An Observational Study of the Characteristics of Taxi Floating Car Data Compared to Radar Sensor Data TONY KARLSSON

Transcription

1 An Observational Study of the Characteristics of Taxi Floating Car Data Compared to Radar Sensor Data TONY KARLSSON Master of Science Thesis Stockholm, Sweden 2012

2 An Observational Study of the Characteristics of Taxi Floating Car Data Compared to Radar Sensor Data TONY KARLSSON DD221X, Master s Thesis in Computer Science (30 ECTS credits) Degree Progr. in Computer Science and Engineering 270 credits Royal Institute of Technology year 2012 Supervisor at CSC was Jens Lagergren Examiner was Anders Lansner TRITA-CSC-E 2012:051 ISRN-KTH/CSC/E--12/051--SE ISSN Royal Institute of Technology School of Computer Science and Communication KTH CSC SE Stockholm, Sweden URL:

3 Abstract In Stockholm, each taxi from a studied taxi company is equipped with a Global Positioning System (GPS) device, transmitting its GPS position approximately once every minute. With the help of a set of road segments representing the road network and a map-matching algorithm, the GPS positions are map-matched to the road segments, to determine the route each taxi is driving on the road network. With the help of the map-matching algorithm the speed for each road segment the taxi is driving on in its route is calculated. The goal of this study is to investigate the relationship between the speed calculated from the taxis with the speed generated from the radar sensors located on parts of the E4 motorway in Stockholm. The problem is that radar sensors measure the speed at a fixed point on the road network, and the speed from the taxis are measured between two points on the road network and are therefore not directly comparable. The data from the radar sensors and taxis are first analyzed, and then aggregated into 5 minutes periods for 7 days in July, To investigate the relationship between the two data sets the average difference of the speed from the taxis and the radar sensors are analyzed under different conditions. The average difference of the speed is analyzed during different times of the day, with different levels of traffic congestions and for different number of taxi cars passing each radar sensor. Some statistical relationships between speed calculated from the taxis and the speed from the radar sensors were found. And several new factors that could have an impact on the results were identified. For speed of radar sensors and taxis below 100 km/h, a statistical relationship between the speed from the taxis and the speed from the radar sensors was identified. The study conducted was an observational study, therefore the certainty of the results are unknown and further studies are needed to identify the factors that can affect the results. A controlled experimental study need to be conducted to be able to draw any conclusions of a casual relationship between the two data sets. The flow from the taxis at the radar sensors are also compared to the estimated flow from the radar sensors and the penetration rate of taxis that are passing the radar sensors were calculated as 0.5%.

4 Referat En observationsstudie om egenskaperna av trafikdata genererad av taxibilar jämfört med radarsensordata Varje taxibil från ett studerat taxibolag i Stockholm sänder ut sin Global Positioning System (GPS)-position ungefär varje minut. Med hjälp av länkar som representerar vägnätet och en algoritm är det möjligt att para ihop GPS-positionerna med länkarna och få fram vilken väg varje taxibil kört på. Därefter är det möjligt att beräkna hastigheten för varje länk, varje taxibil körde på under sin färd. Målet med denna studie var att undersöka sambandet mellan hastigheten av länkarna, beräknat med hjälp av taxibilarna, och hastigheten från radarsensorer som sitter utplacerade på delar av bland annat E4:an i Stockholm. Problemet är att radarsensorer mäter hastigheten på en punkt på vägen, till skillnad från taxibilarna, där hastigheten beräknas för en länk som består av en sträcka mellan två punkter på vägnätet. Först analyserades data från radarsensorerna och taxibilarna, sedan aggregerades data från sju dagar i juli, 2010 i 5 minutersintervall för att underlätta jämförelsen mellan de båda datamängderna. För att undersöka sambandet mellan hastigheten av länkarna, beräknad med hjälp av taxibilar och hastigheten från radarsensorer, undersöktes hur hastighetsskillnaden mellan dem ändrades under olika förutsättningar i trafiken. Hastighetsskillnaden undersöktes under olika tider på dygnet, under olika grader av trängsel, och beroende på hur många taxibilar som passerade en radarsensor i ett visst aggregeringsintervall. Vissa samband mellan hastigheten från taxibilarna och hastigheten från radarsensorerna kunde hittas men det visade sig att det fanns andra faktorer som kunde påverka resultatet. För hastighetsmätningar under 100 km/h, såg det ut att finnas ett statistiskt samband mellan hastigheten från taxibilarna och radarsensorerna. Storleken och tillförlitligheten på detta statistiska samband gick inte avgöra. Eventuella faktorer som kan påverka jämförelsen av hastigheten behöver identifieras och ett kontrollerat experiment behöver sedan utföras för att se hur de olika faktorerna påverkar resultatet. Antalet taxibilar från det specifika taxibolaget i jämförelse med det uppskattade antalet fordon som passerade radarsensorerna beräknades till cirka 0.5%.

5 Contents 1 Introduction Background Questions Goal and Objective Background Theory Traffic Sensor Technologies Fixed Point Sensors Floating Car Data Comparison Methodology Complete Roadway Road Segment by Road Segment Fixed Point Sensor and Fixed Point Sensor Fixed Point Sensor and Road Segment Summary I Data Preparation 15 3 Traffic Data Sources Stockholm Motorway Control System Radar Sensor Data Preparation Radar Sensor Data Analysis and Characteristics Road Segments Taxi Floating Car Data Floating Car Data Analysis and Characteristics Comparison Preparation Aggregation Methodology Aggregation of Radar Sensor Data Aggregation of Floating Car Data Association of Road Segments and Radar Sensors Characteristics of the Association

6 II Comparison 33 5 Statistical Background Regression Analysis Causality Observational Study Comparison Methodology Average Taxi Penetration Rate Taxi and Traffic Average Speed Comparison Certainty on all the Traffic Certainty during Time of the Day Certainty during Congestion Results Taxi Penetration Rate Certainty of Speed Measurement of Taxis Certainty of Speed Measurement during Different Hours of the Day Certainty of Speed Measurement of Taxis during Congestion Discussion and Conclusion Discussion Conclusion Future Work Bibliography 54 Appendices 55 A List of Gantries 56 B Taxi Penetration Rate 61 C Certainty of Speed Measurement 65

7 Chapter 1 Introduction The study conducted as a part of a master s thesis was initiated by the Division of Traffic and Logistics, of the Department of Transport Science, at the School of Architecture and the Built Environment, at the Royal Institute of Technology (KTH). The Division of Traffic and Logistics is doing research on Intelligent Transportation Systems (ITS). 1.1 Background In the field of ITS there are various methods to measure the average speed and flow of traffic on the road network. The purpose is to determine the level of congestion, travel time and average speed at fixed points or on parts of the road network. One measurement method are fixed point detectors such as loop detectors and radar sensors, measuring the average flow and average speed of the traffic at fixed points on the road network. Another type of measurement method is based on floating car data, collected from a vehicle fleet such as a taxi fleet or a bus fleet. In the case of a taxi fleet, the floating car data is collected from the dispatch system of the taxis which are sending their Global Positioning System (GPS)-position to central dispatch for the taxi company. Since taxis has to adopt their speed to the general traffic, the taxi cars can be seen as floating along the traffic and therefore be used as an approximation of the general traffic. In Stockholm each taxi from a studied taxi company is equipped with a dispatch system including a GPS device, which is sending its GPS position and timestamp approximately once every minute to central dispatch. Central dispatch uses the GPS positions of all the taxis to locate the nearest taxi to a customer when the customer is calling central dispatch for taxi service. The Division of Traffic and Logistics is receiving a copy of this data as consecutive GPS positions of each taxi. The consecutive GPS positions for each taxi is map-matched to a digital road network using a map-matching algorithm, producing the most likely route each taxi was driving on the road network. As a part of the Motorway Control System in Stockholm there are a number of 1

8 fixed point radar sensors located on the E4 motorway in Stockholm. Each radar sensor is measuring the average speed of the vehicles passing by each minute. The radar sensors are also measuring the flow, the number of vehicles passing by the radar sensor each minute. The Division of Traffic and Logistics is receiving a copy of the radar sensor data as well. Before this study was started the radar sensor data had not been analyzed at the Division of Traffic and Logistics and its characteristics was unknown. The radar sensor data and the floating car data are two different kind of data sets. Each radar sensor is measuring the average speed of vehicles passing by a fixed point on the road network. But the taxi floating car data is measuring the speed between two points on the road network and the radar sensor data and floating car data are therefore not directly comparable. 1.2 Questions The questions this study will try to answer are the following: What is the fraction of taxis compared to the general traffic? Per day? Per hour? How many taxis are needed to have certainty in the speed measurement from the taxis? Does the number of taxis needed to have certainty in the speed measurement change during the time of the day? Does the number of taxis needed to have certainty in the speed measurement change when there is congestion? The main source of traffic information such as the level of congestion, average speed and travel times on parts of the road network are based on data from fixed point sensors, such as radar sensors. In Stockholm the fixed point radar sensors are located on parts of the main arterial roads. By answering the questions specified above, it will be possible to determine the reliability of the taxi floating car data. If the certainty of speed measurements from the taxi floating car data is reliable, then the taxi floating car data can be used as a measurement method to determine the level of congestion, average speed and travel times on parts of the road network where there are no fixed point radar sensors. 1.3 Goal and Objective The goal of this study is to learn more of the relationship between the radar sensor data generated by the radar sensors and the floating car data generated by the taxi fleet from the studied taxi company. The studied taxi company was choosen since 2

9 the taxi floating car data from this taxi company was available at the Division of Traffic and Logistics. The report for the study is divided into three parts. Background Theory To find out how different kind of traffic sensors are compared and validated with one another, a literature study is conducted on the subject of comparing one type of traffic sensor data with another kind of traffic sensor data. The background theory will present some of the technologies used in ITS. Data Preparation Since the characteristics of the radar sensor data are unknown, a first step is to insert the radar sensor data into a database and analyze its characteristics. The purpose is to identify erroneous data as well as to find a suitable period of time for comparison. After the radar sensor data has been analyzed, the radar sensor data and floating car data are prepared for comparison. Before the radar sensor data can be compared to the taxi floating car data, the radar sensors are associated with the road segments of the E4 motorway. The second step is to aggregate the taxi floating car data and the radar sensor data into a suitable period of time and then a method of comparison has to be found with support from the literature. Comparison Once the radar sensor data and the taxi floating car data have been aggregated into a suitable period of time, the two data sets are compared with one another. To answer the research questions of this study the comparison is focusing on finding out how many taxis are needed to have certainty in the taxi speed measurements compared to the speed measurements from the radar sensors. The comparison part will present the methodology used during the comparison, and how the results are generated from the comparison. Once the radar sensor data has been compared with the taxi floating car data, the results from the comparison are presented. The presentation of the results is grouped by the research questions, with one section for each question. To answer the research questions the results are presented in a form of a statistical analysis. With the help of the literature study, the results from the comparison are discussed and conclusions are presented. 3

10 Chapter 2 Background Theory This chapter will present and explain the various traffic sensor technologies found in the literature. Both the traffic sensor technologies based on floating car data and fixed point sensors will be presented. The traffic data is used by the ITS, but generated by the various traffic sensors. This chapter will also explain how the traffic sensors generate traffic data such as the travel time between two points on the road network. How traffic sensors measure and estimate the average speed of the traffic travelling on a part of the road, or measure and estimate the number of vehicles passing by a certain point on the road network. The literature on the subject of comparing one traffic sensor technology with another traffic sensor technology will be presented and discussed. 2.1 Traffic Sensor Technologies For the purpose of traffic surveillance, effective management of the road network, reduce congestion and estimate travel time, different kind of technologies are used to collect traffic data. The road network can be divided into road segments linked together, where each road segment is a part of the road between two points. A road segment can vary in length and either be a straight line or an arc with a slight curve. Traffic data can be collected using fixed point sensors or from floating car data, and be used to calculate the average speed and flow at a fixed point or of a road segment Fixed Point Sensors The most common type of technology for traffic surveillance has been fixed point sensors. A fixed point sensor is a sensor installed at a fixed position on the road network. The fixed point sensors, in most cases function by aggregating the speed for all vehicles passing by the fixed point sensor in a specific time period, such as 5 minutes and reports the aggregated average speed to the ITS. The aggregated average speed for all vehicles passing by a fixed point sensor in a specific time period 4

11 will here be called the fixed point average speed. The aggregated number of vehicles that pass by the fixed point sensor in a specific time period will from here on be called the fixed point traffic flow. The fixed point traffic flow is in most cases also reported to the ITS in a 5 minutes period. Examples of fixed point sensors that will be explained in more detail are loop detectors, radar sensors and license plate recognition system sensors. Loop detectors, also know as magnetic loop detectors or inductive loop detectors are the most common type of fixed point sensors. A loop detector consists of a coil or loop of wire buried near the surface of the road [1, 10]. When a vehicle is passing by the loop detector, a change of inductance can be detected in the wire. The change of inductance can then be used to identify individual vehicles and calculate the speed of the vehicle, which can then be used to calculate both the fixed point average speed and the fixed point traffic flow. Loop detectors have been in use for several decades and have up until recently been the main source of traffic information [11, 6]. Radar sensors are another example of fixed point sensors that are used in traffic surveillance. A radar sensor can measure the speed of an individual vehicle with the help of a radar beam that is reflected upon the vehicle and back to the radar sensor. Another technology used in traffic surveillance is the automatic license plate recognition (ALPR) system, consisting of two cameras placed at two distinct points of the road network. The ALPR system can with the help of image processing, calculate the travel time and average speed of an individual vehicle between the two cameras, by identifying the license plate of the vehicle. Since a loop detector is a magnetic detector buried in the ground, the cost of maintenance and installation is high [4, 7]. And since the cost of installation and maintenance is high the use of loop detectors is limited to the main arterial roads of the network and the coverage of the traffic surveillance is therefore limited [1, 8]. A general problem with fixed point sensors is that estimation of traffic conditions on the complete network is based on the fixed point average speed and fixed point traffic flow, reported from a small number of sensors, located at fixed points on the road network [6]. An example of this problem is when fixed point sensors located just after a major intersection is used to estimate the average speed of a longer part of a road. Some vehicles has to stop just before passing the intersection and can then increase its speed once the vehicle has passed the intersection. If the fixed point sensor located just after the intersection is used to estimate the average speed of a longer part of the road, starting just after the intersection, the average speed will most likely be underestimated [7]. The ALPR system share some disadvantages with the fixed point sensors such as high installation and maintenance costs as well as limited coverage Floating Car Data Using floating car data is a more recent technology in traffic surveillance and it can be used to estimate the average speed and traffic flow of road segments on the 5

12 road network. Floating car data is measured from a subset of vehicles in the traffic, and can be seen as vehicles that float with the traffic stream and therefore capture the characteristics of the general traffic [2]. Floating car data can be collected using various methods. One such method is using a taxi fleet equipped with a Global Positioning System (GPS) device, that can record and transmit its route as consecutive GPS positions while driving in the traffic [2]. With the help of a digital road network stored as road segments, each GPS position can be matched to a position on the road segment using a map-matching algorithm. An increasing Figure 2.1. An example of how floating car data is map-matched to the road segments of a road network. The figure show the same taxi at three distinct points in time. For each point in time the taxi is transmitting its GPS position and timestamp to a central computer. The figure also show five road segments as dotted lines, linked together at the intersections, representing the road network. The central computer use a map-matching algorithm to determine the route the taxi was driving as the sequence: road segment A, road segment C, road segment E. With the help of the timestamps, the average speed for each road segment can be computed. number of fleet operators such as taxi fleet operators or bus fleet operators, use the GPS technology to keep track of their vehicles, as well as to direct them. The increasing use of the GPS technology is as a side effect producing floating car data with almost zero additional costs [2]. With the help of a vehicle fleet, the segment average speed and the segment taxi flow can be calculated. The segment average speed is the average speed of the floating cars driving on the road segment during a specific time period. The segment taxi flow is the number of floating taxi cars driving on the road segment during a specific time period. An example of the map-matching of the floating car data is presented in figure 2.1 The floating cars are only a subset of all the vehicles driving in the traffic, and therefore the floating cars can only be used to estimate the average speed and flow of the general traffic. 6

13 By the increasing use of cell phones the possibility to use them as a traffic sensor has increased [1, 8]. One such technology uses the anonymous information exchanged between the cell phones and the cell phone Radio Base Stations (RBS) [11]. When the communication of a cell phone has been handed over from one RBS to another RBS. The segment average speed and segment taxi flow can be calculated with the help of a digital road network and a map-matching algorithm in the same way as for taxi floating car data. An advantage of using floating car data compared to fixed point sensors is the wide coverage, making it possible to collect traffic information for the complete road network [7]. A possible problem with this technology is the lack of reliability of the data received when there is a low penetration rate of vehicles collecting the floating car data [5]. The penetration rate of vehicles is the number of floating cars in percent of the total number of vehicles driving on the road network. Several studies are mentioned in the literature that concluded that a penetration rate of at least 5% of cell phones in the vehicles is needed to arrive at a good estimation of travel times for the traffic [4, 8]. On the contrary one study mentioned in the literature, concluded that only a penetration rate of 1% of vehicles is required to be able to calculate reliable information from the floating car data [7]. With the increasing use of built in GPS devices in cell phones, the possibility to use them as a mean to obtain traffic information has increased [8]. A problem with using the GPS device in cell phones is the privacy concern. Even by anonymously recording sequent GPS positions the travel pattern and home address of the user can be found by analyzing the consecutive GPS positions [8]. The solution to this problem as proposed in the literature, is to use virtual trip lines which are preprogrammed geographical points in the software of the cell phone spread out on roads with high traffic [8]. When the user is passing a virtual trip line the speed and travel time is calculated and sent anonymously to a server and privacy is protected since only speed measurements are taken on certain points on the roadways with high traffic. A general problem with using cell phones to obtain traffic data is that the cell phone might not always be travelling in a car. Therefore, first the transportation mode of the cell phones is identified. Then the cell phones travelling on trains, or buses travelling in special bus lanes not suited for obtaining traffic information for the general traffic is filtered. A penetration rate of 2 3% of cell phones in vehicles is estimated to be enough to receive good results from measurements of traffic speed from the cell phones [8]. 2.2 Comparison Methodology The traffic data generated from the traffic sensor technologies that can be of interest in traffic surveillance are: The segment taxi flow The segment average speed 7

14 The fixed point average speed The fixed point traffic flow The travel time between two points of a road Traffic data can be generated from various fixed point sensors and floating car data sources, each with its own characteristics. Characteristics that can be of interest are: The accuracy of the speed measurements from a fixed point sensor. The number of valid sensor readings of the fixed point sensor. The accuracy of the segment average speed calculated from the floating car data. How the segment average speed change with the number of floating vehicles compared to ground truth data. How the segment average speed change with the size of the segment compared to ground truth data. How the segment average speed change for different parts of the road network compared to ground truth data. To be able to see the difference in characteristics of the various traffic sensor technologies the traffic data generated from one traffic sensor technology has to be compared to another traffic sensor technology. To be able to compare data from two distinct traffic sensor technologies, the data has to be comparable. Some methods of comparison manage to show a higher detail of the characteristics than other comparison methods Complete Roadway In several studies in the literature, traffic data from two distinct traffic sensor technologies were compared over a complete roadway [1, 3, 2, 16, 11]. Either by computing the average speed over the complete roadway from two distinct traffic sensor technologies. Or by comparing the computed travel time from the start of the roadway to the end of the roadway from two distinct traffic sensor technologies. One study compared the travel times, computed from cell phone RBS information, for the complete Ayalon freeway in Israel consisting of 4 to 5 lanes in 2005, with the travel times computed from the fixed point average speed reported by loop detectors located on the same freeway [1]. To calculate the travel times from the cell phones, the roadway is first split up into road segments, and then the travel time for each road segment is calculated using a map-matching algorithm. The travel time for the complete roadway is then calculated by summing up the travel time 8

15 for each road segment. To calculate the travel times from the loop detectors the distance between two consecutive loop detectors is divided by two. And to create a road segment for each loop detector, the first half of the distance is associated with the first loop detector, and the second half of the distance is associated with the second loop detector. And then for each road segment, the travel times are calculated by dividing the fixed point average speed from the loop detector, with the length of the road segment the loop detector is located on, and then inverting. The travel time for the complete roadway is then compared for every aggregated 5 minutes time period. By comparing the travel times for the complete roadway the study concluded that there is a good agreement during non-congested conditions. But during congested conditions, when travel time for the complete roadway is calculated as 18 minutes, the difference in travel time calculated from the cell phones and the loop detectors, is 3 4 minutes, which the study state as quite acceptable [1]. Another study compares the travel times of a complete roadway computed from floating car data collected from taxis in Hamburg during 2006 with test drives of two vehicles [3]. The study was conducted with the help of test drives using two vehicles, each equipped with a GPS logger which collected the positions of the vehicle with a frequency of 5 s. To calculate travel times from both the floating car data from taxis and the consecutive GPS positions from the test drives, the GPS positions are map-matched to a digital road network, using a map-matching algorithm. From the comparison it could be concluded that there is a good agreement between the travel times calculated from the taxi floating car data and those travel times calculated from the test drives. But as could be seen in a previous study, the travel times had more errors during congested conditions [1]. The current travel times was also compared with the historical travel times and the study concluded that they agree most of the time, thus hinting that the historical travel times can be used as a complement when there is missing traffic data [3]. Another similar study was conducted in the city of Nuremberg, Germany in 2005, comparing the travel times calculated from floating car data from taxis with travel times calculated from an ALPR system [2]. To compare the traffic data, the travel times during the whole day is aggregated into 15 minutes periods. The study concluded that overall the average travel times for the complete roadway are calculated reliably. Traffic congestion lasting for a longer period of time was successfully detected by the floating car data produced by taxis, but traffic congestion that lasted for a shorter period of time was on the other hand not always detected. Another study was conducted on two roadways in the city center of Düsseldorf, Germany during 2006, where the average speed calculated from taxi floating car data is evaluated by comparing it to the speed calculated from an ALPR system [16]. In contrast to previous studies the estimated average speed from floating car data was much lower than the average speed calculated from the ALPR system for the whole period [1, 2]. But the trend of the average speed was similar, following the same pattern. In the study it is explained that since origin-destination data is used for the floating car data in the comparison and that waiting time at the origin 9

16 and destination could affect the average speed calculations [16]. The average error between the average speed calculated from the taxi floating car data and the average speed calculated from the ALPR system is 12 km/h. Historical average speed values generated from taxi floating car data is also compared, and the historical values are found to follow the current estimated values closely. A study conducted on a short roadway in the city of Antwerp, Belgium, where floating car data generated from cell phone RBS information was compared with traffic data from loop detectors in a similar way as the study conducted on the Ayalon freeway in Israel [11]. Travel time for each road segment of the roadway was calculated from the RBS information and then summed up to obtain the travel time for the complete roadway. Travel times derived from the loop detectors were calculated by inverting the speed on the road segment the loop detector was located on. The travel times calculated from the RBS information for the complete roadway are then compared with the travel times calculated from the loop detectors. The study concluded that there is a good agreement between the average speeds from the two sources of traffic data. But that the average speeds from the RBS information are slightly lower and fluctuating more than the average speeds from the loop detectors [11]. All five studies which compared data over a complete roadway presented their comparison as a graph, showing the difference in speed or travel time during the time of the day on the studied roadway. Making it possible to distinguish variations during the time of the day. One study presented some further statistical analyses and a regression analysis [1]. Because the comparisons are conducted on a complete roadway, it is difficult to see how much the structure of the road and traffic conditions on different parts of the roadway affected the results in the comparison Road Segment by Road Segment In two studies, the method of comparing the average segment speed from one traffic sensor technology with the average segment speed from another traffic sensor technology is used as a mean to analyse the characteristics of the traffic data [10, 19]. In the first study, a comparison of the floating car data produced by taxis in the city of Shenzhen, China during 7 days in December 2006, was conducted [10]. Measuring the segment average speed for each road segment with the help of a stop watch and comparing it with segment average speed computed from floating car data. The comparison was conducted on a roadway consisting of 116 km of urban high-speed road and 138 km of thoroughfare. The comparison was conducted by presenting the error distribution of the difference in segment average speed of the road segments in percent. 36.6% of the road segments had less than 10% error, 23.6% of the road segments had between 10 20% error, 20.3% of the road segments had between 20 30% error, and 19.5% had more than 30% error. To present the results of the comparison between the segment average speeds, calculated for the road segments, the road segments were divided into groups of high-speed road and thoroughfare. And the results show that high-speed roads has less errors than the 10

17 thoroughfare. In the second study, conducted in the city of Cheongju, Korea for 31 days in 2004, the segment average speed, calculated from RBS hand over information from cell phones are compared pairwise with the segment average speed from floating car data generated from 10 probe vehicles [19]. In a similar way as in the previous study conducted in the city of Shenzhen, China, the comparison is conducted by presenting the average error of the segment average speed for each road segment. The conclusion of the study in the city of Cheongju, Korea, was that cell phones can be used as an alternative to estimate the segment average speeds. But the study also concluded that the map-matching can affect the accuracy of the result. And that when a cell phone is communicating with an RBS located far away, the mapmatching is getting more inaccurate, which affects the accuracy of the estimated segment average speed. The comparison for both of these studies are presented in a similar way. The first study presented the error distribution across road segments in percent and the other study presented the average errors for all the road segments used in the study. The method of presenting the results using the error distribution across road segments make it possible, to some extent, to see how the comparison is affected by the characteristics of distinct road segments. But the method do not manage to demonstrate how the results are varying during the time of the day and to which extent that effect the comparison Fixed Point Sensor and Fixed Point Sensor In a study conducted on freeway I-880 in Union City, California, USA during 8 hours in February, 2008, the speed measured at virtual trip lines from 100 vehicles, each carrying a GPS-enabled Nokia N95 cell phone is compared with the fixed point average speed from loop detectors [8]. The 17 virtual trip lines are placed at the same locations as the 17 loop detectors and the comparison was presented as two velocity fields, the first constructed from the 17 virtual trip lines and the second from the 17 loop detectors. In the velocity field, each loop detector represent the road segment of the road the loop detector is located on, and the length of that road segment depended on the distance to the next and previous loop detector. During comparison with the speed measurements from the virtual trip lines, differences in the speed measurements for some of the loop detectors are noticed [8]. One reason for the speed difference is the fact that the segment average speeds from the loop detectors and the segment average speeds from the virtual trip lines are calculated using different methods. Another reason suggested in the literature, Virtual trip lines collect their velocity from a proportion of all vehicles crossing that location, while loop detectors collect data from all vehicles. If this proportion is too small, it might not be statistically representative of the entire population [8]. 11

18 2.2.4 Fixed Point Sensor and Road Segment Several studies from the literature, used the method of comparing traffic data calculated from a fixed point sensor, with traffic data from some other sensor technology, such as floating car data when doing their comparison [1, 4, 6, 15]. In a study conducted on the 14 km long Ayalon freeway in Israel, consisting of 4 lanes in each direction, the road is divided into road segments of length 300 m to m [1]. The study on the Ayalon freeway in Istael has been mentioned in a previous subsection in this report when comparing the travel time for the complete roadway. The freeway has 10 interchanges and 60 loop detectors placed approximately 500 m apart. RBS hand over information from cell phones are used to calculate the travel time for each road segment of the road. In the study the methodology for comparing traffic data from a fixed point sensor with that based on RBS hand over information was discussed. The study concluded that since the two data sets has different characteristics the results from a comparison can only be used as a starting point for evaluating the data, and that the comparison is not sufficient to be able draw any conclusions. For that reason a graphical representation is used in the study, in a form of a velocity field as a mean to compare the speeds from the two data sets along the Ayalon freeway. The velocity field for the loop detector data is then compared to the velocity field of the floating car data from the RBS hand over information. The segment average speed of each road segment is used to calculate the velocity field of the floating car data. And for the loop detector velocity field the speed is calculated for a road segment of 500 m centered at the detectors location, aggregating the speed of all lanes from that detector during the last 5 minutes. In a study conducted on a 9 km part of the Gardiner expressway in Toronto, Canada, containing 14 loop detectors, data from 14 days in 2008 is compared with floating car data from RBS hand over information [4]. The RBS hand over information is used to calculate the segment average speed for each road segment of the roadway. In contrast to the study conducted in Israel [1], the study in Toronto, Canada compared 5 minutes aggregated average speed for each loop detector with the 5 minutes aggregated average speed of the road segment the loop detector was located on. The comparison of the segment average speeds, with the fixed point average speeds from the loop detectors was carried out by first calculating the average speed difference and presenting it in a table. And then presenting a graph for one loop detector showing the fixed point average speed of the loop detector compared with the segment average speed during the time of the day. Both the study conducted in Israel and the study conducted in Toronto conclude that the speed pattern of the two different data sets overall is similar [1, 4]. The study conducted in Toronto, Canada, noticed that the speeds measured by the RBS hand over information were slightly lower than the speed measured from the loop detectors. And mentioned that it is expected since speeds from RBS hand over information were calculated across a larger distance compared to loop detectors which measure the speed at a fixed point. Both studies mentioned that the floating car data from 12

19 the RBS hand over information was more noisy. In the study conducted in Israel, mentioned that the noise from the floating car data was larger on the first section of the road where an on-ramp is connected and mentioned that as a possible explanation. And in the study conducted in Toronto, Canada, mentioned that there were noise from the floating car data in the early morning and late night periods, and explained the noise by low traffic and low penetration rate of RBS hand over information during that period. In another study the segment average speeds calculated from preprogrammed pseudo bus stops, are compared with the speeds calculated from loop detectors, with the purpose to test different loop detector data collection methods [6]. Each pseudo bus stop is placed with equal distance of every two consecutive loop detector, placing each loop detector near the center of a road segment. When comparing the average segment speeds calculated with the fixed point average speeds reported by the loop detectors, the results show that there is a slight variation in the comparison. But the study concluded that aggregating the fixed point average speed from the loop detectors over 5 minutes is better than aggregating over 1 minute. In a study conducted on the I-4 freeway in Florida, USA, the road segment travel time calculated from the data of a vehicle equipped with a GPS, was compared with travel time computed from the 5 minutes aggregated fixed point average speed measurements of a loop detector [15]. The comparison showed that the two data sets are highly consistent but as the studies noted, there are only 6 observations made from the GPS-equipped vehicle, which is a relatively small amount. When fixed point sensor data was compared with floating car data the validations are presented in a couple of different ways. In two studies from the literature, a graphical representation is used for presenting the comparison over a distance of the road and time of the day [4, 1]. This made it easier to see how the characteristics of the roadway and the sensors at different locations and time affected the results. In contrast, two other studies used a regression analysis to present their comparison which better manage to show the overall results of the comparison [6, 15]. But did not manage to show any details of the characteristics of the results Summary If the traffic data from one traffic sensor technology is compared to the traffic data from another traffic sensor technology with different characteristics, the comparison may produce some errors. One study from the literature compared the average segment speeds calculated from floating car data generated by a bus fleet, with fixed point average speeds generated from loop detectors and stated, Clearly the transformation of a fixed point parameter to report conditions over an inhomogeneous segment carry with it potential for error, but the magnitude of this error is not well understood [6]. Another study from the literature pointed this out as well and stated In practice, a new speed measurement technology can only be compared to another technology for measuring speeds and, as each technology has its own characteristics in terms of latency, smoothing, errors and so forth, the two data sets 13

20 are rarely directly comparable [4]. After the data from virtual trip lines and loop detectors were compared, one study noted that the loop detectors reported lower speeds and stated Because of the previous considerations, loop detector measurements are not considered as ground truth in this study. A data analysis is carried out only to observe the main features of both type of measurements, and not to determine the accuracy of measurement [8]. For the comparisons in the literature presented as a velocity field or a graph, more details of the variation of speed during the time of the day for each of the loop detectors in the study was visible. This made it easier to draw conclusions on how the characteristics of the traffic data affected the comparison. Compared to comparisons made on a complete roadway which made it difficult to draw any conclusions on how the characteristics of the traffic sensors and road affected the comparison. Since only a small number of floating cars are used for representing the flow of traffic on the complete road network, the floating cars will most likely not be able to cover the complete road network during all times of the day. Therefore several studies are suggesting that floating car data should be complemented with historical floating car data for each road segment where the penetration rate of floating cars is low [3, 2, 10, 5]. 14

21 Part I Data Preparation 15

22 Chapter 3 Traffic Data Sources This chapter will explain the two kind of traffic sensor technologies that are used for answering the research questions specified for this study. This chapter will also explain how the traffic data from the two traffic sensor technologies were obtained and an analysis of the traffic data will be presented. One traffic data source are the fixed point radar sensors which are a part of the Stockholm Motorway Control System, placed on parts of the Stockholm road network. The data generated by the fixed point radar sensors, will from here on be referred to as the radar sensor data. The other source of traffic data are the taxi cars from a studied taxi company driving on the streets of Stockholm, generating taxi floating car data. A digital representation of the road network in the form of road segments was provided to this study, where each road segment represent either a complete road or a part of a road. 3.1 Stockholm Motorway Control System One of the traffic data sources used are the microwave radar sensors, which are a part of the Stockholm Motorway Control System, based on the MTM-2 (Motorway Traffic Management) system used in the Netherlands. The Stockholm Motorway Control System was installed on parts of six different roads during the period The radar sensors are placed on gantries (see figure 3.1) over each lane in each direction of the road. For most of the radar sensors there is also a variable message sign located above each radar sensor, on the gantry, showing recommended speed, or lane control signs when necessary. The number of gantries and radar sensors on each of the six different roads are presented in table 3.1. In figure 3.2 the location of the six different roads which has radar sensors installed can be seen in relation to the city of Stockholm. Each radar sensor is continuously collecting traffic data, and sending a radar sensor reading to the Stockholm Motorway Control System each minute. A radar sensor reading consists of the fixed point average speed and the fixed point traffic 16

23 Figure 3.1. An example of a gantry with a variable message sign above each lane. There is one radar sensor for each lane, all mounted on the same gantry. Table 3.1. Stockholm Motorway Control System Road Radar Sensors Gantries E E4 (excluding on- and off-ramp) Riksväg 75 (Södra länken) Riksväg 73 (Nynäsvägen) Länsväg 222 (Värmdöleden 12 6 Länsväg 226 (Huddingevägen) Länsväg flow during the last minute. The purpose of the Stockholm Motorway Control System is to increase network capacity of the road network and increase safety by using the variable message signs for queue warnings, incident management, and speed reduction during congestion [13]. The system is controlled by a traffic control center and an automatic incident detection system which uses the data from the radar sensor to display different speed and lane control signs on the variable message signs when necessary. Since the Stockholm Motorway Control System is installed on six different roads, the traffic conditions such as traffic intensity, number of lanes, and the number of taxis travelling on each road may vary. Therefore only the radar sensors located on the E4 motorway are used in this study. Radar sensors that are located on an on- or off-ramp are not used since the fixed point average speed from those radar sensors are more difficult to compare to the taxi floating car data. The length of the part of the E4 motorway in Stockholm that has radar sensors is approximately 26 km long 17

24 265 E4 Sollentuna Danderyd Solna Lidingö Stockholm E Nacka Figure 3.2. The location of the roads with radar sensors in relation to the city of Stockholm. The solid line is the E4 motorway, and the dashed lines are the parts of the 5 other roads with radar sensors. and the distance between the gantries are on average 333 m. The shortest distance between two gantries is 65 m and the longest distance is 690 m. There are in total 155 gantries placed on the E4 motorway in Stockholm, 74 of the gantries are in the northbound direction and 81 of the gantries are in the southbound direction. The 155 gantries has in total 511 radar sensors, where each gantry has either 2, 3, 4 or 5 radar sensors, one for each lane. At positions on the E4 motorway where there is an on- or off-ramp, the gantry at such a location is in most cases placed either right before or right after the on- or off-ramp. The radar sensor data is sent to a central system and then forwarded as an extensible Markup Language (XML) message to the Division of Transport and Logistics at KTH. The Division of Transport and Logistics at KTH has for some time been storing the XML messages by concatenating them into files stored on a server. The radar sensor data was provided to this study in the form of concatenated XML files. The location of each radar sensor is available in a Keyhole Markup Language (KML) file which can be viewed using Google Earth 1, viewing the placement of each radar sensor together with a satellite image of the surrounding area