CN105427592A - Electronic navigation map turning cost calculation method based on floating car - Google Patents

Abstract

The invention provides an electronic navigation map turning cost calculation method based on a floating car. The method comprises steps that floating car time space data in a city zone is extracted; coordinate system calibration pre-processing for the floating car time space data is carried out; registering for the floating car driving locus and an electronic map and path reconstruction are carried out; an average road segment driving speed and pass time are extracted; turning cost is calculated, and navigation information is provided. Rapid and accurate processing on the real-time GPS data and mass historical data of large-scale city taxis or buses can be realized, relatively rapid and accurate estimation on the city road speed can be realized, related traffic parameters are extracted, rationality and relevance of the city road network structure are analyzed, traffic induction, traffic management and traffic program services for large cities are realized, and service and application of the geographical information system in the traffic field are reinforced and promoted.

Description

Map of navigation electronic based on Floating Car turns to cost computing method

Technical field

The present invention relates to a kind of map of navigation electronic based on Floating Car and turn to cost computing method, belong to intelligent transportation application.

Background technology

Floating Car refers to and is loaded with global positioning satellite system (GlobalNavigationSatelliteSystem, GNSS) (as GPS and the Big Dipper), and by vehicle that car number and GNSS data send by wireless communication system, this data sent are called floating car data, and it contains the data such as numbering, position, speed, course, acquisition time, passenger carrying status of vehicle.Low frequency floating car data refers generally to the floating car data that sampling time interval is greater than 30 seconds, and this data acquisition modes can save wireless telecommunications expense and data space, is suitable for large-scale floating vehicle data acquisition.

Floating car data is a kind of typical space-time data, and it reflects position and the running status of vehicle, by obtaining the traffic behavior of road with the combination of road network Geographic Information System (GIS) data.Have compared with the traffic information collection modes such as floating car data and traditional coil, microwave, video can round-the-clock, round-the-clock, digitally carry out information acquisition and do not destroy or reequip the advantage of road infrastructure, in addition can also realize quick, the lasting covering of city system-wide net for the traffic information collection means using urban taxi as Floating Car, data are easy to realize rapid automatized process.

At present, in a lot of big city, with security needs, GNSS locating module and wireless network communication module have generally been installed on taxi to manage, every platform taxi timing acquiring GNSS data, and send it back taxi management center in real time, these data contain the numbering of vehicle, position, the information such as instantaneous velocity and travel direction, the state of every platform taxi can not only be monitored, and the traffic behavior of road residing for vehicle can also be reflected by process.But because taxis quantity is large, transport condition uncertain (carrying, zero load, wait), orientate example as with a kind of GPS in GNSS, factors such as gps data acquisition time interval long (40 seconds-120 seconds), these magnanimity floating car datas also do not use well.To the road driving VELOCITY EXTRACTION aspect that the research of this data is only limitted to map match and calculates based on average instantaneous velocity, to lack more accurately, more deep research and analysis.

Summary of the invention

The object of the invention is to overcome the deficiencies in the prior art, thus provide a kind of map of navigation electronic based on Floating Car to turn to cost computing method.The method makes full use of real-time and the reliability of floating car data, by floating vehicle travelling track and map Rapid matching, Real-time solution city road network speed time space distribution, can process rapidly and accurately the carrying out of the real-time GNSS data of the extensive taxi in city and mass historical data, realize the accurate estimation of urban road speed, there is provided the traffic parameters such as vehicle flow in time, for big city real-time traffic is dredged and the application services such as traffic related information issue.

The object of the invention is to be realized by technical scheme below: a kind of map of navigation electronic based on Floating Car turns to cost computing method, and it comprises the following steps:

Step 1: extract Floating Car space-time data in urban area

Gather and vehicle-mounted GPS positioning system has been installed and the Floating Car space-time data of the bus travelled on major urban arterial highway or taxi, comprise time, place and travel speed;

Step 2: coordinate system calibration pre-service is carried out to Floating Car space-time data

Floating Car conduct road is set up the banded buffer zone with one fixed width, the banded buffer zone width of road is considered as the error band of floating car data, the floating car data outside buffer zone is weeded out, only retain the floating car data in buffer zone;

Step 3: floating vehicle travelling track and electronic chart are carried out registration and path re-establishing

1) utilize map of navigation electronic geometry and attribute data, adopt road network map gridding algorithm to carry out floating car data with floating car data and slightly mate, determine the coupling section that Floating Car is possible;

2) utilize the thick matching result of floating car data, the thicker matching result of navigation electronic chart topological data and same the previous data of car carries out inter-two-point path search;

3) utilize inter-two-point path Search Results and same the previous path re-establishing result of calculation to be selected of car to calculate and travel cost, form new path candidate;

4) if this path is exclusive path, so just as current floating vehicle travelling path and traveling cost, and using the starting point of this result as same car subsequent path; If this path is not exclusive path, so again obtain floating car data again;

Step 4: extract road-section average travel speed and transit time

Computing method are as follows:

$\stackrel{\‾}{V_i}\left(t\right)=\frac{\underset{j=1}{\overset{m}{\Σ}}\underset{k=1}{\overset{n_{(i,j)}}{\Σ}}{V}_{(i,j)}^k}{n_i\left(t\right)}\·f\left(t\right)$

$\stackrel{\‾}{T_i}\left(t\right)=\frac{L_i}{\stackrel{\‾}{V_i}\left(t\right)}$

Wherein, k represents floating vehicle number, represent the instantaneous row vehicle speed of Floating Car j kth sampled point on the i of section, for Floating Car j is in the number of section i up-sampling point, n _it () represents section i all Floating Car sampling number summations in time interval t;

$n_i\left(t\right)=\underset{j=1}{\overset{m}{\Σ}}{n}_j^i;$

F (t) represents be the function about time t, when the data of Floating Car collection are in object time interval, the value of f (t) is 1, otherwise is 0;

Step 5: calculate and turn to cost to provide navigation information

1) in the data structure of navigation road network data, traffic lights information is added;

2) reorganize the topological relation of navigation road network, adjacent segments, except annexation, increases corresponding direction information;

3) link travel time at the different direction information of traffic lights crossing intersection part same section band is calculated according to floating vehicle travelling path and road-section average travel speed extraction algorithm.

The invention has the beneficial effects as follows: can process rapidly and accurately the extensive taxi in city or bus real-time GPS data and mass historical data, realize urban road speed more accurately to estimate, extract relevant traffic parameter, analyze rationality and the relevance of city road network structure, for city traffic induction, traffic administration, traffic programme service, strengthen and promote that Geographic Information System is at the Service and application of field of traffic.Can urban traffic control be directly used in, traffic guidance, traffic programme, alleviate urban road congestion; Can provide accurate thematic space-time data analytical model, to Public Traveling, location-based service etc. provide technology and Data support.

Figure of description

Fig. 1 is Floating Car electronic map match involved by patent of the present invention and path re-establishing real time process flow figure.

Embodiment

The present invention is described in further detail to provide embodiment below.

Map of navigation electronic based on Floating Car turns to cost computing method, and it comprises the following steps:

Step 1: extract Floating Car space-time data in urban area

Gather and vehicle-mounted GPS positioning system has been installed and the Floating Car space-time data of the bus travelled on major urban arterial highway or taxi, comprise time, place and travel speed.

Step 2: coordinate system calibration pre-service is carried out to Floating Car space-time data

The GPS locator data of sending due to Floating Car there is GPS positioning error and various interference causes the position of partial floating car data anchor point to occur drift, so need to carry out calibrating coordinates pre-service.Wherein, pretreated method is, Floating Car conduct road is set up the banded buffer zone with one fixed width, the banded buffer zone width of road is considered as the error band of floating car data, floating car data outside buffer zone is weeded out, only retains the floating car data in buffer zone.

Step 3: floating vehicle travelling track and electronic chart are carried out registration and path re-establishing, as shown in Figure 1:

1) utilize map of navigation electronic geometry and attribute data, adopt road network map gridding algorithm to carry out floating car data with floating car data and slightly mate, determine the coupling section that Floating Car is possible;

This step is that gps data map slightly mates, and determines the coupling section that floating car data is possible.Because normal GPS data exists the positioning error of about 20 meters, therefore can not determine at complicated road area and mate section accurately.If thick matching condition is loose, although can ensure not omit correct coupling section, candidate road section is many, increases the complexity of subsequent algorithm and calculates actual; If parameter is too strict, the quantity in thick coupling section can reduce, but likely misses correct coupling section.So the feature of Water demand floating car data, carry out suitable floating car data matching degree computation model and optimum configurations, determine all possible coupling section of floating car data, and reject the impossible coupling section of floating car data as far as possible, complete the thick coupling of floating car data.

2) the thick matching result of floating car data is utilized, the thicker matching result of navigation electronic chart topological data and same the previous data of car, adopt route searching method to carry out inter-two-point path search;

3) utilize inter-two-point path Search Results and same the previous path re-establishing result of calculation to be selected of car to calculate and travel cost, form new path candidate;

4) if this path is exclusive path, so just as current floating vehicle travelling path and traveling cost, and using the starting point of this result as same car subsequent path; If this path is not exclusive path, so again obtain floating car data again;

Multipath problem is caused by low frequency floating car data sampling location error, accidental, for the multipath problem that a certain Floating Car occurs, often just do not exist other Floating Car, therefore can compare with the traveling cost of other Floating Car neighbouring in the path that synchronization is determined, find out and travel the driving path of the maximum path of cost similarity as reality.

In this step, first estimate by the thick coupling of floating car data the driving path that Floating Car is possible, then pass through feasibility and the rationality computation and analysis determination driving path in path, ensure that the reliability of map matching result.

Step 4: extract road-section average travel speed and transit time

Computing method are as follows:

$\stackrel{\‾}{V_i}\left(t\right)=\frac{\underset{j=1}{\overset{m}{\Σ}}\underset{k=1}{\overset{n_{(i,j)}}{\Σ}}{V}_{(i,j)}^k}{n_i\left(t\right)}\·f\left(t\right)$

$\stackrel{\‾}{T_i}\left(t\right)=\frac{L_i}{\stackrel{\‾}{V_i}\left(t\right)}$

Wherein, k represents floating vehicle number, represent the instantaneous row vehicle speed of Floating Car j kth sampled point on the i of section, for Floating Car j is in the number of section i up-sampling point, n _it () represents section i all Floating Car sampling number summations in time interval t;

$n_i\left(t\right)=\underset{j=1}{\overset{m}{\Σ}}{n}_j^i;$

F (t) represents be the function about time t, when the data of Floating Car collection are in object time interval, the value of f (t) is 1, otherwise is 0;

Step 5: calculate and turn to cost to provide navigation information

1) in the data structure of navigation road network data, traffic lights information is added;

2) reorganize the topological relation of navigation road network, adjacent segments, except annexation, increases corresponding direction information;

3) link travel time at the different direction information of traffic lights crossing intersection part same section band is calculated according to floating vehicle travelling path and road-section average travel speed extraction algorithm.

In path re-establishing algorithm, algorithm the most consuming time is that floating car data slightly mates and inter-two-point path calculates, and the inventive method improves the speed of whole algorithm from these two aspects:

One is the speed that raising low frequency Floating Car is slightly mated, and the inventive method utilizes road network map gridding algorithm can realize gps data Fast Coarse coupling, is not affecting on the basis of computing velocity, is reducing the consumption of memory source.

Two is the speed of path computing between raising two continuous floating car data point.The reliability basis ensureing path computing is studied the algorithm of the node determined fast in minimum region of search and region, is improved the speed of path computing by the quantity reducing search node.Point-to-point transmission the oval area is a kind of route scope of classics, but in actual computation process, determine that the node in elliptical region and the calculated amount required for topological relation are comparatively large, have impact on computing velocity.The present invention invents the Grid square structure intending a structure system-wide networking section topology, determines node needed for path search algorithm and topological relation fast according to the position of two terminals.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; Although with reference to preferred embodiment to invention has been detailed description, those of ordinary skill in the field are to be understood that: still can modify to the specific embodiment of the present invention or carry out equivalent replacement to portion of techniques feature; And not departing from the spirit of technical solution of the present invention, it all should be encompassed in the middle of the technical scheme scope of request of the present invention protection.

Claims (1)

1. the map of navigation electronic based on Floating Car turns to cost computing method, it is characterized in that: it comprises the following steps:

Step 1: extract Floating Car space-time data in urban area

Gather and vehicle-mounted GPS positioning system has been installed and the Floating Car space-time data of the bus travelled on major urban arterial highway or taxi, comprise time, place and travel speed;

Step 2: coordinate system calibration pre-service is carried out to Floating Car space-time data

Floating Car conduct road is set up the banded buffer zone with one fixed width, the banded buffer zone width of road is considered as the error band of floating car data, the floating car data outside buffer zone is weeded out, only retain the floating car data in buffer zone;

Step 3: floating vehicle travelling track and electronic chart are carried out registration and path re-establishing

1) utilize map of navigation electronic geometry and attribute data, adopt road network map gridding algorithm to carry out floating car data with floating car data and slightly mate, determine the coupling section that Floating Car is possible;

2) utilize the thick matching result of floating car data, the thicker matching result of navigation electronic chart topological data and same the previous data of car carries out inter-two-point path search;

3) utilize inter-two-point path Search Results and same the previous path re-establishing result of calculation to be selected of car to calculate and travel cost, form new path candidate;

4) if this path is exclusive path, so just as current floating vehicle travelling path and traveling cost, and using the starting point of this result as same car subsequent path; If this path is not exclusive path, so again obtain floating car data again;

Step 4: extract road-section average travel speed and transit time

Computing method are as follows:

$\stackrel{\‾}{V_i}\left(t\right)=\frac{\underset{j=1}{\overset{m}{\Σ}}\underset{k=1}{\overset{n_{\left(i,j\right)}}{\Σ}}{V}_{(i,j)}^k}{n_i\left(t\right)}\·f\left(t\right)$

$\stackrel{\‾}{T_i}\left(t\right)=\frac{L_i}{\stackrel{\‾}{V_i}\left(t\right)}$

Wherein, k represents floating vehicle number, represent the instantaneous row vehicle speed of Floating Car j kth sampled point on the i of section, for Floating Car j is in the number of section i up-sampling point, n _it () represents section i all Floating Car sampling number summations in time interval t;

$n_i\left(t\right)=\underset{j=1}{\overset{m}{\Σ}}{n}_j^i;$

F (t) represents be the function about time t, when the data of Floating Car collection are in object time interval, the value of f (t) is 1, otherwise is 0;

Step 5: calculate and turn to cost to provide navigation information

1) in the data structure of navigation road network data, traffic lights information is added;

2) reorganize the topological relation of navigation road network, adjacent segments, except annexation, increases corresponding direction information;

3) link travel time at the different direction information of traffic lights crossing intersection part same section band is calculated according to floating vehicle travelling path and road-section average travel speed extraction algorithm.