WO2011120193A1

WO2011120193A1 - Method, system and device for providing traffic information

Info

Publication number: WO2011120193A1
Application number: PCT/CN2010/000411
Authority: WO
Inventors: Wei Qui; Leiming Xu; Thomas Wenzel
Original assignee: Siemens Aktiengesellschaft
Priority date: 2010-03-31
Filing date: 2010-03-31
Publication date: 2011-10-06

Abstract

This invention discloses a method, system and device for providing traffic information. The method includes: calculating appearance of one or more first type traffic sensors in a positioning system, and determining a middle area in a street network according to the appearance of the one or more first type traffic sensors; selecting multiple third type traffic sensors within the middle area determined from the one or more first type traffic sensors; obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from a cellular system for the multiple third type traffic sensors, and forming mapping relation between road segments in the street network and traffic patterns; and extracting a handover sequence of a second type traffic sensor from its operational data and finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns.

Description

Method, System and Device for Providing Traffic

Information

Field of the Invention

This invention relates to floating car data (FCD) techniques, and more particularly relates to a method for providing traffic information, a system for traffic information service and a traffic information processing device.

Background of the Invention

Traffic information service is to generate traffic information by an existing Floating Car Data (FCD) system, which processes online Taxi-GPS locations available in a Taxi-dispatching centre. However, the appearance of taxies (which can be taken as a kind of traffic sensors) is mainly in inner-city, and the number of taxies outside the inner city goes down gradually below a threshold. Therefore, it is not sufficient for the existing FCD system to generate qualified traffic information according to the Taxi-GPS locations. Other cost^Lefficient data source should be found to provide ubiquitous coverage of traffic information in order to keep the competitive edge of the traffic information service.

Cellular terminals (e.g. cellular phone, smart phone, or other wireless device connected to a cellular network) carried by vehicles are widely distributed over the street network of the whole city. In recent years, a couple of ideas/systems were coming up with the similar basic principle of "using cellular terminals as traffic sensors, extracting their movement information from their operational data existed in the cellular network, and then generating traffic information." With the widely distributed cellular terminals, a potential coverage over the whole city, inside and outside, is obtained. Although good performance was validated in simple area (i.e. relative simple networks, typically in outer-city area & highways) for all these similar ideas/systems, the performance in dense area (typically in inner cities) is still a challenge.

In dense area, the structure and the radio environment of the cellular network are complex and dynamic, which bring lots of dynamics on mobility management behaviors of the cellular terminals, e.g. the large dynamics of the handover area, the dynamics of the handover sequence when driving along a street etc. It should be noted that the dynamics on mobility management behaviors is part of operational data of the cellular terminals. With static-information (e.g. cell map) or historic statistic- information (e.g. pre-measured or defined pattern) on the cellular network, the matching of the traffic sensors' locations onto the street network and thereafter the traffic estimation will be a big challenge. That is, in a cellular-based system, a very rough pattern is used for mapping, which is created by e.g. a static cell map or a statistic method using dedicated cars to do some pre-measurement and hard to be up- to-date. Therefore, map matching is a challenge.

Furthermore, the system with the above-mentioned principle can not be a good solution for an area in-between the inner-city and outer-city, as illustratively depicted in Figure 1 (i.e. area B). Hereinafter, the inner-city is considered as an inner area (i.e. area A) while the outer-city is considered as an outer area (i.e. area C). Moreover, the area in-between the inner-city and outer-city can be called a middle area. Summary of the Invention

The basis for the present invention is to solve the weak performance of a prior system, especially in a middle area. In other words, the system for traffic information service in the present invention is to make a seamless connection between an existing FCD system and a cellular-based system (also named as a Highway FCD system or a mobile FCD system).

The technical solution of the present invention is as follows.

A method for providing traffic information includes:

calculating appearance of one or more first type traffic sensors in a positioning system, and determining a middle area in a street network according to the appearance of the one or more first type traffic sensors;

wherein the one or more first type traffic sensors are among a plurality of second type traffic sensors, each of the plurality of second type traffic sensors includes a communication module, and each of the one or more first type traffic sensors further includes a positioning module;

selecting multiple third type traffic sensors within the middle area determined from the one or more first type traffic sensors; obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from a cellular system for the multiple third type traffic sensors, and forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and traffic patterns; and

extracting a handover sequence of a second type traffic sensor from its operational data and finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns.

Optionally, determining a middle area in a street network according to the appearance of the one or more first type traffic sensors includes:

dividing the street network into grids, and counting the number of first type traffic sensors within a grid during a first time interval;

comparing the number of first type traffic sensors with a first threshold and a second threshold; and

determining that the grid belongs to the middle area if the number of first type traffic sensors is lower than the first threshold and higher than the second threshold.

Optionally, selecting multiple third type traffic sensors within the middle area determined from the one or more first type traffic sensors includes:

obtaining a GPS location of a first type traffic sensor within a second time interval and selecting the first type traffic sensor whose GPS location is within the middle area as a third type traffic sensor.

Optionally, obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from a cellular system for the multiple third type traffic sensors includes:

configuring one or more first identities for identifying the communication module included in a second type traffic sensor in the cellular system;

configuring a second identity for identifying the positioning module included in a first type traffic sensor in the positioning system;

storing the one or more first identities and the second identity of each of the multiple third type traffic sensors into an identity list;

searching track reports of the one or more first type traffic sensors from a dispatching center of the positioning system and picking out records of the first type traffic sensors whose first identities have been stored in the identity list; and searching operational data of the plurality of second type traffic sensors from the cellular system, and picking out records of the second type traffic sensors whose second identities have been stored in the identity list.

Optionally, forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and traffic patterns includes:

mapping GPS locations obtained from the track reports into the street network to find a target road segment;

aggregating handover sequences of the multiple third type traffic sensors passing through the target road segment obtained from the operational data to create a handover sequence pattern; and

establishing a mapping relation between the handover sequence pattern and the target road segment.

Optionally, finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns includes:

determining whether the handover sequence matches one of the traffic patterns, and providing the road segment having the mapping relation with the traffic pattern that the handover sequence matches as the traffic information of the second type traffic sensor.

In an embodiment, the traffic sensor is a floating car and the positioning system is a GPS.

A system for traffic information service is provided in the present invention, including: a positioning system including one or more first type traffic sensors, a cellular system and a traffic information processing device;

wherein the one or more first type traffic sensors are among a plurality of second type traffic sensors, each of the plurality of second type traffic sensors includes a communication module, and each of the one or more first type traffic sensors further includes a positioning module; and

the traffic information processing device is adapted for:

determining a middle area in a street network according to appearance of the one or more first type traffic sensors;

selecting multiple third type traffic sensors from the one or more first type traffic sensors within the determined middle area; obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from the cellular system for the multiple third type traffic sensors;

forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and traffic patterns; and extracting a handover sequence of a second type traffic sensor from its operational data and finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns.

A traffic information processing device is provided in the present invention, including:

a location calibration module, adapted for accessing track reports of one or more first type traffic sensors in a positioning system, accessing operational data of a plurality of second type traffic sensors in a cellular system, and generating traffic patterns for instructing a cellular location module to locate a second type traffic sensor into a street network;

the cellular location module, adapted for extracting a handover sequence of a second type traffic sensor from its operational data and finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns generated by the location calibration module;

wherein the one or more first type traffic sensors are among the plurality of second type traffic sensors, each of the plurality of second type traffic sensors includes a communication module, and each of the one or more first type traffic sensors further includes a positioning module.

The device further includes:

a GPS location module, adapted for finding traffic information of a first type traffic sensor from the track reports provided by the positioning system.

Optionally, the location calibration module includes:

a first sub-module, adapted for determining a middle area in the street network according to appearance of the one or more first type traffic sensors, and selecting multiple third type traffic sensors from the one or more first type traffic sensors within the determined middle area;

a second sub-module, adapted for obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from the cellular system for the multiple third type traffic sensors;

a third sub-module, adapted for forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and the traffic patterns.

Optionally, the third sub-module is adapted for:

Brief Description of Drawings

FIG. 1 is a schematic diagram illustrating the division of an inner area, outer area and middle area;

FIG. 2 is a schematic diagram illustrating a system for traffic information service in an embodiment of the present invention;

FIG. 3 is a schematic diagram illustrating the structure of Module X in an embodiment of the present invention;

FIG. 4 is a flowchart illustrating an exemplary implementation of Sub-module

X. l ;

FIG. 5 is a flowchart illustrating an exemplary implementation of Sub-module

X.2;

FIG. 6 is a flowchart illustrating an exemplary implementation of Sub-module

X.3. Detailed Description of the Invention

This invention is hereinafter further described in detail with reference to the accompanying drawings as well as embodiments so as to make the objective, technical solution and merits thereof more apparent.

In an embodiment of the present invention, a system for traffic information service shown in Figure 2 includes: a traffic information processing device, a cellular network with cellular structure (e.g. GSM/GPRS) and a plurality of traffic sensors. In an embodiment of the present invention, the plurality of traffic sensors may be floating cars.

The plurality of floating cars can be divided into two types: Type I floating cars and Type II floating cars. Specifically, Type I floating cars are equipped with positioning module and communication module. A track report of Type I floating car includes at least car ID, one or more positions of the car and timestamp. The track report of Type I floating car is generated and transmitted via the cellular network to a certain entity (entities), such as the traffic information processing device. Type II floating cars are carried with communication modules (e.g. cell phones). It should be pointed out that, the plurality of floating cars are all Type II floating cars, and Type I floating cars are a subset of Type II floating cars.

Logically, the traffic information processing device in an embodiment of the present invention includes three modules: Module A, Module B and Module X.

Module A (for example, also named as a GPS location module) is adapted to logically access track reports of Type I floating cars and generate corresponding traffic information. For example, Module A is a Taxi-FCD system in the prior art, and implements the procedure of the Taxi-FCD system.

Module B (for example, also _;namedj.as a cellular location module) is adapted to logically access operational data (e.g. mobility management related data) of the cellular network related to Type II floating cars (including Type I floating cars), and perform traffic estimation for generating corresponding traffic information. For example, Module B is responsible for implementing the procedure of a mobile FCD system. Module X (for example, also named as a location calibration module) is adapted to logically access track reports of Type I floating cars, logically access operational data of the cellular network related to Type II (including Type I) floating cars lively, and generate information about cellular network dynamics with the help of available information of Type I floating cars for assisting Module B in improving traffic estimation.

That is, both of Type I floating cars and Type II floating cars have communication modules and their mobility information (i.e. location, e.g. cell sequence of handover) can be extracted from their operational data in the cellular network. Moreover, Type I floating cars have additional track reports for indicating their positions, usually much accurate (e.g. GPS) than location derived from the operational data. With the help of the context in the track reports of Type I floating cars, the mobility information (from the operational data) of these Type I floating cars can lively reflect dynamics of cellular network related to a street network (including traffic situation in some road segments of the street network). After proper processing, the information about the dynamics of the¹ cellular network can assist in improving the traffic estimation of Module B.

Specifically, Module X has the following sub-modules: Sub-module X. l , Sub- module X.2 and Sub-module X.3.

Sub-module X.1 is adapted to select a subset of Type I floating cars according to predefined criteria/rules, and generate an indication of getting related part of information from Type I track report and from the operational data for the selected Type I floating cars. In an exemplary embodiment, Sub-module X.1 can be called as a traffic sensor selection unit.

Sub-module X.2 is adapted to extract the part of information from Type I track report and from the operational data according to the indication produced by Sub- module X.2. Further, Sub-module X.2 is adapted to assemble and arrange the extracted part of Type I track report and operational data for each selected Type I floating car into a single record. That is, each of the selected Type I floating cars has a corresponding record. In an exemplary embodiment, Sub-module X.2 can be called as a related information extracting unit. Sub-module X.3 is adapted to process the records for all the selected Type I floating cars generated by Sub-module X.2 to form a living pattern reflecting cellular network dynamics, and provide the living pattern to Module B for improving traffic estimation. In an exemplary embodiment, Sub-module X.3 can be called as a living pattern generating unit.

Accordingly, an exemplary implementation of Sub-module X. l is as shown in Figure 4.

Step 401 : Calculating Taxi-GPS distribution/appearance.

Step 402: Determining the boundary of a middle area, such as area B as illustratively shown in Figure 1.

In real practice, this step is done by e.g. setting two thresholds and comparing the appearance of traffic sensors with the two thresholds. If the appearance (e.g. the number of GPS points within a grid during a time interval) is lower than an upper threshold but higher than a lower threshold, then it is decided that this grid belongs to the middle area. Through adjusting the upper threshold and the lower threshold, the range of middle area would change. For example, when the upper threshold is set to be large enough, the middle area may cover areas A and B in some extreme cases; when the lower threshold is set to be zero, the middle area may cover areas B and C.

The boundary could be static if the time interval for appearance calculation is long enough (e.g. using historical data over a year for appearance calculation). Also, the boundary could be more fine (i.e. varying with time) if the time interval is adjusted to be shorter. The boundary could also be a combination of static and dynamic.

Step 403: Selecting taxies which can contribute to probe the cellular network dynamics in area B.

In this step, a simplest way for selection is to find a taxi who has a GPS location in area B within an observing time interval.

Step 404: Storing the relationship between the ID of a selected taxi and the ID of a communication device of the cellular network carried in the selected taxi into a database of Sub-module X.1 , and outputting an ID list via checking the database. Here, the communication device's ID (also named as network ID) may be such as a MSISDN, IMSI, TMSI, etc. These IDs can be got from a cellular network operator, e.g. from an HLR or VLR etc. Moreover, since some network ID is temporal, e.g. TMSI, the database may be synchronized to relevant network entities like VLR/HLR. In an embodiment of the present invention, the network ID may include a unique network ID and a temporal network ID. The ID list contains two types of information: IDs of the selected taxies for¹ monitoring and IDs of their communication devices whose operational data can be identified. An exemplary format of an item in the ID list is: (Taxi-ID, IMSI, TMSI).

Figure 5 shows an exemplary implementation of Sub-module X.2.

Step 501: Searching Taxi-GPS track reports coming from a dispatching center, picking out the records of taxies whose IDs are listed in the ID list, sorting the records by taxi along time and outputting the sorted result.

For example, the output in step 501 may be (Taxi-IDl, t il, GPS location l 1) - (Taxi-IDl, t_12, GPS_location_12) ...

Step 502 (parallel with step 501): Searching operational data coming from the cellular network, picking out the records (e.g. Handover-based information) including network IDs listed in the ID list, sorting the records by network ID along time and outputting the sorted result. For example, the output in step 502 may be (Taxi-IDl, t'_ll, IMSI1, TMSI1,

GCI_11^GCI_12) (Taxi-IDl, t' l 2, IMSI1, TMSI1, GCI_12^GCI_13) - ...

Step 503: Arranging the two outputs together and sorting them by time. For example, the output in step 503 may be:

1. (Taxi-IDl, t il, GPSJocation l 1)

2. (Taxi-IDl, t'_l 1, IMSI1, TMSI1, GCI_11^GCI_12)

3. (Taxi-IDl, t_12, GPS_location_12)

4. (Taxi-IDl, t'_12, IMSI1, TMSI1, GCI_12^GCI_13) -

5....

Figure 6 illustrates an exemplary implementation of Sub-module X.3. Step 601 : Mapping GPS locations output by Sub-module X.2 onto the street network to find a target road segment.

Step 602: Aggregating with weightings handover sequences of taxies passing the target road segment within a certain time interval output by Sub-module X.2.

Step 603: Creating a living handover sequence pattern, and storing the pattern into a database of Sub-module X.3 (i.e. DB2), wherein the pattern is related to a road segment and a time interval.

Based on the assumption that each road segment is related to a statistical traffic pattern, the traffic pattern (such as a handover sequence pattern) can be treated as the signature of a road segment. That is, there is a mapping relation between a traffic pattern and a road segment. In real practice, due to the cellular network dynamics (e.g. variation of radio propagation scenario, capacity dynamics of a cell, dynamic adjustment of BTS configuration etc.), the signature of a road segment will vary from time to time, i.e. the signature is living. Then, with Module X and available Type I floating cars, it is possible to provide a living handover sequence pattern in the present invention to improve mapping accuracy.

When a Type II floating car drives through the street network, a handover sequence within a certain time interval is extracted from the operational data of the Type II floating car. Then, this floating car is mapped onto a corresponding road segment by comparing the handover sequence with one or more traffic patterns stored in DB2, wherein the traffic patterns are updated according to actual demand or from time to time for guaranteeing the accuracy of mapping. For example, when the handover sequence of a taxi is celll-cell2-cell4, while one of the traffic patterns corresponding to Haidian District of Beijing (an example of a road segment) is cell 1 - cell2-cell3-cell4, it is determined that the handover sequence matches the traffic pattern, which is defined by Module X in real time. Then, traffic information of the taxi is "the taxi is in Haidian District of Beijing".

The foregoing is only preferred embodiments of the present invention and is not for use in limiting the present invention. Any modification, equivalent replacement or improvement made under the spirit and principles of the present invention is included in the protection scope thereof.

Claims

1. A method for providing traffic information, comprising:

selecting multiple third type traffic sensors within the middle area determined from the one or more first type traffic sensors;

obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from a cellular system for the multiple third type traffic sensors, and forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and traffic patterns; and

2. The method according to claim 1 , wherein determining a middle area in a street network according to the appearance of the one or more first type traffic sensors comprises:

3. The method according to claim 1 , wherein selecting multiple third type traffic sensors within the middle area determined from the one or more first type traffic sensors comprises: obtaining a GPS location of a first type traffic sensor within a second time interval and selecting the first type traffic sensor whose GPS location is within the middle area as a third type traffic sensor.

4. The method according to claim 1 , wherein obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from a cellular system for the multiple third type traffic sensors comprises:

searching track reports of the one or more first type traffic sensors from a dispatching center of the positioning system and picking out records of the first type traffic sensors whose first identities have been stored in the identity list; and

searching operational data of the plurality of second type traffic sensors from the cellular system, and picking out records of the second type traffic sensors whose second identities have been stored in the identity list.

5. The method according to claim 1 , wherein forming, according to the track reports and the operational data obtained, mapping relation between road segments in the street network and traffic patterns comprises:

mapping GPS locations obtained from the track reports into the street network to find a target road segment; i

6. The method according to any of claims 1-5, wherein finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns comprises:

7. The method according to any of claims 1 -5, wherein the traffic sensor is a floating car and the positioning system is a GPS.

8. A system for traffic information service, comprising: a positioning system including one or more first type traffic sensors, a cellular system and a traffic information processing device;

the traffic information processing device is adapted for:

determining a middle area in a street network according to appearance of the one or more first type traffic sensors; ^' .

selecting multiple third type traffic sensors from the one or more first type traffic sensors within the determined middle area;

obtaining track reports of the positioning modules from the positioning system and obtaining operational data of the communication modules from the cellular system for the multiple third type traffic sensors;

9. A traffic information processing device, comprising:

the cellular location module, adapted for extracting a handover sequence of a second type traffic sensor from its operational data and finding traffic information of the second type traffic sensor via comparing the handover sequence with the traffic patterns generated by the location calibration module; wherein the one or more first type traffic sensors are among the plurality of second type traffic sensors, each of the plurality of second type traffic sensors includes a communication module, and each of the one or more first type traffic sensors further includes a positioning module.

10. The device according to claim 9, further comprising:

1 1. The device according to claim 9, wherein the location calibration module comprises:

12. The device according to any of claims 9-1 1 , wherein the third sub-module is adapted for: