1.1 Current Development
Urban traffic has been growing all over the world in the past few years, and it puts a lot of pressure on cities’ traffic control and planning, resulting in reduction of transportation efficiency. Moreover, traffic congestion has several negative impacts, such as air pollution, noises, and fuel consumption. According to the urban mobility report [1], drivers in the US wasted 4.16 billion hours of time and 2.81 billion gallons of fuel in 2007 due to traffic congestion, and the congestion costs are increasing.
When traffic congestion is becoming a serious problem to our lives, there is a strong need to improve transportation efficiency.
In order to solve the traffic congestion problem, several methods have been developed to collect traffic information, such as inductive loops, infrared sensors and video detections. Inductive loops and infrared sensors are embedded in a road network to detect vehicles passing over. Video detections are another form of vehicle detection methods which use traffic cameras to detect vehicles by means of image processing techniques. Using these methods, traffic system managers like TANFB Traffic Information System [2] can provide traffic information to road users for route planning decisions. The three types of methods for traffic detection have been a major part of most intelligent transportations systems (ITS) during the last few decades.
However, they are too expensive to construct and maintain. It is difficult to widely use them in urban cities because of economic reasons.
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An alternative method for collecting traffic information is Floating Car Data (FCD). The basic idea of FCD is to collect real-time traffic information by locating vehicles via mobile phones or GPS devices. FCD uses probe cars as mobile sensors to collect traffic information, which is based on the exchange of information between probe cars traveling along a road network and a central server. Unlike the traditional traffic data collection techniques mentioned above, FCD is very cost-effective, that is, there is no need to build additional devices along a road to obtain traffic information.
Contrary to traditional techniques, it has much wider road network coverage. Some service providers, such as TomTom [3], IntelliOne [4], ITIS Holdings plc [5] and Mediamobile [6], have developed applications based on FCD recently.
1.2 Motivation
In general, FCD falls into two categories: floating cellular data and global position system-based probe cars. The principle of floating cellular data is to locate vehicles by means of triangulation or other technologies such as handover [7].
GPS-based probe cars use GPS receivers to measure location and speed. The main difference between these two approaches is that floating cellular data approach does not need additional hardware. GPS-based probe cars rely on on-board GPS devices for location measurement. Floating cellular data, however, does not require special devices in cars, because most driving vehicles are already equipped mobile phones nowadays. Although floating cellular data is superior to GPS-based in the point of view of availability, it has critical weakness—low accuracy. Accuracy of floating cellular data is especially low in urban areas. Density of road network is high in urban areas, whereas the sector cell size is large, which may suffer from locating problems.
As a result, it is difficult to obtain useful traffic information in urban areas by using
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floating cellular data. In this thesis, we only consider the GPS-based approach because of accuracy concerns.
To obtain real-time information, probe cars need to periodically report their conditions to a traffic information center (TIC) in a short period. In a traffic information system using FCD, probe cars send their current conditions, such as coordinates, timestamp, speed and heading, to the TIC via wireless communication methods. Upon using received traffic conditions, the TIC is able to generate and update traffic information, and then broadcasting to all road users. The road users that receive traffic information can decide the best route immediately. For example, the road users can drive different roads in order to avoid traffic accident or traffic jam ahead. In addition, a traffic information system based on FCD can provide travel time easily. Therefore, drivers are able to know how long it takes to reach their destinations, which benefits road users a lot.
To select the periodical report interval, one needs to consider the tradeoff between the amount of report messages and the real-timeliness of the traffic information generated by the TIC. In urban cities, there are many probe cars which report their information to a TIC, which puts loads on the TIC. The TIC needs to receive and process significant amount of messages. When traffic flow increases, the TIC requires additional storage space and communication bandwidths. Therefore, a traffic information system based on such policy has scalability problems. One direct solution is to make probe cars report their conditions in a longer period, therefore reducing the amount of messages which are sent to the TIC. However, it is difficult to obtain real-time information; in other words, the collected information does not reflect traffic conditions immediately.
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Although many studies have been done on FCD generating traffic information, few studies investigated how to select an optimal report interval. Most traffic information systems, however, collect traffic information by using periodical report policies, which needs to consider the trade-off relationship and problems mentioned above. In a traffic information system, the essential factor is real-timeliness, which helps road users to realize traffic conditions immediately. Therefore, periodical report policies are not suitable for real-time information systems deployed in urban areas. To conclude our discussion so far, it is worth paying attention to design a FCD-based traffic information system which offloads the TIC and maintains the real-timeliness of information generated by the TIC.
1.3 Objective
In this thesis, we present a novel traffic report system and method with fast feedback to reduce the number of traffic reports from probe cars and maintain the real-timeliness of the traffic information generated by the TIC. Our traffic information system has the following features:
(1) Providing real-time traffic information.
(2) Reducing communication requirements for FCD-based traffic information systems.
(3) A conditional report policy for probe cars.
Our contribution of this thesis is to propose a report policy that not only reduces communication requirements but also maintains the real-timeliness of the traffic information. We believe our work is valuable since there are few traffic information
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systems that can offload TIC and maintain the real-timeliness of information at the same time. We will describe the report policy in details in later chapters.
1.4 Summary
The remaining part is organized as follows. Chapter 2 describes the current work in Floating Car Data related to our system. Chapter 3 describes our system design in details. Chapter 4 discusses the results in our system. Finally, we give our conclusions in Chapter 5.
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