at some locations e.g. at BMS 17 and 29, the BMS were failed to detect and record data compared to another ones. This could be happened due to various reasons such as from malfunction of software, hardware or BMS components, communication problems, or environmental constraints.

5 Table 2 Total Mac-ID and Unique Mac-ID from each Bluetooth Scanner Scanner # Data on 4/2/2016 Data on 5/2/2016 Total MAC Unique MAC Total MAC Unique MAC 1 147,480 5, ,445 5, ,689 2,218 23,438 2, ,172 5,147 63,910 5, ,453 5, ,822 5, ,840 4,039 71,976 4, ,794 5, ,560 5, ,969 1,505 9,089 1, , , ,107 8, ,833 8, ,014 7, ,371 7, ,118 5, ,036 5, ,880 5,123 96,723 5, ,326 5,306 62,534 5, ,843 7, ,968 7, ,546 2,892 18,217 2, ,700 7, ,109 7, ,206 4,929 66,918 5, ,121 2,312 88,497 4, ,501 6, ,559 7, ,873 4,555 63,682 4, ,059 5,454 97,082 5, ,598 1,919 61,141 4, ,580 3, ,968 3, ,090 4,873 84,430 5, ,791 6,724 97,477 7, ,925 4,751 58,901 4, ,842 4,521 62,266 4, ,656 4,785 48,810 2, ,559 2,770 93,819 2, ,965 5,953 78,813 6, ,886 4,859 66,398 5, ,445 1,804 32,685 1, ,998 4, ,814 4, ,739 3, ,317 4, ,780 3,239 40,548 3, ,251 7, ,293 7, ,199 3,914 63,106 3, ,363 4,714 80,117 4,513

6 3. FRAMEWORK FOR EXTRACTING TRAVEL TIME FROM BMS DATA In this study BT data was collected from BMS system installed on urban roadway in Bangkok CBD. Details of recoded data are aforementioned in previous section. The framework for segment travel time estimation is depicted in Figure 3 which includes the following steps. Bluetooth Matching Removing the questionable IDs Filtering Removing the questionable trips (Upper and Lower thresholds) Removing the outlier trips (MAD) Smoothing Travel Time Estimation Fig. 3 Framework for travel time estimation from BT data Data Matching: Due to each BT device can enter the same BMS zone multiple time per day therefore the trip exit time of each BT device from BMS zone are need to be extracted. In our study, the 30 minutes time gap is set as the threshold in separating trips which means the record is considered as last detected time of trip at BMS when there are no other record from the same BT device can be discovered in that BMS zone within 30 minute from last detected time (if record from same device is discovered at same BMS after 30 minute from last detected time it will be considered as another trips). Segment travel time or travel time from upstream to downstream BMSs can be calculated by matching and find the time difference of the same BT device recorded at those BMSs. Data Filtering: The objective of filtering process is to remove the questionable and outlier data from the samples. This process comprises 3 tasks as follows; Removing questionable ID: After the matching process, travel times from questionable BT devices such as the cloned devices (from logistic company

7 etc.) with same ID that can be found at several locations in the same time were removed. Removing questionable trips: This step aims at removing outlier trip by setting upper and lower boundaries for trip time to track the trip that faster and slower than usual trips. For instance, the trips those travel faster than speed limit available on roadways, or use another route instead of direct route for passing the distance between two BMSs, or from stopping vehicles. Removing outlier trips: By applying Median Absolute Deviation (MAD) method (Gather and Fried, 2004; Khoei, A. M. et al., 2013) which is a robust measure of the spread out of data. Let travel time values as univariate data, the MAD is the median of the absolute deviations from the data's median. MAD median X median(x) (1) i k MAD (2) where k is a constant scale factor which depends on type of the distribution (in case of normal distribution k is taken to be ). In this research the 15 minutes moving time window was selected for calculating MAD. The suggested k value from previous study is from 1 to 5. For this study the k = 2 is applied in Eq. (2). The upper and lower boundaries for filtering outlier trips can be calculated by adding and subtracting from MAD. The trip travel times beyond these boundaries are considered as outlier values and removed. Data Smoothing: This step aims to reduce or eliminate short-term volatility and extract real trends and patterns from travel time data by applying exponential smoothing. s t y ( 1 ) s 1, t 0 (3) t t where s t is the output of the exponential smoothing, is the smoothing factor, y t is recorded travel time at time t, ( 1 ) is the damping factor. In this study the value of 0.55 was applied regarding its smallest mean square error. Travel time estimation: In this study we have divided time of day into 96 intervals (15 minutes per interval) from :00-0:14:59, 0:15:00-0:29:59,, 23:45:00-23:59:59. The travel time for interval i ( TT i ) can be calculated by averaging all the travel time of trips within that interval as follow: n 1 TTi s t for i 1, 2, 3,..., 96 (4) n t 1

8 4. TRAVELTIME ESTIMATION RESULTS The travel time for a road segment from BMS 15 to BMS 16 is presented in Figure 4. Figure 4(a) and 4(b) depicts the raw data recorded from individual vehicle and the filtered data from proposed framework, respectively. Figure 4(c) presents the estimated travel time for each time interval (15 minutes/interval). These results indicate the potential of BMS in providing travel time or traffic state information on urban road network Travel time (sec) Travel time (sec) :00 6:00 9:00 12:00 15:00 18:00 21:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 Time of day (a) Time of day (b) 2000 Travel time (sec) :00 6:00 9:00 12:00 15:00 Time of day (c) 18:00 21:00 Fig. 4 Travel time from BMS system (a) travel time before filtering (b) travel time after filtering (c) estimated travel time for each interval

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